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Statistical biases in optic flow

Statistical biases in optic flow

引用

conference on computer Vision and pattern recognition (CVPR)

作者： C. Fermuller R. Pless Y. Aloimonos Center for Automation Research University of Maryland College Park MD USA

The computation of optical flow from image derivatives is biased in regions of non uniform gradient distributions. A least-squares or total least squares approach to computing optic flow from image derivatives even in regions of consistent flow can lead to a systematic bias dependent upon the direction of the optic flow, the distribution of the gradient directions, and the distribution of the image noise. The bias a consistent underestimation of length and a directional error. Similar results hold for various methods of computing optical flow in the spatiotemporal frequency domain. The predicted bias in the optical flow is consistent with psychophysical evidence of human judgment of the velocity of moving plaids, and provides an explanation of the Ouchi illusion. Correction of the bias requires accurate estimates of the noise distribution; the failure of the human visual system to make these corrections illustrates both the difficulty of the task and the feasibility of using this distorted optic flow or undistorted normal flow in tasks requiring higher lever processing.

关键词： image motion analysis Optical distortion Optical noise Optical computing Distributed computing Humans Least squares methods Spatiotemporal phenomena Frequency domain analysis Psychology

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Deformable shape detection and description via model-based region grouping

Deformable shape detection and description via model-based r...

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conference on computer Vision and pattern recognition (CVPR)

作者： Lifeng Liu S. Sclaroff Computer Science Department Boston University Boston MA USA

A method for deformable shape detection and recognition is described. Deformable shape templates are used to partition the image into a globally consistent interpretation, determined in part by the minimum description length principle. Statistical shape models enforce the prior probabilities on global, parametric deformations for each object class. Once trained, the system autonomously segments deformed shapes from the background, while not merging them with adjacent objects or shadows. The formulation can be used to group image regions based on any image homogeneity predicate; e.g., texture, color or motion. The recovered shape models can be used directly in object recognition. Experiments with color imagery are reported.

关键词： Shape Deformable models image segmentation Merging Color Computational complexity Lighting computer science image processing image edge detection

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Subset selection for active object recognition

Subset selection for active object recognition

引用

conference on computer Vision and pattern recognition (CVPR)

作者： J. Winkeler B.S. Manjunath S. Chandrasekaran Department of Electrical and Computer Engineering University of California Santa Barbara CA USA

This paper presents an algorithm for constructing object representations suitable for recognition. The system automatically selects a representative subset of the views of the object while constructing the eigenspace basis. These views are actively located for object identification and pose determination. All processing.is performed on-line. The camera is actively positioned during both representation and recognition. When tested with 240 views for each of seven objects, the system achieves 100% accurate object recognition and pose determination. These results are shown to degrade gracefully as conditions deteriorate.

关键词： Object recognition Cameras System testing Greedy algorithms image reconstruction Degradation image recognition Solid modeling Face recognition Sampling methods

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Real-time Gabor-type filtering using analog focal plane image processors

Real-time Gabor-type filtering using analog focal plane imag...

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conference on computer Vision and pattern recognition (CVPR)

作者： B.E. Shi Department of Electrical and Electronic Engineering Hong Kong University of Science and Technology Kowloon Hong Kong China

We describe focal plane image processors which filter images in space by filters which are similar to even and odd Gabor filters. All processing.is done using analog circuits fabricated on the same die as the photosensors allowing combined sensing and processing.at rates as high as 5000 frames per second. Both 1D and 2D sensors have been designed, fabricated and tested. In the 2D sensor the tuned orientation can be steered and the filter response scaled by adjusting external bias voltages controlling parameters such as the gains of the analog processing.circuits. Because both even and odd Gabor-type filter outputs are calculated, sensors can be used in Gabor phase-based algorithms. As a simple example, we have embedded a 1D sensor on a mobile robot platform to perform image fixation.

关键词： Gabor filters Filtering image sensors Band pass filters image processing Analog computers Space technology Analog circuits Mobile robots Mechanical sensors

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Simultaneous image classification and restoration using a variational approach

Simultaneous image classification and restoration using a va...

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conference on computer Vision and pattern recognition (CVPR)

作者： C. Samson L. Blanc-Feraud J. Zerubia G. Aubert Ariana project Sophia-Antipolis France Laboratoire J.A. Dieudonné Université de Nice Sophia-Antipolis Nice France

Herein, we present a variational model devoted to image classification coupled with an edge-preserving regularization process. In the last decade, the variational approach has proven its efficiency in the field of edge-preserving restoration. In this paper, we add a classification capability which contributes to provide images compound of homogeneous regions with regularized boundaries. The soundness of this model is based on the works developed on the phase transition theory in mechanics. The proposed algorithm is fast, easy to implement and efficient. We compare our results on both synthetic and satellite images with the ones obtained by a stochastic model using a Potts regularization.

关键词： image classification image restoration Satellites Stochastic processes Acoustic noise image processing image edge detection Active shape model image segmentation Active contours

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3D deformable image matching using multiscale minimization of global energy functions

3D deformable image matching using multiscale minimization o...

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conference on computer Vision and pattern recognition (CVPR)

作者： O. Musse F. Heitz J.-P. Armspach Institut de Physique Biologique CNRS UPRES-A 7004 Strasbourg I University Strasbourg France

This paper presents a hierarchical framework to perform deformable matching of three dimensional (3D) images. 3D shape deformations are parameterized at different scales, using a decomposition of the continuous deformation vector field over a sequence of nested subspaces, generated from a single scaling function. The parameterization of the field enables to enforce smoothness and differentiability constraints without performing explicit regularization. A global energy function, depending on the reference image and the transformed one, is minimized via a coarse-to-fine algorithm over this multiscale decomposition. Contrary to standard multigrid approaches, no reduction of image data is applied. The continuous field of deformation is always sampled at the same resolution, ensuring that the same energy function is handled at each scale and that the energy decreases at each step of the minimization.

关键词： image matching image registration Biomedical imaging Shape Biology Energy resolution Yield estimation image processing Motion estimation Layout

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3D trajectory recovery for tracking multiple objects and trajectory guided recognition of actions

3D trajectory recovery for tracking multiple objects and tra...

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conference on computer Vision and pattern recognition (CVPR)

作者： R. Rosales S. Sclaroff Computer Science Department Boston University USA

A mechanism is proposed that integrates low-level (image processing., mid-level (recursive 3D trajectory estimation), and high level (action recognition) processes. It is assumed that the system observes multiple moving objects via a single, uncalibrated video camera. A novel extended Kalman filter formulation is used in estimating the relative 3D motion trajectories up to a scale factor. The recursive estimation process provides a prediction and error measure that is exploited in higher-level stages of action recognition. Conversely, higher-level mechanisms provide feedback that allows the system to reliable segment and maintain the tracking of moving objects before, during, and after occlusion. The 3D trajectory, occlusion, and segmentation information are utilized in extracting stabilized views of the moving object. Trajectory-guided recognition (TGR) is proposed as a new and efficient method for adaptive classification of action. The TGR approach is demonstrated using "motion history images" that are then recognized via a mixture of Gaussian classifier. The system was tested in recognizing various dynamic human outdoor activities; e.g., running, walking, roller blading, and cycling. Experiments with synthetic data sets are used to evaluate stability of the trajectory estimator with respect to noise.

关键词： Trajectory Recursive estimation image recognition image processing Cameras Motion estimation Feedback Maintenance image segmentation Data mining

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Optimal multi-scale matching

Optimal multi-scale matching

引用

conference on computer Vision and pattern recognition (CVPR)

作者： M.S. Lew T.S. Huang Leiden Institute for Advanced Computer Science Leiden University Leiden Netherlands Beckman Institute University of Illinois Urbana-Champaign Urbana IL USA

The coarse-to-fine search strategy is extensively used in current reported research. However, it has the same problem as any hill climbing algorithm, most importantly, it often finds local instead of global minima. Drawing upon the artificial intelligence literature, we applied an optimal graph search, namely A*, to the problem. Using real stereo and video test sets, we compared the A* method to both template and hill climbing. Our results show that A* has greater accuracy than the ubiquitous coarse-to-fine hill climbing pyramidal search algorithm in both stereo matching and motion tracking.

关键词： Stereo vision Cameras Tracking Stereo image processing Biological system modeling Video compression image coding Motion analysis computer science Artificial intelligence

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Radiometric self calibration

Radiometric self calibration

引用

conference on computer Vision and pattern recognition (CVPR)

作者： T. Mitsunaga S.K. Nayar Media Processing Laboratories Sony Corporation Japan Department of Computer Science Columbia University USA

A simple algorithm is described that computes the radiometric response function of an imaging system, from images of an arbitrary scene taken using different exposures. The exposure is varied by changing either the aperture setting or the shutter speed. The algorithm does not require precise estimates of the exposures used. Rough estimates of the ratios of the exposures (e.g. F-number settings on an inexpensive lens) are sufficient for accurate recovery of the response function as well as the actual exposure ratios. The computed response function is used to fuse the multiple images into a single high dynamic range radiance image. Robustness is tested using a variety of scenes and cameras as well as noisy synthetic images generated using 100 randomly selected response curves. Automatic rejection of image areas that have large vignetting effects or temporal scene variations make the algorithm applicable to not just photographic but also video cameras.

关键词： Radiometry Calibration Layout Cameras Apertures Lenses Fuses Dynamic range Robustness Testing

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Surface reconstruction from multiple aerial images in dense urban areas

Surface reconstruction from multiple aerial images in dense ...

引用

conference on computer Vision and pattern recognition (CVPR)

作者： M. Fradkin M. Roux H. Maitre U.M. Leloglu Département TSI Ecole Nationale Supérieure des TéIécommunications Paris France Metu Tübitak Bilten Ankara Turkey

Accurate 3D surface models of dense urban areas are essential for a variety of applications, such as cartography, urban planning and monitoring mobile communications, etc. Since manual surface reconstruction is very costly and time-consuming, the development of automated algorithms is of great importance. While most of existing algorithms focus on surface reconstruction either in rural or sub-urban areas, we present an approach dealing with dense urban scenes. The approach utilizes different image-derived cues, like multiview stereo and color information, as well as the general scene knowledge, formulated in data-driven reasoning and geometric constraints. Another important feature of our approach is simultaneous processing.of 2D and 3D data. Our approach begins with two independent tasks: stereo reconstruction using multiple views and region-based image segmentation, resulting in generation disparity and segmentation maps, respectively. Then, the information derived from the both maps is utilized for generation of a dense elevation map, through robust verification of planar surface approximations for the detected regions and imposition of geometric constraints. The approach has been successfully tested on complex residential and industrial scenes.

关键词： Surface reconstruction image reconstruction Layout image segmentation Urban areas Urban planning Monitoring Mobile communication Stereo image processing Robustness

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