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June 2017
  • 15.06.17 Invitation to Alon Steren's lecture
    Time and Place: 21/06/2017, 14:30, Taub 601
    Speaker: Alon Shtern
    Title: Shape Correspondence using Spectral Methods and Deep Learning
    Supervisor: Prof. Ron Kimmel

    The interest in acquiring and analyzing the geometry of the world is ever increasing, fueling a wide range of computer vision algorithms in the field of geometry processing. Spectral analysis has become key component in many applications involving non-rigid shapes modeled as two-dimensional surfaces, and recently, convolutional neural networks have shown remarkable success in a variety of computer vision tasks. We designed a set of methods and tools that use these paradigms for applications such as shape correspondence, nonrigid deformations, and volumetric optical flow. In this talk we will present three different ways to infer point-to-point correspondences between deformable shapes, which is a fundamental operation in the field of geometry processing.

    A well-established approach to address the non-rigid shape correspondence problem is to define a measure of dissimilarity between the shapes. One way for measuring distance between two non-rigid shapes is to embed their two-dimensional surfaces into some common Euclidean space, defining the comparison task as a problem of rigid matching in that space. In the first part of this talk we review a novel spectral embedding, named the "Spectral Gradient Fields Embedding", which exploits the local interactions between the eigenfunctions of the Laplace-Beltrami operator and the extrinsic geometry of the surface.

    Next, we analyze the applicability of the spectral kernel distance, as a measure of dissimilarity between surfaces, for solving the shape matching problem. To align the spectral kernels, we developed the Iterative Closest Spectral Kernel Maps (ICSKM) algorithm. ICSKM extends the Iterative Closest Point (ICP) algorithm to the class of deformable shapes. Instead of aligning the shapes in the three dimensional Euclidean domain, this method estimates the transformation that best fits the embeddings of the shapes into the spectral domain.

    Volumetric optical flow is a different way to address the matching problem of a three-dimensional dynamic scene. In the last part of the talk we introduce a multi-scale optical flow based deep learning architecture for predicting the next frame of a sequence of volumetric images. The fully differentiable model consists of specific crafted modules that are trained on small patches in an unsupervised manner. The approach, called "V-Flow", is useful for analyzing the temporal dynamics of three-dimensional images in applications that involve, for example, motion of viscous fluid substances or volumetric medical imaging.
  • 13.06.17 Aaron Wetzler's PhD Seminar on Computational Geometric Vision

    Who: Aaron Wetzler (EE/CS-Technion)

    Time and Place: Thursday 22/06, 11.30 in Taub 337


    PhD Seminar: Computational Geometric Vision


    By combining geometric principles for shape analysis with modern sensing techniques, large-scale datasets and powerful

    computational architectures we show various new ways of enabling computers to better perceive, interpret and comprehend

    the geometry of the world around them. Specifically we explore the topics of reconstruction, filtering and semantic

    processing within the context of computational geometric vision.

    Reconstruction - We start by discussing the problem of sensing and reconstructing three-dimensional geometry. We develop a method of performing efficient photometric stereo which can model non-linear and near-source lighting setups while avoiding directly computing normal fields. We then look into to two alternative approaches for reconstructing geometry on a smartphone using projected light.

    Filtering - Image and shape data which is obtained through modern cameras and sensors is typically noisy. We contribute a new patch based data denoising framework called the Beltrami Patch filter for denoising grayscale, color images and extend it to depth fields and 3D meshes. We then modify the approach by reformulating the differential operators used as trainable kernels in a deep neural network and unrolling the update step through time. We demonstrate state of the art results and highlight the fact that other PDE based methods could take advantage of the same basic idea.

    Semantic processing - In the last part of this talk we discuss the problem of localizing and identifying self similar geometric objects in a complex visual space. We specifically focus on the problem of identifying fingertips on articulating hands observed by depth cameras. We describe how we used high accuracy magnetic sensors to annotate large quantities of training data for both front facing and ego-centric hand motion. To perform learning efficiently we turn to random forests and contribute a new approach for efficiently mapping the training of a random decision tree on billions of training samples with trillions of features to a single multi-GPU computing node. Similarly for inference we describe a novel pipelined FPGA hardware implementation.

    PhD work supervised by Prof. Ron Kimmel

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