-Thresholding is an important form of image segmentation and is a first step in the processing of images for many applications. The selection of suitable thresholds is ideally an automatic process, requiring the use of some criterion on which the selection is based. Most such criteria are only based on the one-dimensional (1D) gray-level histogram of image. In an effort to use more information ...

The histogram specification turns a shape of a histogram into that we want to specify. This technique can be applied usefully in various image processing fields such as machine vision. However, the histogram specification technique has its basic limits. For instance, the histogram does not have location information of pixels. Also, the accuracy of specification drops because of quantization err...

This paper presents a novel approach to speech data normalization by introducing interpolation for histogram equalization. We study different ways of histogram interpolation that inhence this normalization technique. We found that using a special weighting factor to combine current and past test sentence statistics improved speech recognition performance. For the testing that used weighted hist...

Magnetic resonance imaging (MRI) of the brain is a precious tool to facilitate physician’s diagnoses and care for a mixture of brain diseases including stroke, cancer, and epilepsy. It presents exact information to discover the diseases. Histogram equalization is one of the important steps in image enhancement technique for MRI. This paper compares different methods like Brightness Preserving B...

An essential aspect of texture analysis is the extraction features that describe distribution values in local, spatial regions. We present a localized histogram layer for artificial neural networks. Instead computing global histograms as done previously, proposed directly computes and parameters are estimated during backpropagation. compare our method with state-of-the-art encoding methods such...