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In the special sports camera, there are subframes. A lens is composed of multiple frames. It will be unclear if a frame is cut out. The definition of video screenshots lies in the quality of video. To get clear screenshots, we need to find clear video. The purpose of this paper is to analyze and evaluate the quality of sports video images. Through the semantic analysis and program design of video using computer language, the video images are matched with the data model constructed by research, and the real-time analysis of sports video images is formed, so as to achieve the real-time analysis effect of sports techniques and tactics. In view of the defects of rough image segmentation and high spatial distortion rate in current sports video image evaluation methods, this paper proposes a sports video image evaluation method based on BP neural network perception. The results show that the optimized algorithm can overcome the slow convergence of weights of traditional algorithm and the oscillation in error convergence of variable step size algorithm. The optimized algorithm will significantly reduce the learning error of neural network and the overall error of network quality classification and greatly improve the accuracy of evaluation. Sanda motion video image quality evaluation method based on BP (back propagation) neural network perception has high spatial accuracy, good noise iteration performance, and low spatial distortion rate, so it can accurately evaluate Sanda motion video image quality. [ABSTRACT FROM AUTHOR]

Electric shovels are widely used in the mining industry to dig ore, and the teeth in shovels' bucket can be lost due to the tremendous pressure exerted by ore materials during operation. When the teeth fall off and enter the crusher with other ore materials, serious damages to crusher gears and other equipment happen, which causes millions of economic loss, because it is made of high-manganese steel. Thus, it is urgent to develop an efficient and automatic algorithm for detecting broken teeth. However, existing methods for detecting broken teeth have little effect and most research studies depended on sensor skills, which will be disturbed by closed cavity in shovel and not stable in practice. In this paper, we present an intelligent computer vision system for monitoring teeth condition and detecting missing teeth. Since the pixel-level algorithm is carried out, the amount of calculation should be reduced to improve the superiority of the algorithm. To release computational pressure of subsequent work, salient detection based on deep learning is proposed for extracting the key frame images from video flow taken by the camera installed on the shovel including the teeth we intend to analyze. Additionally, in order to more efficiently monitor teeth condition and detect missing teeth, semantic segmentation based on deep learning is processed to get the relative position of the teeth in the image. Once semantic segmentation is done, floating images containing the shape of teeth are obtained. Then, to detect missing teeth effectively, image registration is proposed. Finally, the result of image registration shows whether teeth are missing or not, and the system will immediately alert staff to check the shovel when teeth fall off. Through sufficient experiments, statistical result had demonstrated superiority of our presented model that serves more promising prospect in mining industry. [ABSTRACT FROM AUTHOR]

With the development of computer science, especially the application of 3D scanning technology in garment design, intelligent modeling is realized, which is impossible to achieve in traditional design methods. In this paper, we propose the 3D model construction of human garments based on the motion recovery structure method. The eigenmatrix is obtained from the camera parameters, and the transformation matrix is calculated by matching the image feature points with the help of scale-invariant feature conversion algorithm to realize the 3D reconstruction technology of human garments based on multiview image sequences. The effectiveness of this method is verified through experiments, and it has good robustness and accuracy. Through the form of style modeling, the design thinking and method can be extended to form a more reasonable garment structure and guide the innovation of garment production mode. [ABSTRACT FROM AUTHOR]

We propose a constrained linear curvature image registration model to explicitly control the deformation according to the transformed Jacobian matrix determinant using point-by-point inequality constraints in this paper. In addition, an effective numerical method is proposed to solve the resulting inequality constrained optimization model. Finally, some numerical examples are given to prove the obvious advantages of the curvature image registration model with inequality constraints. [ABSTRACT FROM AUTHOR]

Multimodality brain image registration technology is the key technology to determine the accuracy and speed of brain diagnosis and treatment. In order to achieve high-precision image registration, a fast subpixel registration algorithm based on single-step DFT combined with phase correlation constraint in multimodality brain image was proposed in this paper. Firstly, the coarse positioning at the pixel level was achieved by using the downsampling cross-correlation model, which reduced the Fourier transform dimension of the cross-correlation matrix and the multiplication of the discrete Fourier transform matrix, so as to speed up the coarse registration process. Then, the improved DFT multiplier of the matrix multiplication was used in the neighborhood of the coarse point, and the subpixel fast location was achieved by the bidirectional search strategy. Qualitative and quantitative simulation experiment results show that, compared with comparison registration algorithms, our proposed algorithm could greatly reduce space and time complexity without losing accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a new feature descriptor that is suitable for image matching under nonlinear intensity changes. The proposed approach consists of the following three steps. First, a binary local patch clustering transform response is employed as the transform space. The value of the new space exhibits a high similarity after changes in intensity. Then, a random binary pattern coding method extracts raw feature histograms from the new space. Finally, the discrimination of the proposed feature descriptor is enhanced by using a multiple spatial support region-based binning method. Experimental results show that the proposed method is able to provide a more robust image matching performance under nonlinear intensity changes. [ABSTRACT FROM AUTHOR]