Showing papers on "Image gradient published in 1988"

A statistical and geometrical edge detector for SAR images

Abstract: A constant-false-alarm-rate (CFAR) edge detector based on the ratio between pixel values is described. The probability distribution of the image obtained by applying the edge detector is derived. Hence, the decision threshold can be theoretically determined for a given probability of false alarm as a function of the number of looks of the image under study and the size of the processing neighborhood. For a better and finer detection, the edge detector operates along the four usual directions over windows of increasing sizes. A test performed, for a given direction, on a radar image of an agricultural scene shows good agreement with the theoretical study. The operator is compared with the CFAR edge detectors suitable for radar images. >

Integrating region growing and edge detection

Abstract: The authors present a method that combines region growing and edge detection for image segmentation. They start with a split-and-merge algorithm where the parameters have been set up so that an oversegmented image results. Then region boundaries are eliminated or modified on the basis of criteria that integrate contrast with boundary smoothness, variation of the image gradient along the boundary, and a criterion that penalizes for the presence of artifacts reflecting the data structure used during segmentation (quadtree, in this case). >

Enhancement of chest radiographs with gradient operators

Abstract: Reference is made to the Sobel and Roberts gradient operators used to enhance image edges. Overall, the Sobel operator was found to be superior to the Roberts operator in edge enhancement. A theoretical explanation for the superior performance of the Sobel operator was developed based on the concept of analyzing the x and y Sobel masks as linear filters. By applying pill-box, Gaussian, or median filtering prior to applying a gradient operator, noise was reduced. The pill-box and Gaussian filters were more computationally efficient than the median filter with approximately equal effectiveness in noise reduction. >

Color image processing apparatus with image corrector

Abstract: A color copying machine has a scanning-type proximity linear image sensor unit for optically scanning an input image and producing an image read signal, and a color printer unit for producing a copy image corresponding to the input image at limited levels of printing density. An image type discrimination circuit receives the image read signal, and at least partially discriminates the type of image based on a pattern matching technique, thereby producing a discrimination signal for specifying the image type in a pixel region of a predetermined size of the input image. An adaptive signal processing circuit also receives the image read signal, and performs different image signal correction processing in accordance with the discriminated image type, thus producing a corrected image signal, which is supplied to a multi-value dithering processor. The multi-value dithering processing circuit performs multi-value dither processing of the corrected image signal, and produces a printer drive signal for defining a printing pixel pattern for each pixel of the input image.

Grain distribution detecting device for grain separator

Abstract: PURPOSE: To improve the control accuracy of the partition plate of the separator and to detect a grain distribution economically by inputting image information in an near infrared region obtained from a border area and obtaining a binarized border image. CONSTITUTION: The detecting device consists of an image pickup means 50 which picks up an image of the grain border area of rough rice and unpolished rice, and an image processing means 100 which processes the image information sent from the means 50 and generates a distribution image of the rough rice and unpolished rice, and image information in the near infrared region is used as the processing image information. Then the arithmetic part 101 of the means 100 processes the image signal from the means 50 to obtain a gradation image, a binarizing image processing part 102 generates a binarized image based on the gradation image, and an evaluation part 103 evaluates the grain distribution with the binarized image. Further, the means 50 consists of a black-and-white television camera 51 which uses a CCD as the image pickup device and a visible- light cut filter 52 which is provided in front of the camera. COPYRIGHT: (C)1990,JPO&Japio

Context dependent edge detection

Abstract: To simulate the edge perception ability of human eyes and detect scene edges from an image, context information must be used in the edge detection process. To accomplish the optimal use of the context, the authors introduce an edge detection scheme which uses the context of the whole image. The edge context for each pixel is the set of all row monotonically increasing paths through the pixel. The edge detector assigns a pixel that edge state having highest edge probability among all the paths. Experiments indicate the validity of the edge detector. Upon comparing the performance of the context dependent edge detector with the context free second directional derivative zero-crossing edge operator, the authors find that the context dependent edge detector is superior. >

Image enhancement using sliding histograms

Abstract: Image enhancement methods based on local histogram analysis are discussed. It is shown that this problem is closely connected to that of contour preserving image smoothing and may be solved by a preliminary image decomposition. A method for image decomposition into background and texture components is proposed. The results obtained are used for image smoothing, enhancement, object and contour detection, and color image enhancement.

Method of and apparatus for processing color image

Abstract: There is provided a color image processing apparatus in which, by making use of an image reading apparatus of a digital full-color copying apparatus, the designated color to be obtained after conversion is input and stored as component ratio of color-separated image data, and monochromatic image data proportional to the gradation levels, e.g., density, lightness, luminance and so forth are obtained from the color separated image data produced from the original image, the monochromatic image data thus obtained being converted into the color separation data corresponding to the component ratio of the designated color to be obtained after conversion, thereby making it possible to preserve the hue of the designated color and to effect color conversion to the designated color having gradation proportional to the gradation of the original image.

Analysis of thin section images

Abstract: A system for analyzing thin section images is disclosed. A microscope scans a thin section impregnated by a dyed epoxy and develops a digital representation of the color image of the thin section. A classifier defines the areas of the image which are identified as pore space. The color image is then transformed into a monochromatic image by transforming the color image onto the principal axis representing the brightness of the color image. Each discrete feature representing grain space or pore space in the monochromatic image is defined by analyzing the uniform brightness distribution of the region. Finally, the boundary of each discrete region can be processed to ascertain the size, shape, orientation and roundness of the region.

Image processing apparatus and methods for making a creative image from an original image

Abstract: An image processing apparatus and method in which an object is imaged to produce signals and the signals are divided into groups according to areas of the image having edges of high spatial frequency, i.e., large change in same-color characteristics. The signals in selected areas are changed to produce a reproduced image with desired color effects. For example, an edge may be detected in the original image, and the division of the image into areas effected by a closed curve using the edge. The subsequent processing may include changing a color characteristic of at least one of the areas. The color characteristic might, for example, be an average of various values of that characteristic throughout the area in question. The identification of a high-spatial-frequency area can be performed in various ways, e.g., by summing edge components of the original image which extend in a plurality of directions.

Edge Detection Based On Multi-Edge Models

Abstract: The DRF method for edge detection is developed in more general cases, which can be easily implemented by parallel line processors for real time image processing.

Road Edge Extraction Using a Plan-View Image Transformation.

Abstract: A new technique to extract road edges in the roadfollowing algorithm for autonomous road vehicle navigation is described. It is based on finding road edges on a subsampled plan-view of a portion of the road ahead of the vehicle. The method is illustrated in the real-time identification of road edges using a fast vertical edge detector and link operator applied to the transformed plan view. Location of both road edges at 20 frames per second is demonstrated.

Pyramidal Edge Detection And Image Representation

Abstract: We present a scheme of extracting edge information from parallel spatial frequency bands. From these we create an integrated image of most significant edges of different scales. The frequency bands are realized using the formalism of a Gaussian pyramid in which the levels represent a bank of spatial lowpass filters. The integrated edge image is created in a top-down algorithm, starting from the smallest version of the image. The sequential algorithm uses mutual edge information of two consecutive levels to control the processing in the lower one. This edge detection algorithm constitutes an image-dependent nonuniform processing scheme. Computational results show that only 20%-50% of the operations are needed to create an edge pyramid, compared to the number required in the regular scheme. The proposed generic scheme of image-dependent processing can be also implemented with operators other than edge detectors to exploit the advantages inherent in biological processing of images.

Automatic edge extraction using locally adaptive threshold

Abstract: The authors describe an automatic edge extraction algorithm using a new edge operator called the E/sup K/ edge operator, and a locally adaptive threshold subject to brightness and gradient value in a picture. The proposed algorithm is capable of extracting one-pixel-thick edges and giving a rather pleasing representation of the original grey-level picture. This algorithm is compared to the well known Sobel gradient operator and Keskes' zero-crossing operator.< >

Method and device for image reduction in image processing

Abstract: Dither element values predetermined at all pixels of a dither matrix pattern prepared for an equality function are outputted one by one each time on n×n unit matrix of a read-out image obtained from a given original image by an image reading device such as an image scanner is subjected to extraction of brightness feature in 1/n image reducing mode. Thus, even a halftone image such as a photograph can be effectively reduced to scale an reproduced as a reduced image on a monitor display or the like.

An Expert System For Image Segmentation

Abstract: Probably the most critical problem in image understanding is the segmenting images into disjoint regions of uniform gray-tone or texture. This paper describes the design of a new image segmentation system which uses an expert-system approach for solving this problem. The system is composed of two parts: (1) an image processing tool set which processes a given image by localized brightness compensation and heuristic edge extraction, and (2) a knowledge-base controller which is composed of an inference engine, a rule base, and a hypothesis base. The localized brightness compensation preprocesses the input image under control of the knowledge-base controller to obtain improved image quality. The heuristic edge extraction obtains spatially-accurate, one-pixel-wide boundaries of uniform-property subregions. These boundaries are, then, encoded using specially designed codes which facilitate the application of rules for final region formation. The combined use of the image processing tools and the knowledge-base controller makes the segmented region boundaries one pixel wide, spatially accurate, without edge gaps, and without noisy micro-edges inside segmented regions. The paper is illustrated with results by applying the system to the segmentation of actual high-resolution aerial imagery.

An Integrated Image Segmentation/Image Analysis System

Abstract: The paper describes an integrated image segmentation/image analysis system. A segmentation algorithm which operates by tuning it's output to a pre-defined mathematical optimum is firstly outlined. Implementation of a rule-based image analysis system which makes use of the data computed during the segmentation process is then discussed. A pyramidal data structure is suggested in which the image data flows from the base upwards with the control data used in analysing the image moving in the reverse direction. It is hoped that by means of this approach the image analysis process will be capable of exerting a degree of control over the segmentation algorithm leading to a more flexible system.

Parallel Optical Pyramidal Image Processing

Abstract: Pyramidal processing is a form of multiresolution image analysis in which a primary image is decomposed into a set of different resolution image copies. Pyramidal processing aims to extract and interpret significant features of an image appearing at different resolutions. Digital pyramidal image processing, because of the large number of convolution type operations, is time-consuming. On the other hand, optical pyramidal processors, because of their ease in performing convolution operation, are preferable in real-time image understanding applications. Two methods of optical pyramidal image generation, a Fourier spectrum filtering based, and a local averaging using 2D lens array, are presented. Preliminary experimental results for optical Gaussian, Laplacian and other fivadtree pyramidal image processing are shown. Experimental results, using commercial lifiuid crystal TVs, for a real-time pyramid image generation are presented.

Description of the color image by using stable view points

Abstract: A method is described for the color image description by using stable view points of an image obtained after Gaussian smoothing. The basic idea is to determine the view points and corresponding viewing fields at various levels from the color information and define the local image by using these points and fields so that the image matching can be realized. This approach may be developed into a generalized method for image recognition. >

Multi-Pipeline Architecture For Real-Time Low-Level Image Processing

Abstract: We shall present two kinds of multi-pipeline architecture for real-time low-level image processing. The first one is to be used in the general local operations of image processing with a fixed 3*3 window size. The second one is to be used in a less general local operations with a dynamic window size. The image processing functions executable in these architectures include Sobel edge operator, Laplacian high-pass filter, smoothing operation, erosion, and dilation, etc. The raw image is inputted into the frame buffer of the parallel processor. Different schemes are designed to set up the data flow path among the processing elements or cells.

Context dependent edge detection

Abstract: To obtain the optimal use of context, an edge detection scheme is introduced which uses the context of the whole image. The edge context for each pixel is the set of all row-monotonically-increasing paths through the pixel. The edge detector assigns to a pixel the edge state having the highest edge probability among all the paths. Experiments indicate the validity of the edge detector. A comparison with the performance of the context-free second-directional derivative zero-crossing edge operator shows that the context-dependent edge detector is superior. >

A Hierarchical Relaxation Method For Edge Detection

Abstract: Eoge features can be used in" the scer:.ce analysis and computer v is i.on. In this paper, we propose a new hierarch ical relaxation method for edge detection . Using multiresolution informations of the image hierarch ical re.:,Jresentatiun, we can chang the wei ghted coefficients by ccnsidering the values a"t the high level of the py­ ram ids as a measure of the local edge features in the images, then satisactory detection can be obtained at all points, and the convergency speed of the relaxation process can be also improved greatly. This method also has tne adv antage of obtaination of compact border of obj ects in the images.