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computer vision methods for visual MIMO optical system

Computer vision methods for visual MIMO optical system

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IEEE computer Society Conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Wenjia Yuan Kristin Dana Michael Varga Ashwin Ashok Marco Gruteser Narayan Mandayam State University of New Jersey Rutgers USA

Cameras have become commonplace in phones, laptops, music-players and handheld games. Similarly, light emitting displays are prevalent in the form of electronic billboards, televisions, computer monitors, and hand-held devices. The prevalence of cameras and displays in our society creates a novel opportunity to build camera-based optical wireless communication systems based on a concept called visual MIMO. We extend the common term MIMO from the field of communications (“multiple-input multiple-output”) that is typically used to describe multiple antenna, multiple transmitter radio frequency communications channel. In the visual MIMO communications paradigm, the transmitters are light-emitting devices such as electronic displays and cameras are the receivers. In this paper we discuss and address several challenges in creating a visual MIMO channel. These challenges include: (1) electronic display detection, (2) embedding the transmission signal in the display video, and (3) system characterization for electronic display appearance.

关键词： Cameras MIMO Visualization Image segmentation Monitoring Measurement Optical transmitters

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Biometric score fusion through discriminative training

Biometric score fusion through discriminative training

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IEEE computer Society Conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Vivek Tyagi Nalini Ratha IBM Research India New Delhi India IBM Thomas J. Watson Research Center Hawthorne NY USA

In the multibiometric systems, various matcher/modality scores are fused together to provide better performance than the individual matcher scores. In the authors have proposed a likelihood ratio test (LRT) based fusion technique for the biometric verification task that outperformed several other classifiers. They model the genuine and the imposter densities by the finite Gaussian mixture models (GMM, a generative model) whose parameters are estimated using the maximum likelihood (ML) criteria. Lately, the discriminative training methods and models have been shown to provide additional accuracy gains over the generative models, in multiple applications such as the speech recognition, verification and text analytics. These gains are based on the fact that the discriminative models are able to partially compensate for the unavoidable mismatch, which is always present between the specified statistical model (GMM in this case) and the true distribution of the data which is unknown. In this paper, we propose to use a discriminative method to estimate the GMM density parameters using the maximum accept and reject (MARS) criteria. The test results using the proposed method on the NIST-BSSRI multimodal dataset indicate improved verification performance over a very competitive maximum likelihood (ML) trained system proposed in.

关键词： Hidden Markov models Training pattern recognition Estimation Lead Biological system modeling Mars

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Real-time RGB-D mapping and 3-D modeling on the GPU using the random ball cover data structure

Real-time RGB-D mapping and 3-D modeling on the GPU using th...

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International Conference on computer vision Workshops (ICCV Workshops)

作者： Dominik Neumann Felix Lugauer Sebastian Bauer Jakob Wasza Joachim Hornegger Pattern Recognition Lab Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg Germany

The modeling of three-dimensional scene geometry from temporal point cloud streams is of particular interest for a variety of computer vision applications. With the advent of RGB-D imaging devices that deliver dense, metric and textured 6-D data in real-time, on-the-fly reconstruction of static environments has come into reach. In this paper, we propose a system for real-time point cloud mapping based on an efficient implementation of the iterative closest point (ICP) algorithm on the graphics processing unit (GPU). In order to achieve robust mappings at real-time performance, our nearest neighbor search evaluates both geometric and photometric information in a direct manner. For acceleration of the search space traversal, we exploit the inherent computing parallelism of GPUs. In this work, we have investigated the fitness of the random ball cover (RBC) data structure and search algorithm, originally proposed for high-dimensional problems, for 6-D data. In particular, we introduce a scheme that enables both fast RBC construction and queries. The proposed system is validated on an indoor scene modeling scenario. For dense data from the Microsoft Kinect sensor (640 × 480 px), our implementation achieved ICP runtimes of

关键词： Iterative closest point algorithm Graphics processing unit Runtime Nearest neighbor searches Real time systems Data structures Solid modeling

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Robust object tracking via online learning of adaptive appearance manifold

Robust object tracking via online learning of adaptive appea...

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International Conference on computer vision Workshops (ICCV Workshops)

作者： Jianwei Ding Yongzhen Huang Kaiqi Huang Tieniu Tan National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781467300629

Appearance modeling plays a critical role in robust object tracking, which should be adaptive to various appearance changes. We propose a new appearance model based on adaptive appearance manifold for object tracking. The adaptive appearance manifold consists of several submanifolds and each is approximated with a low dimensional linear subspace. The initial appearance model is constructed using location information of target object in the first frame, and no prior knowledge is needed. We design an efficient dynamic structure for the adaptive appearance manifold, which can reduce time of comparison between a new observation and the appearance model. The appearance model is incrementally learned online using the input sequence image. We integrate our new appearance model with the particle filtering framework. Several public challenging videos are used to test our tracking algorithm. The experimental results demonstrate that our algorithm is robust to illumination change, pose variation, partial occlusion and clutter background. And the speed of our algorithm is also very fast.

关键词： Target tracking Manifolds Adaptation models Lighting Robustness Videos

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Asymmetric distances for binary embeddings

Asymmetric distances for binary embeddings

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Conference on computer vision and pattern recognition (CVPR)

作者： Albert Gordo Florent Perronnin Computer Vision Center Universitat Autònoma Barcelona Spain Textual and Visual Pattern Analysis Xerox Research Centre Europe France

In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database signatures, it has been noted that this is not strictly a requirement. Indeed, asymmetric schemes which binarize the database signatures but not the query still enjoy the same two benefits but may provide superior accuracy. In this work, we propose two general asymmetric distances which are applicable to a wide variety of embedding techniques including Locality Sensitive Hashing (LSH), Locality Sensitive Binary Codes (LSBC), Spectral Hashing (SH) and Semi-Supervised Hashing (SSH). We experiment on four public benchmarks containing up to 1M images and show that the proposed asymmetric distances consistently lead to large improvements over the symmetric Hamming distance for all binary embedding techniques. We also propose a novel simple binary embedding technique - PCA Embedding (PCAE) - which is shown to yield competitive results with respect to more complex algorithms such as SH and SSH.

关键词： Databases Kernel Euclidean distance Principal component analysis Approximation methods Table lookup Random access memory

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Robust airlight estimation for haze removal from a single image

Robust airlight estimation for haze removal from a single im...

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IEEE computer Society Conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Matteo Pedone Janne Heikkilä Machine Vision Group University of Oulu Finland

Present methods for haze removal from a single image require the estimation of two physical quantities which, according to the commonly used atmospheric scattering model, are transmission and airlight. The visual quality of images de-hazed with such methods is highly dependent on the accuracy of estimation of the aforementioned quantities. In this paper we propose a new method for reliable airlight color estimation that could be used in digital cameras to automatically de-haze images by removing unrealistic color artifacts. The main idea of our method is based on novel statistics gathered from natural images regarding frequently occurring airlight colors. The statistics are used to introduce a minimization cost functional which has a closed form solution, and is easy to compute. We compare our approach with current methods present in literature, and show its superior robustness with both images with artificially added haze, and real hazy photos.

关键词： Image color analysis Estimation Robustness Atmospheric modeling Scattering Minimization Channel estimation

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Low resolution facial image recognition via multiple kernel criterion

Low resolution facial image recognition via multiple kernel ...

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Asian Conference on pattern recognition (ACPR)

作者： Chuan-Xian Ren Dao-Qing Dai Hong Yan Center of Computer Vision and Department of Mathematics Sun Yat-Sen University Guangzhou China Department of Electric Engineering City University of Hong Kong Hong Kong China

Practical face recognition systems are sometimes confronted with low-resolution (LR) images. Most existing feature extraction algorithms aim to preserve relational structure among objects of the input space in a linear embedding space. However, it has been a consensus that such complex visual learning tasks will be well be solved by adopting multiple descriptors to more precisely characterize the data for improving performance. In this paper, we addresses the problem of matching LR and high-resolution images that are difficult for conventional methods in practice due to the lack of an efficient similarity measure, and a multiple kernel criterion (MKC) is proposed for LR face recognition without any super-resolution (SR) preprocessing. Different image descriptors including RsL2, LBP, Gradientface and IMED are considered as the multiple kernel generators and the Gaussian function is exploited as the distance induced kernel. MKC solves this problem by minimizing the inconsistency between the similarities captured by the multiple kernels, and the nonlinear objective function can be alternatively minimized by a constrained eigenvalue decomposition. Experiments on benchmark databases show that our MKC method indeed improves the recognition performance.

关键词： Kernel Image resolution Face recognition Face Feature extraction Databases Optimization

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A FUZZY IDENTIFICATION METHOD OF CONTOURS BASED ON CHAIN-CODE FEATURES

A FUZZY IDENTIFICATION METHOD OF CONTOURS BASED ON CHAIN-COD...

引用

第三届全国智能视觉监控学术会议

作者： Qi Dong Guangping Xu Liu Jing Yanbing Xue Hua Zhang Key Laboratory of Intelligence Computing and New Software Technology TianjinChina300384 Key Laboratory of Computer Vision and SystemMinistry of EducationTianjinChina300384 School of Computer and CommunicationTianjin University of TechnologyTianjinChina300384

ISBN: (纸本)9781457718342

The contour analysis and identification are the important aspects in visual surveillance *** paper proposes a fuzzy identification method of ***,according to the description of a contour based on the chain-code method,the proposed method utilizes the statistical features of contours including the chain-code entropy and chain-code space distribution entropies,from which the feature vector of a contour is ***,the method generates the contour pattern from some contour samples and uses the approaching principle to identify a *** our method integrates effectively multiple statistical feathers of its chain-code and employs a fuzzy pattern recognition technique,the experiments show quantitatively that it can achieve good results from various metrics.

关键词： Image Contour Fuzzy pattern Chain Code Object recognition

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A novel approach for delineation and feature extraction in QRS complex of ECG signal

A novel approach for delineation and feature extraction in Q...

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International Conference on Image Information Processing

作者： Sayak Pramanik Rupendra Nath Mitra Sucharita Mitra Bidyut Baran Chaudhuri Department of Electronics & Communication Institute of Engineering and Management Kolkata India Department of Electronics Netaji Nagar Day College Kolkata India Network Planning & Optimization Nokia Siemens Networks Private Limited Kolkata India Computer Vision & Pattern Recognition Unit Indian Statistical Institute Kolkata India

This paper reports the development of a software suite to be accessed in future with any General Packet Radio Service (GPRS) enabled mobile phone or Personal Digital Assistant (PDA) for the extraction and analysis of disease-related features from the images of paper based ECG records. In India and other developing countries, the cheaper paper based ECG machines are mainly used. Also in rural areas of these countries - where there is acute dearth of qualified cardiologists, the cardiac diseases are still the major silent killers. One way of addressing this problem is cellular medicine which necessitates an intelligent cardiac parameter extraction algorithm. In our bid to address exactly this requirement, a software is developed with the help of few image processing techniques. At first, the background noises i.e. the gridlines are removed by thresholding technique. Binarization and thinning has been done for extraction of digital time data and then Discrete Wavelet Transform (DWT) and water reservoir based pattern recognition technique are subsequently used to delineate other important ECG parameters for ECG interpretation.

关键词： Electrocardiography Feature extraction pattern recognition Information processing Reservoirs Strips

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A Painting Based Technique for Skew Estimation of Scanned Documents

A Painting Based Technique for Skew Estimation of Scanned Do...

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International Conference on Document Analysis and recognition

作者： Alireza Alaei Umapada Pal P. Nagabhushan Fumitaka Kimura Department of Studies in Computer Science University of Mysore Mysore India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Graduate School of Engineering Mie University Japan

In this paper, we propose an efficient skew estimation technique based on Piece-wise Painting Algorithm (PPA) for scanned documents. Here we, at first, employ the PPA on the document image horizontally and vertically. Applying the PPA on both the directions, two painted images (one for horizontally painted and other for vertically painted) are obtained. Next, based on statistical analysis some regions with specific height (width) from horizontally (vertically) painted images are selected and top (left), middle (middle) and bottom (right) points of such selected regions are categorized in 6 separate lists. Utilizing linear regression, a few lines are drawn using the lists of points. A new majority voting approach is also proposed to find the best-fit line amongst all the lines. The skew angle of the document image is estimated from the slope of the best-fit line. The proposed technique was tested extensively on a dataset containing various categories of documents. Experimental results showed that the proposed technique achieved more accurate results than the state-of-the-art methodologies.

关键词： Estimation Painting Fitting Linear regression Text analysis Writing

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