Several vision problems can be reduced to the problem of fitting a linear surface of low dimension to data, including the problems of structure-from-affine-motion, and of characterizing the intensity images of a Lambe...
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ISBN:
(纸本)0780342364
Several vision problems can be reduced to the problem of fitting a linear surface of low dimension to data, including the problems of structure-from-affine-motion, and of characterizing the intensity images of a Lambertian scene by constructing the intensity manifold. For these problems, one must deal with a data matrix with some missing elements. In structure-from-motion, missing elements will occur if some point features are not visible in some frames. To construct the intensity manifold missing matrix elements will arise when the surface normals of some scene points do not face the light source in some images. We propose a novel method for fitting a low rank matrix to a matrix with missing elements. We show experimentally that our method produces good results in the presence of noise. These results can be either used directly, or can serve as an excellent starting point for an iterative method.
The vast majority of corner and edge detectors measure image intensity gradients in order to estimate the positions and strengths of features. However, many of the most popular intensity gradient estimators are inhere...
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ISBN:
(纸本)0818672587
The vast majority of corner and edge detectors measure image intensity gradients in order to estimate the positions and strengths of features. However, many of the most popular intensity gradient estimators are inherently and significantly anisotropic. In spite of this, few algorithms take the anisotropy into account, and so the set of features uncovered is typically sensitive to rotations of the image, compromising recognition, matching (e.g. stereo), and tracking. We introduce an effective technique for removing unwanted anisotropies from analytical gradient estimates, by measuring local intensity gradients in four directions rather than the more traditional two. In experiments using real image data, our algorithm reduces the gradient anisotropy associated with conventional analytical gradient estimates by up to 85%, yielding more consistent feature topologies.
Tire's paper describes a representation for people and animals, called a body plan, which is adapted to segmentation and to recognition in complex environments. The representation is an organized collection of gro...
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ISBN:
(纸本)0780342364
Tire's paper describes a representation for people and animals, called a body plan, which is adapted to segmentation and to recognition in complex environments. The representation is an organized collection of grouping hints obtained from a combination of constraints on color and texture and constraints on geometric properties such as the structure of individual parts and the relationships between parts. Body plans can be learned from image data, using established statistical learning techniques. The approach is illustrated with two examples of programs that successfully use body plans for recognition: one example involves determining whether a picture contains a scantily clad human, using a body plan built by hand;We other involves determining whether a picture contains a horse, using a body plan learned front image data. In both cases, the system demonstrates excellent performance on large, uncontrolled test sets and very large and diverse control sets.
We present a set of algorithms and a search strategy for the robust content-based retrieval of multispectral satellite images. Since the property of interest in these images is usually the physical characteristics of ...
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ISBN:
(纸本)0818672587
We present a set of algorithms and a search strategy for the robust content-based retrieval of multispectral satellite images. Since the property of interest in these images is usually the physical characteristics of ground cover, we use representations and methods that are invariant to illumination and atmospheric conditions. The representations and algorithms are derived for this application from a physical model for the formation of multispectral satellite images. The use of several representations and algorithms is necessary to interpret the diversity of physical and geometric structure in these images. Algorithms are used that exploit multispectral distributions, multispectral spatial structure, and labeled classes. The performance of the system is demonstrated on a large set of multispectral satellite images taken over different areas of the United States under different illumination and atmospheric conditions.
We study the problem of estimating rigid motion from a sequence of monocular perspective images obtained by navigating around an object while fixating a particular feature point. We cast the problem in the framework o...
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ISBN:
(纸本)0818672587
We study the problem of estimating rigid motion from a sequence of monocular perspective images obtained by navigating around an object while fixating a particular feature point. We cast the problem in the framework of "epipolar geometry", and propose a filter based upon implicit dynamical model for recursively estimating motion under the fixation constraint. This allows us to compare the quality of the estimates directly against the ones obtained assuming a general rigid motion simply by changing the geometry of the parameter space, while maintaining the same structure of the recursive estimator. We also present a closed-form static solution from two views, and a recursive estimator of the relative pose between the viewer and the scene.
Automatic video browsing requires algorithms for detecting a variety of events, including production effects (e.g., scene breaks and captions) and moving objects. We present new methods that use edges and motion for d...
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ISBN:
(纸本)0818672587
Automatic video browsing requires algorithms for detecting a variety of events, including production effects (e.g., scene breaks and captions) and moving objects. We present new methods that use edges and motion for detecting production effects and computing motion segmentation. Production effects, such as cuts, dissolves, wipes and captions, can be detected by looking for new edges that are far from previous edges. A global motion computation is used to register consecutive images. We have also developed a method for motion segmentation, which does not require computing local optical flow. Our methods run at several frames per second on a Sparc workstation, and tolerate compression artifacts.
Planar pose measurement from images is an important problem for automated assembly and inspection. In addition to accuracy and robustness, ease of use is very important for real world applications. Recently, Murase an...
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ISBN:
(纸本)0818672587
Planar pose measurement from images is an important problem for automated assembly and inspection. In addition to accuracy and robustness, ease of use is very important for real world applications. Recently, Murase and Nayar have presented the 'parametric eigenspace' for object recognition and pose measurement based on training images. Although their system is easy to use, it has potential problems with background clutter and partial occlusions. We present an algorithm that is robust in these terms. It uses several small features on the object rather than a monolithic template. These 'eigenfeatures' are matched using a median statistic, giving the system robustness in the face of background clutter and partial occlusions. We demonstrate our algorithm's pose measurement accuracy with a controlled test, and we demonstrate its detection robustness on cluttered images with the objects of interest partially occluded.
Motion of an observer relative to objects in a scene provides information about the structure of the scene. Changing patterns of shading due to motion relative to the light source provide information about surface str...
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ISBN:
(纸本)0818672587
Motion of an observer relative to objects in a scene provides information about the structure of the scene. Changing patterns of shading due to motion relative to the light source provide information about surface structure, albedos, and light sources. One can stratify this photometric information into affine, unitary, and metric structure, much like the stratification of structure from motion [1]. For Lambertian surfaces, if either motion or photometry give us more than affine structure, the two cues can be combined to yield full metric information. Edge constraints plus unitary photometry also give us full metric photometry. Affine structure alone contains much of the quantitative structure information, allowing us to judge such things as the ordinal relationships between the albedos.
We present the Incremental Focus of Attention (IFA) architecture for adding robustness to software-based, real-time, motion trackers. The framework provides a structure which, when given the entire camera image to sea...
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ISBN:
(纸本)0818672587
We present the Incremental Focus of Attention (IFA) architecture for adding robustness to software-based, real-time, motion trackers. The framework provides a structure which, when given the entire camera image to search, efficiently focuses the attention of the system into a narrow set of possible states that includes the target state. IFA offers a means for automatic tracking initialization and reinitialization when environmental conditions momentarily deteriorate and cause the system to lose track of its target. Systems based on the framework degrade gracefully as various assumptions about the environment are violated. In particular, multiple tracking algorithms are layered so that the failure of a single algorithm causes another algorithm of less precision to take over, thereby allowing the system to return approximate feature state information.
The problem of finding the closest point in high-dimensional spaces is common in computational vision. Unfortunately, the complexity of most existing search algorithms, such as k-d tree and R-tree, grows exponentially...
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ISBN:
(纸本)0818672587
The problem of finding the closest point in high-dimensional spaces is common in computational vision. Unfortunately, the complexity of most existing search algorithms, such as k-d tree and R-tree, grows exponentially with dimension, making them impractical for dimensionality above 15. In nearly all applications, the closest point is of interest only if it lies within a user specified distance ε. We present a simple and practical algorithm to efficiently search for the nearest neighbor within Euclidean distance ε. Our algorithm uses a projection search technique along with a novel data structure to dramatically improve performance in high dimensions. A complexity analysis is presented which can help determine ε in structured problems. Benchmarks clearly show the superiority of the proposed algorithm for high dimensional search problems frequently encountered in machine vision, such as real-time object recognition.
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