An Adaptive Fuzzy Contrast Enhancement Algorithm with Details Preserving

This paper modifies the Adaptive Contrast Enhancement Algorithm with Details Preserving (ACEDP) technique by integrating a fuzzy element in the image type selection. The proposed technique, named the Adaptive Fuzzy Contrast Enhancement with Details Preserving (AFCEDP) technique, first computes the degree of membership of the input image to three categories, i.e. low-, middle- or high-level images. The AFCEDP technique then clips the histogram at different plateau limits that are computed from both the degree of membership and the clipping functions. The classification of an image in the ACEDP technique is done based solely on the intensity range of the maximum number of pixels, which may be inaccurate. In the proposed AFCEDP technique, the image type classification is handled in a better way with the integration of a fuzzy element. The performance of the proposed AFCEDP technique was compared with the conventional ACEDP technique and several state-of-art techniques described in the literature. The simulation results revealed that the AFCEDP technique demonstrates good capability in contrast enhancement and detail preservation. In addition, the experiments using cervical cell images and HEp-2 cell images showed great potential of the AFCEDP technique as a technique for enhancing medical microscopic images

Automatic System for Improving Underwater Image Contrast and Color Through Recursive Adaptive Histogram Modification

Contrast and color are important attributes to extract and acquire much information from underwater images. However, normal underwater images contain bright foreground and dark background areas. Previous enhancement methods enhance the foreground areas but retain darkness and blue-green illumination of background areas. This study proposes a new method of enhancing underwater image, which is called recursive adaptive histogram modification (RAHIM), to modify image histograms column wisely in accordance with Rayleigh distribution. Modifying image saturation and brightness in the hue–saturation–value color model increases the natural impression of image color through the human visual system. Qualitative and quantitative evaluations prove the effectiveness of the proposed method. Comparison with state-of-the-art methods shows that the proposed method produces the highest average entropy, measure of enhancement (EME), and EME by entropy with the values of 7.618, 28.193, and 6.829, respectively

Design and implementation of a t-way test data generation strategy with automated execution tool support

To ensure an acceptable level of quality and reliability of a typical software product, it is desirable to test every possible combination of input data under various configurations. However, due to the combinatorial explosion problem, exhaustive testing is practically impossible. Resource constraints, cost factors, and strict time-to-market deadlines are some of the main factors that inhibit such a consideration. Earlier research has suggested that a sampling strategy (i.e., one that is based on a t-way parameter interaction) can be effective. As a result, many helpful t-way sampling strategies have been developed and can be found in the literature. Several advances have been achieved in the last 15 years, which have, in particular, served to facilitate the test planning process by systematically minimizing the test size required (based on certain t-way parameter interactions). Despite this significant progress, the integration and automation of strategies (from planning process to execution) are still lacking. Additionally, strategizing to sample (and construct) a minimum test set from the exhaustive test space is an NP-complete problem; that is, it is often unlikely that an efficient strategy exists that could regularly generate an optimal test set. Motivated by these challenges, this paper discusses the design, implementation, and validation of an efficient strategy for t-way testing, the GTWay strategy. The main contribution of GTWay is the integration of t-way test data generation with automated (concurrent) execution as part of its tool implementation. Unlike most previous methods, GTWay addresses the generation of test data for a high coverage strength (t > 6)

A robust structure identification method for evolving fuzzy system

This paper proposes a robust structure identification method (RSIM) based on incremental partitioning learning. RSIM starts with an open region (initial domain) that covers all input samples. The initial region starts with one fuzzy rule without fuzzy terms and then evolves through incremental partitioning learning, which creates many subregions for system error minimization. The three major contributions of the proposed RSIM are as follows: It locates sufficient splitting points provided through a robust partitioning technique, determines the optimum trade-off between accuracy and complexity through a novel partition-selection technique, minimizes global error through global least square optimization. These contributions offer many remarkable advantages. First, RSIM provides a solution for the curse of dimensionality. Second, RSIM can also be applied to low-dimensional problems. Third, RSIM seeks to produce few rules with low number of conditions to improve system readability. Fourth, RSIM minimizes the number of fired rules. Therefore, RSIM can achieve low-level complexity systems. Three low-dimension and six high-dimension and real-life benchmarks are used to evaluate the performance of RSIM with state-of-the art methods. Although RSIM has high interpretability, the results prove that RSIM exhibits greater accuracy than other existing methods

Determination of Important Topographic Factors for Landslide Mapping Analysis Using MLP Network

Landslide is one of the natural disasters that occur in Malaysia. Topographic factors such as elevation, slope angle, slope aspect, general curvature, plan curvature, and profile curvature are considered as the main causes of landslides. In order to determine the dominant topographic factors in landslide mapping analysis, a study was conducted and presented in this paper. There are three main stages involved in this study. The first stage is the extraction of extra topographic factors. Previous landslide studies had identified mainly six topographic factors. Seven new additional factors have been proposed in this study.They are longitude curvature, tangential curvature, cross section curvature, surface area, diagonal line length, surface roughness, and rugosity. The second stage is the specification of the weight of each factor using two methods. The methods are multilayer perceptron (MLP) network classification accuracy and Zhou’s algorithm. At the third stage, the factors with higher weights were used to improve the MLP performance. Out of the thirteen factors, eight factors were considered as important factors, which are surface area, longitude curvature, diagonal length, slope angle, elevation, slope aspect, rugosity, and profile curvature.Theclassification accuracy of multilayer perceptron neural network has increased by 3% after the elimination of five less important factors

Enhancement Of The Low Contrast Image Using Fuzzy Set Theory

This paper presents a fuzzy grayscale enhancement technique for low contrast image. The degradation of the low contrast image is mainly caused by the inadequate lighting during image capturing and thus eventually resulted in nonuniform illumination in the image

Illumination Estimation Based Color to Grayscale Conversion Algorithms

In this paper, a new adaptive approach, namelythe illumination estimation approach is introduced into the colorto grayscale conversion technique. In this approach, someassumptions will be made to calculate the weight contribution ofred, green, and blue components during the conversion process.Two color to grayscale conversion algorithms are developedunder this approach, namely the Gray World Assumption Colorto Grayscale Conversion (GWACG) and Shade of GrayAssumption Color to Grayscale (SGACG) conversion algorithms.Based on the extensive experimental results, the proposedalgorithms outperform the conventional conversion techniquesby producing resultant grayscale images with higher brightness,contrast, and amount of details preserved. For this reason, theseproposed algorithms are suitable for pre- and post- processing ofdigital images

Automatic Segmentation And Detection Of Mass In Digital Mammograms

This paper presents an automated system for mass segmentation and detection in mammograms. Initially, breast segmentation is applied to separate the breast and non-breast area. Then, image enhancement is employed to improve the contrast of the tissues structure in mammograms. Finally, constraint region growing based on local statistical texture analysis is applied to detect and segment out the mass from the mammograms

Automatic Segmentation And Detection Of Mass In Digital

This paper presents an automated system for mass segmentation and detection in mammograms

Development of an automated unit testing tool for java program.

Software testing relates to the process of executing a program or system with the intent of finding errors. Covering as much as 25 to 35 percent of the development costs and resources, software testing is an integral part of the software development lifecycle. Pengujian softwer melibatkan proses melarikan sesuatu program atau sistem dengan tujuan untuk mengesan kesalahan. Dengan kos pembangunan dan sumber meliputi 25 hingga 30 peratus, pengujian adalah antara bahagian utama dalam kitar hayat pembangunan softwer