There are several images that do not have uniform brightness which pose a challenging problem for image enhancement systems. As histogram equalization has been successfully used to correct for uniform brightness problems, a histogram equalization method that utilizes human visual system based thresholding(human vision thresholding) as well as logarithmic processing techniques were introduced la...

We propose GLGM (gray-level & gradient-magnitude) histogram as a novel image histogram for thresholding. GLGM histogram explicitly captures the gray level occurrence probability and spatial distribution property simultaneously. Different from previous histograms that also consider the spatial information, GLGM histogram employs the Fibonacci quantized gradient magnitude to characterize spatial ...

In this research work, to understand the types of cancer cell and attempt to analyses the biopsy slides. In this method to identify cancer parts just using simple technique of isolation of insignificant portion of biopsy slide by cancer cell level and object level segmentation and classification. Many features used in the cancer cell detection and classification of biopsy image are inspired by ...

In computer vision, detection and tracking of targets is very complex problem and demands sophisticated solutions. Unmanned Aerial Vehicles (UAVs) are increasingly being used for reconnaissance and Surveillance. This framework mainly consists of image matching for reconnaissance and Surveillance. This framework mainly consists of image matching for Digital Scene Matching Area Correlation (DSMAC...

This paper proposes a global multi-level thresholding method for image segmentation. As a criterion for this, the traditional method uses the Shannon entropy, originated from information theory, considering the gray level image histogram as a probability distribution, while we applied the Tsallis entropy as a general information theory entropy formalism. For the algorithm, we used the artificia...

Two critical steps in fingerprint recognition are binarization and thinning of the image. The need for real time processing motivates us to select local adaptive thresholding approach for the binarization step. We introduce a new hardware for this purpose based on pipeline architecture. We propose a formula for selecting an optimal block size for the thresholding purpose. To decrease minutiae f...

So far we have focussed on evaluating the performance of algorithms of irreversible image steganography in the ECCs domain and Transform domain for the grayscale and color images. In this Chapter we shall cover the two type of reversible image steganographic algorithms using the Companding technique (also called Reversible Thresholding Method) based on SLTPerceptual and Robust. We shall also gi...

Thresholding has to be done in medical images for various reasons. However, different images have different characteristics making the traditional process of thresholding by one algorithm a very challenging task. That is because any thresholding method may be perform well for some images but for sure it will not be suitable for all images. In this paper, intelligent thresholding by training a n...

Computer apparition plays the most important role in human perception, which is limited to only the visual band of the electromagnetic spectrum. The need for Radar imaging systems, to recover some sources that are not within human visual band, is raised. This paper present new algorithm for Synthetic Aperture Radar (SAR) images segmentation based on thresholding technique. Entropy based image t...

Binarization is a process of separation of pixel values of an input image into two pixel values like white as background and black as foreground. It is an important part in image processing and it is the first step in many document analysis and OCR processes. Most of the binarization techniques associate a certain intensity value called threshold which separate the pixel values of the concerned...