Histogram equalization

About: Histogram equalization is a research topic. Over the lifetime, 5755 publications have been published within this topic receiving 89313 citations.

Watermarking by histogram specification

Abstract: The paper investigates the use of image histograms as watermarks. First, the problem of exact histogram specification is addressed and a method for exact histogram specification, consistent with the human perception of brightness, is developed. Next, two watermarking techniques based on exact histogram specification are proposed. The first one directly considers image histograms as watermarks. Thus, a particular histogram is assigned as a watermark and images are further transformed to have exactly the assigned histogram. Since quite large variations in image histogram are not perceived by humans, an unlimited number of invisible watermarks can be defined for which images appear visually non-distorted. Besides, by selecting histograms which are variations of uniform histogram, the transformed images are not only uniquely marked but also enhanced. The second approach conserves, for each image, its original histogram. The watermarking procedure consists of two histogram specification transforms: a transform to the assigned watermark followed by an inverse transform to recover the original histogram. Since image recovery after a histogram specification transform is not exact, the error obtained after the two consecutive transforms is further used to track each watermark.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Apparatus for and method of feature extraction for image recognition

Abstract: An apparatus for and method of performing a most informative feature extraction (MIFE) method in which a facial image is separated into sub-regions, and each sub-region makes individual contribution for performing facial recognition. Specifically, each sub-region is subjected to a sub-region based adaptive gamma (SadaGamma) correction or sub-region based histogram equalization (SHE) in order to account for different illuminations and expressions. A set of reference images is also divided into sub-regions and subjected to the SadaGamma correction or SHE. A comparison is made between the each corrected sub-region and each corresponding sub-region of the reference images. Based upon the comparisons made individually for the sub-regions of the facial image, one of the stored reference images having the greatest correspondence is chosen. While usable individually, using the MIFE and/or SadaGamma correction or SHE together achieves a lower error ratio in face recognition under different expressions, illuminations and occlusions.

Image representations for object detection using kernel classifiers

Abstract: This paper presents experimental comparisons of various image representations for object detection using kernel classifiers In particular it discusses the use of support vector machines (SVM) for object detection using as image representations raw pixel values, projections onto principal components, and Haar wavelets General linear transformations of the images through the choice of the kernel of the SVM are considered Experiments showing the effects of histogram equalization, a non-linear transformation, are presented Image representations derived from probabilistic models of the class of images considered, through the choice of the kernel of the SVM, are also evaluated Finally, we present a feature selection method using SVMs, and show experimental results

Infrared image adaptive inverse histogram enhancement technology

Abstract: In infrared images, when the traditional histogram equalizes the image, the detail pixels are easily immerged by the background pixels, resulting in the image being too bright and too dark. Based on this situation, an adaptive inverse histogram equalization algorithm was proposed in this paper. The algorithm enhanced image details by inverse statistics, adaptive selection threshold and segmentation mapping. Compared with the traditional histogram equalization algorithm, the inverse histogram equalization algorithm significantly improve the image visual effect in different gray level distributions and enhance the details of different areas of the image to different degrees. Moreover, under the premise of achieving better image processing effects, this algorithm can still guarantee real-time performance and high efficiency by optimizing calculation methods, and is suitable for FPGA hardware transplantation.