Showing papers on "Image gradient published in 1982"
01 Dec 1982-Graphical Models \/graphical Models and Image Processing \/computer Vision, Graphics, and Image Processing
TL;DR: A parametric implementation of cubic convolution image reconstruction is presented which is generally superior to the standard algorithm and which can be optimized to the frequency content of the image.
Abstract: Cubic convolution, which has been discussed by Rifman and McKinnon (1974), was originally developed for the reconstruction of Landsat digital images. In the present investigation, the reconstruction properties of the one-parameter family of cubic convolution interpolation functions are considered and thee image degradation associated with reasonable choices of this parameter is analyzed. With the aid of an analysis in the frequency domain it is demonstrated that in an image-independent sense there is an optimal value for this parameter. The optimal value is not the standard value commonly referenced in the literature. It is also demonstrated that in an image-dependent sense, cubic convolution can be adapted to any class of images characterized by a common energy spectrum.
349 citations
01 Jan 1982
TL;DR: Analysis of SEASAT images with coregistered LANDSAT images indicates that the radar data can make a significant contribution to rock-type discrimination, especially if textural measures are incorporated.
Abstract: Radar images haveunique radiometric andgeometric char- acteristics whichpresent unique problems andopportunities forgeolog- ical application. Thispaper reviews preprocessing andanalytical tech- niques founduseful orpromising forapplications ofradar images to geologic problems suchasrock-type discrimination. Theuseofcoher- entmonochromatic illumination inradarimages results inimage speckle noise whichinterferes withcharacterization oftheimaged sur- face. Median value filtering oftheradar images removes speckle with minimal edgeeffects andresolution degradation. Variations inradar sceneillumination duetouncompensated sensor platform motions or antenna pattern effects canbesomewhat corrected forbymeanand variance equalization inadirection perpendicular totheresulting image gradient. Registration ofradar images toamapbaseandcompensation ofterrain induced image distortion canbeaccomplished byregistration todigital elevation models andknowledge ofimaging geometry. Analy- sisofSEASATimages withcoregistered LANDSATimages indicates that theradar data canmakeasignificant contribution torock-type discrimi- nation, especially iftextural measures areincorporated. Thesensitivity ofradar backscatter tolocal slopes makesradar images anexcellent me- diumfromwhichtoextract textural measures. Threetechniques for extraction ofthetextural datainherent intheradar images arepre- sented. Computation ofimage tonevariance overvarious areas can numerically encode image texture. Hue-saturation-intensity split spec- trumprocessing displays low-frequency variations incolor while pre- serving high-frequency detail. TheuseofFourier bandpass filtering re- sults inaradar image texture mapwhich separates geologic units based uponspatial frequencies characteristic oftheunit.
68 citations
TL;DR: In this article, the authors present three techniques for extraction of the textural data inherent in the radar images and demonstrate that the radar data can make a significant contribution to rock type discrimination, especially if textural measures are incorporated.
Abstract: Radar images have unique radiometric and geometric characteristics which present unique problems and opportunities for geological application. This paper reviews preprocessing and analytical techniques found useful or promising for applications of radar images to geologic problems such as rock-type discrimination. The use of coherent monochromatic illumination in radar images results in image speckle noise which interferes with characterization of the imaged surface. Median value filtering of the radar images removes speckle with minimal edge effects and resolution degradation. Variations in radar scene illumination due to uncompensated sensor platform motions or antenna pattern effects can be somewhat corrected for by mean and variance equalization in a direction perpendicular to the resulting image gradient. Registration of radar images to a map base and compensation of terrain induced image distortion can be accomplished by registration to digital elevation models and knowledge of imaging geometry. Analysis of SEASAT images with coregistered LANDSAT images indicates that the radar data can make a significant contribution to rock-type discrimination, especially if textural measures are incorporated. The sensitivity of radar backscatter to local slopes makes radar images an excellent medium from which to extract textural measures. Three techniques for extraction of the textural data inherent in the radar images are presented. Computation of image tone variance over various areas can numerically encode image texture. Hue-saturation-intensity split spectrum processing displays low-frequency variations in color while preserving high-frequency detail.
67 citations
Patent•
17 Mar 1982
TL;DR: In this paper, a method for smoothing retouch in electronic color image reproduction employs a coordinate pen used by the retoucher as a retouch brush, to identify image point coordinates within an image point area.
Abstract: A method for smoothing retouch in electronic color image reproduction employs a coordinate pen used by the retoucher as a retouch brush, to identify image point coordinates within an image point area. The color values associated with the image point coordinates are changed as a function of the color values of the nearby image point coordinates. The size of the areas which have their color values changed is operator-selectable. The color values of the areas which are changed can be matched to the marginal values of the area, or may be replaced by a mean value of the color values within the area. By this means image contours may be flattened, and the effective noise in the image surfaces may be minimized.
22 citations
01 May 1982
TL;DR: It is shown how the techniques of Digital Image Analysis can be efficiently used in the interpretation of seismic cross-sections to cope with the characteristics of seismic signals.
Abstract: We show how the techniques of Digital Image Analysis can be efficiently used to help in the interpretation of seismic cross-sections. The problem of automatically finding homogeneous facies is in fact directly tied to Image Processing problems such as edge detection, texture analysis, segmentation and classification. We briefly overview some existing tools, outline some new techniques which had to be invented to cope with the characteristics of seismic signals and demonstrate on many examples the power of our approach.
22 citations
TL;DR: This paper addresses the problem of combining range and intensity data for scene analysis by using edge maps of the range image and of the intensity image to place both sources of information in the same form.
7 citations
01 Jun 1982
TL;DR: The facet model was used to accomplish step edge detection and its underlying grey tone intensity surface was estimated on the basis of the pixels in its neighborhood to determine whether or not a pixel should be marked as a step edge pixel.
Abstract: The facet model was used to accomplish step edge detection. The essence of the facet model is that any analysis made on the basis of the pixel values in some neighborhood has its final authoritative interpretation relative to the underlying grey tone intensity surface of which the neighborhood pixel values are observed noisy samples. Pixels which are part of regions have simple grey tone intensity surfaces over their areas. Pixels which have an edge in them have complex grey tone intensity surfaces over their areas. Specially, an edge moves through a pixel only if there is some point in the pixel's area having a zero crossing of the second directional derivative taken in the direction of a non-zero gradient at the pixel's center. To determine whether or not a pixel should be marked as a step edge pixel, its underlying grey tone intensity surface was estimated on the basis of the pixels in its neighborhood.
4 citations
03 May 1982
TL;DR: An edge detection and line fitting procedure which ascribes a direction, a measure of gradient and quality of fit to the edge within a square segment of a controlled size or "scope".
Abstract: The detection and tracing of edges of varying diffusion is a problem of importance in image analysis. In particular, it is of interest for the segmentation of meteorological and physiological pictures where the boundaries of objects are possibly not well defined or are obscured to a varying extent by noise. We present an edge detection and line fitting procedure which ascribes a direction, a measure of gradient and quality of fit to the edge within a square segment of a controlled size or "scope". To detect and fit edges to diffuse objects the scope is adaptively altered based on the confidence of fit to permit tracing of the object's boundary. We discuss predictor-corrector procedures for performing this edge tracing where predicted and calculated lines and confidences are used to generate a better fitting line. The performance of the procedures is demonstrated using both synthetic and satellite meteorological images.
1 citations
01 May 1982
TL;DR: A model for the reconstruction of real color textures is presented, an extension of a bidimensionnal Markov model previously proposed for the synthesis of gray level textures, to limit the number of colors to avoid computationnal complexity.
Abstract: A model for the reconstruction of real color textures is presented. It is an extension of a bidimensionnal Markov model previously proposed for the synthesis of gray level textures. The main problem is to limit the number of colors to avoid computationnal complexity. This is solved by operating a suboptimal quantization of the color information included in the real texture: i) firstly by transforming the color components into a perceptive uniform space, based on a vision model; ii) then by optimally clustering this color space.