Evolvable Reconfigurable Hardware Framework for Edge Detection

Systems on Reconfigurable Chips contain rich resources of logic, memory, and processor cores on the same fabric. This platform is suitable for implementation of Evolvable Reconfigurable Hardware Architectures (ERHA). It is based on the idea of combining reconfigurable Field Programmable Gate Arrays (FPGA) along with genetic algorithms (GA) to perform the reconfiguration operation. This architecture is a suitable candidate for implementation of early-processing stage operators of image processing such as filtering and edge detection. However, there are still fundamental issues need to be solved regarding the on-chip reprogramming of the logic. This paper presents a framework for implementing an evolvable hardware architecture for edge detection on Xilinx Virtex–4 chip. Some preliminary results are discussed

Edge Detection via Edge-Strength Estimation Using Fuzzy Reasoning and Optimal Threshold Selection Using Particle Swarm Optimization

An edge is a set of connected pixels lying on the boundary between two regions in an image that differs in pixel intensity. Accordingly, several gradient-based edge detectors have been developed that are based on measuring local changes in gray value; a pixel is declared to be an edge pixel if the change is significant. However, the minimum value of intensity change that may be considered to be significant remains a question. Therefore, it makes sense to calculate the edge-strength at every pixel on the basis of the intensity gradient at that pixel point. This edge-strength gives a measure of the potentiality of a pixel to be an edge pixel. In this paper, we propose to use a set of fuzzy rules to estimate the edge-strength. This is followed by selecting a threshold; only pixels having edge-strength above the threshold are considered to be edge pixels. This threshold is selected such that the overall probability of error in identifying edge pixels, that is, the sum of the probability of misdetection and the probability of false alarm, is minimum. This minimization is achieved via particle swarm optimization (PSO). Experimental results demonstrate the effectiveness of our proposed edge detection method over some other standard gradient-based methods

FINGERPRINT ENHANCEMENT USING FUZZY LOGIC AND DEEP NEURAL NETWORKS

Department of Computer Science and EngineeringFingerprint recognition analysis is one of the most leading preferred prodigious biometric advancement which has drawn generous consideration in biometrics. In this work, fingerprint Intensification is performed which is defined by Fuzzy logic technique and recognize the matching image with its unique characteristics extracted and classify the features extracted from a fuzzy enhanced image along with three major types of Neural Networks which are Feed Forward Artificial Neural Network, Neural Network, Recurrent Neural Network in order to classify the unique features extracted from a fingerprint image. This work efficiently expresses the results with Fuzzy logic enhancement and Neural Networks classifiers. Its principle goal is to improve the image using Fuzzy and extricate the spurious minutiae detected and classify the different features generated using GLCM and DWT. This work displays a framework of unique finger impression classification based on particular characteristics for extricating different features and three types of Neural Network for classification. Fuzzy technique is used for the fuzzy based image enhancement to urge the clear see of the unique finger impression. Fingerprint Image Intensification is the procedure to enhance the distorted images to encourage the recognizable proof. The motivation behind the work is to enrich the quality of the distorted condition image generated from any fingerprint sensor, as Images can be corrupted due to various conditions and one of the principal issues is the resolution of the fingerprint sensor generating noisy images. High-quality pictures are vital for the exact coordinating of unique finger impression pictures. But unique mark pictures are seldom of idealizing refinement. As it may be corrupted or debased due to varieties of the skin, impression state and condition. In this way, unique finger impression images must be improved before utilized. The idea behind this work fingerprint image intensification process is to improve the quality of distorted and noisy fingerprint images generated from a low-cost fingerprint sensor. Execution of current ???ngerprint acknowledgment frameworks is vigorously in???uenced by the precision of their characteristic???s extraction evaluation. These days, there are more ways to deal with ???ngerprint analysis with worthy outcomes. Issues begin to emerge in low-quality conditions where the dominant part of the conventional strategies dependent on examining the surface of ???ngerprint can't handle this issue so e???ectively as Neural Networks. Fuzzy logic technique is implemented first to remediate the distorted picture and enhance it with the implementation of GLCM and DWT2 algorithm features of an image is extracted, post to which three types of Neural Network Classification is performed to analyze the accuracy of the image generated from the extracted feature parameters and match the test and trained result with the implementation of Neural Networks and classify the outcome results. The three Neural Network used is Artificial Neural Network (ANN), Neural Network (NN), Recurrent Neural Network (RNN). This algorithm works efficiently to identify the fingerprint matching from the predefined trained images from the fuzzy enhanced image generated. Experiments are performed (in MATLAB 2019 student version) to make sure the extraction process should not get the false minutiae and preserve the true extracted features Fuzzy based Image Enhancement method makes sure the feature traits of the image is intensified. Better improvement proves the quality improvement further incrementing the highest accuracy determined in the classification further. This work can be used in a wide area of applications in biometrics as it is a combined work of distorted fingerprints enhancement, false feature removal, true feature extraction, matching of the images for identification purpose and classification using Neural Networks. Experiments show results which are quite promising and gives a direction of the subsequent further analysis in future work.clos

Fuzzy edge detector using entropy optimization

This paper proposes a fuzzy-based approach to edge detection in gray-level images. The proposed fuzzy edge detector involves two phases - global contrast intensification and local fuzzy edge detection. In the first phase, a modified Gaussian membership function is chosen to represent each pixel in the fuzzy plane. A global contrast intensification operator, containing three parameters, viz., intensification parameter t, fuzzifier fh and the crossover point xc, is used to enhance the image. The entropy function is optimized to obtain the parameters fh, and xc using the gradient descent function before applying the local edge operator in the second phase. The local edge operator is a generalized Gaussian function containing two exponential parameters, α and β. These parameters are obtained by the similar entropy optimization method. By using the proposed technique, a marked visible improvement in the important edges is observed on various test images over common edge detectors