Showing papers on "Image gradient published in 1987"

Method and apparatus for image processing with fed-back error correction

Abstract: There is an image processing apparatus for digitally processing an image. This apparatus comprises: a binarization circuit to binarize image data by a predetermined threshold value; a processor to correct errors generated in binarization; a first detector to detect an edge direction of the image from the image data; and a second detector to detect an edge quantum of the image from the image data. The process corrects the error data in accordance with the edge direction detected by the first detector or the edge quantum detected by the second detector. The errors to be corrected by the processor are the errors between the output concentration data after the binarization and the image concentration data. The processor adds weight coefficients to the error data in a predetermined range stored in a memory and then adds the weighted error data to image data to be newly binarized. The sum of the weight coefficients which are used in the weighting process is set to "1". With this apparatus, a high quality apparatus can be reproduced with a high fidelity from an original including many edges.

Imaging system including means to provide a display image with shadows and three dimensional perspectives by a combination of voxel and surface method

Abstract: A three-dimensional image display method for displaying a three-dimensional image including depth and distance information which consists of the steps of obtaining a densified three-dimensional image according to the distance information of an original three-dimensional image using a voxel method and modifying the densified three-dimensional image using a surface method. The surface method consists of the steps of determining each minute image element group including plural image elements proximate to each other over all image elements forming the densified three-dimensional image obtained under the voxel method, computing a density gradient defined by the each minute image element group based on density data of the plural image elements, substituting each modified density data modified according to the density gradient for the density data of the plural image elements obtained under voxel method, and displaying the modified density data on a display. A system for accomplishing this method is also provided.

A new heuristic edge extraction technique

Abstract: The problem of detecting intensity changes within images is canonical in computer vision. This research paper presents a new heuristic edge extraction (HEE) technique for solving the problem. The HEE consists of the following processes: (1) preprocessing, (2) Sobel edge detection, (3) thinning edge operation, (4) AI A* algorithm (minimum-cost-searching scheme), and (5) chain coding. The preprocessing improves image quality to facilitate edge extraction. Sobel edge detection is used for obtaining the spatially accurate multipixel-wide edges within input images. The thining edge operation yields one-pixel-wide edges by processing the Sobel edges. AI A* algorithm extracts optimal curve edge segments based on the use of both thinned edge intensity and Sobel edge direction (i.e. gradient direction). Finally, the chain coding technique is applied to encode extracted optimal curve edge segments. The HEE provides, therefore, spatially accurate, high-quality, and exactly one-pixel-wide edges as system output.

A method of acquiring 3-D data of an object from stereo images

Abstract: A method of acquiring 3-D data of an object via binocular technique is presented in this paper. It attempts to improve the accuracy of 3-D measurement of the passive technique and the main feature of this method are: 1) accurate (subpixel) location of the noisy image edge segments by parameterization; 2) using generalized cones for 3-D measurement with an accuracy beyond resolution limit of the digital sensor.

Aspects of directional filtering and applications in image processing

Abstract: This paper gives a brief overview of an approach to several problems of image processing based on directional filtering. Definitions and possible implementation routes will be given. Direction filtering allows the extraction from an image of edge structures within a limited interval of directions. It is shown that a powerful edge detection scheme based on one implementation of this filtering may lead to very interesting solutions to various problems such as coding, restoration and texture discrimination.

Extracting method for feature of gradation image

Abstract: PURPOSE:To eliminate the influence by the environment, and also, to facilitate the extraction of a feature by operating a difference between an original image, and a processed image which has executed a space filter processing to the original image, and deriving a variable density image. CONSTITUTION:By a camera 2, a three-dimensional image is inputted, and by an image processor 3 consisting of an image memory 7 and an image processing processor 8, an average value of total three picture elements of one object picture element from the left side of an original image and one picture element each of the right and left is derived and processed by a using a spatial filter 20 of, for instance, 3X3, and a smoothed image is obtained. Subsequently, the smoothed image and the original image are brought to an inter-image operating, and from an obtained gradation image, a feature is extracted. In such a way, by a spatial filter of an arbitrary shape, an arbitrary smoothed image is obtained by a repeated processing of a spatial filter of a small area, and by deriving a difference to the original image, a two-dimensional floating binarization is realized, and the feature can be extracted without being influenced by the environment.

Image Polynomial And Its Applications To Image Restoration

Abstract: This paper proposes the image polynomial as an image descriptor. An image polynomial is defined by a power series whose coefficients coincide with gray levels of a digital image. And orders of an image polynomial agree with locations of pixels of a digital image. The image polynomial is also suitable to describe a shift-invariant image formation system. Furthermore, we derive an iterative image restoration algorithm which yields the original clear image by a finite number of iteration.