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Predictive vector quantization of 3-D mesh geometry by representation of vertices in local coordinate systems
2007
Journal of Visual Communication and Image Representation
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Experimental results verify the advantage of the use of the local coordinate system over the global one. ...
In this work, the prediction error vectors are represented in a local coordinate system in order to cluster them around a subset of a 2-D planar subspace and thereby increase block coding efficiency. ...
The visual distortion vs. rate curves are displayed in Fig. 7 for the coding of the triangular test models, Venus Head, Horse and Bunny. ...
doi:10.1016/j.jvcir.2007.03.001
fatcat:hijijuvmcjehjifogmf3fedcty
Representation is representation of similarities
1998
Behavioral and Brain Sciences
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I propose a unified approach to visual representation, addressing the need for superordinate and basic-level categorization and for the identification of specific instances of familiar categories. ...
Representation in terms of similarities to reference shapes supports processing (e.g., discrimination) of shapes that are radically different from the reference ones, without the need for the computationally ...
a global mapping (even a space-variant one) is not a good model of the primate visual system insofar as translation invariance is concerned. ...
doi:10.1017/s0140525x98001253
fatcat:ybnbqivwwnc5rdwqefit2oqlzq
Compressive Visual Representations
[article]
2021
arXiv
pre-print
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Learning effective visual representations that generalize well without human supervision is a fundamental problem in order to apply Machine Learning to a wide variety of tasks. ...
Our experiments confirm that adding compression to SimCLR and BYOL significantly improves linear evaluation accuracies and model robustness across a wide range of domain shifts. ...
Entropy Bottleneck
In order to test our hypothesis that compression can improve visual representation quality, we need to
be able to measure and control the amount of compression in our visual representations ...
arXiv:2109.12909v3
fatcat:cb3ngpw4bbegro3arubpsiqv4q
Learning a Fixed-Length Fingerprint Representation
[article]
2019
arXiv
pre-print
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Coupled with a re-ranking scheme, the DeepPrint rank-1 search accuracy on the NIST SD4 dataset against a gallery of 1.1 million fingerprints is comparable to the top COTS matcher, but it is significantly ...
We present DeepPrint, a deep network, which learns to extract fixed-length fingerprint representations of only 200 bytes. ...
(ii) The representations extracted in [27] require the arduous process of minutiae-detection, patch extraction, patch-level inference, and an aggregation network to build a single global feature representation ...
arXiv:1909.09901v2
fatcat:e5bitszt7jeopjrlpzr5nhtbee
Rethinking the Role of Top-Down Attention in Vision: Effects Attributable to a Lossy Representation in Peripheral Vision
2012
Frontiers in Psychology
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In particular, our texture tiling model (TTM) represents images in terms of a fixed set of "texture" statistics computed over local pooling regions that tile the visual input. ...
Recent work suggests that there is a significant loss of information in early stages of visual processing, especially in the periphery. ...
FIGURE 4 | (A) In visual search, we propose that on each fixation (red cross), the visual system computes a fixed set of summary statistics over each local patch. ...
doi:10.3389/fpsyg.2012.00013
pmid:22347200
pmcid:PMC3272623
fatcat:e7yx6hndjjcpbppjfbiebitij4
End-to-end Learning of Deep Visual Representations for Image Retrieval
[article]
2017
arXiv
pre-print
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At the end of the training process, the proposed architecture produces a global image representation in a single forward pass that is well suited for image retrieval. ...
Our representations can also be heavily compressed using product quantization with little loss in accuracy. For additional material, please see www.xrce.xerox.com/Deep-Image-Retrieval. ...
Concurrently, methods that aggregate local patches to build a global image representation have been considered. ...
arXiv:1610.07940v2
fatcat:ecuxhrf6bffjfawbvoo7lgnmny
Image Super-Resolution Via Sparse Representation
2010
IEEE Transactions on Image Processing
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In addition, the local sparse modeling of our approach is naturally robust to noise, and therefore the proposed algorithm can handle super-resolution with noisy inputs in a more unified framework. ...
The learned dictionary pair is a more compact representation of the patch pairs, compared to previous approaches, which simply sample a large amount of image patch pairs [1], reducing the computational ...
[14] proposed a two-step statistical approach integrating the global PCA model and a local patch model. ...
doi:10.1109/tip.2010.2050625
pmid:20483687
fatcat:gxaj3q5ucnaphiyquaiy6y4qyy
Spectral Analysis of Latent Representations
[article]
2019
arXiv
pre-print
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We propose a metric, Layer Saturation, defined as the proportion of the number of eigenvalues needed to explain 99% of the variance of the latent representations, for analyzing the learned representations ...
Saturation is based on spectral analysis and can be computed efficiently, making live analysis of the representations practical during training. ...
We interpret this as a symptom of the convergence of the model towards a local minimum. ...
arXiv:1907.08589v1
fatcat:ip4li4bvbncmrohjkcyxibtnhu
A polygon soup representation for multiview coding
2010
Journal of Visual Communication and Image Representation
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Starting from a sequence of multi-view video plus depth (MVD) data, the proposed quad-based representation takes into account, in a unified manner, different issues such as compactness, compression, and ...
Second, a selective elimination of the quads is performed in order to reduce inter-view redundancies and thus provide a compact representation. ...
local weights used to perform local adaptive blending of view contribution. ...
doi:10.1016/j.jvcir.2010.01.003
fatcat:x3isiu7snfeyjmkufvvtgmwv4i
SIFT-based local image description using sparse representations
2009
2009 IEEE International Workshop on Multimedia Signal Processing
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This is accomplished by searching for a sparse approximation of the input SIFT descriptors. ...
histogram of occurence of the different visual words in the image. ...
The methods rely on a sparse representation of SIFT descriptors computed on MSER regions. ...
doi:10.1109/mmsp.2009.5293301
dblp:conf/mmsp/ZepedaKG09
fatcat:tmidm2ubfvdxjpd2x7h2pyexra
Techniques for the graph representation of spectral imagery
2011
2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)
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representation of the image. ...
Contrary to the typical approaches of utilizing the first order statistics, mixture models, and linear subspaces, the methods described in this paper utilize the spectral data structure to generate a graph ...
An additional impact of the advanced methods is the decrease in total neighbors searched and in edges used (i.e. less computing resources). ...
doi:10.1109/whispers.2011.6080912
dblp:conf/whispers/MercovichAM11
fatcat:as57cpquyjfyvoxuvjsk7zagoq
Compressed Holistic ConvNet Representations for Detecting Loop Closures in Dynamic Environments
2020
IEEE Access
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In this paper, (1) We proposed a flexible loop closure detection workflow based on the holistic representations; (2) In this workflow, a post-processing method is applied to the raw holistic ConvNet representations ...
Detecting loop closures in dynamic environments is a severe challenge for the simultaneous localization and mapping (SLAM) system. ...
INTRODUCTION The visual simultaneous localization and mapping (SLAM) have been widely studied in the past few years especially in the fields of robotics and computer vision [1] . ...
doi:10.1109/access.2020.2982228
fatcat:duuvmup6cfbz7ibghj4uqxlxvy
SURREAL: Subgraph Robust Representation Learning
2019
Applied Network Science
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It preserves both local and global connectivity patterns, and addresses the issue of high-degree nodes that may incidentally connect a pair of nodes in a graph. ...
Representation learning algorithms aim to preserve local and global network structure by identifying node neighborhoods. ...
However, merely adopting the SkipGram model for graph representation learning seems to be insufficient in capturing local and global connections (Perozzi et al. 2014; Tang et al. 2015; Grover and Leskovec ...
doi:10.1007/s41109-019-0160-1
fatcat:jhiluwpebrfnlk22kyqhxjk4wy
Learning Generalisable Omni-Scale Representations for Person Re-Identification
[article]
2021
arXiv
pre-print
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Further, to determine the optimal placements of these IN layers in the architecture, we formulate an efficient differentiable architecture search algorithm. ...
An effective person re-identification (re-ID) model should learn feature representations that are both discriminative, for distinguishing similar-looking people, and generalisable, for deployment across ...
In [40] , [41] , [42] , CNNs are branched to learn representations from global and local image regions. ...
arXiv:1910.06827v5
fatcat:f5hua6mrkncpdbkpfw5t2dtmyq
Image Capture and Representation
[chapter]
2014
Computer Vision Metrics
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This chapter surveys a range of topics dealing with capturing, processing, and representing images, including computational imaging, 2D imaging, and 3D depth imaging methods, sensor processing, depth-field ...
Readers with a strong background in the area of 2D and 3D imaging may benefit from a light reading of this chapter. ...
The bundle adjustment process can perform either a local adjustment over a limited set of recent frames or global adjustment over all the frames during times of low scene motion when time permits. ...
doi:10.1007/978-1-4302-5930-5_1
fatcat:2fwvd5xe3ffblgiw4oj4vs3idq
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