Topic
Histogram equalization
About: Histogram equalization is a research topic. Over the lifetime, 5755 publications have been published within this topic receiving 89313 citations.
Papers published on a yearly basis
Papers
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TL;DR: A mathematical model that follows a power law has been developed and comparisons between the proposed technique and both of the histogram equalization and histogram hyperbolization techniques have been made.
27 citations
01 Jan 2004
TL;DR: A generative probabilistic model of the appearance of a nonrigid object and an iterative procedure for searching for the maximum likelihood estimate of the position and shape of the tracked object in a new image are presented.
Abstract: In this paper we present a generative probabilistic model of the appearance of a nonrigid object and an iterative procedure for searching for the maximum likelihood (ML) estimate of the position and shape of the tracked object in a new image. The shape of the object in an image is approximated by an ellipse that is described by a full covariance matrix. The appearance of the object is described by a color-histogram. The algorithm is used for tracking persons in image sequences.
27 citations
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03 Mar 2011Abstract: We propose a novel Histogram Equalization (HE) to improve contrast of images. As most existing HE methods still suffer from over-enhancement caused by a quantum jump, the proposed method focuses on robustness to deal with the problem in various image conditions. To achieve the goal, we adjust the curve shape of the output mapping function by properly combining the curve shape of the null mapping function and that of the normal mapping function from the conventional HE according to the weighting value. Experimental results show that the proposed method well endures difficult conditions and provides moderate image quality.
27 citations
24 Mar 1994
TL;DR: This paper analyzes measures relevant to extending color histogram indexing to large databases: capacity (how many distinguishable histograms can be stored) and sensitivity (how the average number of retrieved images depends on the retrieval threshold).
Abstract: Color histogram matching has been shown to be a promising way of quickly indexing into a large image database. Yet, few experiments have been done to test the method on truly large databases, and even if they were performed, they would give little guidance to a user wondering if the technique would be useful with his or her database. In this paper we de ne and analyze measures relevant to extending color histogram indexing to large databases: capacity (how many distinguishable histograms can be stored) and sensitivity (how the average number of retrieved images depends on the retrieval threshold). The theoretical results lead to a practical test procedure which enables a user to determine the performance of color histogram indexing on a large database by looking at a small, randomly-chosen subset of the images. We suggest how our analysis can be extended to other feature-based indexing techniques. Capacity and Sensitivity of Color Histogram Indexing 1
27 citations
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TL;DR: A simple algorithm is presented that selectively adjusts the local gradients in affected regions of the filtered image so that they are consistent with those in the original image.
Abstract: We present a method for restoring antialiased edges that are damaged by certain types of nonlinear image filters. This problem arises with many common operations such as intensity thresholding, tone mapping, gamma correction, histogram equalization, bilateral filters, unsharp masking, and certain nonphotorealistic filters. We present a simple algorithm that selectively adjusts the local gradients in affected regions of the filtered image so that they are consistent with those in the original image. Our algorithm is highly parallel and is therefore easily implemented on a GPU. Our prototype system can process up to 500 megapixels per second and we present results for a number of different image filters.
27 citations