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A Generalized Probabilistic Framework for Compact Codebook Creation

A Generalized Probabilistic Framework for Compact Codebook C...

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IEEE Conference on computer vision and pattern recognition

作者： Lingqiao Liu Lei Wang Chunhua Shen School of Engineering Australian National University Australian Center Visual Technologies University of Adelaide

ISBN: (纸本)9781457703942

Compact and discriminative visual codebooks are preferred in many visual recognition tasks. In the literature, a few researchers have taken the approach of hierarchically merging visual words of a initial large-size codebook, but implemented this idea with different merging criteria. In this work, we show that by defining different class-conditional distribution function and parameter estimation method, these merging criteria can be unified under a single probabilistic framework. More importantly, by adopting new distribution functions and/or parameter estimation methods, we can generalize this framework to produce a spectrum of novel merging criteria. Two of them are particularly focused in this work. For one criterion, we adopt the multinomial distribution to model each object class, and for the other criterion we propose a max-margin-based parameter estimation method. Both theoretical analysis and experimental study demonstrate the superior performance of the two new merging criteria and the general applicability of our probabilistic framework.

关键词： merger Compact Compact pattern Frameworks codebook Parameter estimation Statistical analysis decentralised control Distribution function Theoretical analysis

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A Robust Method for Vector Field Learning with Application to Mismatch Removing

A Robust Method for Vector Field Learning with Application t...

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IEEE Conference on computer vision and pattern recognition

作者： Ji Zhao Jiayi Ma Jinwen Tian Jie Ma Dazhi Zhang Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology

ISBN: (纸本)9781457703942

We propose a method for vector field learning with outliers, called vector field consensus (VFC). It could distinguish inliers from outliers and learn a vector field fitting for the inliers simultaneously. A prior is taken to force the smoothness of the field, which is based on the Tiknonov regularization in vector-valued reproducing kernel Hilbert space. Under a Bayesian framework, we associate each sample with a latent variable which indicates whether it is an inlier, and then formulate the problem as maximum a posteriori problem and use Expectation Maximization algorithm to solve it. The proposed method possesses two characteristics: 1) robust to outliers, and being able to tolerate 90% outliers and even more, 2) computationally efficient. As an application, we apply VFC to solve the problem of mismatch removing. The results demonstrate that our method outperforms many state-of-the-art methods, and it is very robust.

关键词： outliers inliers Vector field voltage frequency conversion expectation-maximisation algorithm Hilbert space Smoothness

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Aggregating Gradient Distributions into Intensity Orders: A Novel Local Image Descriptor

Aggregating Gradient Distributions into Intensity Orders: A ...

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IEEE Conference on computer vision and pattern recognition

作者： Bin Fan Fuchao Wu Zhanyi Hu National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781457703942

A novel local image descriptor is proposed in this paper, which combines intensity orders and gradient distributions in multiple support regions. The novelty lies in three aspects: 1) The gradient is calculated in a rotation invariant way in a given support region;2) The rotation invariant gradients are adaptively pooled spatially based on intensity orders in order to encode spatial information;3) Multiple support regions are used for constructing descriptor which further improves its discriminative ability. Therefore, the proposed descriptor encodes not only gradient information but also information about relative relationship of intensities as well as spatial information. In addition, it is truly rotation invariant in theory without the need of computing a dominant orientation which is a major error source of most existing methods, such as SIFT. Results on the standard Oxford dataset and 3D objects have shown a significant improvement over the state-of-the-art methods under various image transformations.

关键词： Error Sources Descriptors graph transformation Imagery (Psychotherapy) Image images Invariant Gradient

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Boosted Local Structured HOG-LBP for Object Localization

Boosted Local Structured HOG-LBP for Object Localization

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IEEE Conference on computer vision and pattern recognition

作者： Junge Zhang Kaiqi Huang Yinan Yu Tieniu Tan National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781457703942

Object localization is a challenging problem due to variations in object's structure and illumination. Although existing part based models have achieved impressive progress in the past several years, their improvement is still limited by low-level feature representation. Therefore, this paper mainly studies the description of object structure from both feature level and topology level. Following the bottom-up paradigm, we propose a boosted Local Structured HOGLBP based object detector. Firstly, at feature level, we propose Local Structured Descriptor to capture the object's local structure, and develop the descriptors from shape and texture information, respectively. Secondly, at topology level, we present a boosted feature selection and fusion scheme for part based object detector. All experiments are conducted on the challenging PASCAL VOC2007 datasets. Experimental results show that our method achieves the state-of-the-art performance.

关键词： Feature extraction Descriptors illumination Lighting topology Pascal based detector

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Supervised Local Subspace Learning for Continuous Head Pose Estimation

Supervised Local Subspace Learning for Continuous Head Pose ...

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IEEE Conference on computer vision and pattern recognition

作者： Dong Huang Markus Storer Fernando De la Torre Horst Bischof Robotics Institute Carnegie Mellon University Institute for Computer Graphics and Vision Graz University of Technology

ISBN: (纸本)9781457703942

Head pose estimation from images has recently attracted much attention in computer vision due to its diverse applications in face recognition, driver monitoring and human computer interaction. Most successful approaches to head pose estimation formulate the problem as a nonlinear regression between image features and continuous 3D angles (i.e. yaw, pitch and roll). However, regression-like methods suffer from three main drawbacks: (1) They typically lack generalization and overfit when trained using a few samples. (2) They fail to get reliable estimates over some regions of the output space (angles) when the training set is not uniformly sampled. For instance, if the training data contains under-sampled areas for some angles. (3) They are not robust to image noise or occlusion. To address these problems, this paper presents Supervised Local Subspace Learning (SL~2), a method that learns a local linear model from a sparse and non-uniformly sampled training set. SL~2 learns a mixture of local tangent spaces that is robust to under-sampled regions, and due to its regularization properties it is also robust to over-fitting. Moreover, because SL~2 is a generative model, it can deal with image noise. Experimental results on the CMU Multi-PIE and BU-3DFE database show the effectiveness of our approach in terms of accuracy and computational complexity.

关键词： image noise pose estimation Head images computer human interaction Subspace image feature linear models Nonlinear Regression computer vision complexity classes

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13th International Conference on Advanced Concepts for Intelligent vision Systems, ACIVS 2011

13th International Conference on Advanced Concepts for Intel...

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13th International Conference on Advanced Concepts for Intelligent vision Systems, ACIVS 2011

ISBN: (纸本)9783642236860

The proceedings contain 67 papers. The special focus in this conference is on Advanced Concepts for Intelligent vision Systems. The topics include: Swarm Intelligence Based Searching Schemes for Articulated 3D Body Motion Tracking;combining Linear Dimensionality Reduction and Locality Preserving Projections with Feature Selection for recognition Tasks;a New Anticorrelation-Based Spectral Clustering Formulation;simultaneous Partitioned Sampling for Articulated Object Tracking;a Geographical Approach to Self-Organizing Maps Algorithm Applied to Image Segmentation;a Multi-Layer ‘Gas of Circles’ Markov Random Field Model for the Extraction of Overlapping Near-Circular Objects;evaluation of Image Segmentation Algorithms from the Perspective of Salient Region Detection;robust Active Contour Segmentation with an Efficient Global Optimizer;a Method to Generate Artificial 2D Shape Contour Based in Fourier Transform and Genetic Algorithms;supervised Visual Vocabulary with Category Information;image Segmentation Based on Electrical Proximity in a Resistor-Capacitor Network;hierarchical Blurring Mean-Shift;curve-Skeletons Based on the Fat Graph Approximation;DTW for Matching Radon Features: A pattern recognition and Retrieval Method;ridges and Valleys Detection in Images Using Difference of Rotating Half Smoothing Filters;analysis of Wear Debris through Classification;fourier Fractal Descriptors for Colored Texture Analysis;efficiency Optimization of Trainable Feature Extractors for a Consumer Platform;salient Region Detection Using Discriminative Feature Selection;image Analysis Applied to Morphological Assessment in Bovine Livestock;nonparametric Estimation of Fisher Vectors to Aggregate Image Descriptors;quantifying Appearance Retention in Carpets Using Geometrical Local Binary patterns;enhancing the Texture Attribute with Partial Differential Equations: A Case of Study with Gabor Filters;video Stippling.

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What Makes a Chair a Chair?

What Makes a Chair a Chair?

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IEEE Conference on computer vision and pattern recognition

作者： Helmut Grabner Juergen Gall Luc Van Gool Computer Vision Laboratory ETH Zurich ESAT - PSI/IBBT K.U. Leuven

ISBN: (纸本)9781457703942

Many object classes are primarily defined by their functions. However, this fact has been left largely unexploited by visual object categorization or detection systems. We propose a method to learn an affordance detector. It identifies locations in the 3d space which "support" the particular function. Our novel approach "imagines" an actor performing an action typical for the target object class, instead of relying purely on the visual object appearance. So, function is handled as a cue complementary to appearance, rather than being a consideration after appearance-based detection. Experimental results are given for the functional category "sitting". Such affordance is tested on a 3d representation of the scene, as can be realistically obtained through SfM or depth cameras. In contrast to appearance-based object detectors, affordance detection requires only very few training examples and generalizes very well to other sittable objects like benches or sofas when trained on a few chairs.

关键词： seat sofas functional categories object class BENCH benches Detection systems actor detector

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A Two-Stage Reconstruction Approach for Seeing Through Water

A Two-Stage Reconstruction Approach for Seeing Through Water

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IEEE Conference on computer vision and pattern recognition

作者： Omar Oreifej Guang Shu Teresa Pace Mubarak Shah Computer Vision Lab University of Central Florida Night Vision and Electronic Sensors Directorate

ISBN: (纸本)9781457703942

Several attempts have been lately proposed to tackle the problem of recovering the original image of an underwater scene using a sequence distorted by water waves. The main drawback of the state of the art [18] is that it heavily depends on modelling the waves, which in fact is ill-posed since the actual behavior of the waves along with the imaging process are complicated and include several noise components;therefore, their results are not satisfactory. In this paper, we revisit the problem by proposing a data-driven two-stage approach, each stage is targeted toward a certain type of noise. The first stage leverages the temporal mean of the sequence to overcome the structured turbulence of the waves through an iterative robust registration algorithm. The result of the first stage is a high quality mean and a better structured sequence;however, the sequence still contains unstructured sparse noise. Thus, we employ a second stage at which we extract the sparse errors from the sequence through rank minimization. Our method converges faster, and drastically outperforms state of the art on all testing sequences even only after the first stage.

关键词： Waves waves state of the art imaging processes seeing Sight Noise

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Deterministically Maximizing Feasible Subsystem for Robust Model Fitting with Unit Norm Constraint

Deterministically Maximizing Feasible Subsystem for Robust M...

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IEEE Conference on computer vision and pattern recognition

作者： Yinqiang Zheng Shigeki Sugimoto Masatoshi Okutomi Department of Mechanical and Control Engineering Tokyo Institute of Technology

ISBN: (纸本)9781457703942

Many computer vision problems can be accounted for or properly approximated by linearity, and the robust model fitting (parameter estimation) problem in presence of outliers is actually to find the Maximum Feasible Subsystem (MaxFS) of a set of infeasible linear constraints. We propose a deterministic branch and bound method to solve the MaxFS problem with guaranteed global optimality. It can be used in a wide class of computer vision problems, in which the model variables are subject to the unit norm constraint. In contrast to the convex and concave relaxations in existing works, we introduce a piecewise linear relaxation to build very tight under- and over-estimators for square terms by partitioning variable bounds into smaller segments. Based on this novel relaxation technique, our branch and bound method can converge in a few iterations. For homogeneous linear systems, which correspond to some quasi-convex problems based on L_∞-L_∞-norm, our method is non-iterative and certainly reaches the globally optimal solution at the root node by partitioning each variable range into two segments with equal length. Throughout this work, we rely on the so-called Big-M method, and successfully avoid potential numerical problems by exploiting proper parametrization and problem structure. Experimental results demonstrate the stability and efficiency of our proposed method.

关键词： mode matching Branch and bound methods images computer vision SUB-SYSTEM Relaxation Techniques optimal solution

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Improving Video Stabilization in the Presence of Motion Blur

Improving Video Stabilization in the Presence of Motion Blur

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National Conference on computer vision, pattern recognition, Image Processing and Graphics (NCVPRIPG)

作者： Manish Okade P.K. Biswas Department of Electronics and Electrical Communication Engineering Indian Institute of Technology Kharagpur India

In this paper we propose the idea of deblurring for efficient feature matching in the context of video stabilization. This is achieved by incorporating a motion deblurring block prior to the feature extraction and matching stage in the stabilization pipeline. The effect of motion blur on feature extraction and matching has not been investigated so far as most of the approaches have looked into deblurring as a post processing step. After preprocessing the blurred frames, the scale invariant feature transform points are used to find correspondence to get an estimate of the camera motion. Smoothing of the motion parameters to retain the desired motion is performed using a gaussian filter. Finally, inverse of the resulting image transform is carried out to obtain a stable video sequence. We compare our method to existing approaches and show how the inserted block improves the video stabilization performance. Interframe Transformation Fidelity (ITF) is used to show the superiority of our proposed approach.

关键词： Feature extraction Cameras Video sequences Motion estimation Indexes Motion compensation pattern matching

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