Showing papers on "Image gradient published in 1985"

Image processing apparatus with high- and low-resolution image sensors and an edge detector

Abstract: An image processing apparatus has image input systems for inputting image data at high and low resolutions, a high pass filter for filtering the low-resolution image data, an image property discrimination processor for discriminating an edge in the high-pass filtered low-resolution image data, and a signal selection circuit for selecting the image of high or low resolution in accordance with the discrimination result of the discrimination processor The apparatus processes image signals so as to reproduce optimum quality images for all types of original images including character and halftone images

A Gaussian-weighted multiresolution edge detector

Abstract: A Hueckel-like edge detector is developed which weights the neighborhood of a point according to a Gaussian function. This edge detector is found to be a generalization of both a Gaussian weighted gradient edge detector and of a zero crossing edge detector. A hierarchical structure, called a pyramid, allows a fast implementation of the operator in which versions of the operator with many different neighborhood sizes are applied to an image simultaneously.

Edge Information in Color Images Based on Histograms of Differences

Abstract: : A new measure of edge information for color images based on cumulative histograms of absolute color differences is proposed. A multispectral version of the Symmetric Nearest Neighbor filter for edge-preserving smoothing and methods for image segmentation and edge detection are developed based on this measure. Experimental results show that the performance of the new algorithms is very good. The analysis of color images has received little attention in computer vision research, in spite of the facts that color plays an important role in human vision and that color should also provide much useful information for many image analysis applications.

An image processing method for scanning electron microscopy based on the information transmission theory

Abstract: Based on the information transmission theory, topographic image signals in scanning electron microscopy are used to evaluate contrast, gradient, acutance, and Laplacian operator, the total of which represent the image sharpness of an edge line. One may consider the impulse and step functions as an input to the Gaussian system function of a low-pass filter, the impulse and step response functions possibly representing a single spot and image contrast of an edge profile, respectively. It is shown that the response function of acutance defined as the power of the gradient normalized by density is a more realistic representation of image edge sharpness. Also, edge sharpness can be greatly enhanced by utilizing the Laplacian operator through digital image processing for a disk specimen model with a rounded edge. Contrast increased by specimen tilt, and an edge effect due to side-scattered electrons, as well as the signal attenuation by specimen collection, are consistently obtained as the response function in the system. The exact measurement of spot size and edge-to-edge resolution, and image sharpness improvement, are derived by digital image processing.

Detecting method for surface defect

Abstract: PURPOSE: To discriminate easily an inspection object part of a material to be inspected, without preparing many standard patterns, by obtaining a surface image having a difference between an average luminance of a removed part of the material to be inspected and an average luminance of the remaining part, deriving a mask image by binary-coding, enlarging and reducing a surface image, and detecting a surface defect by AND of the mask image and a binary-coded image of the surface image. CONSTITUTION: A material to be inspected 1 is brought to an image pickup by a television camera 4, and its surface image (original image) is obtained. This surface image is inputted to a mask image generator 5 provided on an image processor 20, its surface image is brought to binary-coding, enlargement and reduction processings by the mask image generator 5, and a mask image based on the surface image is obtained. The obtained mask image is brought to image recording to a mask image recorder 6. On the other hand, the original image is inputted to a defect emphasizing device 7, as well, and by the defect emphasizing device 7, a binary-coded image of the surface image is obtained, and brought to image recording to a binary-coded image recorder 8. Subsequently, by a synchronizing synthesizer 9, the mask image is synthesized with the binary-coded image, and in case a surface defect exists, only a part (b) having the defect goes to '1'. COPYRIGHT: (C)1987,JPO&Japio

Comparing the laplacian zero crossing edge detector with the second directional derivative edge detector

Abstract: We present evidence that the Laplacian Zero-Crossing operator does not use neighborhood information as effectively as the second directional derivative edge operator. We show that the use of a Gaussian smoother with standard deviation 5.0 for the Laplacian of Gaussian edge operator with a neighborhood size of 50 × 50 both misses and misplaces edges on an aerial image of a mobile home park. Our results of the Laplacian edge detector on a noisy test checkerboard image are also not as good as the second directional derivative edge operator. We conclude by discussing a number of open issues on edge operator evaluation.

Recent Advances in White-Light Image Processing

Abstract: Publisher Summary The basic principle of a white-light image processor is very suitable for color image processing. This chapter describes a technique that permits certain image processing operations to be carried out by a spectrally broadband light source––white light. This method is capable of performing image processing that obeys the concept of coherent light. The proposed white-light image processing system is linear in complex amplitude. The white-light processor is operating in a partially coherent mode so the image can be processed in complex amplitude. The coherence requirement for white-light image processing is analyzed through the partial coherence theory. Some of the recent advances in white-light image processing—such as the advantage of capacity, color, simplicity, and cost—are also described in the chapter. The color image deblurring, color image subtraction, color image retrieval, and pseudocolor encoding can be easily carried out by the proposed white-light processing technique. The white-light image processing operations can be evaluated from the stand point of conventional linear systems.

Fast generation of chain-code image descriptions

Abstract: A method is described for the rapid reduction of intensity matrix image data to chain-coded descriptors The method also yields some topographic and numerical information about regions in the image In this method, the chain code is generated and stored in an array which is isomorphic to the image array Computation other than that associated with the intensity classification (feature) operator is minimized by the use of decision tables for chain code manipulation The method is capable of processing a 300×200 gray scale image in two seconds using a gradient and threshold operator on a 500K instruction per second processor

An edge detection algorithm of color images using the reduction of brightness and chroma

Abstract: In the present layout scanner, the operations for changing color partially, or extracting a portion of it, have been done interactively by specifying the location through a digitizer. Usually, image analysis techniques and artificial intelligence techniques are applied to region extraction and region matching operations. This paper describes an edge detection algorithm which is the most basic operation of color image processing. First, attention is given to effective gray scale intensity analysis of the gray level image. Then the concept of the reduction of brightness and chroma is considered as an addition of the differences in terms of hue and chroma to the differences in terms of brightness. Based on that concept, the difference equation is derived and the edge detection experiments are reported.

New Class Of Features For Pattern Recognition And Image Analysis

Abstract: A class of features, called "edge features," has been developed and applied to several problems of practical interest in image processing. These features are derived from a vector-valued function of the image called the "edge spectrum-" The edge spectrum at coordinate (x.,y) of the image describes the distribution of edge directions near (x,y). Several applications of edge features are discussed. One is considered in some detail. This application is to identify friendly aircraft descending for landing on an aircraft carrier. Identification is achieved by measuring wingspan - a good discriminant between the A6, A7, E2C and F.14. aircraft. For this purpose an edge feature was designed for locating the wing tips in the image. Wingspan was converted to physical dimension using range information and the known parameters of the optical system.