Topic
Histogram equalization
About: Histogram equalization is a research topic. Over the lifetime, 5755 publications have been published within this topic receiving 89313 citations.
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08 Dec 1994TL;DR: In this article, a two-tone image signal is used to determine whether or not an image part is identical to an object by rotating the object and the length and horizontal length of the circumscribing rectangle are compared with the threshold values.
Abstract: A circumscribing rectangle is obtained for a black continuous image part using a two-tone image signal. If it is determined that the image part is possibly one which is obtained as a result of rotating the object, lengths of sides of the image part are compared with threshold values. If it is determined that the image part is not one which is obtained as a result of rotating the object, the height and horizontal length of the circumscribing rectangle are compared with the threshold values. Thus, it is determined whether or not the image part is identical to the object. An RGB chromaticity histogram is produced for each of small regions of an input color image. Each of the chromaticity histograms of the small regions is compared with reference ones. As a result of the comparison, an identification number of the reference histogram having the highest similarity to that of the small region among those constituting the input image is given to the small region. A histogram of the identification numbers thus is produced for the input color image. The thus-produced histogram is used to determine which one of a plurality of objects is identical to the input color image.
44 citations
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27 Nov 2008
TL;DR: In this paper, an image recognition system using t-test and a method thereof are provided to decide efficiently the face image of the respective different image situation on the intelligibility lock through the image situation classification of face image selecting the recognizer drawing the image situations of the optimum which is suitable for each image situation by classifying the corresponding recognizer using ttest image situations acknowleged through image recognition of situation module.
Abstract: An image recognition system using t-test and a method thereof are provided to decide efficiently the face image of the respective different image situation on the intelligibility lock through the image situation classification of the face image selecting the recognizer drawing the image situation of the optimum which is suitable for each image situation by classifying the corresponding recognizer using t-test image situations acknowleged through the image recognition of situation module. An image recognition system using t-test comprises the followings: the vision recognition module(110) loading the face image(10); the image normalization module(120) normalizing loaded face image as described above; the image pre-processing module(130) removing the interference included in the normalized face image as described above through the histogram equalization and producing vector data; the image situation classification module(140) divided into the number in which user designates the image situation included in vector data through the clustering using the method for the K-means grouping; the image recognition of situation module(150) calculating average the classified image situation as described above through the Euclidean distance equation based on center and recognizing clearly the image situation based on the calculated mean value; the recognizer fusion module(160) selecting the recognizer which is suitable for each image situation based on the acknowleged image situations as described above through the recognizer classification using t-test.
44 citations
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24 Jun 1998
TL;DR: In this article, the problem of obtaining a high quality output image with a little processing load by producing a histogram based on pixel data of an original image, detecting pixel data corresponding to a prescribed number of degrees is detected by accumulating prescribed pixel values and image processing correction is performed based on the detected pixel data.
Abstract: PROBLEM TO BE SOLVED: To obtain a high quality output image with a little processing load by producing a histogram based on pixel data of an original image, detecting pixel data corresponding to a prescribed number of degrees obtd. by accumulating prescribed pixel values and performing image processing correction based on the detected pixel data. SOLUTION: A histogram is produced based on the pixel data of an original image, pixel data corresponding to a prescribed number of degrees is detected by accumulating prescribed pixel values and image processing correction is performed based on the detected pixel data. In this system, a printer driver 103 performs image correction processing of color information of an image plotting instruction that is included in an inputted plotting instruction group by means of image correction processing 120. Correction processing 121 for a printer is performed by making a plotting instruction into a raster with the color information that is subjected to image correction processing and produces a raster image on an RGB bit page memory. And, CMYK data depending on a printer characteristic is produced by performing masking processing, etc., of each pixel and is transferred to a printer 105.
44 citations
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30 Apr 2009TL;DR: In this paper, a method for contrast enhancement of digital images is provided, where a threshold gray level for each region is determined and a mapping curve for the region is generated based on the threshold level, which is then applied to each pixel in the region to enhance contrast.
Abstract: Methods for contrast enhancement of digital images are provided. A method of adaptive histogram equalization is provided that determines weighting factors for discriminating between sub-regions of a digital image to be more enhanced or less enhanced. Another method for content adaptive local histogram equalization is provided that uses a mapping function in which the dynamic range is not changed by the transformation. A third method for contrast enhancement is provided that includes dividing a digital image into a plurality of regions of pixels, and for each region in the plurality of regions, determining a threshold gray level for the region, generating a mapping curve for the region based on the threshold gray level, and applying the generated mapping curve to each pixel in the region to enhance contrast.
44 citations
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TL;DR: A novel evolutionary framework for image enhancement, automatic global thresholding, and segmentation is presented, which is here applied to different clinical scenarios involving bimodal MR image analysis: uterine fibroid segmentation in MR guided Focused Ultrasound Surgery, and brain metastatic cancer segmentsation in neuro-radiosurgery therapy.
44 citations