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Continuous tracking within and across camera streams

Continuous tracking within and across camera streams

引用

conference on computer Vision and pattern recognition (CVPR)

作者： Jinman Kang I. Cohen G. Medioni IRIS Computer Vision Group University of Southern California Los Angeles CA USA

This paper presents a new approach for continuous tracking of moving objects observed by multiple, heterogeneous cameras. Our approach simultaneously processes video streams from stationary and pan-tilt-zoom cameras. The detection of moving objects from moving camera streams is performed by defining an adaptive background model that takes into account the camera motion approximated by an affine transformation. We address the tracking problem by separately modeling motion and appearance of the moving objects using two probabilistic models. For the appearance model, multiple color distribution components are proposed for ensuring a more detailed description of the object being tracked. The motion model is obtained using a Kalman filter (KF) process, which predicts the position of the moving object. The tracking is performed by the maximization of a joint probability model. The novelty of our approach consists in modeling the multiple trajectories observed by the moving and stationary cameras in the same KF framework. It allows deriving a more accurate motion measurement for objects simultaneously viewed by the two cameras and an automatic handling of occlusions, errors in the detection and camera handoff. We demonstrate the performances of the system on several video surveillance sequences.

关键词： Cameras Streaming media Object detection Layout computer vision Motion detection Tracking Video surveillance image resolution computerized monitoring

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Linear sparse feature based face detection in gray images

Linear sparse feature based face detection in gray images

引用

ieee International conference on image processing

作者： Xiaofeng Lu Nanning Zheng Songfeng Zheng The Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China The Institute of Artificial Intelligence and Robotics Xi'un Jiaotong University Xi'an P. R. China

ISBN: (纸本)0780377508

A very simple algorithm is used to construct an over complete set of linear sparse feature based classifiers, and AdaBoost algorithm is adopted to select part of them to form a strong classifier. During the course of feature extraction and selection, the new method minimizes the classification error directly, whereas most previous works cannot do this. An important difference between this method and other methods is that the sparse features are learned from the training set, instead of being arbitrarily defined. Experiments demonstrate that the new algorithm performs quite well.

关键词： computer vision Face detection Detectors Face recognition Training data Artificial intelligence Intelligent robots Feature extraction pattern recognition Humans

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Learning Bayesian network classifiers for facial expression recognition both labeled and unlabeled data

Learning Bayesian network classifiers for facial expression ...

引用

conference on computer Vision and pattern recognition (CVPR)

作者： I. Cohen N. Sebe F.G. Gozman M.C. Cirelo T.S. Huang Beckman Institute University of Illinois at Urbana-Champaign IL USA Leiden Institute of Advanced Computer Science Leiden University The Netherlands Escola Politecnica Universidade de Sao Paulo Sao Paulo Brazil

Understanding human emotions is one of the necessary skills for the computer to interact intelligently with human users. The most expressive way humans display emotions is through facial expressions. In this paper, we report on several advances we have made in building a system for classification of facial expressions from continuous video input. We use Bayesian network classifiers for classifying expressions from video. One of the motivating factor in using the Bayesian network classifiers is their ability to handle missing data, both during inference and training. In particular, we are interested in the problem of learning with both labeled and unlabeled data. We show that when using unlabeled data to learn classifiers, using correct modeling assumptions is critical for achieving improved classification performance. Motivated by this, we introduce a classification driven stochastic structure search algorithm for learning the structure of Bayesian network classifiers. We show that with moderate size labeled training sets and large amount of unlabeled data, our method can utilize unlabeled data to improve classification performance. We also provide results using the Naive Bayes (NB) and the Tree-Augmented Naive Bayes (TAN) classifiers, showing that the two can achieve good performance with labeled training sets, but perform poorly when unlabeled data are added to the training set.

关键词： Bayesian methods Face recognition Humans Feature extraction image databases Tracking Labeling Emotion recognition computer science computer networks

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Retina Encoder Tuning and Data Encryption for Learning Retina Implants

Retina Encoder Tuning and Data Encryption for Learning Retin...

引用

International Joint conference on Neural Networks 2003

作者： Baruth, Oliver Eckmiller, Bolf Neumann, Dirk Division of Neuroinformatics Department of Computer Science University of Bonn Römerstr. 164 D-53117 Bonn Germany

A Retina Encoder (RE) as part of a visual prosthesis (retina implant) for blind subjects with retinal degenerative disorders was implemented by an array of 256 tunable spatio-temporal filters (ST) to map visual patterns PI onto encoded output patterns (Fig. 1) as functional approximation of retinal information processing. These RE-output signals were fed into a Visual System Module (VM) to simulate the mapping (by the central visual system) of the retinal output onto visual percepts P2, which were visualized on a monitor. Alternative tuning strategies for roaming within the large spatio-temporal state space of RE were developed and tested. Initially, VM as neural network was trained to generate a P2 very similar to a selected set of P1s by feeding the corresponding RE-outputs into VM. RE tuning was tested by presenting a given small set of P1s to the serial coupling of RE and VM and by monitoring a modified Hamming distance between P2 and P1, while the ST-parameters were tuned. Human volunteers with normal vision were asked to approximate P2 to P1 directly, with random search, or by means of dialog-based tuning with an evolutionary algorithm (EA). Typically, dialog-based EA tuning reached the optimal similarity between P2 and P1 (limited by the quality of VM) within less than 200 iterations. In contrast, manual tuning required considerably more iteration steps and converged to a smaller similarity between P2 and P1. Structure and function of our tunable RE offer not only an optimization of visual perception, but may also be important to serve as technical Encryption Unit (EU) in connection with the human central visual system as biological Decryption Unit (DU);an important feature to meet the authentication requirements of active medical devices.

关键词： image coding

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Dynamic depth recovery using belief propagation

Dynamic depth recovery using belief propagation

引用

ieee International conference on image processing

作者： Yihua Xu Dongsheng Wang Heung-yeung Songde Ma Multimodal User Interface Group National Laboratory of Pattern Recognition Chinese Academy of Sciences

ISBN: (纸本)0780377508

In this paper, we study the dynamic stereo problem, i.e. to recover the shape of dynamic scene from multiple synchronized image sequences. To incorporate both spatial and temporal information for depth recovery, we propose a statistical framework that uses pixel process model to encode temporal coherence, and Markov random fields (MRFs) for spatial coherence. In this framework, the dynamic depth recovery problem is finally formulated as an optimization problem, and is optimized by using the belief propagation algorithm. Experimental results with the real dynamic scenes illustrate our method's ability of robust shape recovery.

关键词： Belief propagation Layout Shape Stereo vision Robustness image motion analysis image sequences Markov random fields User interfaces computer interfaces

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Noise Adaptive Channel Smoothing of Low-Dose images

Noise Adaptive Channel Smoothing of Low-Dose Images

引用

ieee computer society conference on computer Vision and pattern recognition Workshops (CVPRW)

作者： Hanno Scharr Michael Felsberg Per-Erik Forssen Intel Research Santa Clara CA USA Computer Vision Laboratory Linköping University Linkoping Sweden

Many nano-scale sensing techniques and image processing.applications are characterized by noisy, or corrupted, image data. Unlike typical camera-based computer vision imagery where noise can be modeled quite well as additive, zero-mean white or Gaussian noise, nano-scale images suffer from low intensities and thus mainly from Poisson-like noise. In addition, noise distributions can not be considered symmetric due to the limited gray value range of sensors and resulting truncation of over- and underflows. In this paper we adapt B-spline channel smoothing to meet the requirements imposed by this noise characteristics. Like PDE-based diffusion schemes it has a close connection to robust statistics but, unlike diffusion schemes, it can handle non-zero-mean noises. In order to account for the multiplicative nature of Poisson noise the variance of the smoothing kernels applied to each channel is properly adapted. We demonstrate the properties of this technique on noisy nano-scale images of silicon structures and compare to anisotropic diffusion schemes that were specially adapted to this data.

关键词： Smoothing methods Noise Kernel Robustness Spline Channel estimation computer vision

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Detection of 3D symmetry axis from fragments of a broken pottery bowl

Detection of 3D symmetry axis from fragments of a broken pot...

引用

International conference on Acoustics, Speech, and Signal processing.(ICASSP)

作者： F. Yao G. Shao Shimane University Matsue Japan Seinan Gakuin University Fukuoka Japan

This paper relates a method to detect three-dimensional (3D) symmetry axis from the fragment of a broken pottery bowl, which is symmetric in the original status (that is, before broken). Firstly, the fragment is extracted from the range image by applying a bias processing. and its contour is detected. Next, the corner points of the 3D fragment are extracted from its contour. Then, the contour of the 3D fragment is separated into 3D curve segments at the corner points, and the partial mouth-edge is determined by reasoning its center coordinates. Finally, by shrinking the range image, a series of partial curves which are in parallel with the partial mouth-edge, is obtained. The symmetry axis is thought as a line passing through the center points of these partial curves, in 3D space. We tested this method by using the real-world range data. The experiment results are satisfactory.

关键词： Shape image segmentation pattern recognition Testing pattern analysis Humans Intelligent systems computer vision Decision making Object detection

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Boosting linear discriminant analysis for face recognition

Boosting linear discriminant analysis for face recognition

引用

ieee International conference on image processing

作者： J. Lu K.N. Plataniotis A.N. Venetsanopoulos Bell Canada Multimedia Laboratory The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto Canada

In this paper, we propose a new algorithm to boost performance of traditional linear discriminant analysis (LDA)-based face recognition (FR) methods in complex FR tasks, where highly nonlinear face pattern distributions are often encountered. The algorithm embodies the principle of "divide and conquer", by which a complex problem, is decomposed into a set of simpler ones, each of which can be conquered by a relatively easy solution. The Ad-aBoost technique is utilized within this framework to: 1) generalize a set of simple FR sub-problems and their corresponding LDA solutions; 2) combine results from the multiple, relatively weak, LDA solutions to form a very strong solution. Experimentation performed on the FERET database indicates that the proposed methodology is able to greatly enhance performance of the traditional LDA-based method with an averaged improvement of correct recognition rate (CRR) up to 9% reported.

关键词： Boosting Linear discriminant analysis Face recognition Laboratories Strontium Databases Covariance matrix Robustness NIST Optimization methods

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A relevance feedback algorithm based on the clustering and Parzen window

A relevance feedback algorithm based on the clustering and P...

引用

ieee International conference on image processing

作者： Hyung il Koo Nam Ik Cho Seoul National University Seoul South Korea

ISBN: (纸本)0780377508

A relevance feedback algorithm based on the nonparametric approach is proposed. In the feature space, the algorithm generates multiple hyper-spheres around the regions where the images relevant with the query are densely populated, whereas the conventional algorithm searches the images in a single hyper-ellipsoid region. Then the Parzen window approach is applied to estimate the probability of relevance of each image in these multiple clusters (hyper-spheres). As a result, the relevance region in the feature space expands rapidly and covers arbitrarily shaped spaces with a small number of parameters. Also, since the user needs to determine only the positive images not the ambiguous negative ones, it is more convenient to use compared to some of the existing algorithms requiring negative feedback.

关键词： Clustering algorithms Information retrieval image retrieval Training data image databases Negative feedback Feature extraction computer science education Probability density function pattern recognition

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Handwritten Amharic Bank Check recognition Using Hidden Markov Random Field

Handwritten Amharic Bank Check Recognition Using Hidden Mark...

引用

ieee computer society conference on computer Vision and pattern recognition Workshops (CVPRW)

作者： Worku Alemu Siegfried Fuchs Image Processing and Recognition Group Artificial Intelligence Institute Dresden Germany

Amharic, a working language in Ethiopia, has its own writing system which is totally different from that of the Latin alphabet based languages. Amharic handwriting recognition is challenging due to the huge number of symbols, significant interclass similarity and also intra-class variability. In this paper the application of Hidden Markov Random Field (HMRF) for handwriting recognition of the legal amount field of Amharic bank check is presented. The three main contributions of this paper are the following. First, a new feature extraction technique is used which tries to extract natural features as perceived by human beings. The features extracted by this technique show a significant performance improvement. Second, a classification technique by estimating likelihood using a method known as pseudo-marginal probability is developed. The third contribution is the application of contextual information based on the syntactical structure of Amharic checks. Such context information is important in recognition process because even humans fail to recognize symbols correctly without any context. A noticeable difference is observed between results obtained with and without the application of contextual information. On the whole, despite the huge interclass similarity and also intra-class variability of handwritten Amharic characters, attractive results are found.

关键词： Pixel Feature extraction Hidden Markov models Computational modeling Handwriting recognition pattern recognition Law

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