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检索条件"任意字段=2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006"

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Starburst: A hybrid algorithm for video-based eye tracking combining feature-based and model-based approaches

Starburst: A hybrid algorithm for video-based eye tracking c...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Dongheng Li D. Winfield D.J. Parkhurst Iowa State University Ames IA USA

Knowing the user’s point of gaze has significant potential to enhance current human-computer interfaces, given that eye movements can be used as an indicator of the attentional state of a user. The primary obstacle of integrating eye movements into today’s interfaces is the availability of a reliable, low-cost open-source eye-tracking system. Towards making such a system available to interface designers, we have developed a hybrid eye-tracking algorithm that integrates feature-based and model-based approaches and made it available in an open-source package. We refer to this algorithm as "starburst" because of the novel way in which pupil features are detected. This starburst algorithm is more accurate than pure feature-based approaches yet is signi?cantly less time consuming than pure modelbased approaches. The current implementation is tailored to tracking eye movements in infrared video obtained from an inexpensive head-mounted eye-tracking system. A validation study was conducted and showed that the technique can reliably estimate eye position with an accuracy of approximately one degree of visual angle.

关键词： Costs Tracking Open source software Algorithm design and analysis computer vision Keyboards computer interfaces Hardware Human computer interaction Availability

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An Evaluation of Motion in *** Selective Attention

引用

ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： T.J. Williams B.A. Draper Computer Science Department Colorado State University Fort Collins CO USA

The Difference of Gaussian (DoG) saliency maps originally proposed by Koch and Ullman had information channels for intensity, opponent colors, and edge orientations, but from the beginning it was suggested that additional channels could and should be added. This paper addresses selective attention in video sequences, and adds motion channels to the saliency maps. The resulting attention windows display better-than-random correlation to the eye fixations of human subjects. This paper is not the first to add motion data to saliency maps. It is, however, the first paper we know of to explicitly compare the performance of saliency maps with and without a motion channel. The surprising negative result is that adding motion channels does not improve the performance of saliency-based selective attention, as measured by correspondence with human eye fixations. We draw two conclusions from this experiment: (1) although motion is clearly a critical attentional cue, the saliency map model may not extend as easily to motion data as some people (ourselves included) thought; and (2) the standard evaluation metric for selective attention algorithms - better than random correlation to human eye fixations - is outdated. Our community needs to focus on comparative studies between algorithms instead.

关键词： Humans computer vision Video sequences Layout computer science Displays Motion measurement Spatial resolution Pipelines Belts

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Beyond Lambert: reconstructing specular surfaces using color

Beyond Lambert: reconstructing specular surfaces using color

引用

conference on computer vision and pattern recognition (cvpr)

作者： S.P. Mallick T.E. Zickler D.J. Kriegman P.N. Belhumeur Computer Science and Engineering University of California San Diego La Jolla CA USA Engineering and Applied Sciences Harvard University Cambridge USA Computer Science Columbia University New York NY USA

We present a photometric stereo method for non-diffuse materials that does not require an explicit reflectance model or reference object. By computing a data-dependent rotation of RGB color space, we show that the specular reflection effects can be separated from the much simpler, diffuse (approximately Lambertian) reflection effects for surfaces that can be modeled with dichromatic reflectance. Images in this transformed color space are used to obtain photometric reconstructions that are independent of the specular reflectance. In contrast to other methods for highlight removal based on dichromatic color separation (e.g., color histogram analysis and/or polarization), we do not explicitly recover the specular and diffuse components of an image. Instead, we simply find a transformation of color space that yields more direct access to shape information. The method is purely local and is able to handle surfaces with arbitrary texture.

关键词： Surface reconstruction Reflectivity Image color analysis Image reconstruction Photometry Reflection Histograms Image analysis Independent component analysis Polarization

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ARTag, a fiducial marker system using digital techniques

ARTag, a fiducial marker system using digital techniques

引用

conference on computer vision and pattern recognition (cvpr)

作者： M. Fiala National Research Council Canada Ottawa Canada

Fiducial marker systems consist of patterns that are mounted in the environment and automatically detected in digital camera images using an accompanying detection algorithm. They are useful for augmented reality (AR), robot navigation, and general applications where the relative pose between a camera and object is required. Important parameters for such marker systems is their false detection rate (false positive rate), their inter-marker confusion rate, minimal detection size (in pixels) and immunity to lighting variation. ARTag is a marker system that uses digital coding theory to get a very low false positive and inter-marker confusion rate with a small required marker size, employing an edge linking method to give robust lighting variation immunity. ARTag markers are bi-tonal planar patterns containing a unique ID number encoded with robust digital techniques of checksums and forward error correction (FEC). This proposed new system, ARTag has very low and numerically quantifiable error rates, does not require a grey scale threshold as does other marker systems, and can encode up to 2002 different unique ID's with no need to store patterns. Experimental results are shown validating this system.

关键词： Robustness Forward error correction Digital cameras Detection algorithms Augmented reality Robotics and automation Navigation Robot vision systems Codes Joining processes

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Bayesian object detection in dynamic scenes

Bayesian object detection in dynamic scenes

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conference on computer vision and pattern recognition (cvpr)

作者： Y. Sheikh M. Shah Computer Vision Laboratory School of Computer Science University of Central Florida Orlando FL USA

Detecting moving objects using stationary cameras is an important precursor to many activity recognition, object recognition and tracking algorithms. In this paper, three innovations are presented over existing approaches. Firstly, the model of the intensities of image pixels as independently distributed random variables is challenged and it is asserted that useful correlation exists in the intensities of spatially proximal pixels. This correlation is exploited to sustain high levels of detection accuracy in the presence of nominal camera motion and dynamic textures. By using a non-parametric density estimation method over a joint domain-range representation of image pixels, multi-modal spatial uncertainties and complex dependencies between the domain (location) and range (color) are directly modeled. Secondly, temporal persistence is proposed as a detection criteria. Unlike previous approaches to object detection which detect objects by building adaptive models of the only background, the foreground is also modeled to augment the detection of objects (without explicit tracking) since objects detected in a preceding frame contain substantial evidence for detection in a current frame. Third, the background and foreground models are used competitively in a MAP-MRF decision framework, stressing spatial context as a condition of pixel-wise labeling and the posterior function is maximized efficiently using graph cuts. Experimental validation of the proposed method is presented on a diverse set of dynamic scenes.

关键词： Bayesian methods Object detection Layout Pixel Cameras Object recognition Technological innovation Random variables Motion detection Uncertainty

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3D and Moving Target Extraction from Dynamic Pushbroom Stereo Mosaics

3D and Moving Target Extraction from Dynamic Pushbroom Stere...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Zhigang Zhu Hao Tang Bing Shen G. Wolberg Department of Computer Science City College of New York NY USA

In this paper, we propose a dynamic pushbroom stereo mosaic approach for representing and extracting 3D structures and independent moving targets from urban 3D scenes. Our goal is to acquire panoramic mosaic maps with motion tracking information for 3D (moving) targets using a light aerial vehicle equipped with a video camera flying over an unknown area for urban surveillance. In dynamic pushbroom stereo mosaics, independent moving targets can be easily identified in the matching process of stereo mosaics by detecting the "out-ofplace" regions that violate epipolar constraints and/or give 3D anomalies. We propose a segmentation-based stereo matching approach with natural matching primitives to estimate the 3D structure of the scene, particularly the ground structures (e.g., roads) on which humans or vehicles move, and then to identify moving targets and to measure their 3D structures and movements.

关键词： Vehicle dynamics Data mining Layout Target tracking Cameras Surveillance Humans Land vehicles Road vehicles Particle measurements

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Tracking multiple colored blobs with a moving camera

Tracking multiple colored blobs with a moving camera

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conference on computer vision and pattern recognition (cvpr)

作者： A.A. Argyros M.I.A. Lourakis Foundation for Research and Technology-Hellas Vassilika Vouton Institute for Computer Science Crete Greece

This paper concerns a method for tracking multiple blobs exhibiting certain color distributions in images acquired by a possibly moving camera. The method encompasses a collection of techniques that enable modeling and detecting the blobs possessing the desired color distribution(s), as well as inferring their temporal association across image sequences. Appropriately colored blobs are detected with a Bayesian classifier, which is bootstrapped with a small set of training data. Then, an online iterative training procedure is employed to refine the classifier using additional training images. Online adaptation of color probabilities is used to enable the classifier to cope with illumination changes. Tracking over time is realized through a novel technique, which can handle multiple colored blobs. Such blobs may move in complex trajectories and occlude each other in the field of view of a possibly moving camera, while their number may vary over time. A prototype implementation of the developed system running on a conventional Pentium IV processor at 2.5 GHz operates on 320/spl times/240 live video in real time (30Hz). It is worth pointing out that currently, the cycle time of the tracker is determined by the maximum acquisition frame rate that is supported by our ieee 1394 camera, rather than the latency introduced by the computational overhead for tracking blobs.

关键词： Cameras Humans Lighting Face detection Fingers Streaming media Tracking computer science Image sequences Bayesian methods

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A projector-camera system with real-time photometric adaptation for dynamic environments

A projector-camera system with real-time photometric adaptat...

引用

conference on computer vision and pattern recognition (cvpr)

作者： K. Fujii M.D. Grossberg S.K. Nayar NTT Cyber Space Laboratories NTT Corporation Yokosuka Japan Department Computer Science City College and Graduate Center City University of New York New York NY USA Department Computer Science Columbia University New York NY USA

Projection systems can be used to implement augmented reality, as well as to create both displays and interfaces on ordinary surfaces. Ordinary surfaces have varying reflectance, color, and geometry. These variations can be accounted for by integrating a camera into the projection system and applying methods from computer vision. The methods currently applied are fundamentally limited since they assume the camera, projector, and scene are static. In this paper, we describe a technique for photometrically adaptive projection that makes it possible to handle a dynamic environment. We begin by presenting a co-axial projector-camera system whose geometric correspondence is independent of changes in the environment. To handle photometric changes, our method uses the errors between the desired and measured appearance of the projected image. A key novel aspect of our algorithm is that we combine a physics-based model with dynamic feedback to achieve real time adaptation to the changing environment. We verify our algorithm through a wide variety of experiments. We show that it is accurate and runs in real-time. Our algorithm can be applied broadly to assist HCI, visualization, shape recovery, and entertainment applications.

关键词： Cameras Photometry Real time systems Machine vision computer displays

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Detecting and Tracking Eyes Through Dynamic Terrain Feature Matching

Detecting and Tracking Eyes Through Dynamic Terrain Feature ...

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Jun Wang Lijun Yin Department of Computer Science State University of New York Binghamton Binghamton NY USA

Automatic eye detection and tracking is an important component in the advanced human-computer interface design. In this paper, we present a novel approach for detecting and tracking eyes through matching their terrain features. Regarded as a 3D terrain surface, eye region exhibits certain intrinsic traits when using a so-called topographic representation. With the topographic classification of terrain features, we generate a terrain map for each facial image and extract eye candidates from the terrain map. Our algorithm mainly consists of two parts. First, eye locations are estimated from the candidate positions using an appearancebased object recognition technique. Second, a mutual information based fitting function is defined to describe the similarity between two terrain surfaces. By optimizing the fitting function, eye locations are updated for each frame in a video sequence. The distinction of the proposed approach lies in that both eye detection and eye tracking are performed in a terrain map domain rather than an original intensity image domain. The robustness of the approach is demonstrated under various imaging conditions and with different facial appearances using a web camera.

关键词： Eyes Surface topography Surface fitting Data mining Mutual information Robustness Cameras Object detection Humans computer science

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Jensen-Shannon boosting learning for object recognition

Jensen-Shannon boosting learning for object recognition

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conference on computer vision and pattern recognition (cvpr)

作者： Xiangsheng Huang S.Z. Li Yangsheng Wang CASIA-SAIT HCI Joint Lab Institute of Automation Chinese Academy and Sciences Beijing China Institute of Automation Chinese Academy and Sciences Beijing China

In this paper, we propose a novel learning method, called Jensen-Shannon Boosting (JSBoost) and demonstrate its application to object recognition. JSBoost incorporates Jensen-Shannon (JS) divergence [Y. Bubner et al. (2001)] into AdaBoost learning. JS divergence is advantageous in that it provides more appropriate measure of dissimilarity between two classes and it is numerically more stable than other measures such as Kullback-Leibler (KL) divergence (see [Y. Bubner et al. (2001)]). The best features are iteratively learned by maximizing the projected JS divergence, based on which best weak classifiers are derived. The weak classifiers are combined into a strong one by minimizing the recognition error. JSBoost learning is demonstrated with face object recognition using a local binary pattern (LBP) [M. Pietikainen et al. (2004)] based representation. JSBoost selects the best LBP features from thousands of candidate features and constructs a strong classifier based on the selected features. JSBoost empirically produces better face recognition results than other AdaBoost variants such as RealBoost [R.E. Schapire et al. (1998)], GentleBoost [J. Friedman et al. (2000)] and KL-Boost [C. Liu et al. (2003)], as demonstrated by experiments.

关键词： Boosting Object recognition Automation Linear discriminant analysis Human computer interaction Learning systems Face recognition Upper bound pattern recognition Feature extraction

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