Application of Gabor Image Recognition Technology in Intelligent Clothing Design

Aiming at the complex problem of image recognition feature extraction, this paper proposes an intelligent clothing design model based on parallel Gabor image feature extraction algorithm. Based on the intelligent parallel mode, the algorithm decomposes and merges the calculation process of the image Gabor transformation, decomposes the entire image Gabor feature extraction calculation process into a parallel part and a nonparallel part, and accelerates the parallel part by using multiple cores. The calculation results are then combined to achieve the purpose of multicore parallel acceleration of the entire calculation process. Secondly, based on the consideration of improving the real-time performance of the intelligent clothing design system, combined with the existing multicore environment, this paper uses the intelligent model to design and implement the image parallel Gabor feature extraction algorithm and uses image processing and analysis technology to analyze the visual elements of traditional clothing and identify and quantify to form a relatively complete clothing visual element evaluation system, which provides a basis for large-scale collection and automated evaluation of clothing visual effects, as well as clothing trend tracking and prediction. Experiments show that the algorithm can effectively shorten the calculation time of Gabor image feature extraction and can obtain a good speedup in a multicore environment. At the same time, it combines with a multiscale intelligent clothing classification algorithm, on the basis of the VS2008 platform, combined with OpenCV 2.0, designed and implemented an intelligent clothing design system, and conducted experiments and system tests. The experimental results show that the algorithm given in this paper can accurately segment fabric defects from the background, which proves that the detection algorithm has a good detection effect. Simulation results show that the algorithm proposed in this paper can more accurately identify the state of clothing features, and the real-time performance of intelligent clothing design in a multicore environment has been improved to a certain extent.

Automated Diabetic Retinopathy Detection using CS Based SVM Techniques

Diabetes is one of the metabolic maladies where a patient has high glucose either brought about by body inability to create enough insulin or the cells inability to react to deliver insulin. This ceaseless ailment may prompt long haul inconveniences and death. It can cause high danger of kidney disappointment, nervous system harm, visual impairment and coronary illness. During the ongoing years there have been numerous examinations on programmed finding of diabetic retinopathy utilizing a few features and techniques. In this work at first the color fundus image can be utilized for processing, methods, for example, Median filtering, Morphological transformation, or Histogram equalization used to improve the nature of the image. Then to detect microaneurysms, blood vessels and optic disc using the techniques like morphological thresholding transformation, and the features are extracted from Grey level Co-occurrence Matrix (GLCM), Gabor Feature extraction and Linear Binary Pattern (LBD).At long last, for classify the various phases of diabetic retinopathy, SVM (support Vector Machine) Algorithm will be utilized, the outcomes are optimized by Cuckoo Search (CS) calculation.