There are many applications in which the automatic detection and recognition of text embedded in images is useful. These applications include digital libraries, multimedia systems, Information Retrievial Systems, and Geographical Information Systems. When machine generated text is printed against clean backgrounds, it can be converted to a computer readable form (ASCII) using current Optical Character Recognition (OCR) technology. However, text is often printed against shaded or textured backgrounds or is embedded in images. Examples include maps, advertisements, photographs, videos and stock certificates. Current document segmentation and recognition technologies cannot handle these situations well. In this paper, a four-step system which automatically detects and extracts text in images is proposed. First, a texture segmentation scheme is used to focus attention on regions where text may occur. Second, strokes are extracted from the segmented text regions. Using reasonable heuristics...

Image segmentation is very essential and critical to image processing and pattern recognition. This survey provides a summary of color image segmentation techniques available now. Basically, color segmentation approaches are based on monochrome segmentation approaches operating in di erent color spaces. Therefore, we rst discuss the major segmentation approaches for segmenting monochrome images: histogram thresholding, characteristic feature clustering, edge detection, region-based methods, fuzzy techniques, neural networks, etc. � then review some major color representation methods and their advantages/disadvantages� nally summarize the color image segmentation techniques using di erent color representations. The usage of color models for image segmentation is also discussed. Some novel approaches such as fuzzy method and physics based method are investigated as well.

We propose a new definition of the total variation norm for vector valued functions which can be applied to restore color and other vector valued images. The new TV norm has the desirable properties of (i) not penalizing discontinuities (edges) in the image, (ii) rotationally invariant in the image space, and (iii) reduces to the usual TV norm in the scalar case. Some numerical experiments on denoising simple color images in RGB color space are presented. 1 Introduction During gathering and transfer of image data some noise and blur is usually introduced into the image. Several reconstruction methods based on the total variation (TV) norm have been proposed and studied for intensity (gray scale) images, see [9, 14, 21, 26, 29]. Since these methods have been successful in reducing noise and blur without smearing sharp edges for intensity images, it is natural to extend the TV norm to handle color and other vector valued images. Why do we need color restoration? It can be argued that si...

We propose a new automatic image segmentation method. Color edges in an image are first obtained automatically by combining an improved isotropic edge detector and a fast entropic thresholding technique. After the obtained color edges have provided the major geometric structures in an image, the centroids between these adjacent edge regions are taken as the initial seeds for seeded region growing (SRG). These seeds are then replaced by the centroids of the generated homogeneous image regions by incorporating the required additional pixels step by step. Moreover, the results of color-edge extraction and SRG are integrated to provide homogeneous image regions with accurate and closed boundaries. We also discuss the application of our image segmentation method to automatic face detection. Furthermore, semantic human objects are generated by a seeded region aggregation procedure which takes the detected faces as object seeds.

Image segmentation, i.e., identification of homogeneous regions in the image, has been the subject of considerable research activity over the last three decades. Many algorithms have been elaborated for gray scale images. However, the problem of segmentation for colour images, which convey much more information about objects in scenes, has received much less attention of scientific community. While several surveys of monochrome image segmentation techniques were published, similar comprehensive surveys for colour images, to our knowledge, did not emerge. This report

A color image edge detection algorithm is proposed in this paper based on the idea that use global color information to guide local gradient computation. Major chromatic components of an image are first extracted through cluster analysis. According to these color clusters, a set of linear chromatic transforms are generated. An appropriate chromatic transform is chosen for each pixel to maximize the gradient magnitude. In this way, edges are treated as transitions from one cluster to another. The algorithm is implemented and experimental results for real color images are included. 1. INTRODUCTION Color perception is an important part of the image cognition process. Under certain circumstances, human vision system chooses color rather than shapes and textures as the major discrimination attribute [1][4]. For this reason, color image edge detection algorithms have received more attention recently and several algorithms have been proposed [2] [3] [5] [6] [7]. In some of these algorithms, ...

AbstractÐFor over 30 years researchers in computer vision have been proposing new methods for performing low-level vision tasks such as detecting edges and corners. One key element shared by most methods is that they represent local image neighborhoods as constant in color or intensity with deviations modeled as noise. Due to computational considerations that encourage the use of small neighborhoods where this assumption holds, these methods remain popular. This research models a neighborhood as a distribution of colors. Our goal is to show that the increase in accuracy of this representation translates into higher-quality results for low-level vision tasks on difficult, natural images, especially as neighborhood size increases. We emphasize large neighborhoods because small ones often do not contain enough information. We emphasize color because it subsumes gray scale as an image range and because it is the dominant form of human perception. We discuss distributions in the context of detecting edges, corners, and junctions, and we show results for each. Index TermsÐEdge detection, junction detection, corner detection, earth mover's distance, color distributions, perceptual color distance.

The research described in this thesis describes work towards making Active Contours fully autonomous mechanisms capable of tracking biological targets in real-world situations. The problem of tracking the apparent motion of cauliflower plants photographed from a tractor was of special interest. In order to reach this simple goal it was necessary to complete research in a number of different areas.

Copyright © 2004

Abstract not found