A hardware array processor designed principally for imageprocessing.applications, called BASE 8, has been developed. BASE 8 has the appearance of a fully parallel Binary Array Processor to the programmer, however it ...
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A hardware array processor designed principally for imageprocessing.applications, called BASE 8, has been developed. BASE 8 has the appearance of a fully parallel Binary Array Processor to the programmer, however it processes a large array in a sequence of 8 multiplied by 8 blocks. A BASE 8 processor is designed to be connected to and controlled by a minicomputer such as a PDP 11. It greatly enhances the performance of the host computer for some imageprocessing.algorithms and also provides a flexible hardware model for Binary Array Processor architecture research. The hardware organization of BASE 8 is described and its performance for some imageprocessing.algorithms is given.
We describe an efficient method of improving the performance of vision algorithms operating on video streams by reducing the amount of data captured and transferred from image sensors to analysis servers in a data-awa...
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ISBN:
(纸本)9781728193601
We describe an efficient method of improving the performance of vision algorithms operating on video streams by reducing the amount of data captured and transferred from image sensors to analysis servers in a data-aware manner. The key concept is to combine guided, highly heterogeneous sampling with an intelligent Scene Cache. This enables the system to adapt to spatial and temporal patterns in the scene, thus reducing redundant data capture and processing. A software prototype of our framework running on a general-purpose embedded processor enables superior object detection accuracy (by 56%) at similar energy consumption (slight improvement of 4%) compared to an H.264 hardware accelerator.
A framework for object segmentation in vector-valued images is presented in this paper. The first scheme proposed is based on geometric active contours moving towards the objects to be detected in the vector-valved im...
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ISBN:
(纸本)0818672587
A framework for object segmentation in vector-valued images is presented in this paper. The first scheme proposed is based on geometric active contours moving towards the objects to be detected in the vector-valved image. Objects boundaries are obtained as geodesics or minimal weighted distance curves in a Riemannian space. The metric in this space is given by a definition of edges in vector-valued images. The curve flow corresponding to the proposed active contours holds formal existence, uniqueness, stability, and correctness results. The techniques is applicable for example to color and texture images. The scheme automatically handles changes in the deforming curve topology. We conclude the paper presenting an extension of the color active contours which leads to a possible image flow for vector-valued image segmentation. The algorithm is based on moving each one of the image level-sets according to the proposed color active contours. This extension also shows the relation of the color geodesic active contours with a number of partial-differential-equations based imageprocessing.algorithms as anisotropic diffusion and shock filters.
An efficient procedure which integrates feature selection and binary decision tree construction is presented. The nonparametric approach is based on the Kolmogorov-Smirnov criterion which yields an optimal classificat...
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An efficient procedure which integrates feature selection and binary decision tree construction is presented. The nonparametric approach is based on the Kolmogorov-Smirnov criterion which yields an optimal classification decision at each node. By combining the feature selection with the design of the classifier, only the most informative features are retained for classification.
A novel depth-from-focus technique is introduced that needs only a single image. It is based on a precise knowledge of the 3-D point spread function and requires objects of uniform brightness and simple shapes. Using ...
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ISBN:
(纸本)0818658258
A novel depth-from-focus technique is introduced that needs only a single image. It is based on a precise knowledge of the 3-D point spread function and requires objects of uniform brightness and simple shapes. Using adequate low-level imageprocessing.techniques, the true area of the object and the distance from the focal plane is obtained from parameters such as the apparent (blurred) area of the object and the mean brightness in this area. The technique has been applied to measure the size distribution of bubbles submerged by breaking waves. A depth criterion is used to define a virtual measuring volume that is roughly proportional to the size of the bubbles.
In this paper, we exploit some previous theoretical results about decision tree pruning to derive a color segmentation algorithm which avoids some of the common drawbacks of region merging techniques. The algorithm ha...
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ISBN:
(纸本)0769512720
In this paper, we exploit some previous theoretical results about decision tree pruning to derive a color segmentation algorithm which avoids some of the common drawbacks of region merging techniques. The algorithm has both statistical and computational advantages over known approaches. It authorizes the processing.of 512 x 512 images in less than a second on conventional PC computers. Experiments are reported on thirty-five images of various origins, illustrating the quality of the segmentations obtained.
We offer a novel strategy to adapt the perceptual organization process to an object and its context in a scene. Given a set of training images of an object in context, a learning process decides on the relative import...
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ISBN:
(纸本)0818684976
We offer a novel strategy to adapt the perceptual organization process to an object and its context in a scene. Given a set of training images of an object in context, a learning process decides on the relative importance of the basic Gestalt relationships such as proximity, parallelness, similarity. symmetry, closure, and common region towards segregating the object from the background. This learning is accomplished using a team of stochastic automata in a N-player cooperative game framework. The grouping process which is based on graph partitioning is able to form large groups from relationships defined over a small set of primitives and is fast. We demonstrate the robust performance of the grouping system on a variety of real images. Among the interesting conclusions is the significant role of photometric attributes in grouping and the ability to perform figure-ground segmentation from a set of local relations, each defined over a small number of primitives.
There are two major formulations of image alignment using gradient descent. The first estimates an additive increment to the parameters (the additive approach), the second an incremental warp (the compositional approa...
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ISBN:
(纸本)0769512720
There are two major formulations of image alignment using gradient descent. The first estimates an additive increment to the parameters (the additive approach), the second an incremental warp (the compositional approach). We first prove that these two formulations are equivalent. A very efficient algorithm was recently proposed by Hager and Belhumeur using the additive approach that unfortunately can only be applied to a very restricted class of warps. We show that using the compositional approach an equally efficient algorithm (the inverse compositional algorithm) can be derived that can be applied to any set of warps which form a group. While most warps used in computer vision form groups, there are a certain warps that do not. Perhaps most notable is the set of piecewise affine warps used in Flexible Appearance Models (FAMs). We end this paper by extending the inverse compositional algorithm to apply to FAMs.
Landuse classification is an important problem in the remote sensing field. It can be used in a wide range of applications. In this paper we propose a hybrid method fusing edges and regions information for the landuse...
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ISBN:
(纸本)0769523722
Landuse classification is an important problem in the remote sensing field. It can be used in a wide range of applications. In this paper we propose a hybrid method fusing edges and regions information for the landuse classification of multispectral images. It mainly includes the steps of image pre-processing. initial segmentation and region merging. Especially, a novel spatial mean shift procedure is proposed so that some information can be extracted and used in the successive steps. Aiming at the multispectral images processing. we also design a band weighting strategy that give a proper weight to each band adaptively according to the region to be processed. Experimental results on the Landsat TM and ETM+ images validate the performance of the proposed method.
Quantitative analysis of lung tissue micrographs aims primarily at the determination of three-dimensional properties of the lung from two-dimensional micrographs. A computer-based approach to the determination of lung...
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Quantitative analysis of lung tissue micrographs aims primarily at the determination of three-dimensional properties of the lung from two-dimensional micrographs. A computer-based approach to the determination of lung tissue boundaries and triple points and end points from the micrograph is presented. The lung tissue boundaries are determined by histogram enhancement approach and zoom thresholding technique. The binary picture representing the lung tissue is then undergoing a thinning process to generate its skeleton from which the triple points and end points are automatically determined. The processing.algorithms are developed into a software package implemented on a PDP-11/40 minicomputer.
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