Research on Remote Sensing Image Matching with Special Texture Background

The purpose of image registration is to find the symmetry between the reference image and the image to be registered. In order to improve the registration effect of unmanned aerial vehicle (UAV) remote sensing imagery with a special texture background, this paper proposes an improved scale-invariant feature transform (SIFT) algorithm by combining image color and exposure information based on adaptive quantization strategy (AQCE-SIFT). By using the color and exposure information of the image, this method can enhance the contrast between the textures of the image with a special texture background, which allows easier feature extraction. The algorithm descriptor was constructed through an adaptive quantization strategy, so that remote sensing images with large geometric distortion or affine changes have a higher correct matching rate during registration. The experimental results showed that the AQCE-SIFT algorithm proposed in this paper was more reasonable in the distribution of the extracted feature points compared with the traditional SIFT algorithm. In the case of 0 degree, 30 degree, and 60 degree image geometric distortion, when the remote sensing image had a texture scarcity region, the number of matching points increased by 21.3%, 45.5%, and 28.6%, respectively and the correct matching rate increased by 0%, 6.0%, and 52.4%, respectively. When the remote sensing image had a large number of similar repetitive regions of texture, the number of matching points increased by 30.4%, 30.9%, and −11.1%, respectively and the correct matching rate increased by 1.2%, 0.8%, and 20.8% respectively. When processing remote sensing images with special texture backgrounds, the AQCE-SIFT algorithm also has more advantages than the existing common algorithms such as color SIFT (CSIFT), gradient location and orientation histogram (GLOH), and speeded-up robust features (SURF) in searching for the symmetry of features between images.

Robust Object Detection in Military Infrared Image

The innovation of this paper is that we put forward a new algorithm of object detection form military infrared images with texture background according to the Mean-shift smooth and segmentation method combined with eight directions difference clustering. According to the texture characteristics of background, smoothing and clustering is carried out to extract the characteristics of object. The experimental results show that the algorithm is able to extract the object information form complex infrared texture background with better self-adapting and robustness. Future research particularly lies in raising the accuracy of object extracted.

Online Visual Inspectionsystem for OLED Defects

The OLED defects generated in the manufacturing process restrict the development of OLED industry, machine vision based automatic OLED-inspection equipment can rapidly detect these defects and help to improve the OLED manufacturing process. The OLED images have the features of repeating texture background, uneven overall brightness of the image and the defects without obvious edge. In addition, an uncertain change in light and position of the inspection system increases the difficulty of the detection. Therefore, we propose the method to detect defects which take advance of the human eye characteristics of the Gabor filter and the unsupervised and fast segmentation features of the Fuzzy C-Means FCM algorithm. Through the combined 2-step segmentation, most OLED defects can be detected. The experimental tests are performed to validate the effectiveness of the proposed method. The result of the experiment shows that this method works well which can meet the requirements of robustness, automation of the fast and reliable of an online inspection system.

Texture Deviation Mapping Based on Detail Enhancement

To provide with an effective technique for non-photorealistic rendering for computer generated images with artistic appearances from 2D images motivates our work in this paper. The methods proposed in this paper are inspired by the image deviation mapping constructed from a single texture background image. We establish our method for obtaining artistic appearances taking the deviation mapping as the underlying basis. Based on the simple linear filtering convolution operation, which is well suited of progressive coarsening of images and for detail extraction, and the image’s detail, such as edge and tone can be preserved in the final artistic appearance. This method has the exact computational complexity, and this technique is easily to implement and the rendering speed is fast.

Texture Based Infrared Military Target Extraction

The innovation of this paper is that it forwards a new algorithm of target extraction form military infrared images with texture background according to the Mean-shift smooth and segmentation method combined with eight directions difference clustering. According to the texture characteristics of background image, smoothing and clustering are both carried out to extract the characteristics of target. The method is relatively simple making it easy for practical applications. The experimental results show that the algorithm is able to extract the target information form complex military infrared texture background with better self-adapting and robustness.