A new criterion for multilevel thresholding is proposed. The criterion is based on the consideration of two factors. The first one is the discrepancy between the thresholded and original images and the second one is the number of bits required to represent the thresholded image. Based on a new maximum correlation criterion for bilevel thresholding, the discrepancy is defined and then a cost function that takes both factors into account is proposed for multilevel thresholding. By minimizing the cost function, the classification number that the gray-levels should be classified and the threshold values can be determined automatically. In addition, the cost function is proven to possess a unique minimum under very mild conditions. Computational analyses indicate that the number of required mathematical operations in the implementation of our algorithm is much less than that of maximum entropy criterion. Finally, simulation results are included to demonstrate their effectiveness. >

A new criterion for automatic multilevel thresholding

A description is given of a gray-level threshold selection method for image segmentation that is based on the maximum entropy principle. The optimal threshold value is determined by maximizing the a posteriori entropy subject to certain inequality constraints which are derived by means of spectral measures characterizing uniformity and the shape of the regions in the image. For this purpose, the authors use both the gray-level distribution and the spatial information of an image. The effectiveness of the method is demonstrated by its performance on some real-world images. An extension of this method to chromatic images is provided. >

A gray-level threshold selection method based on maximum entropy principle

If a continuous straight line segment is digitized on a regular grid, obviously a loss of information occurs. As a result, the discrete representation obtained (e.g., a chaincode string) can be coded more conveniently than the continuous line segment, but measurements of properties (such as line length) performed on the representation have an intrinsic inaccuracy due to the digitization process. In this paper, two fundamental properties of the quantization of straight line segments are treated. 1) It is proved that every ``straight'' chaincode string can be represented by a set of four unique integer parameters. Definitions of these parameters are given. 2) A mathematical expression is derived for the set of all continuous line segments which could have generated a given chaincode string. The relation with the chord property is briefly discussed.

/pdf/discrete-representation-of-straight-lines-28dt73rf4n.pdf

Discrete Representation of Straight Lines

A practical vision system for controlling the positioning of a robot arm recognizes and locates objects. The vision system processes binary images, but recognizes objects based on boundary features such as lines, arcs, corners and holes instead of "blob features" such as area and best-fit ellipse. Consequently, the vision system can process two common situations not handled by blob analysis: merged blobs due to touching or overlapping parts and incomplete blobs due to low image contrast. The microprocessor-based system is interfaced to the robot system and can recognize up to five parts per second.

Vision system for distinguishing touching parts

A thresholding technique is developed for segmenting digital images with bimodal reflectance distributions under nonuniform illumination. The algorithm works in a raster format, thus making it an attractive segmentation tool in situations requiring fast data throughput. The theoretical base of the algorithm is a recursive Taylor expansion of a continuously varying threshold tracking function.

An Iterative Thresholding Algorithm for Image Segmentation

robots that \"see\" and \"feel\" can perform more complex tasks, industry has employed various computer vision techniques to enhance the abilities of intelligent machines. The recent widespread interest in robotics and automation in the US originates from American industry's most fundamental problem: a staggering drop in productivity. From 1947 to 1965, US productivity increased at an average rate of 3.4 percent a year. The growth rate decreased to 2.3 percent in the following decade, then dropped to below one percent in the late 1970's and down to-0.9 percent in 1980. Japan's productivity growth, the contrasting example most often cited in the literature, has been climbing at an average annual rate of about 7.3 percent. ' Although there are many ways to influence manufacturing productivity and product quality-regulatory, fiscal, and social-the emphasis in the following discussion is technological. In particular, we are interested in the computer vision aspects of industrial inspection and robot control. The principal motivation behind computer vision is increased flexibility and lower cost. The use of sensing technology to endow-a machine with a greater degree of \"intelligence\" in dealing with its environment is receiving increased attention. A robot that can \"see\" and \"feel\" should be easier to train in the performance of complex tasks while at the same time requiring less stringent control mechanisms than preprogrammed machines. A sensory, trainable system is also adaptable to a much larger variety of tasks, thus achieving a degree of universality that ultimately translates into lower production and maintenance costs. The computer vision process can be divided into five principal areas: sensing, segmentation, description, recognition , and interpretation. These categories are suggested to a large extent by the way computer vision systems are generally implemented. It is not implied that human vision and reasoning can be so neatly subdivided nor that these processes are carried out independently of each other. For instance, we can logically assume that recognition and interpretation are highly interrelated functions in a human. These relationships, however, are not yet understood to the point where they can be mod-eled analytically. Thus, the subdivision of functions discussed below can be viewed as a practical (albeit limited) approach for implementing state-of-the-art computer vision systems, given our level of understanding and the analytical tools currently available in this field. Visual sensing Imaging devices. Visual information is converted to electrical signals by visual sensors. The most commonly used visual sensors are vidicon cameras …

Safabakhsh

Papers

Computer Vision Techniques for Industrial Applications and Robot Control