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.

Gabor Feature Representation and Deep Convolution Neural Network for Marine Vessel Classification

The Vessel Surveillance System (VSS), a crucial tool for fisheries monitoring, controlling, and surveillance, has been required to use for the reservation of the current depressed state of the world's fisheries by fisheries management agencies. An important issue in the vessel surveillance system is the classification of vessels. However, several factors, such as lighting, congestion, and sea state, will affect the vessel's appearance, making it more difficult to classify vessels. There are two main methods for conventional classifications of vessels: the traditional-based- characteristics method and the convolutional neural networks-used method. In this paper, we combine Gabor feature representation (GFR) and deep convolution neural network (DCNN) to classify vessels. Gabor filters in different directions and ratios are used to extract vessel characteristics to create a new image of vessels, which is DCNN's input. The visible and infrared spectrums (VAIS) dataset, the world's first publicly available dataset for paired infrared and visible vessel images, was used to validate the proposed method (GFR-DCNN). The numerical results showed that GFR-DCNN is more accurate than other methods.

Random Collective Representation-Based Detector with Multiple Features for Hyperspectral Images

Collaborative representation-based detector (CRD), as the most representative anomaly detection method, has been widely applied in the field of hyperspectral anomaly detection (HAD). However, the sliding dual window of the original CRD introduces high computational complexity. Moreover, most HAD models only consider a single spectral or spatial feature of the hyperspectral image (HSI), which is unhelpful for improving detection accuracy. To solve these problems, in terms of speed and accuracy, we propose a novel anomaly detection approach, named Random Collective Representation-based Detector with Multiple Feature (RCRDMF). This method includes the following steps. This method first extract the different features include spectral feature, Gabor feature, extended multiattribute profile (EMAP) feature, and extended morphological profile (EMP) feature matrix from the HSI image, which enables us to improve the accuracy of HAD by combining the multiple spectral and spatial features. The ensemble and random collaborative representation detector (ERCRD) method is then applied, which can improve the anomaly detection speed. Finally, an adaptive weight approach is proposed to calculate the weight for each feature. Experimental results on six hyperspectral datasets demonstrate that the proposed approach has the superiority over accuracy and speed.