Cheng-Yi Yu

Extension Neural Network Approach to Classification of Brain MRI

Magnetic Resonance Image (MRI) has been widely used for clinical applications in recent years. With the ability of scanning the same section by multiple frequencies, MRI makes it possible to generate several images on the same section. Despite of accessible abundant information, MRI also makes it more difficult to judge the location of every tissue. MRI will complicate the judgment due to strong noise. In order to resolve this problem, this paper endeavors to classify them via the help of Extension Neural Network (ENN), This paper has to demonstrate the advantages of Extension Theory, Statistical theory is considered as a judgment method, whereby obtaining experimental data of Extension Neural Network and perceptron for subsequent comparison. It has proved that Extension is superior to the other algorithms in terms of classification.

Adaptive inverse hyperbolic tangent algorithm for dynamic contrast adjustment in displaying scenes

Contrast has a great influence on the quality of an image in human visual perception. A poorly illuminated environment can significantly affect the contrast ratio, producing an unexpected image. This paper proposes an Adaptive Inverse Hyperbolic Tangent (AIHT) algorithm to improve the display quality and contrast of a scene. Because digital cameras must maintain the shadow in a middle range of luminance that includes a main object such as a face, a gamma function is generally used for this purpose. However, this function has a severe weakness in that it decreases highlight contrast. To mitigate this problem, contrast enhancement algorithms have been designed to adjust contrast to tune human visual perception. The proposed AIHT determines the contrast levels of an original image as well as parameter space for different contrast types so that not only the original histogram shape features can be preserved, but also the contrast can be enhanced effectively. Experimental results show that the proposed algorithm is capable of enhancing the global contrast of the original image adaptively while extruding the details of objects simultaneously.

A Fuzzy Cerebellar Model Articulation Controller Using a Strategy-Adaptation-Based Bacterial Foraging Optimization Algorithm for Classification Applications

This paper proposes a fuzzy cerebellar model articulation controller using a strategy-adaptation-based bacterial foraging optimization (SABFO) algorithm to solve classification problems. A strategic approach to the chemotaxis step in the SABFO algorithm was adopted: in this approach, each virtual bacterium swims on different run-lengths, and bacterial diversity is increased. The simulation results indicated that the performance of the proposed method was more favorable than that of other methods.

Image contrast enhancement based on a three-level adaptive inverse hyperbolic tangent algorithm

An image contrast enhancement algorithm using a three-scale adaptive inverse hyperbolic tangent (3SAIHT) scheme is proposed. It has long been known that the human vision system heavily depends on details and edges in understanding its perception of scenes. The main goal of this article is to develop a contrast enhancement technique to restore a blurred and dark image so as to improve visual quality. The proposed technique consists of two steps, sub-scaling and contrast enhancement where the sub-scaling is performed by sub-band processing, while the contrast enhancement is accomplished by an AIHT algorithm to bring out hidden details in a processed image. Experimental results show that the proposed method performs better than other techniques.

Image Contrast Enhancement by Hybrid 3SAIHT and CLAHE Algorithm

Human visual perception is insensitive to certain shades of gray but can distinguish among 20 to 30 shades of gray under a given adaptation level. In this paper, we propose an image fusion pipeline that generates a high vision quality image by fusing the Three-Scale Adaptive Inverse Hyperbolic Tangent (3SAIHT) and the Contrast-Limited Adaptive Histogram Equalization (CLAHE) algorithms to increase detail and edge information. Fusion results are clearer and better with regard to display quality and contrast enhancement.

Eight-Scale Image Contrast Enhancement Based on Adaptive Inverse Hyperbolic Tangent Algorithm

The Eight-Scale parameter adjustment is a nature extending of Adaptive Inverse Hyperbolic Tangent (8SAIHT) algorithm. It has long been known that the Human Vision System (HVS) heavily depends on detail and edge in the understanding and perception of scenes. Our main goal is to produce a contrast enhancement technique to recover an image from a blurred and darkness, also improve visual quality at the same time. Eight-scale coefficients adjustments can provide a further local refinement in detail under the Adaptive Inverse Hyperbolic Tangent (AIHT) algorithm. The proposed 8SAIHT method is using the sub-band to calculate the local mean and local variance before the AIHT algorithm is performed. We also show that this approach is convenient and effective to do the enhancement process for a various types of images. The 8SAIHT is also capable of enhancing the local contrast of the original image adaptively while extruding more on the details of objects simultaneously.

Remove Strong Light and Atomized Algorithms for Digital Image

In this study using dark channel prior for single image remove strong light and atomized. The method used to dark channel estimate transfer and Soft Matting transmittance, of a method for thinning processing, obtain a depth map image of strong light. the light reflected from the scene is affected surface of airborne particles, which leads to a decrease in image contrast, while the ambient scene light will be scattered in the air suspended particles, the scattered light into the imaging device and will cause the image color drift, the overall look of the image becomes blurred images and details of the contents illegible gray color bias. Image of outdoor scenes are degraded by the medium. Can be effective remove strong light, so that the overall image contrast in natural.

A Study of Digital Image Enlargement and Enhancement

Most image enlargement techniques suffer the problem of zigzagged edges and jagged images following enlargement. Humans are sensitive to the edges of objects; if the edges in the image are sharp, the visual is considered to be high quality. To solve this problem, this paper presents a new and effective method for image enlargement and enhancement based on adaptive inverse hyperbolic tangent (AIHT) algorithm. Conventional image enlargement and enhancement methods enlarge the image using interpolation, and subsequently enhance the image without considering image features. However, this study presents the method based on Adaptive Inverse Hyperbolic Tangent algorithm to enhance images according to image features before enlarging the image. Experimental results indicate that the proposed algorithm is capable of adaptively enhancing the image and extruding object details, thereby improving enlargements by smoothing the edge of the objects in the image.

Image Tracking and Analysis Algorithm by Independent Component Analysis

Along with digitizing and multimedia era, the image has not changed from the original entity into any changes can be dealt with digital preservation methods. Although the digital image capture technology means more and more developed, but there are still many variables affect the quality of an image. An image quality usually depends on the users usage or changes in the natural environment. Due to the natural environment of the most common factors that influence is light, so an image of the brightness distribution over the target object caused by extreme hardly recognizable condition common. Therefore, we will use the independent component analysis of an input color images Red, Green, and Blue three Color Space to the main component analysis, in order to achieve the target tracking and analysis.