the authors present a method for 3-D object recognition from 2-D image sequences. the system uses feature points tracked over three or more views to compute structural invariants, which serve as 3-D shape representati...
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the authors present a method for 3-D object recognition from 2-D image sequences. the system uses feature points tracked over three or more views to compute structural invariants, which serve as 3-D shape representations. Object recognition is performed by using these Euclidean invariants as indices into a high-dimensional shape table. the use of indexing eliminates any need for matching models to images. In addition, the representation of 3-D objects is extracted from 2-D views, eliminating the cumbersome burden of having to obtain 3-D models. the proposed scheme was implemented using a mixed database of real and simulated objects. Experiments are outlined that show good recognition results on real objects and simulated objects corrupted with noise.< >
For processing solar corona X-ray images a new method has been introduced. With regard to the special character of the original images an algorithm of the local optimization of histogram has been developed. To preserv...
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A framework called Cresceptron is introduced for automatic algorithm design through learning of concepts and rules, thus deviating from the traditional mode in which humans specify the rules constituting a vision algo...
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A framework called Cresceptron is introduced for automatic algorithm design through learning of concepts and rules, thus deviating from the traditional mode in which humans specify the rules constituting a vision algorithm. Withthe Cresceptron, humans as designers need only to provide a good structure for learning, but they are relieved of most design details. the Cresceptron has been tested on the task of visual recognition by recognizing 3-D general objects from 2-D photographic images of natural scenes and segmenting the recognized objects from the cluttered image background. the Cresceptron uses a hierarchical structure to grow networks automatically, adaptively, and incrementally through learning. the Cresceptron makes it possible to generalize training exemplars to other perceptually equivalent items. Experiments with a variety of real-world images are reported to demonstrate the feasibility of learning in the Cresceptron.< >
the authors present techniques for recognizing instances of 3-D object classes from sets of 3-D feature observations. recognition of a class instance is structured as a search of an interpretation tree in which geomet...
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the authors present techniques for recognizing instances of 3-D object classes from sets of 3-D feature observations. recognition of a class instance is structured as a search of an interpretation tree in which geometric constraints on pairs of sensed features not only prune the tree, but are used to determine upper and lower bounds on the model parameter values of the instance. A real-valued constraint propagation network unifies the representations of the model parameters, model constraints and feature constraints, and provides a simple and effective mechanism for accessing and updating parameter values. recognition of objects with multiple internal degrees of freedom, including non-uniform scaling and stretching, articulations, and subpart repetitions, is demonstrated for two different types of real range data: 3-D edge fragments from a stereo vision system, and position/surface normal data derived from planar patches extracted from a range image.< >
A shape recognition approach is presented. Uncertainty handling, combining, and propagation form the heart of the method. Multiple knowledge sources extract information from the segmented image and increase knowledge ...
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A shape recognition approach is presented. Uncertainty handling, combining, and propagation form the heart of the method. Multiple knowledge sources extract information from the segmented image and increase knowledge about undefined shapes. Knowledge sources have to be tuned to discriminate shape classes, and a critical number of independent knowledge sources guarantees the classification. Information provided by the knowledge sources is stored in the Shafer form of probability mass assignment. Dempster's rule is used to update belief in classes. A brief theoretical overview is given. Combined with a heuristic, this method achieves interesting results as well as a short execution time. An example derived from an application in the PROMEthEUS project, consisting of traffic sign recognition on a motorway, illustrates this method.< >
For both 3-D reconstruction and prediction of image coordinates, cameras can be calibrated implicitly without involving their physical parameters. the authors present a two-plane method for such a complete calibration...
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For both 3-D reconstruction and prediction of image coordinates, cameras can be calibrated implicitly without involving their physical parameters. the authors present a two-plane method for such a complete calibration, which models all kinds of lens distortions. First, the modeling is done in a general case without imposing the pinhole constraint. Epipolar curves considering lens distortions are introduced and are found in a closed form. then, a set of constraints of perspectivity is derived to constrain the modeling process. Withthese constraints, the camera physical parameters can be related directly to the modeling parameters. Extensive experimental comparisons of the methods withthe classic photogrammetric method and Tsai's method relating to the aspects of 3-D measurement, the effect of the number of calibration points, and the prediction of image coordinates, are made using real images from 15 different depth values.< >
the authors synthesize a new approach to 3-D object shape recovery by integrating qualitative shape recovery techniques and quantitative physics-based shape estimation techniques. they first use qualitative shape reco...
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the authors synthesize a new approach to 3-D object shape recovery by integrating qualitative shape recovery techniques and quantitative physics-based shape estimation techniques. they first use qualitative shape recovery and recognition techniques to provide strong fitting constraints on physics-based deformable model recovery techniques. Previously developed techniques of fitting deformable models to occluding image contours are then extended to the case of image data captured under general orthographic, perspective, and stereo projections. Experimental results are presented to illustrate the shape recovery approach.< >
the authors present a quantitative analysis of the viewpoint consistency constraint (VCC), which is the fundamental principle behind model-based methods for recognizing 3-D objects from 2-D data. It defines a measure ...
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the authors present a quantitative analysis of the viewpoint consistency constraint (VCC), which is the fundamental principle behind model-based methods for recognizing 3-D objects from 2-D data. It defines a measure of viewpoint consistency error (VCE), based on a formal model of image feature errors. Existing methods for establishing feature correspondences using the VCC are discussed. the poor performance of incremental methods is demonstrated and attributed to the failure to ensure that global consistency improves during search. A more reliable method, viewpoint consistency ascent, which uses the VCE explicitly as a heuristic for a state-space search, is presented. the two algorithms are compared in an experimental study. the approach to quantitative analysis of alternative algorithms is illustrated, which may be applied to model based object recognition more generally.< >
the author addresses the problems of reconstructing 3-D space in a projective framework from two views and of artificially generating novel views of the scene from two given views. It is shown that withthe correspond...
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the author addresses the problems of reconstructing 3-D space in a projective framework from two views and of artificially generating novel views of the scene from two given views. It is shown that withthe correspondences coming from four non-coplanar points in the scene and the corresponding epipoles, it is possible to define and reconstruct a projective invariant, referred to as projective depth, that can be used later to reconstruct the projective or affine structure of the scene or directly to generate novel views of the scene. the derivation has the advantage that the viewing transformation matrix need not be recovered in the course of computations.< >
the authors present the theory of probabilistic relaxation for matching symbolic structures, derive as limiting cases the various heuristic formulas used by researchers in matching problems, and state the conditions u...
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the authors present the theory of probabilistic relaxation for matching symbolic structures, derive as limiting cases the various heuristic formulas used by researchers in matching problems, and state the conditions under which they apply. they successfully apply the theory to the problem of matching and recognizing aerial road network images based on road network models and to the problem of edge matching in a stereo pair. For this purpose, each line network is represented by an attributed relational graph where each node is a straight line segment characterized by certain attributes and related with every other node via a set of binary relations.< >
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