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Table Of Integrals Series And Products

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

The PASCAL Visual Object Classes Challenge

An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

The computer vision and pattern recognition communities have recently witnessed a surge of feature-based methods in object recognition and image retrieval applications. These methods allow representing images as collections of “visual words” and treat them using text search approaches following the “bag of features” paradigm. In this article, we explore analogous approaches in the 3D world applied to the problem of nonrigid shape retrieval in large databases. Using multiscale diffusion heat kernels as “geometric words,” we construct compact and informative shape descriptors by means of the “bag of features” approach. We also show that considering pairs of “geometric words” (“geometric expressions”) allows creating spatially sensitive bags of features with better discriminative power. Finally, adopting metric learning approaches, we show that shapes can be efficiently represented as binary codes. Our approach achieves state-of-the-art results on the SHREC 2010 large-scale shape retrieval benchmark.

Shape google: Geometric words and expressions for invariant shape retrieval

Kinship Verification of two or more people has shown to be a complicated problem, though it is widely used in various practical tasks and applications. The areas of the use-cases vary. Among them are applications for homeland security, automatic family recognition, youth and elder matching or predicting and more. We propose using Deep Learning approach to deal with the problem of Kin Verification, such to provide a logical explanation for solving the problem with a novel mechanism for training on the FIW data-set. Our method obtains state-of-the-art for the FIW challenge for the restricted-image setting11

Kin-Verification Model on FIW Dataset Using Multi-Set Learning and Local Features

Facial Landmark Point Localization using Coarse-to-Fine Deep Recurrent Neural Network

This paper suggests a novel segmentation algorithm for separating moving objects from the background in video sequences without any prior information of the sequence nature. We formulate the problem as a connectivity analysis of region adjacency graph (RAG) based on temporal information. The nodes of the RAG represent homogeneous regions and the edges represent temporal information, which is obtained by frames comparison iterations. Connectivity analysis of the RAG nodes is performed after each frames comparison by a breadth first search (BFS) based algorithm. The set of nodes, which achieve maximum weight of theirs surrounding edges are considered as moving object. The number of comparisons that are needed for temporal information is automatically determined.

/pdf/automatic-adaptive-segmentation-of-moving-objects-based-on-1fzdvb2css.pdf

Automatic Adaptive Segmentation of Moving Objects Based on Spatio-Temporal Information.

Symmetry detection and analysis in 3D images is a fundamental task in a gamut of scientific fields such as computer vision, medical imaging and pattern recognition to name a few. In this work, we present a computational approach to 3D symmetry detection and analysis. Our analysis is conducted in the Fourier domain using the pseudo-polar Fourier transform. The pseudo-polar representation enables to efficiently and accurately analyze angular volumetric properties such as rotational symmetries. Our algorithm is based on the analysis of the angular correspondence rate of the given volume and its rotated and rotated-inverted replicas in their pseudo-polar representations. We also derive a novel rigorous analysis of the inherent constraints of 3D symmetries via groups-theory based analysis. Thus, our algorithm starts by detecting the rotational symmetry group of a given volume, and the rigorous analysis results pave the way to detect the rest of the symmetries. The complexity of the algorithm is O(N 3log?(N)), where N×N×N is the volumetric size in each direction. This complexity is independent of the number of the detected symmetries. We experimentally verified our approach by applying it to synthetic as well as real 3D objects.

http://www.cs.tau.ac.il/~amir1/PS/3D-Symmetry-detection1.pdf

3-D Symmetry Detection and Analysis Using the Pseudo-polar Fourier Transform

We present a method for improving a Non Local Means operator by computing its low-rank approximation. The low-rank operator is constructed by applying a filter to the spectrum of the original Non Local Means operator. This results in an operator which is less sensitive to noise while preserving important properties of the original operator. The method is efficiently implemented based on Chebyshev polynomials and is demonstrated on the application of natural images denoising. For this application, we provide a comprehensive comparison of our method with leading denoising methods.

Yosi Keller

Papers

Kin-Verification Model on FIW Dataset Using Multi-Set Learning and Local Features

Facial Landmark Point Localization using Coarse-to-Fine Deep Recurrent Neural Network

Automatic Adaptive Segmentation of Moving Objects Based on Spatio-Temporal Information.

3-D Symmetry Detection and Analysis Using the Pseudo-polar Fourier Transform

An algorithm for improving Non-Local Means operators via low-rank approximation