Multi scale entropy based adaptive fuzzy contrast image enhancement for crowd images

Contrast enhancement is a very important issue in image processing, pattern recognition and computer vision. Fuzzy logic based techniques perform enhancement using more detailed information of grayness of an image. However, these methods do not perform well on images taken in uncontrolled environment which pose different challenges such as illumination variation, perspective distortion and viewpoint variation. In this paper, we have worked to devise a more robust image enhancement method using fuzzy logic. We propose a novel multi scale entropy based measurement performed using fuzzy logic image processing and utilize it to define and enhance the contrast. For this purpose, we present a mathematical formula to calculate contrast using an adaptive amplification constant. Our approach uses both the local and global entropy information. We have experimented our algorithm on images from Crowd Counting UCF dataset, which contains very dense crowds and complex texture that stands in line with the challenges targeted in this paper. The results show an improved quality than original dataset images and prove that our method enhances the images with a more dynamic ranged contrast as well as better visual results

Perceptual Copyright Protection Using Multiresolution Wavelet-Based Watermarking And Fuzzy Logic

In this paper, an efficiently DWT-based watermarking technique is proposed to embed signatures in images to attest the owner identification and discourage the unauthorized copying. This paper deals with a fuzzy inference filter to choose the larger entropy of coefficients to embed watermarks. Unlike most previous watermarking frameworks which embedded watermarks in the larger coefficients of inner coarser subbands, the proposed technique is based on utilizing a context model and fuzzy inference filter by embedding watermarks in the larger-entropy coefficients of coarser DWT subbands. The proposed approaches allow us to embed adaptive casting degree of watermarks for transparency and robustness to the general image-processing attacks such as smoothing, sharpening, and JPEG compression. The approach has no need the original host image to extract watermarks. Our schemes have been shown to provide very good results in both image transparency and robustness.Comment: 13 pages, 7 figure

Fuzzy Logic and Singular Value Decomposition based Through Wall Image Enhancement

Singular value decomposition based through wall image enhancement is proposed which is capable of discriminating target, noise and clutter signals. The overlapping boundaries of clutter, noise and target signals are separated using fuzzy logic. Fuzzy inference engine is used to assign weights to different spectral components. K-means clustering is used for suitable selection of fuzzy parameters. Proposed scheme significantly works well for extracting multiple targets in heavy cluttered through wall images. Simulation results are compared on the basis of mean square error, peak signal to noise ratio and visual inspection

Application of a Mamdani-type fuzzy rule-based system to segment periventricular cerebral veins in susceptibility-weighted images

This paper presents an algorithm designed to segment veins in the periventricular region of the brain in susceptibility-weighted magnetic resonance images. The proposed algorithm is based on a Mamdani-type fuzzy rule-based system that enables enhancement of veins within periventricular regions of interest as the first step. Segmentation is achieved after determining the cut-off value providing the best trade-off between sensitivity and specificity to establish the suitability of each pixel to belong to a cerebral vein. Performance of the algorithm in susceptibility-weighted images acquired in healthy volunteers showed very good segmentation, with a small number of false positives. The results were not affected by small changes in the size and location of the regions of interest. The algorithm also enabled detection of differences in the visibility of periventricular veins between healthy subjects and multiple sclerosis patients. © Springer International Publishing Switzerland 2016.Postprint (author's final draft

Fuzzy-logic-based control, filtering, and fault detection for networked systems: A Survey

This paper is concerned with the overview of the recent progress in fuzzy-logic-based filtering, control, and fault detection problems. First, the network technologies are introduced, the networked control systems are categorized from the aspects of fieldbuses and industrial Ethernets, the necessity of utilizing the fuzzy logic is justified, and the network-induced phenomena are discussed. Then, the fuzzy logic control strategies are reviewed in great detail. Special attention is given to the thorough examination on the latest results for fuzzy PID control, fuzzy adaptive control, and fuzzy tracking control problems. Furthermore, recent advances on the fuzzy-logic-based filtering and fault detection problems are reviewed. Finally, conclusions are given and some possible future research directions are pointed out, for example, topics on two-dimensional networked systems, wireless networked control systems, Quality-of-Service (QoS) of networked systems, and fuzzy access control in open networked systems.This work was supported in part by the National Natural Science Foundation of China under Grants 61329301, 61374039, 61473163, and 61374127, the Hujiang Foundation of China under Grants C14002 andD15009, the Engineering and Physical Sciences Research Council (EPSRC) of the UK, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany