In this paper we present a new representation for 3D free-form contours in the conformal geometric algebra G 4, 1 . this new representation allows to extract local geometrical feature information which is used to solv...
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ISBN:
(纸本)0769525210
In this paper we present a new representation for 3D free-form contours in the conformal geometric algebra G 4, 1 . this new representation allows to extract local geometrical feature information which is used to solve the correspondence problem for pose estimation applications. Under perspective projection, local features are extracted from a projected contour segment and compared withimage features obtained from the monogenic signal. We tested our approach using synthetical and real data for several pose estimation algorithms
Segmentation of images into disjoint regions and interpretation of the regions for semantic meanings are two central tasks in an image analysis system. Typically, the segmentation and interpretation are performed sepa...
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ISBN:
(纸本)0769525210
Segmentation of images into disjoint regions and interpretation of the regions for semantic meanings are two central tasks in an image analysis system. Typically, the segmentation and interpretation are performed separately withthe interpretation as a post processing of segmentation. In this paper, we use an iterative method that keeps refining the segmentation and producing semantic class labels at the same time. the segmentation algorithm is based on a region growing technique and the interpretation is a Markov random field (MRF) based classification. the two processes are integrated under the Bayesian framework, with both aiming at reducing a defined energy. the interactions between the two are bidirectional by letting the interpretation result have some degree of control on the region growing process. Various features can hence be efficiently combined, and accurate classifications are obtained for operational synthetic aperture radar (SAR) sea ice applications
the proposed algorithm segments planar structures out of data gained from 3D laser range scanners, typically used in robotics. the approach first fits planar patches to the dataset, using a new, extended expectation m...
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the proposed algorithm segments planar structures out of data gained from 3D laser range scanners, typically used in robotics. the approach first fits planar patches to the dataset, using a new, extended expectation maximization (EM) algorithm. this algorithm solves the classical EM problems of insufficient initialization by iteratively determining the number and positions of patches in a split and merge framework. Determining the fitting quality of the gained patches, the approach then allows for segmentation of planar surfaces out of the 3D environment. the result is a set of 2D objects, which can be used as input for classical computer vision applications, in particular for object recognition. Our approach makes it possible to apply classical tools of 2D imageprocessing to solve problems of 3D robot mapping, e.g. landmark recognition
We describe a new omnidirectional stereo imaging system that uses a concave lens and a convex mirror to produce a stereo pair of images on the sensor of a conventional camera. the light incident from a scene point is ...
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ISBN:
(纸本)0769525210
We describe a new omnidirectional stereo imaging system that uses a concave lens and a convex mirror to produce a stereo pair of images on the sensor of a conventional camera. the light incident from a scene point is split and directed to the camera in two parts. One part reaches camera directly after reflection from the convex mirror and forms a single-viewpoint omnidirectional image. the second part is formed by passing a subbeam of the reflected light from the mirror through a concave lens and forms a displaced single viewpoint image where the disparity depends on the depth of the scene point. A closed-form expression for depth is derived. Since the optical components used are simple and commercially available, the resulting system is compact and inexpensive. this, and the simplicity of the required imageprocessingalgorithms, make the proposed system attractive for real-time applications, such as autonomous navigation and object manipulation. the experimental prototype we have built is described
Foundation principles of multichannel real-time information processing system, which is solving a problem of automatic object tracking, are considered in this work. imageprocessingalgorithms, which are used for thre...
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ISBN:
(纸本)0819458287
Foundation principles of multichannel real-time information processing system, which is solving a problem of automatic object tracking, are considered in this work. imageprocessingalgorithms, which are used for three-channel system foundation and optimally approaching for system core - Microprocessor Neuro Matrix NM6403 are realized. Information processing is accomplished with using of Hopfield neural network, one 384x288 frame processing time is about 100... 140 ms.
Iterative deconvolution algorithms exhibit increased ringing artifacts at higher number of iterations. Quantitative image analysis techniques such as optical flow algorithms are sensitive to these artifacts. We propos...
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ISBN:
(纸本)0769523595
Iterative deconvolution algorithms exhibit increased ringing artifacts at higher number of iterations. Quantitative image analysis techniques such as optical flow algorithms are sensitive to these artifacts. We propose to use an image quality metric to identify the terminal step of iterative restoration algorithms. Frequency based metrics have difficulties in distinguishing the ringing artifacts from the image features. Spatial analysis techniques require extensive processing making them unsuitable for real-time image quality measurement as needed in iterative image restoration. We propose a novel ringing metric using binary morphological operators and demonstrate the method on the images of random cotton fibers acquired using white light confocal microscope.
Many machine learning algorithms for clustering or dimensionality reduction take as input a cloud of points in Euclidean space, and construct a graph withthe input data points as vertices. this graph is then partitio...
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ISBN:
(纸本)0262195348
Many machine learning algorithms for clustering or dimensionality reduction take as input a cloud of points in Euclidean space, and construct a graph withthe input data points as vertices. this graph is then partitioned (clustering) or used to redefine metric information (dimensionality reduction). there has been much recent work on new methods for graph-based clustering and dimensionality reduction, but not much on constructing the graph itself. Graphs typically used include the fullyconnected graph, a local fixed-grid graph (for image segmentation) or a nearest-neighbor graph. We suggest that the graph should adapt locally to the structure of the data. this can be achieved by a graph ensemble that combines multiple minimum spanning trees, each fit to a perturbed version of the data set. We show that such a graph ensemble usually produces a better representation of the data manifold than standard methods;and that it provides robustness to a subsequent clustering or dimensionality reduction algorithm based on the graph.
Iterative deconvolution algorithms exhibit increased ringing artifacts at higher number of iterations. Quantitative image analysis techniques such as optical flow algorithms are sensitive to these artifacts. We propos...
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Iterative deconvolution algorithms exhibit increased ringing artifacts at higher number of iterations. Quantitative image analysis techniques such as optical flow algorithms are sensitive to these artifacts. We propose to use an image quality metric to identify the terminal step of iterative restoration algorithms. Frequency based metrics have difficulties in distinguishing the ringing artifacts from the image features. Spatial analysis techniques require extensive processing making them unsuitable for real-time image quality measurement as needed in iterative image restoration. We propose a novel ringing metric using binary morphological operators and demonstrate the method on the images of random cotton fibers acquired using white light confocal microscope.
In this paper a novel medical imageprocessing system is discussed. the core of the system is developed using a 16-bit fixed-point parallel architecture B-spline signal processing system. the statistical measure of fi...
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ISBN:
(纸本)0769522645
In this paper a novel medical imageprocessing system is discussed. the core of the system is developed using a 16-bit fixed-point parallel architecture B-spline signal processing system. the statistical measure of finite word length effect is analytically developed. A modified algorithm for the reduced hardware reprogrammable interpolator has been designed. Finally some suitable modification in the hardware is made to reduce the power consumption.
In this paper, an improved FCM clustering algorithm is proposed. Unlike a traditional FCM clustering algorithm whose convergence is sensitive to its initial parameters, the proposed algorithm based on fuzzy decision t...
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ISBN:
(纸本)0780366468
In this paper, an improved FCM clustering algorithm is proposed. Unlike a traditional FCM clustering algorithm whose convergence is sensitive to its initial parameters, the proposed algorithm based on fuzzy decision theory can automatically and adaptively select these parameters with optimal values. the simulation results indicate that the modified algorithm not only overcomes the ill phenomena of the FCM algorithms available now, but also is robust to the selection of the weighting constants.
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