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Learning Important Spatial Pooling Regions for Scene Classification 27

Learning Important Spatial Pooling Regions for Scene Classif...

引用

27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Lin, Di Lu, Cewu Liao, Renjie Jia, Jiaya Chinese Univ Hong Kong Hong Kong Hong Kong Peoples R China Hong Kong Univ Sci & Technol Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781479951178

We address the false response influence problem when learning and applying discriminative parts to construct the mid-level representation in scene classification. It is often caused by the complexity of latent image structure when convolving part filters with input images. this problem makes mid-level representation, even after pooling, not distinct enough to classify input data correctly to categories. Our solution is to learn important spatial pooling regions along with their appearance. the experiments show that this new framework suppresses false response and produces improved results on several datasets, including MIT-Indoor, 15-Scene, and UIUC 8-Sport. When combined with global image features, our method achieves state-of-the-art performance on these datasets.

关键词： pattern recognition

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Towards Pose Robust Face recognition

Towards Pose Robust Face Recognition

引用

26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Yi, Dong Lei, Zhen Li, Stan Z. Chinese Acad Sci Inst Automat Ctr Biometr & Secur Res Beijing 100864 Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100864 Peoples R China

ISBN: (纸本)9780769549897

Most existing pose robust methods are too computational complex to meet practical applications and their performance under unconstrained environments are rarely evaluated. In this paper, we propose a novel method for pose robust face recognition towards practical applications, which is fast, pose robust and can work well under unconstrained environments. Firstly, a 3D deformable model is built and a fast 3D model fitting algorithm is proposed to estimate the pose of face image. Secondly, a group of Gabor filters are transformed according to the pose and shape of face image for feature extraction. Finally, PCA is applied on the pose adaptive Gabor features to remove the redundances and Cosine metric is used to evaluate the similarity. the proposed method has three advantages: (1) the pose correction is applied in the filter space rather than image space, which makes our method less affected by the precision of the 3D model;(2) By combining the holistic pose transformation and local Gabor filtering, the final feature is robust to pose and other negative factors in face recognition;(3) the 3D structure and facial symmetry are successfully used to deal with self-occlusion. Extensive experiments on FERET and PIE show the proposed method outperforms state-of-the-art methods significantly, meanwhile, the method works well on LFW.

关键词： Gabor filters face recognition feature extraction hidden feature removal pose estimation principal component analysis solid modelling

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iCub World: Friendly Robots Help Building Good vision Data-Sets

iCub World: Friendly Robots Help Building Good Vision Data-S...

引用

26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Fanello, Sean Ryan Ciliberto, Carlo Santoro, Matteo Natale, Lorenzo Metta, Giorgio Rosasco, Lorenzo Odone, Francesca Ist Italiano Tecnol iCub Facil Genoa Italy Univ Genoa DIBRIS Genoa Italy Italian Inst Technol MIT LCSL I-16163 Genoa Italy

ISBN: (纸本)9780769549903

In this paper we present and start analyzing the iCub World data-set, an object recognition data-set, we acquired using a Human-Robot Interaction (HRI) scheme and the iCub humanoid robot platform. Our set up allows for rapid acquisition and annotation of data with corresponding ground truth. While more constrained in its scopes - the iCub world is essentially a robotics research lab - we demonstrate how the proposed data-set poses challenges to current recognition systems. the iCubWorld data-set is publicly available (1).

关键词： human-robot interaction humanoid robots object recognition robot vision

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White-Box Evaluation of computer vision Algorithms through Explicit Decision-Making

引用

7th International conference on computer vision Systems

作者： Zanibbi, Richard Blostein, Dorothea Cordy, James R. Rochester Inst Technol Dept Comp Sci Rochester NY 14623 USA Queens Univ Sch Comp Kingston ON K7L 3N6 Canada

ISBN: (纸本)9783642046667

Traditionally computer vision and pattern recognition algorithms are evaluated by measuring differences between final interpretations and ground truth. these black-box evaluations ignore intermediate results, making it difficult to use intermediate results in diagnosing errors and optimization. We propose "opening the box," representing vision algorithms as sequences of decision points where recognition results are selected from a set of alternatives. For this purpose, we present a domain-specific language for pattern recognition tasks, the recognition Strategy Language (RSL). At run-time, an RSL interpreter records a complete history of decisions made during recognition, as it applies them to a set of interpretations maintained for the algorithm. Decision histories provide a rich new Source of information: recognition errors may be traced back to the specific decisions that caused them, and intermediate interpretations may be recovered and displayed. this additional information also permits new evaluation metrics that include false negatives (correct hypotheses that the algorithm generates and later rejects), Such as the percentage of ground truth hypotheses generated (historical recall), and the percentage of generated hypotheses that are correct (historical precision). We illustrate the approach through all analysis of cell detection in two published table recognition algorithms.

关键词： Performance Evaluation Document recognition Table recognition Scripting Languages Domain-Specific Languages

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ChaLearn Looking at People RGB-D Isolated and Continuous Datasets for Gesture recognition 29

ChaLearn Looking at People RGB-D Isolated and Continuous Dat...

引用

29th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Wan, Jun Li, Stan Z. Zhao, Yibing Zhou, Shuai Guyon, Isabelle Escalera, Sergio Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100864 Peoples R China Macau Univ Sci & Technol Macau Peoples R China UPSud Orsay France Univ Paris Saclay Paris France ChaLearn Barcelona Spain Univ Barcelona Comp Vis Ctr E-08007 Barcelona Spain

ISBN: (纸本)9781509014378

In this paper, we present two large video multi-modal datasets for RGB and RGB-D gesture recognition: the ChaLearn LAP RGB-D Isolated Gesture Dataset (IsoGD) and the Continuous Gesture Dataset (ConGD). Both datasets are derived from the ChaLearn Gesture Dataset (CGD) that has a total of more than 50000 gestures for the "one-shot-learning" competition. To increase the potential of the old dataset, we designed new well curated datasets composed of 249 gesture labels, and including 47933 gestures manually labeled the begin and end frames in sequences. Using these datasets we will open two competitions on the CodaLab platform so that researchers can test and compare their methods for "user independent" gesture recognition. the first challenge is designed for gesture spotting and recognition in continuous sequences of gestures while the second one is designed for gesture classification from segmented data. the baseline method based on the bag of visual words model is also presented.

关键词： Gesture recognition

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Discriminative Blur Detection Features 27

Discriminative Blur Detection Features

引用

27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Shi, Jianping Xu, Li Jia, Jiaya Chinese Univ Hong Kong Hong Kong Hong Kong Peoples R China Lenovo R&T Image & Visual Comp Lab Hong Kong Peoples R China

ISBN: (纸本)9781479951178

Ubiquitous image blur brings out a practically important question - what are effective features to differentiate between blurred and unblurred image regions. We address it by studying a few blur feature representations in image gradient, Fourier domain, and data-driven local filters. Unlike previous methods, which are often based on restoration mechanisms, our features are constructed to enhance discriminative power and are adaptive to various blur scales in images. To avail evaluation, we build a new blur perception dataset containing thousands of images with labeled ground-truth. Our results are applied to several applications, including blur region segmentation, deblurring, and blur magnification.

关键词： Image blur analysis blur detection blur feature

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Pyramid-based Visual Tracking Using Sparsity Represented Mean Transform 27

Pyramid-based Visual Tracking Using Sparsity Represented Mea...

引用

27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Zhe Wong, Kin Hong Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781479951178

In this paper, we propose a robust method for visual tracking relying on mean shift, sparse coding and spatial pyramids. Firstly, we extend the original mean shift approach to handle orientation space and scale space and name this new method as mean transform. the mean transform method estimates the motion, including the location, orientation and scale, of the interested object window simultaneously and effectively. Secondly, a pixel-wise dense patch sampling technique and a region-wise trivial template designing scheme are introduced which enable our approach to run very accurately and efficiently. In addition, instead of using either holistic representation or local representation only, we apply spatial pyramids by combining these two representations into our approach to deal with partial occlusion problems robustly. Observed from the experimental results, our approach outperforms state-of-theart methods in many benchmark sequences.

关键词： pattern recognition

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Surface-from-Gradients: An Approach Based on Discrete Geometry Processing 27

Surface-from-Gradients: An Approach Based on Discrete Geomet...

引用

27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Xie, Wuyuan Zhang, Yunbo Wang, Charlie C. L. Chung, Ronald C. -K. Chinese Univ Hong Kong Dept Mech & Automat Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781479951178

In this paper, we propose an efficient method to reconstruct surface-from-gradients (SfG). Our method is formulated under the framework of discrete geometry processing. Unlike the existing SfG approaches, we transfer the continuous reconstruction problem into a discrete space and efficiently solve the problem via a sequence of least-square optimization steps. Our discrete formulation brings three advantages: 1) the reconstruction preserves sharp-features, 2) sparse/incomplete set of gradients can be well handled, and 3) domains of computation can have irregular boundaries. Our formulation is direct and easy to implement, and the comparisons with state-of-the-arts show the effectiveness of our method.

关键词： pattern recognition

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Learning a Manifold as an Atlas

Learning a Manifold as an Atlas

引用

26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Pitelis, Nikolaos Russell, Chris Agapito, Lourdes Univ London Sch EECS London WC1E 7HU England

ISBN: (纸本)9780769549897

In this work, we return to the underlying mathematical definition of a manifold and directly characterise learning a manifold as finding an atlas, or a set of overlapping charts, that accurately describe local structure. We formulate the problem of learning the manifold as an optimisation that simultaneously refines the continuous parameters defining the charts, and the discrete assignment of points to charts. In contrast to existing methods, this direct formulation of a manifold does not require "unwrapping" the manifold into a lower dimensional space and allows us to learn closed manifolds of interest to vision, such as those corresponding to gait cycles or camera pose. We report state-of-the-art results for manifold based nearest neighbour classification on vision datasets, and show how the same techniques can be applied to the 3D reconstruction of human motion from a single image.

关键词： computer vision image motion analysis image reconstruction learning (artificial intelligence) optimisation

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Measuring Crowd Collectiveness

Measuring Crowd Collectiveness

引用

26th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Zhou, Bolei Tang, Xiaoou Wang, Xiaogang Chinese Univ Hong Kong Dept Informat Engn Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China

ISBN: (纸本)9780769549897

Collective motions are common in crowd systems and have attracted a great deal of attention in a variety of multidisciplinary fields. Collectiveness, which indicates the degree of individuals acting as a union in collective motion, is a fundamental and universal measurement for various crowd systems. By integrating path similarities among crowds on collective manifold, this paper proposes a descriptor of collectiveness and an efficient computation for the crowd and its constituent individuals. the algorithm of the Collective Merging is then proposed to detect collective motions from random motions. We validate the effectiveness and robustness of the proposed collectiveness descriptor on the system of self-driven particles. We then compare the collectiveness descriptor to human perception for collective motion and show high consistency. Our experiments regarding the detection of collective motions and the measurement of collectiveness in videos of pedestrian crowds and bacteria colony demonstrate a wide range of applications of the collectiveness descriptor(1).

关键词： image motion analysis optimisation video signal processing

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