This paper describes a texture coding technique mainly suitable for segmentation-based coding schemes. The main features of the proposed technique are its efficiency in terms of bits per pixel for homogeneous regions ...
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ISBN:
(纸本)081941638X
This paper describes a texture coding technique mainly suitable for segmentation-based coding schemes. The main features of the proposed technique are its efficiency in terms of bits per pixel for homogeneous regions and its ability to deal with local inhomogeneities that may be present in the image. The basic idea of the coding strategy is to divide the image into blocks and to classify the blocks in two categories: Referable and Nonreferable. Referable means that the block can be approximated by one block of the already transmitted texture and nonreferable is defined by opposition. Nonreferable blocks are transmitted with a general purpose coding scheme (for example a DCT-based technique) and referable blocks are transmitted by means of a simple transition vector indicating which sample of the transmitted texture has to be translated. We show that this technique is suitable for texture but produces distortions for strong contours. As a result, we propose to use it within a segmentation-based coding scheme where contours are transmitted by another strategy. Finally, the application to sequence coding is discussed. It is shown that this technique is particularly attractive to code the prediction error within a motion compensated video coding scheme.
The guiding principle of this study is to find an optimum way to simplify the contours produced by a second generation coding scheme based on morphological segmentation. For this purpose, evaluations of existing metho...
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ISBN:
(纸本)081941638X
The guiding principle of this study is to find an optimum way to simplify the contours produced by a second generation coding scheme based on morphological segmentation. For this purpose, evaluations of existing methods for contour simplification are carried out first. Based on the human visual phenomenon, a new nonlinear filter by means of majority operation is designed to simplify the contours in order to obtain an optimum compromise between the cost for contour coding and visual quality. Applications for region-based still image coding and video coding are demonstrated. Experimental results have shown an average of 20% reduction of bits for contour coding while keeping good visual quality.
Background normalization is a low-level imageprocessing task typically used to enhance images by eliminating featureless, nonuniform background illumination. Automatic background normalization requires three distinct...
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ISBN:
(纸本)0819415421
Background normalization is a low-level imageprocessing task typically used to enhance images by eliminating featureless, nonuniform background illumination. Automatic background normalization requires three distinct steps: threshold selection and segmentation, reconstruction of background image, and subtraction. This paper presents a new region-based thresholding criterion for background identification and normalization. Experimental results will be presented.
We present here an algorithm which performs radar cross-section estimation by using techniques based on simulated annealing. Standard simulated annealing approaches to image restoration attempt to categorize each imag...
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ISBN:
(纸本)0819415472
We present here an algorithm which performs radar cross-section estimation by using techniques based on simulated annealing. Standard simulated annealing approaches to image restoration attempt to categorize each image element as belonging to one of a small number of predefined image states or values. This is restrictive for tasks such as radar cross-section estimation and we present here an algorithm which is capable of producing a real-valued output. This is achieved by introducing an edge detection stage into the simulated annealing process. The action of the annealing algorithm may be viewed as a filter which adapts to local image structure. We present results which demonstrate this behavior and in so doing allow us to estimate the residual noise levels we might expect.
This paper describes a system developed for segmenting multiband grayscale images into n-class labeled images at high-throughput rates. This system, which we refer to as the segmentation engine, performs supervised im...
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ISBN:
(纸本)0819414786
This paper describes a system developed for segmenting multiband grayscale images into n-class labeled images at high-throughput rates. This system, which we refer to as the segmentation engine, performs supervised image segmentation using algorithms based on the statistical pattern recognition paradigm. So-called 'features' are computed for each pixel and the feature vector thus formed is presented to a statistical classifier, which uses feature information to determine the most probable class of the pixel. algorithms are described for the following: features, automatic feature selection, classification and classifier training. While this paper describes the entire system, the algorithmic approach will be emphasized.
A great number of parallel computer architectures have been proposed, whether they are SIMD machines (Single Instruction Multiple Data) with lots of quite simple processors, or MIMD machines (Multiple Instruction Mult...
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ISBN:
(纸本)0819412775
A great number of parallel computer architectures have been proposed, whether they are SIMD machines (Single Instruction Multiple Data) with lots of quite simple processors, or MIMD machines (Multiple Instruction Multiple Data) containing few, but powerful processors. Each one claims to offer some kind of an optimality at the hardware level. But implementing parallel imageprocessingalgorithms to make them run in real time will remain a real challenge; it addresses rather the control of communication networks between processors (message passing, circuit switching..) or the computing model (e.g. data parallel model). In that respect, our goal here is to point out some algorithmic needs to distribute imageprocessing operators. They will be translated first in terms of programming models, more general then imageprocessing applications, and then as hardware properties of the processor network. In that way, we do not design yet another parallel machine dedicated to imageprocessing, but a more general parallel architecture which one will be able to efficiently implement different kinds of programming models.
Adapted wave form analysis, refers to a collection of FFT like adapted transform algorithms. Given an image these methods provide special matched collections of templates (orthonormal bases) enabling an efficient codi...
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Adapted wave form analysis, refers to a collection of FFT like adapted transform algorithms. Given an image these methods provide special matched collections of templates (orthonormal bases) enabling an efficient coding of the image. Perhaps the closest well known examples of such coding method is provided by musical notation, where each segment of music is represented by a musical score made up of notes (templates) characterized by their duration, pitch, location and amplitude, our method corresponds to transcribing the music in as few notes as possible. The extension of images and video is straightforward. We describe the image by collections of oscillatory patterns of various sizes, locations and amplitudes using a variety of orthogonal bases. These selected basis functions are chosen inside predefined libraries of oscillatory localized functions (trigonometric and wavelet-packets waveforms) so as to optimize the number of parameters needed to describe our object. These algorithms are of complexity N log N opening the door for a large range of applications in signal and imageprocessing, such as compression, feature extraction denoising and enhancement. In particular we describe a class of special purpose compressions for fingerprint images, as well as denoising tools for texture and noise extraction.
A line segment image transform and inverse transform is used to detect and extract quasi- linear features in synthetic aperture radar (SAR) images. The transform is a windowed version of the Radon transform. The trans...
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ISBN:
(纸本)0819411027
A line segment image transform and inverse transform is used to detect and extract quasi- linear features in synthetic aperture radar (SAR) images. The transform is a windowed version of the Radon transform. The transform begins by dividing the original image into overlapped subimages using a simple analysis filter. A Radon transform is applied to each subimage yielding a representation of the subimage in terms of line segments at varying angles and positions. The amplitude of each line segment is used to calculate the detection statistic for that line segment. Line segments whose detection statistics are above a specified threshold are passed, and the filtered subimage is reconstructed using an inverse Radon transform (convolution back projection). Finally, the filtered subimages are recombined to form the filtered version of the original image. The filtering procedure passes quasi-linear features in the original image, and rejected features that are not quasi-linear, such as speckle. The inversion procedure is designed so that the original image is reconstructed if the threshold is set to zero.
This paper describes an image segmentation algorithm and the results obtained using a specially designed robotic head. The head consists of a camera and a laser range-finder mounted on a pan & tilt unit. Addit...
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ISBN:
(纸本)0819412007
This paper describes an image segmentation algorithm and the results obtained using a specially designed robotic head. The head consists of a camera and a laser range-finder mounted on a pan & tilt unit. Additional distance measuring capabilities, offered by the head, have been integrated into the segmentation process. The described method will be used for detecting visual landmarks by an autonomous mobile robot.
Recognition of general unconstrained cursive handwriting remains largely unsolved. We present a system for recognizing off-line cursive English text guided in part by global characteristics of the handwriting. A new m...
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ISBN:
(纸本)0819412015
Recognition of general unconstrained cursive handwriting remains largely unsolved. We present a system for recognizing off-line cursive English text guided in part by global characteristics of the handwriting. A new method for finding the letter boundaries based on minimizing a heuristic cost function is introduced. The function is evaluated at each point along the baseline of the word to find the best possible segmentation points. The algorithm tries to find all the actual letter boundaries and as few additional ones as possible. After a normalization step that removes much of the style variation, the normalized segments are classified by a one hidden layer feedforward neural network. The word recognition algorithms find the segmentation points that are likely to be extraneous and generates all possible final segmentations of the word by either keeping or removing them. Interpreting the output of the neural network as posterior probabilities of letters, it then finds the word that maximizes the probability of having produced the image, over a set of 30,000 words and over all the possible final segmentations. We compared two hypotheses for finding the likelihood of words that are in the lexicon and found that using a Hidden Markov Model of English is significantly less successful than assuming independence among the letters of a word. In our initial test involving multiple writers, 68% of the words were in the top three choices.
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