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2018
Project Title:

OLM - object location memory

Picture of OLM - object location memory
Students:

Bar Neuman, Nitzan Winkler, Jonathan Weizman

Supervisors:

Boaz Sterenfeld and Yaron Honen

Description:
VR Platform for a research following a demand made by a researcher in psychology from Emek Izrael Institution. The experiment focused on the subject’s ability to memorize the objects in the environment and identify the changes in their positions. The environment simulates a Savanna with bushes meant to be memorized and foxes as a distraction.
Project Title:

Matrix

Picture of Matrix
Students:

Elinor Feller

Supervisors:

Boaz Sterenfeld and Yaron Honen

Description:
The matrix effect was introduced in The Matrix film that was released in 1999. It was one of many novel new visual effects that were introduced in this film. In this project, I made the Matrix rain code effect by creating a Shader in Unity.
Project Title:

Tomato Classification using DL

Picture of Tomato Classification using DL
Students:

Shak Morag,Eitan K

Supervisors:

Alon Zvirin

Description:
Diagnosis of crop phenotypes is a widespread problem that affects the life of millions around the world. In our project, our goal was to create a system for classifying different parts of images in order to achieve classification and segmentation of significant plant parts. Our methods included applying neural networks. We've tested a few types of networks, mainly classifier and encoder-decoder. Using the classifier, we achieved very high accuracy (97%- 98%) when classifying parts of the image. Later, using the encoder-decoder architecture we shortened the required time for segmenting an image, from minutes to a few seconds.
Project Title:

360 Video Editing

Picture of 360 Video Editing
Students:

Daniel Weisberg Mitelman, Lorella Matathia

Supervisors:

Boaz Sterenfeld and Yaron Honen

Description:
360 Video Editing is a unique VR tool that gives you an option to edit a video, without losing the special experience. With the controllers, you can interact with the editor. You are able to delete frames, show specific frames and export the edited video. The main functions that the editor provides: • Displaying a video on sphere • Dividing the video into sub-frames • Displaying sub-frames • Splitting video • Showing effects • Exporting final video
Project Title:

Bit Saber

Picture of Bit Saber
Students:

Sapir Mordoch, Bar Uliel, Moran Nisan

Supervisors:

Boaz Sterenfeld and Yaron Honen

Description:
Beat Saber is a unique VR game where your goal is to slash the beats (represented by small cubes) as they are coming at you. The game includes several songs, each with different difficult.     The player uses VR motion controllers to wield a pair of light-sabers to slash the blocks. Each block is colored red or blue to indicate whether the red or blue saber should be used to slash it.    When a block is slashed by the suitable saber, the block is cut and the player get a point, otherwise if the player slashes the block with a saber with the opposite saber the score is reset.
Project Title:

Unity Multiplayer Game

Picture of Unity Multiplayer Game
Students:

Michael Gont, Kiril Gont

Supervisors:

Boaz Sterenfeld and Yaron Honen

Description:
A multiplayer space sim game. Server-Client communication implemented from scratch on top of Unity's low-level networking API (Transport Layer API). Movement is smoothed with linear interpolation.
Project Title:

3D VR Paint

Picture of 3D VR Paint
Students:

Shlomit Sibony, Ran Mansoor, Yuval Shildan

Supervisors:

Boaz Sterenfeld and Yaron Honen

Description:
Create your art via our 3D VR paint. Use the latest technologies including the Manus-VR gloves (wearables) and the HTC Vive. We offer an intuitive and powerful platform which you can paint with both hands, control the painting transform and choose the colors you wish to paint with.
Project Title:

Quad-copter GIP Vision Control Farmework

Picture of Quad-copter GIP Vision Control Farmework
Students:

Nathan Sala and Ofir Zelig

Supervisors:

David Dovrat and Ohad Menashe and Roman Rabinovich

Description:
Drones can be used to various tasks and replace humans in many aspects of our lives. Our goal was to create a system that will enable to control a drone using computer vision that will enable it to fly autonomously. Eventually, we created a framework that wraps all the parts that control the flight functionality of the drone in a way that future projects could use it could focus only on computer vision.
Project Title:

VR Floor Planner

Picture of VR Floor Planner
Students:

Netanel Lev & Dolev Ben Ami

Supervisors:

Boaz Sterenfeld and Yaron Honen

Description:
Design, explore and share floor plans in VR.
Project Title:

Child facial expression detection

Picture of Child facial expression detection
Students:

Eden Benhamou and Deborah Wolhandler

Supervisors:

Alon Zivrin & Michal Zivan

Description:
We present an examination of facial expression detection of children in two different study environments, joint storytelling and yoga. We analyze videos of preschool children from the ENIC lab filmed during 6 months. Our data analysis combines face detection algorithms, artificial neural networks designed for emotion recognition, face recognition algorithm and image processing tools for tracking. We present results of child facial expressions during the recorded video sessions. This project was made in collaboration of ENIC-GIP labs.
Project Title:

HoloLens Face Emotion Detection

Picture of HoloLens Face Emotion Detection
Students:

Ilya Smirnov, Dani Ansheles, Denis Turov

Supervisors:

Boaz Sterenfeld and Yaron Honen

Description:
The project will represent face emotion detection algorithm, that runs on Microsoft HoloLens device. The purpose of the project is providing to User real-time detected emotion as attached smiley near visible faces The project could be used to help people, with inability to identify and describe emotions by themselves. As a result, people suffering from this disorder will be able to understand other people emotions by attached smiley.
Project Title:

An Extrapolated Dynamic String Averaging Method

Picture of An Extrapolated Dynamic String Averaging Method
Students:

Victor Kolobov

Supervisors:

Simeon Reich and Rafał Zalas

Description:
The project deals with projection methods which have their origin in computerized tomography and image processing. These methods solve an optimization problem which seeks a point in the intersection of convex (constraint) sets. They do so by an iterative application of operators to the approximation point. We present a certain acceleration technique called "extrapolation". This technique allows the algorithms to take bigger steps and accelerates their convergence. We show theoretical results for the convergence of methods considered in the string averaging framework. In addition, we present a library that was developed for MATLAB which may allow researchers in the field to devise and experiment with different projection methods.
Project Title:

Symbolic Autoencoder

Picture of Symbolic Autoencoder
Students:

Oryan Barta

Supervisors:

Alon Zvirin and Yaron Honen

Description:
Developing effective unsupervised learning techniques is an essential stepping stone towards next generation machine learning models. Such models would no longer be bottlenecked by their dependence on massive labeled datasets which are often difficult or impossible to obtain. We propose a novel architecture for deep feature extraction from unlabeled data and intelligent labeling of data using an implicitly defined and learned symbolic language. The model can then be used in a semi-supervised context to reduce the amount of labeled data necessary for training.
Project Title:

Augmented Emergency Response

Picture of Augmented Emergency Response
Students:

Avi Kotlicky,Arik Rinberg

Supervisors:

Boaz Sterenfeld and Yaron Honen

Description:
We developed a 3D Augmented Reality Multi-User emergency response application. Our application allows all users to simultaneously see the same scene, while a command center on a PC sees in real time users and their movements displayed on a real time map. The command center can communicate with all the users in scene, display hologram warnings in areas and manage all the users. The users may send warnings and request help from the command center and other users. The users also have a user location system that allows them to keep track of their team.
Project Title:

Architecture VR

Picture of Architecture VR
Students:

Sami Abdu and Liza Rakhlina

Supervisors:

Boaz Sterenfeld and Yaron Honen

Description:
For many architects, the biggest challenge is often giving the client a clear understanding of how the design will look in real life. The idea behind the project is to visualize the Interior and the Exterior design of an architecture model in VR, so that clients could gain an understanding of how a design will look to scale in a fully 3D environment.
Project Title:

Holomessages

Picture of Holomessages
Students:

Eran Tzabar

Supervisors:

Yaron Honen and Boaz Sternfeld

Description:
HoloMessages is an Augmented Reality system for sending and receiving office messages. Using the Hololens device by scanning a QR code at the entrance to the office, you can load user information and leave a message. There are new technologies that enable users to live in a new and interactive Mixed Reality world. The goal is to provide users a simple and necessary office operation, to send and receive messages and integrate the operations into the new technological world. Providing a much more comfortable workplace, which extend your working environment outside of your computer screen to the whole room. Similar when you put sticky notes on your desks that make an extension of your working environment.
Project Title:

Next Generation Image Style Transfer via CNN

Picture of Next Generation Image Style Transfer via CNN
Students:

Adar Elad & Mark Erlich

Supervisors:

Alona Golts

Description:
Image Style Transfer refers to an artistic process in which a content image is modified to include the style taken from a second image. Early signs of this idea appeared in the early 2000's, but the real breakthrough came with the impressive work of Leon Gatys and his co-authors. Their method relies on pre-learned neural networks, that has been trained for image recognition. Our project focuses on expanding and improving the prime weaknesses of Gatys’ algorithm: (i) preservation of edges; (ii) enabling treatment of large images; and (iii) speeding up the whole transfer process
Project Title:

RGB camera based heart-rate estimation using Eulerian Video Magnification (EVM)

Picture of RGB camera based heart-rate estimation using Eulerian Video Magnification (EVM)
Students:

Yonatan Amir, Doron Armon, Neriya Mazzuz

Supervisors:

Ron Slossberg and Gil Shamai

Description:
The purpose of our project is to extract information and relevant medical indicators of a potential patient using an RGB camera. We implemented few techniques in order to extract pulse in real time using a standard web camera based on a number of researches. The main research is "Eulerian Video Magnification for Revealing Subtle Changes in the World", which published at MIT. In addition, we have created a visualization the subtle changes in the patient's facial blood flow.
Project Title:

Real-Time 3D Face Reconstruction

Picture of Real-Time 3D Face Reconstruction
Students:

Shadi Endrawis

Supervisors:

Matan Sela

Description:
Fast and robust three-dimensional reconstruction of facial geometric structure from a single image is a challenging task with numerous applications. In this project, I implemented and experimented with the iterative CNN models for extracting geometric structure from a single image. The next step in the project was to apply the model first to videos, and from there to optimize the system for real-time 3D face reconstruction using a webcam.
Project Title:

Deep Learning of Compressed Sensing Operators with Structural Similarity Loss

Picture of Deep Learning of Compressed Sensing Operators with Structural Similarity Loss
Students:

Yochai Zur

Supervisors:

Amir Adler

Description:
Compressed sensing (CS) is a signal processing framework for efficiently reconstructing a signal from a small number of measurements, obtained by linear projections of the signal. In this paper we present an end-to-end deep learning approach for CS, in which a fully-connected network performs both the linear sensing and non-linear reconstruction stages. During the training phase, the sensing matrix and the non-linear reconstruction operator are jointly optimized using Structural similarity index (SSIM) as loss rather than the standard Mean Squared Error (MSE) loss. We compare the proposed approach with state-of-the-art in terms of reconstruction quality under both losses, i.e. SSIM score and MSE score. Source code available here: https://github.com/yochaiz/SSIM
Project Title:

VR Fruit Ninja

Picture of VR Fruit Ninja
Students:

hadar ben efraim, dror levy

Supervisors:

Boaz Sterenfeld and Yaron Honen

Description:
We developed a VR game that based on the popular game “Fruit Ninja”. Our application simulates several environments. The player’s main goal is to win as much as points as he can, by cutting fruits and avoiding different obstacles. We added few more functionalities to the game so the player can enjoy both the game and the VR experience. The application gives the player an extraordinary experience that enables him to use several senses in order to get a high score.
Project Title:

FindIt - Object Detection and Tracking on HoloLens

Picture of FindIt - Object Detection and Tracking on HoloLens
Students:

Sefi Albo, Bar Albo

Supervisors:

Yaron Honen and Boaz Sternfeld

Description:
FindIt is an HoloLens application for finding objects in home environment. The idea is to help us find missing stuff at home by tracking the objects around us and remember their locations. The app also tracks the objects as they change their location in the room. The app has 3 modes: - Scan – scan the room to find objects. This mode's purpose is to initialize the app's knowledge about the objects the user wants to track. The app is taking photos which will later be processed by Object Detection Model to detect the objects in the room. - View – view the scan results. In this mode, the user can see the detected objects and their location. - Find – find the objects and track changes. This mode is the main usage of our app. When the user says "Find -Object Name-", a box will appear at the exact same place as the real object. Also, the navigation system will guide the user to its location.
Project Title:

VR wizard hunting

Picture of VR wizard hunting
Students:

Alex Salevich, Edi Frankel

Supervisors:

Boaz Sterenfeld and Yaron Honen

Description:
Our project's goal is to overcome this challenge and present a new way of interacting with an application via recognition of motion and shape instead of menus and buttons. Our solution will allow to link the controller motion in 3d space to a certain behaviour in the application. In our case we will implement the idea as a game in which the user plays a wizard that can draw his spells as shapes and for each one of them a predefined spell will be activated.
Project Title:

Hologram Interface

Picture of Hologram Interface
Students:

Alex Salevich

Supervisors:

Boaz Sterenfeld and Yaron Honen

Description:
The old version of this project was a stand alone application with fixed models and no control over the presentation of the holograms. The goal of this project was to extend the application in 2 ways: 1) The app will have a user interface for user control over the presentation at computer which the app is running at. 2) The app will act as a server that can be connect to by clients so that they control the many features of the main application. In order for this to work a second Client side application has been developed as a part of this project.
Project Title:

MultiAR Project

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Students:

Michael Pekel, Ofir Elmakias

Supervisors:

Dr. Matan Sela

Description:
An augmented reality real-time multiplayer where the player is literally part of the game; he can fight creatures or other real players, do quests together and interact indoor and outdoor. The project was based on the Google Tango platform - acquiring data about the environment (walls, planes) in real time and a precise (less than 1 meter) relative position. The game mechanism includes: ‣ Full multiplayer ecosystem - client\server udp communication. ‣ Physics - walls/planes interaction. ‣ Shoot-Hit mechanism - the real players collider position is calculated in real-time as long as the bullet trajectory. ‣ Single player quests + collaborated puzzle quests. ‣ Integration with Google Daydream controller. ‣ Single & stereo ("dual screen VR") modes. Many thanks for our supervisors & supporters: Yaron Honen, Boaz Sternfeld, Matan Sela and Alexander Porotskiy. The demo clip could be found at: https://www.youtube.com/watch?v=EHyYArZsYlA
Project Title:

ExpressionMime

Picture of ExpressionMime
Students:

Dor Granat , Ran Yehezkel

Supervisors:

Matan Sela

Description:
The purpose of our project was to create an application that can detect faces and estimate their pose and expressions. At real-time, we can identify faces and their poses in a video, and render different 3D models of faces with various expressions over the original faces.
Project Title:

Faster and Lighter Online Sparse Dictionary Learning

Picture of Faster and Lighter Online Sparse Dictionary Learning
Students:

Shay Ben-Assayag & Omer Dahary

Supervisors:

Jeremias Sulam

Description:
Sparse representation has shown to be a very powerful model for real world signals, and has enabled the development of applications with notable performance. Combined with the ability to learn a dictionary from signal examples, sparsity-inspired algorithms are often achieving state-of-the-art results in a wide variety of tasks. However, most existing methods are restricted to small dimensions, mainly due to the computational constraints that the dictionary learning problem entails. In the context of image processing, this implies handling small image patches instead of the entire image. A novel work which has circumvented this problem is the recently proposed Trainlets framework, where the authors proposed the Online Sparse Dictionary Learning (OSDL) algorithm that is able to efficiently handle bigger dimensions. This approach is based on a double sparsity model which uses a new cropped Wavelet decomposition as the base dictionary, and an adaptive dictionary learned from examples by employing Stochastic Gradient Descent (SGD) ideas. In continuation to this work, which has shown that dictionary learning can be up-scaled to tackle a new level of signal dimensions, our project is focused on studying and improving OSDL. In this report, we present several modifications to the algorithm which are aimed at dealing with its limitations, results of experiments conducted on high dimensional large datasets, our conclusions and suggestions for future work.
Project Title:

War Room

Picture of War Room
Students:

Natan Yellin, Ay Kay, Dima Trushin

Supervisors:

Boaz Sterenfeld and Yaron Honen

Description:
We developed a 3D Augmented Reality Multi-User application. Our application allows all users to simultaneously see the same scene at different angles. The Users are able to cooperatively manipulate objects and plan events in the scene.
2017
Project Title:

AR Museum

Picture of AR Museum
Students:

Roi Glink, Shay Michali, Elad Alon

Supervisors:

Boaz Sterenfeld and Yaron Honen

Description:
We developed an Oculus Android gearVR application with both entertainment and educational purposes. Our application mainly offer an AR gameplay while still integrating some VR scenes and thus creating a Mixed reality experience. The user will be able to stroll at any random location with the headset and with minimal preparation of specific set of images and key points will visit an “AR Museum”. That experience is gained by our application which replaces these images with art paintings, therefore offering various options for the user to experience in one room. Additionally, the application offers both VR and AR interactions with the user. The user can control the navigation of scenes and the content of the museum to his points of interest. For Example - the user can choose to see Van-Goh paintings and the museum will be all Van-Goh. Afterwards with only one click the user will be able to see Asher paintings and so on.
Project Title:

Image Inpainting Using Pre-trained Classication-CNN

Picture of Image Inpainting Using Pre-trained Classication-CNN
Students:

Adar Elad,Yaniv Kerzhner

Supervisors:

Yaniv Romano

Description:
Image inpainting is an extensively studied problem in image processing, and various tools have been brought to serve it over the years. Recently, effective solutions to this problem based on deep-learning have been added to this impressive list. This paper offers a novel and unconventional solution to the image inpainting problem, still in the context of deep-learning. As opposed to a direct solution of training a CNN to fill-in missing parts in images, this work promotes a solution based on pre-trained classification-oriented CNN. The proposed algorithm is based on the assumption that such CNN's have memorized the visual information they operate upon, and this can be leveraged for our inpainting task. The main theme in the proposed solution is the formulation of the problem as an energy-minimization task in which the missing pixels in the input image are the unknowns. This minimization aims to reduce the distance between the true image label and the one resulting from the network operating on the completed image. A critical observation in our work is the fact that for better inpainting performance, the pre-training of the CNN should be applied on small portions of images (patches), rather than the complete images. This ensures that the network assimilates small details in the data, which are crucial for the inpainting needs. We demonstrate the success of this algorithm on two datasets: MNIST digits and face images (Extended Yale B), showing in both the tendency of this method to operate very well.
Project Title:

Prism Hologram

Picture of Prism Hologram
Students:

Marina Minkin

Supervisors:

Boaz Sterenfeld and Yaron Honen

Description:
As part of the project I created holograms. I built a prism to redirect ray of light, and wrote code to transform inputs to the output format that can be reflected by the prism. The input can be either a 3D model or an input from a webcam
Project Title:

Quad-copter Remote Control

Picture of Quad-copter Remote Control
Students:

Sapir Cohen, Nir Daitch

Supervisors:

David Dovrat

Description:
In this project, our goal is to give the user a simple control scheme to fly a drone. The possible commands are take-off, hover and land. As we will demonstrate, we were able to control our UAV using a small, affordable and easy-to-operate remote controller. Also, using the Raspberry Pi as our computing module, we have created a portable solution that can be mounted on the drone itself.
Project Title:

Multi AR - Real-Time Augmented Reality Multiplayer

Picture of Multi AR - Real-Time Augmented Reality Multiplayer
Students:

Michael Pekel, Ofir Elmakias

Supervisors:

Matan Sela and Boaz Sterenfeld & Yaron Honen

Description:
An augmented reality real-time multiplayer where the player is literally part of the game - he can fight creatures or other real players, do quests together and interact indoor and outdoor. The project was based on the Google Tango platform - acquiring data about the environment (walls, planes) in real time and a precise (less than 1 meter) relative position. The game mechanism includes: ‣ Full multiplayer ecosystem - client\server udp communication. ‣ Physics - walls/planes interaction. ‣ Shoot-Hit mechanism - the real players collider position is calculated in real-time as long as the bullet trajectory. ‣ Single player quests + collaborated puzzle quests. ‣ Integration with Google Daydream controller. ‣ Single & stereo ("dual screen VR") modes. Many thanks for our supervisors & supporters: Yaron Honen, Boaz Sternfeld, Matan Sela & Alexander Porotskiy. The demo clip could be found at: https://www.youtube.com/watch?v=EHyYArZsYlA
Project Title:

Identification of Swarm Members

Picture of Identification of Swarm Members
Students:

Nir Daitch, Sapir Cohen

Supervisors:

David Dovrat

Description:
This project handles the identification of a specific object instance in a swarm of similar objects. We have created an image processing component that recognizes the existence of peers in a given frame- with a main goal of identifying peer drones.
Project Title:

Hybrid Video Coding at High Bit-Rates

Picture of Hybrid Video Coding at High Bit-Rates
Students:

Ron Gatenio, Roy Shchory

Supervisors:

Yehuda Dar

Description:
In this project we explore the prevalent hybrid video-coding concept that joins transform-coding and motion-compensation. Specifically, we study the necessity of transforming the motion-compensation prediction residuals for their coding at high bit-rates. Our research relies on empirical statistics from a simplified motion-compensation procedure implemented in Matlab, and also from the reference software of the state-of-the-art HEVC standard. Our results show that the correlation among the motion-compensation residuals gets lower as the bit-rate increases, supporting the marginal use of transform coding at high bit-rates (i.e., the residuals are directly quantized). We also developed a research tool that provides data from intermediate stages of the HEVC. The data mainly include motion-compensation residuals, motion vector data, block and frame types, and bit-budget of components. It is formatted in a structure suitable for easy usage in Matlab for future research projects.
Project Title:

Perlin City - Procedural 3D City Generation Project

Picture of Perlin City - Procedural 3D City Generation Project
Students:

Alex Nicola, Nati Goel

Supervisors:

Yaron Honen and Boaz Sternfeld

Description:
Based on a known method of creating a single random city block using perlin noise, we have created a method of procedurally generating an infinite city without the intervention of human designer. Perline city is deterministic, realistic and beautiful. To achieve best performance possible, we used object pooling, separation of building objects upon multiple frames using coroutines, detecting when to create and destroy and addition of detailed objects that are displayed and hidden based on distance Download Demo: https://drive.google.com/open?id=0B8C-GqYShcqub1pINW0zNk5obHc
Project Title:

Looming VR

Picture of Looming VR
Students:

Alla Khier,Inbar Donag

Supervisors:

Daniel Raviv

Description:
This is a virtual reality simulation of a vehicle with a camera that uses visual looming cue to navigate and avoid obstacles. We visualized the looming in different ways and compared 2 methods for looming calculation. One using ranges between the camera and the obstacles, and the other using the temporal change of the texture density of the obstacle.
Project Title:

Rate ditorsion optimized tree structures for image compression

Picture of Rate ditorsion optimized tree structures for image compression
Students:

Moshiko Elisof, Sefi Fuchs

Supervisors:

Yehuda Dar

Description:
1D and 2D Signals compression using improved tree coding, exploiting similarities of dyadic blocks by merging them into one tree leaf, allowing for adaptive tree-structure. The studied algorithms compensate for an inherent issue in standard tree-based signal coding - squared blocks which limit the ability to reduce representation bit-cost. Based on: [1]Rate-Distortion Optimized Tree-Structured Compression Algorithms for Piecewise Polynomial Images by Rahul Shukla, Member, IEEE, Pier Luigi Dragotti, Member, IEEE, Minh N. Do, and Martin Vetterli, Fellow, IEEE . [2]Image compression via improved quadtree decomposition algorithms by E. Shusterman and M. Feder
Project Title:

CutOutRL - Visualizing Neural Networks with Scribbles

Picture of CutOutRL - Visualizing Neural Networks with Scribbles
Students:

Yonatan Zarecki ,Ziv Izhar

Supervisors:

Elad Richardson

Description:
Deep neural networks (DNNs) have been very successful in recent years, achieving state-of-the-art results in a wide range of domains, such as voice recognition, image segmentation, face recognition and more. In addition, reinforcement-learning (RL) training methods combined with DNN models (deep RL) have been able to solve a wide variety of games, from PONG to Mario, purely by looking at the pixel values of the screen. Various “games” have been proposed for challenging neural networks, testing their capacity to learn complex tasks. Some tasks are designed to give us human insight about the way the model operates. In this project, we challenged a deep RL model with the task of segmenting an image using scribbles. We force it to achieve good segmentations by using scribble-based segmentation in a way similar to humans. We hope to gain insight on the way the network does segmentation by looking at the scribbles it generates.
Project Title:

Drone package delivery

Picture of Drone package delivery
Students:

Gal peretz , Gal malka

Supervisors:

Ohad Menashe

Description:
This is an open source project that intended to help people to create autonomous drone missions that operate with a pixhawk controller. The project is written in C++ and Python in order to enable fast image processing and operating the drone in real time. The project also includes built in missions. Our goal was to fly to a specific GPS location, scan the area for a bullseye target and land on the center of the target. You can use the framework to create your own missions. The framework includes an API that helps to stream live video over wifi or/and record the video to file. We’ve created this project in the Geomatric Image Processing lab at the Technion. Our goal was to create a simple framework to manage simple and complex missions represented by state machine Read more in our github page or see the project Report and final presentation
Project Title:

VR Newsroom

Picture of VR Newsroom
Students:

Natan Yelin

Supervisors:

Omri Azenkot and Boaz Sterenfeld and Yaron Honen

Description:
VR Newsroom is an experiment in browsing online news in virtual reality. It explores ways that VR can be used to facilitate discovery and exploration of large amounts of content. Online news was chosen for the content of the experiment because of its dynamic nature. Live APIs were chosen so that every time VR Newsroom is loaded different content is displayed. Custom RSS/ATOM feeds are also supported.
Project Title:

Virtual Reality interior designer

Picture of Virtual Reality interior designer
Students:

Frenkel Eduard and Salevich Alexander

Supervisors:

Mata Sela and Yaron Honen

Description:
Our project's goal is to allow a user to experience a sense of presence in a room while designing it.
We intend to build a designing tool using virtual reality which will allow to design a room space while being present inside a virtual room;
The tool will allow the user to design a room interior and be able to experience his work product at the same time.
Our solution will allow to design better suited environment for the customer by letting the designer the ability to show the customer the final design and get his feedback.
Project Title:

DeepFlowers - Online Flower Recognition using Deep Neural Networks

Picture of DeepFlowers - Online Flower Recognition using Deep Neural Networks
Students:

Yonatan Zarecki and Ziv Izhar

Supervisors:

Elad Richardson

Description:
These days, it seems like everyone has their own smartphone and that internet connection is available everywhere even in the most distant corners of nature reserves. A challenging task for nature lovers is the task of flower recognition, even with heavy big and heavy flower guide books it is hard to identify each flower species exactly, and for amateurs finding anything is using these guides can be a monumental task by itself, Differences between flowers species can be very subtle, and not easy to detect even for an expert's eye. Another challenge a flower classifier has to face is the sheer amount of flowers in the world, or in a specific country.

In this project we try and harvest the power of deep convolutional neural networks (CNNs) for our recognition task which have proven to be successful in similar tasks, and using data given to us by Prof. Avi Shmida of the Hebrew University, build a flower recognizer with an open online API for all to use.
Project Title:

Brain 3D Anatomy

Picture of Brain 3D Anatomy
Students:

Tom palny, Shani Levi and Nurit Devir

Supervisors:

Yaron Honen and Boaz Sternfeld and Omri Azencot and Hagai Tzafrir

Description:
Our system consists of three main parts: The first one is to receive a 3-dimensional matrix represents the MRI scan of the brain. We used Matlab in order to create 2-dimensional images from the given matrix. Each image was saved as a PNG file and represent a specific slice of the brain. The second part is to load the images into Unity and create a 3-dimensional object from them. In Order to build this object, we used the Ray Marching algorithm. The last part was to implement the ability to present the object in virtual reality using HTC vive and allow features which will give the user the feeling of the 3-dimensional object. Our project supports the following features: • Rotating – rotate the brain using the handheld controller. • Cutting – Cut the brain along the three X, Y and Z axes. • Zoom – zoom in and zoom out. • Reset button – turn back to the initial model. • Masking – emphasize different parts of the brain according to the user choice: The user can choose one of the two options representing the parts he wants – color it or remove the unwanted parts and see only the wanted one. After using the presentation way, the user can choose the specific part in which he is interested.
Project Title:

TermiNet

Picture of TermiNet
Students:

Itai Caspi

Supervisors:

Aaron Wetzler

Description:
In recent years, the introduction of deep reinforcement learning allowed rapid progress in the pursuit after implementing general AI. One of the long-standing challenges withholding further progress is designing an agent that operates in a hierarchical manner with temporal abstractions over its actions. We present a system which disassembles the learning into multiple sub-skills without external assistance. The system consists of a deep recurrent network which learns to generate action sequences from raw pixels alone, and implicitly learns structure over those sequences. We test the model on a complex 3D first person shooter game environment to demonstrate its effectiveness.
Project Title:

Anatomy VR

Picture of Anatomy VR
Students:

Ksenia Kaganer , Dima Trushinand Adi Mesika

Supervisors:

Boaz Sternfeld and Yaron Honen

Description:
We developed a 3D Anatomic learning application. Our application assist you in the learning process by creating a realistic Virtual Reality environment. You can explore all the human body parts in a very detailed level. Navigate between different body layers, e.g. skin, muscles, bones, internal organs etc. and see all the terminology names of each body part. In addition, you can walk around the body naturally, have a look at the body from every aspect you want and holding a VR plane that slice the body and get a different anatomic cuts.
Project Title:

Villicity

Picture of Villicity
Students:

Ksenia Kaganer,Eran Tzabar

Supervisors:

yaron honen and Avi Parush and Maayan Efrat

Description:
It is widely recognized that in celiac disease, to learn and adhere to the gluten free diet is essential for ensuring a good quality of life.
It is important that the education process adopt strategies to motivate and make the learning effective, particularly for children and adolescent.
In this context, new technologies can help make the learning process more engaging.
The main idea is to use a game approach in order to make the learning and training process more engaging and intuitive.
2016
Project Title:

Puppify - Automatic Generation of Planar Marionettes from Frontal Images

Picture of Puppify - Automatic Generation of Planar Marionettes from Frontal Images
Students:

Elad Richardson and Gil Ben-Shachar

Supervisors:

Anastasia Dubrovina and Aaron Weltzer

Description:
In this project, we propose a method for fully automating the body segmentation process, thus enabling a wide variety of consumer and security applications and removing the friction caused by manual input. The process starts with a deep convolutional network, used to localize body joints, which are refined and stabilized using Reverse Ensembling and skin tone cues. The skeletal pose model is then exploited to create "auto-scribbles": automatically generated foreground/background scribble masks that can be used as inputs for a wide range of segmentation algorithms to directly extract the subject's body from the background. Simple segmentation aware cropping produces individual body part crops which can be used to generate a planar marionette for repositioning and animation.
Project Title:

Snake 3D

Picture of Snake 3D
Students:

Sapir Eltanani and Simona Gluzman

Supervisors:
Description:

3D game which relives the experience of playing the old well known game 'Snake', but this time in a three dimentional world in VR. The game is developed for the leap motion and oculus rift devices.

The aim remains the same, the player has to eat the most of the flying foods to earn points. Every eaten food grows the snake longer, but once the player touches the objects in the world around him or the snake's body he loses.

Project Title:

Imae Segmentation Using Multi-Region Active Contours

Picture of Imae Segmentation Using Multi-Region Active Contours
Students:

Chen Shapira and Tamir Segev

Supervisors:
Description:
Our project focused on the Multi-region Active Contours with a single Level-Set function method. This method allows quick & accurate image segmentation on 2d and 3d images. This is done by dividing the image into multiple regions by calculating a single nonnegative distance function, which is easily extracted using the Voronoi Implicit Interface Method.
Project Title:

Augmented Reality in Road Navigation

Picture of Augmented Reality in Road Navigation
Students:

Doron Halevy

Supervisors:
Description:
In this work, we propose a vision-based solution for globally localizing vehicles on the road using a single on-board camera and exploiting the availability of priorly geo-tagged street view images from the surrounding environment together with their associated local point clouds. Our approach is focused on the integration of image-based localization into a tracking and mapping system in order to provide accurate and globally-registered 6DoF tracking of the vehicle’s position at all times
Project Title:

QTCopter

Picture of QTCopter
Students:

Noam Yogev, Roee Mazor, Efi Shtain, Vasily Vitchevsky, Sergey buh, Alex Bogachenko and Daniel Joseph

Supervisors:
Description:

We built a platform to be used to create autonomous indoor flight capable drones and implemented a system based on computer vision to utilize and demonstrate this platform by performing four tasks, as described by a national contest sponsored by the Pearls of Wisdom voluntary association. Currently, such platforms are researched from commercial and academic points of view, but no finished products have been released.

The computer vision part of the project is responsible for communicating with the quadcopter’s navigation module, in order to guide the quadcopter to the next target, identify key objects and trigger the execution of various required auxiliary actions. The software will run on an ARM-based companion computer running Linux, mounted on the drone. It will receive images from one or multiple high speed cameras on the quadcopter and ROS messages from the navigation module running on the same computer. The OpenCV library will be used to implement the required functionalities.

Project Title:

Mobile Point Fusion

Picture of Mobile Point Fusion
Supervisors:

Aaron Wetzler

Description:
The need for real-time 3D reconstruction is becoming more and more apparent in today's world. Depth Sensors are being marketed today in consumer laptops and tablets. In the near future we expect an increase in availability of mobile devices with depth sensors, and therefore also a need for highly efficient real-time 3D reconstruction methods. Our project's goal is to enable these devices to preform 3D reconstruction in real-time. Our solution uses the input from a moving depth sensor to estimate the camera position and build a 3D model. The implementation harnesses the GPU to achieve real time preformace while taking into account the limitations of mobile devices and putting a strong emphasis on optimizations throughout the pipeline.
Project Title:

Automatic 3D Face Printing

Picture of Automatic 3D Face Printing
Students:

Hila Ben-Moshe and David Gelbendorf

Supervisors:

Alon Zvirin

Description:

Developing automatic process for building a 3D face model from a GIP facial video file. We first use Viola Jones Face Detection Algorithm to detect which frame should we choose from the video. Detection of face features and the movement of face are taken under consideration when choosing the best frame automatically. Than we process the selected frame, including automatic choose of bounding box and with fixing missing eyebrows. Finally, we write the desired model to STL format, a known format to print in 3D.

2015
Project Title:

Image Segmentation and Matting in Realtime on a Mobile Device

Picture of Image Segmentation and Matting in Realtime on a Mobile Device
Students:

Elad Richardson

Supervisors:
Description:
In the our project we've implemented a scribble-based algorithm for extracting object from natural photos and pasting them seamlessly into a different background under the constrains of a mobile device computational power. The algorithm was first developed and tested on a personal computer with the help of openCV’s C++ libraries and was then ported to Android using the Native Development Kit. We used the Android Software Development Kit in order to wrap the algorithm in a user friendly interface to create an application that anybody can use.
Project Title:

Rigid ICP Registration with Kinnect

Picture of Rigid ICP Registration with Kinnect
Students:

Choukroun Yoni and Semmel Elie

Supervisors:
Description:
The main goal of the first part of the project was to perform an Iterative Closest Point registration on two depth maps obtained using the Kinect depth sensor in C++ on the windows platform. The other purposes of this first part was to learn how to integrate alone big libraries (dynamic or not) to the project and to handle with the difficulties of implementing an algorithm on the different classes of the libraries whom do not match necessarily one with the other. The second part of the project was to bond, two by two with the precedent algorithm, different scan frames get by the Kinect with the help of its motor to get a whole body depth image.
Project Title:

Mad Panim

Picture of Mad Panim
Students:

Nadav Toledo

Supervisors:

Eng. Alon Zvirin and Eng. Yaron Honen

Description:
בניית ארגז כלים לאנליזה ראשונית של מודל\משטח פנים תלת-מימדי בניסויים קליניים עבור הרופאים. ארגז הכלים יאפשר לרופאים בין היתר להציג מודל פנים מיטבי של הנבדק לאחר סריקת וידאו של פניו, למצוא נקודות עניין על גבי המודל\משטח ולמדוד מרחקים גאודטים בין הנקודות השונות לפי שיקול דעתו של הרופא. האפליקציה מקבלת קובץ וידאו תלת מימדי ממצלמת גיפ ויודעת באופן אוטומטי לבחור את תמונת העומק הטובה ביותר מתוך סרט הוידאו. בתמונת העומק יבחרו באופן אוטומטי שלוש נקודות במרכז הפנים בעזרת ויולה וג'ונס. בעזרת ASM ימצאו באופן אוטומטי 68 נקודות בכל משטח הפנים המוצג. אלג' שפותח ע"י הסטודנט יודע באופן אוטומטי לחלוטין לבחור מתוך כל הנקודות 12 נקודות מרכזיות (5 בפה, 4 בעיניים, 3 באף). באמצעות הממשק שפותח ניתן לראות את המסלולים על גבי הפנים ,בין הנקודות הנבחרות ולקבל באופן מיידי את המרחק הגאודטי ביניהן (fast marching). בנוסף, ניתן לשמור את כל הנתונים (נקודות,מרחקים,מס' תמונה, הערות וכו') לקובץ אקסל ולעלות אותם במועד מאוחר יותר.
Project Title:

Printed circuit boards detection and image analysis

Picture of Printed circuit boards detection and image analysis
Students:

Giorgio Tabarani and Roi Divon

Supervisors:

Dr. Amir Adler

Description:
In recent years and with recent events in the country, arose the demand to be able to identify printed circuit boards taken by the Israeli Police in crime scenes in order to connect between cases and identify the source of these boards hoping to avoid similiar and unpleasant incidents in the future.

The police takes snapshots of printed circuit boards from every crime scene, mostly distorted circuits due to burns or fractures, and tries to identify their origin from manual inspection and by guessing.

In this project we were asked to develop a basic system which can perform the aforementioned identification automatically given a picture of a shred and a database of pictures of circuit boards which usually appear in crime scenes.

Project Title:

Generating 3D Colored Face Model Using a Kinect Camera

Picture of Generating 3D Colored Face Model Using a Kinect Camera
Students:

Rotem Mordoch and Nadine Toledano and Ori Ziskind

Supervisors:

Matan Sela and Yaron Honen

Description:
The constant development of cheap depth cameras, together with the ongoing integration of them on mobile devices, offers the potential of many new and exciting applications covering various of different fields. This includes personal everyday use, commercial objectives and medical solutions. In our project we propose a system which allows the user to easily create a colored 3D facial model of its own. The objective of this project is to build a user-friendly system for generating a 3D colored facial model. The solution we offer combines open source techniques for face detection in an image and a 3D reconstruction algorithm. We integrate these techniques to create a common algorithm which produces our goal. The system we have built uses depth camera stream to capture a subject’s face on each frame, and uses this information to generate a high quality colored 3D facial model. We demonstrate our results and optimizations to the solution, and offer possible future opportunities to continue our work.
Project Title:

Solving Simultaneous Linear Equations Using GPU

Picture of Solving Simultaneous Linear Equations Using GPU
Students:

Oriel Rosen and Haviv Cohen

Supervisors:

Yaron Honen

Description:
Image processing tends to demand highly complicated computations. Though some programming languages (as Matlab) are very comfortable for mathematical usage, they are less than ideal in terms of performance, leading to programs which run far too much time. The solution to that problem is to use “stronger” tools at the choke-points of the computation. By programming with low-level languages and by using parallelism, we can drastically improve our program’s performance.
Project Title:

Freehand Voxel Carving Scanning on a Mobile Device

Picture of Freehand Voxel Carving Scanning on a Mobile Device
Students:

Alex Fish

Supervisors:

Aaron Wetzler

Description:

3D scanners are growing in their popularity as many new applications and products are becoming a commodity. These applications are normally tethered to a computer and/or require expensive and specialized hardware. Our goal is to provide a 3D scanner which uses only a mobile phone with a camera. We consider the problem of computing the 3D shape of an unknown, arbitrarily shaped scene from multiple color photographs taken at known but arbitrarily distributed viewpoints using a mobile device. The estimated camera orientation and position in 3D space obtained from publicly available SLAM libraries permits us to perform a 3D reconstruction of the observed objects. We demonstrate that it is possible to achieve a good 3D reconstruction on a mobile device.

2014
Project Title:

3D Image Fusion using ICP

Picture of 3D Image Fusion using ICP
Students:

Itay Naor

Supervisors:

Alon Zvirin and Guy Rosman and Yaron Honen

Description:

This project deals with the ICP algorithm and uses it to create a complete three-dimensional model of a rigid object. First, a wrapper for an ICP algorithm included in PCL library which fuses 3D images taken by GIP Technion laboratory camera was written, and its running parameters were optimized. Afterwards, various improvements were implemented for an ICP algorithm using distance function of point to surface, parameters were examined, and fusion algorithm was written. Finally, the program was integrated to the GUI of GIP Technion laboratory .

Project Title:

Puzzle Bingo

Picture of Puzzle Bingo
Students:

Shahar Sagiv, Omri Panizel, Loui Diab and Waseem Ghraye

Supervisors:
Description:
We created a new type of game, which combines the competitive aspect of the Bingo game with the great fun of solving a puzzle. This game is being played simultaneously between 4 players (on 4 different devices) who compete by solving the given puzzle. Every player can see the real time progress of the other players with a map showing their boards. Every Puzzle has a title name and the user have a choice to stop solving the puzzle when he recognize the image, and try to guess the image title. This gives the game educational value, as the players learn to recognize places, animals, and celebrities.
Project Title:

Clinical Ceck-up System

Picture of Clinical Ceck-up System
Students:

Anna Ufliand and Sergey Yusufov

Supervisors:
Description:
המטרה העיקרית של הפרויקט היתה פיתוח מערכת אשר תהיה מסוגלת להתתממשק עם מכשיר לבדיקות רפואיות, אשר פותח בפקולטה להנדסה ביו-רפואית, לקרוא ולהציג את הנתונים מהמכשיר הנ"ל בצורה נוחה, יעילה וכך שיהיה ניתן לגשת לנתונים האלא ממכשירים שונים.כאחד הקריטריונים החשובים עבורינו היה לפתח מערכת אשר תאפשר גמישות מירבית. למכשיר ביו-רפואי יכולות להיות הרבה קונפיגורציות שונות, זו בעצם פלטפורמה המאפשרת הרכבת חיישנים שונים לביצוע בדיקות שונות. היה לנו חשוב להתייחס לכך ולפתח מערכת אשר לא תגביל את סוג וכמות החיישנים אשר בעזרתם מבצעים בדיקות.
Project Title:

Make it stand

Picture of Make it stand
Students:

Dvora Nagler

Supervisors:
Description:

Artists, designers and architects use imbalance to their advantage to produce surprising and elegant designs.
The balancing process is challenging when manipulating geometry in a 3D modeling software,
since volumes are only represented by their boundaries.
Our goal is to modify volume shape such that once printed, the model stands.
To do so, we manipulate the inner voids and sometimes it will not be enough and we will also consider deforming the model.
These two manipulations change the mass distribution and thus the center of mass position.

Project Title:

Solving Classification Problem on Hyperspectral Images

Picture of Solving Classification Problem on Hyperspectral Images
Students:

Talor Abramovich and Oz Gavrielov

Supervisors:
Description:
Hyperspectral Imaging is a spectral imaging method, which includes bands from the visible light as well as infra red. Unlike the 2D color images, which only use red, green and blue, hyperspectral image includes a third dimension of spectrum. This information can be used to classify the objects in the image, and to define the difference between asphalt, plants and water. It could even show the difference between real leaf and a plastic one. In our project we used several classification algorithms, including KNN, PCA and KSVD to classify four hyperspectral images. We compared the results and found which algorithm gives the best classification and which is the most efficient..
2013
Project Title:

Quaternion K-SVD for Color Image Denoising

Picture of Quaternion K-SVD for Color Image Denoising
Students:

Amit Carmeli

Supervisors:
Description:
In this work, we introduce the use of Quaternions within the field of sparse and redundant representations. The Quaternion space is an extension of the complex space, where each element is composed of four parts – a real-part and three imaginary parts. The major difference between Quaternion space H and the complex C space is that the Quaternion space has non-commutative multiplication. We design and implement Quaternion variants of state-of-the-art algorithms OMP and KSVD. We show various results, previously established only for the real or complex spaces, and use them to devise the Quaternion K-SVD algorithm, nicknamed QK-SVD.
Project Title:

Structured Light Based 3D Reconstruction with Priors

Picture of Structured Light Based 3D Reconstruction with Priors
Students:

Itamar Talmi and Ofir Haviv

Supervisors:
Description:
פרויקט זה בא לבחון שימוש באלגוריתם PCA לצורך פתיחת נעילת מכשיר אנדרואיד על ידי זיהוי פנים. פרויקט זה מדגים כיצד ניתן, ע"י למידה של פני האדם כלשהו בתאורות שונות, בניית בסיס PCA של אותו אדם, ובעזרת בסיס זה, לאמת מישהו שמנסה להזדהות בעת פתיחת נעילה של מכשיר אנדרואיד.
Project Title:

CamPong – Smartphone PONG using Camera and Built-in Projector

Picture of CamPong – Smartphone PONG using Camera and Built-in Projector
Students:

Nofar Carmeli and Rom Herskovitz

Supervisors:
Description:
In this project, we introduce the use of a mobile phone, equipped with a camera and a projector, to allow real time hand detection. We present a demo of an interactive pong game that is controlled by the players’ natural hand movements during the game. To the best of our knowledge this is the first known use of a commodity cellular phone that uses an inbuilt projector to perform real time structured light projections coupled with real time image processing.
2012
Project Title:

Open Fusion

Picture of Open Fusion
Students:

Nurit Schwabsky and Vered Cohen

Supervisors:
Description:
OpenFusion is an implementation of Microsoft’s KinectFusion system. This system enables real-time tracking and reconstruction of a 3D scene using a depth sensor. A stream of depth images is received from the camera and compared to the model built so far using the Iterative Closest Point (ICP) algorithm to track the 6DOF camera position. The camera position is then used to integrate the new depth images into the growing volumetric model, resulting in an accurate and robust reconstruction. The reconstructed model is adapted according to dynamic changes in the scene without losing accuracy.
Project Title:

3D Stereo Reconstruction Using iPhone Devices

Picture of 3D Stereo Reconstruction Using iPhone Devices
Students:

Ron Slossberg and Omer Shaked

Supervisors:
Description:
Stereo Reconstruction is a common method for obtaining depth information about a given scene using 2D images of the scene taken simultaneously by two cameras from different views. This process is done by finding corresponding objects which appear in both images and examining their relative positions in the images, based on previous knowledge of the internal parameters of each camera and the relative positions of both cameras. This method relies on the same basic principle that enables our eyes to perceive depth.

 

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