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检索条件"任意字段=27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014"

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Spatio-Temporal Self-Organizing Map Deep Network for Dynamic Object Detection from Videos 30

Spatio-Temporal Self-Organizing Map Deep Network for Dynamic...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Du, Yang Yuan, Chunfeng Li, Bing Hu, Weiming Maybank, Stephen Univ Chinese Acad Sci Chinese Acad Sci CAS Ctr Excellence Brain Sci & Intelligence Tech Natl Lab Pattern RecognitInst Automat Beijing Peoples R China Birkbeck Coll London England

ISBN: (纸本)9781538604571

In dynamic object detection, it is challenging to construct an effective model to sufficiently characterize the spatial-temporal properties of the background. this paper proposes a new Spatio-Temporal Self-Organizing Map (STSOM) deep network to detect dynamic objects in complex scenarios. the proposed approach has several contributions: First, a novel STSOM shared by all pixels in a video frame is presented to efficiently model complex background. We exploit the fact that the motions of complex background have the global variation in the space and the local variation in the time, to train STSOM using the whole frames and the sequence of a pixel over time to tackle the variance of complex background. Second, a Bayesian parameter estimation based method is presented to learn thresholds automatically for all pixels to filter out the background. Last, in order to model the complex background more accurately, we extend the single-layer STSOM to the deep network. then the background is filtered out layer by layer. Experimental results on CDnet 2014 dataset demonstrate that the proposed STSOM deep network outperforms numerous recently proposed methods in the overall performance and in most categories of scenarios.

关键词： Self-organizing feature maps Object detection Videos Dynamics Bayes methods Kernel computer vision

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Noisy Softmax: Improving the Generalization Ability of DCNN via Postponing the Early Softmax Saturation 30

Noisy Softmax: Improving the Generalization Ability of DCNN ...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Chen, Binghui Deng, Weihong Du, Junping Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Beijing Peoples R China

ISBN: (纸本)9781538604571

Over the past few years, softmax and SGD have become a commonly used component and the default training strategy in CNN frameworks, respectively. However, when optimizing CNNs with SGD, the saturation behavior behind softmax always gives us an illusion of training well and then is omitted. In this paper, we first emphasize that the early saturation behavior of softmax will impede the exploration of SGD, which sometimes is a reason for model converging at a bad local-minima, then propose Noisy Softmax to mitigating this early saturation issue by injecting annealed noise in softmax during each iteration. this operation based on noise injection aims at postponing the early saturation and further bringing continuous gradients propagation so as to significantly encourage SGD solver to be more exploratory and help to find a better local-minima. this paper empirically verifies the superiority of the early softmax desaturation, and our method indeed improves the generalization ability of CNN model by regularization. We experimentally find that this early desaturation helps optimization in many tasks, yielding state-of-the-art or competitive results on several popular benchmark datasets.

关键词： Training Noise measurement Annealing Standards Robustness computer vision Telecommunications

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Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression recognition in the Wild 30

Reliable Crowdsourcing and Deep Locality-Preserving Learning...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Li, Shan Deng, Weihong Du, JunPing Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9781538604571

Past research on facial expressions have used relatively limited datasets, which makes it unclear whether current methods can be employed in real world. In this paper, we present a novel database, RAF-DB, which contains about 30000 facial images from thousands of individuals. Each image has been individually labeled about 40 times, then EM algorithm was used to filter out unreliable labels. Crowdsourcing reveals that real-world faces often express compound emotions, or even mixture ones. For all we know, RAF-DB is the first database that contains compound expressions in the wild. Our cross-database study shows that the action units of basic emotions in RAF-DB are much more diverse than, or even deviate from, those of lab-controlled ones. To address this problem, we propose a new DLP-CNN (Deep Locality-Preserving CNN) method, which aims to enhance the discriminative power of deep features by preserving the locality closeness while maximizing the inter-class scatters. the benchmark experiments on the 7-class basic expressions and 11-class compound expressions, as well as the additional experiments on SFEW and CK+ databases, show that the proposed DLP-CNN outperforms the state-of-the-art handcrafted features and deep learning based methods for the expression recognition in the wild.

关键词： expression recognition BENCHMARKS Facial Expression expectation-maximisation algorithm Database Emotions compounds Mug shots

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Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network 30

Regressing Robust and Discriminative 3D Morphable Models wit...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Tran, Anh Tuan Hassner, Tal Masi, Iacopo Medioni, Erard USC Inst Robot & Intelligent Syst Los Angeles CA 90007 USA USC Inst Informat Sci Los Angeles CA 90007 USA Open Univ Israel Raanana Israel

ISBN: (纸本)9781538604571

the 3D shapes of faces are well known to be discriminative. Yet despite this, they are rarely used for face recognition and always under controlled viewing conditions. We claim that this is a symptom of a serious but often overlooked problem with existing methods for single view 3D face reconstruction: when applied "in the wild", their 3D estimates are either unstable and change for different photos of the same subject or they are over-regularized and generic. In response, we describe a robust method for regressing discriminative 3D morphable face models (3DMM). We use a convolutional neural network (CNN) to regress 3DMM shape and texture parameters directly from an input photo. We overcome the shortage of training data required for this purpose by offering a method for generating huge numbers of labeled examples. the 3D estimates produced by our CNN surpass state of the art accuracy on the MICC data set. Coupled with a 3D-3D face matching pipeline, we show the first competitive face recognition results on the LFW, YTF and IJB-A benchmarks using 3D face shapes as representations, rather than the opaque deep feature vectors used by other modern systems.

关键词： three-dimensional displays Shape Face recognition Robustness Training Image reconstruction Training data

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Real-time Action recognition with Enhanced Motion Vector CNNs 29

Real-time Action Recognition with Enhanced Motion Vector CNN...

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2016 ieee conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Bowen Wang, Limin Wang, Zhe Qiao, Yu Wang, Hanli Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen key lab Comp Vis & Pat Rec Beijing Peoples R China Tongji Univ Key Lab Embedded Syst & Serv Comp Minist Educ Shanghai Peoples R China Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland

ISBN: (纸本)9781467388511

the deep two-stream architecture [23] exhibited excellent performance on video based action recognition. the most computationally expensive step in this approach comes from the calculation of optical flow which prevents it to be real-time. this paper accelerates this architecture by replacing optical flow with motion vector which can be obtained directly from compressed videos without extra calculation. However, motion vector lacks fine structures, and contains noisy and inaccurate motion patterns, leading to the evident degradation of recognition performance. Our key insight for relieving this problem is that optical flow and motion vector are inherent correlated. Transferring the knowledge learned with optical flow CNN to motion vector CNN can significantly boost the performance of the latter. Specifically, we introduce three strategies for this, initialization transfer, supervision transfer and their combination. Experimental results show that our method achieves comparable recognition performance to the state-of-the-art, while our method can process 390.7 frames per second, which is 27 times faster than the original two-stream method.

关键词： Optical flows

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Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classification 30

Learning Spatial Regularization with Image-level Supervision...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhu, Feng Li, Hongsheng Ouyang, Wanli Yu, Nenghai Wang, Xiaogang Univ Sci & Technol China Hefei Anhui Peoples R China Univ Sydney Sydney NSW Australia Chinese Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781538604571

Multi-label image classification is a fundamental but challenging task in computer vision. Great progress has been achieved by exploiting semantic relations between labels in recent years. However, conventional approaches are unable to model the underlying spatial relations between labels in multi-label images, because spatial annotations of the labels are generally not provided. In this paper, we propose a unified deep neural network that exploits both semantic and spatial relations between labels with only image-level supervisions. Given a multi-label image, our proposed Spatial Regularization Network (SRN) generates attention maps for all labels and captures the underlying relations between them via learnable convolutions. By aggregating the regularized classification results with original results by a ResNet-101 network, the classification performance can be consistently improved. the whole deep neural network is trained end-to-end with only image-level annotations, thus requires no additional efforts on image annotations. Extensive evaluations on 3 public datasets with different types of labels show that our approach significantly outperforms state-of-the-arts and has strong generalization capability. Analysis of the learned SRN model demonstrates that it can effectively capture both semantic and spatial relations of labels for improving classification performance.

关键词： dimensional relationship images Image classification Semantics computer vision ANNOTATIONS end-to-end

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Neural Sign Language Translation 31

Neural Sign Language Translation

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31st ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Camgoz, Necati Cihan Hadfield, Simon Koller, Oscar Ney, Hermann Bowden, Richard Univ Surrey Guildford Surrey England Rhein Westfal TH Aachen Aachen Germany

ISBN: (纸本)9781538664209

Sign Language recognition (SLR) has been an active research field for the last two decades. However most research to date has considered SLR as a naive gesture recognition problem. SLR seeks to recognize a sequence of continuous signs but neglects the underlying rich grammatical and linguistic structures of sign language that differ from spoken language. In contrast, we introduce the Sign Language Translation (SLT) problem. Here, the objective is to generate spoken language translations from sign language videos, taking into account the different word orders and grammar. We formalize SLT in the framework of Neural Machine Translation (NMT) for both end-to-end and pretrained settings (using expert knowledge). this allows us to jointly learn the spatial representations, the underlying language model, and the mapping between sign and spoken language. To evaluate the performance of Neural SLT we collected the first publicly available Continuous SLT dataset, RWth-PHOENIX-Weather 2014T(1). It provides spoken language translations and gloss level annotations for German Sign Language videos of weather broadcasts. Our dataset contains over .95M frames with >67K signs from a sign vocabulary of >1K and >99K words from a German vocabulary of >2.8K. We report quantitative and qualitative results for various SLT setups to underpin future research in this newly established field. the upper bound for translation performance is calculated at 19.26 BLEU-4, while our end-to-end frame-level and gloss-level tokenization networks were able to achieve 9.58 and 18.13 respectively.

关键词： Assistive technology Videos Gesture recognition Task analysis Decoding Linguistics

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APPLICATION OF 3-D vision TO INDUSTRIAL APPLICATIONS.

APPLICATION OF 3-D VISION TO INDUSTRIAL APPLICATIONS.

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Proceedings - 27th ieee computer Society International conference, COMPCON 83 Fall: Delivering computer Power to End Users.

作者： Mundy, Joseph L.

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Scale Invariant Feature Transform using Oriented pattern 27

Scale Invariant Feature Transform using Oriented Pattern

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ieee 27th Canadian conference on Electrical and computer Engineering (CCECE)

作者： Daneshvar, Mohammad Baghery Babaie-Zadeh, Massoud Ghorshi, Seyed Sharif Univ Technol Sch Sci & Engn Kish Isl Iran Sharif Univ Technol Dept Elect Engn Tehran Iran

ISBN: (纸本)9781479930999

Image matching plays an important role in many aspects of computer vision. Our proposed method is based on Scale Invariant Feature Transform (SIFT) which is one of the popular image matching methods. the main ideas behind our method are removing the excess keypoints, adding oriented patterns to descriptor, and decreasing the size of the descriptors. By doing these changes to SIFT, we would have oriented patterns of keypoints. In addition, the numbers of keypoints have been reduced and the places of keypoints would be selected more accurately, and also the size of the descriptors has been reduced.

关键词： Image matching keypoint feature extraction descriptor oriented pattern

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Analysis of Widely-used Descriptors for Finger-vein recognition

Analysis of Widely-used Descriptors for Finger-vein Recognit...

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9th International conference on computer vision theory and Applications (VISAPP)

作者： Yousefi, Fariba Sivri, Erdal Kaya, Ozgur Suloglu, Selma Kalkan, Sinan Middle East Tech Univ Ankara Turkey SoSoft Ltd Sti Ankara Turkey

ISBN: (纸本)9789897581335

For finger-vein recognition, many successful methods, such as Line Tracking (LT), Maximum Curvature (MC) and Wide Line Detector (WL), have been proposed. Among these, LT has a very slow matching and feature-extraction phase, and LT, MC and WL are translation and rotation dependent. Moreover, we show in the paper, they are affected by noise. To overcome these drawbacks, we propose using popular feature descriptors widely used for several computer vision or pattern recognition (cvpr) problems in the literature. the cvpr descriptors we test include Histogram of Oriented Gradients (HOG), Fourier Descriptors (FD), Zernike Moments (ZM), Local Binary patterns (LBP) and Global Binary patterns (GBP), which have not been applied to the finger-vein recognition problem before. We compare these descriptors against LT, MC, and WL and evaluate their running times, performance and resilience against noise, rotation and translation. We report that the LT and WL methods accuracy are comparable to each other and WL gives the best accuracy, LT method's speed is the slowest. Our results indicate that WL can be used together with ZM and GBP in case of rotation and noise, respectively.

关键词： Finger-vein recognition Histogram of Oriented Gradients Fourier Descriptors Zernike Moments Global Binary patterns

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