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. Dr...
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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.
This paper describes the implementation of a stereo depth measurement algorithm in hardware on field programmable gate arrays (FPGAs). This system generates 8 bit sub-pixel disparities on 256 by 360 pixel images at vi...
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This paper describes the implementation of a stereo depth measurement algorithm in hardware on field programmable gate arrays (FPGAs). This system generates 8 bit sub-pixel disparities on 256 by 360 pixel images at video rate (30 frames/sec). The algorithm implemented is a multi-resolution, multi-orientation phase-based technique called local weighted phase-correlation (Fleet, 1994). Hardware implementation speeds up the performance more than 300 times that of the same algorithm running in software. In this paper, we describe the programmable hardware platform, the base stereo vision algorithm and the design of the hardware. We include various trade-offs required to make the hardware small enough to fit on our system and fast enough to work at video rate. We also show sample outputs from the functioning hardware. Although this paper is specifically focused on phase-based stereo vision FPGA realizations, most of the design issues are common to other DSP and vision applications.
An optimal deformable contour approximation algorithm using a cardinal-form piecewise cubic spline (PCS) curve representation is presented. The approximation is optimal in the sense of least square errors in both loca...
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An optimal deformable contour approximation algorithm using a cardinal-form piecewise cubic spline (PCS) curve representation is presented. The approximation is optimal in the sense of least square errors in both location and orientation. The knots are set automatically at high curvature positions. The sample data are generated by a robust edge fragment detection algorithm which is optimal in the sense of a weighted absolute error. An initial contour placement algorithm uses a penalized maximum likelihood algorithm to group the edge fragments together for an initial contour. A controlled deformable contour algorithm refines the initial contour to cover meaningful edge features.< >
We present a computational, group-theoretic approach to steerable functions. The approach is group-theoretic in that the treatment involves continuous transformation groups for which elementary Lie group theory may be...
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We present a computational, group-theoretic approach to steerable functions. The approach is group-theoretic in that the treatment involves continuous transformation groups for which elementary Lie group theory may be applied. The approach is computational in that the theory is constructive and leads directly to a procedural implementation. For functions that are steerable with n finite number of basis functions under a k-parameter group, the procedure is efficient and is guaranteed to return the minimum number of basis functions. If the function is not steerable, a numerical implementation of the procedure could also be used to compute basis functions that approximately steer the function over a range of transformation parameters. Examples of both applications are demonstrated.
A method is proposed for constructing the joint probability model from a specified first-order distribution and correlation structure. The approach is based on an invertible nonlinear transformation. The model's i...
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A method is proposed for constructing the joint probability model from a specified first-order distribution and correlation structure. The approach is based on an invertible nonlinear transformation. The model's information-theoretic properties (entropy, rate-distortion bound) are examined. The results can be used to analyze and evaluate the performance of an image data compression system. It is believed that this class of model and its information-theoretic properties may make for more realistic modeling of medical images and hence their more efficient processing.< >
Manufacturing activities involve sub stantial numbers of visual tasks for pro cess control, assembly and inspection. Many of these tasks are simple and are done repetitively for prolonged periods, thus are subject to ...
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Manufacturing activities involve sub stantial numbers of visual tasks for pro cess control, assembly and inspection. Many of these tasks are simple and are done repetitively for prolonged periods, thus are subject to various degradations due to visual fatigue. In addition, cost of these operations has been rapidly increasing. There is an obvious need for automation in the area of visual tasks related to manufacturing.
Many computer vision applications have to cope with large dynamic range and changing illumination conditions in the environment. Any attempt to deal with these conditions at the algorithmic level alone are inherently ...
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Many computer vision applications have to cope with large dynamic range and changing illumination conditions in the environment. Any attempt to deal with these conditions at the algorithmic level alone are inherently difficult because of the following: (1) conventional image sensors cannot completely capture wide dynamic range radiances without saturation or underexposure; (2) the quantization process destroys small signal variations especially in shadows; and (3) all possible illumination conditions cannot be completely accounted for. The paper proposes a computational model for brightness perception that deals with issues of dynamic range and noise. The model can be implemented on-chip in analog domain before the signal is saturated or destroyed through quantization. The model is "unified" because a single mathematical formulation addresses the problem of shot and thermal noise, and normalizes the signal range to simultaneously compress the dynamic range, minimize appearance variations due to changing illumination, and minimize quantization noise. The model strongly mimics brightness perception processes in early biological vision.
To make a Euclidean reconstruction of the world seen through a stereo rig, we can either use a calibration grid, and the results will rely on the precision Of the grid and the extracted points of interest, or use self...
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To make a Euclidean reconstruction of the world seen through a stereo rig, we can either use a calibration grid, and the results will rely on the precision Of the grid and the extracted points of interest, or use self-calibration. Past work on self-calibration is focussed on the use of only one camera, and gives sometimes very unstable results. In this paper, we use a stereo rig which is supposed to be weakly calibrated using a method such as the one described in Deriche et al. (1994). Then, by matching two sets of points of the same scene reconstructed from different points of view, we try to find both the homography that maps the projective reconstruction to the Euclidean space and the displacement from the first set of points to the second set of points. We present results of the Euclidean reconstruction of a whole object from uncalibrated cameras using the method proposed here.
Background subtraction is the first step of many video surveillance applications. What is considered background varies by application, and may include regular, systematic, or complex motions. The paper explores the us...
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Background subtraction is the first step of many video surveillance applications. What is considered background varies by application, and may include regular, systematic, or complex motions. The paper explores the use of several different local spatio-temporal models of a background, defined at each pixel in the image. We present experiments with real image data and conclude that appropriate local representations are sufficient to make background models of complicated real world motions. Empirical studies illustrate, for example, that an optical flow-based model is able to detect emergency vehicles whose motion is different from those typically observed in traffic scenes. We conclude that "different models are appropriate for different scenes", but give criteria by which one can choose which model will be best.
This paper considers the problem of modeling and extracting arbitrary deformable contours from noisy images. We propose a global contour model based on a stable and regenerative shape matrix, which is invariant and un...
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This paper considers the problem of modeling and extracting arbitrary deformable contours from noisy images. We propose a global contour model based on a stable and regenerative shape matrix, which is invariant and unique under rigid motions. Combined with Markov random field to model local deformations, this yields prior distribution that exerts influence over a global model while allowing for deformations. We then cast the problem of extraction into posterior estimation and show its equivalence to energy minimization of a generalized active contour model. We discuss pertinent issues in shape training, minimax regularization and initialization by generalized Hough transform. Finally, we present experimental results and compare its performance to rigid template matching.< >
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