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Abstract: Hand joint pose assessment is a critical step in the forensic identification of range of motion, holding significant importance in the field of forensic science. To overcome the limitations of traditional methods that rely heavily on professional physicians, this paper proposes an improved hand pose assessment method based on computer vision. Focusing on 21 hand keypoints, the method addresses challenges such as their small size in images, susceptibility to occlusion, and limited feature information by constructing an optimized detection framework. Specifically, a Small Object Augmented Feature Pyramid is designed to alleviate the difficulty of losing small keypoint features; the SPDConv module is introduced to preserve detailed features; and the Omni-Frequency-Spatial Attention Kernel (Omni-FSAK) module is incorporated to achieve efficient multi-scale feature fusion, thereby significantly enhancing the perception capability for blurred, occluded, and tiny keypoints. In terms of detection head design, in addition to the classification branch, bounding box regression branch, and keypoint prediction branch, we further introduce a Localization Quality Calibrator (LQC). This mechanism fully utilizes the outputs from the classification branch and regression distribution information to evaluate the reliability of prediction results. It effectively compensates for the insufficiency of classification confidence in accurately reflecting localization quality, enabling the model to output high-quality detection and keypoint prediction results more robustly under the same network scale. Experiments on the FreiHAND and KeypointsHand datasets verify the effectiveness of the proposed method. Compared with the baseline method, the accuracy is improved by 3.15% and the recall rate is increased by 1.74%, fully validating the feasibility of the improved scheme.Abstract: Hand joint pose assessment is a critical step in the forensic identification of range of motion, holding significant importance in the field of forensic science. To overcome the limitations of traditional methods that rely heavily on professional physicians, this paper proposes an improved hand pose assessment method based on computer vision. Focus...Learn More
Abstract: Aiming at the complex and diverse interference of image moir é patterns, the difficulty in fully preserving texture details and edge information, and the poor performance and high computational complexity of existing moir é pattern removal models, a new moir é pattern removal network model based on UNet network is proposed, which integrates weighted convolution and multi-scale attention mechanism. In the feature extraction stage, a weighted convolutional layer based on density parameters is introduced to enhance the central region response of the convolutional kernel through adaptive weight adjustment, while suppressing the influence of edge noise. Secondly, in order to more effectively capture multi-scale feature information, spatial attention blocks and multi-scale channel attention modules are used for deep level feature extraction to obtain more detailed and rich feature representations. In the decoding stage, a combination of skip connections and residual connections is used to enhance the robustness of the model through multi-scale output, and finally, Moir é fringes are removed through weighted convolutional layers. The experimental results show that the evaluation scores of the model on three public datasets, UHDM, TIP, and FHDMI, are better than those of mainstream models for removing image moir é patterns, proving that the model effectively improves the effectiveness of image moir é pattern removal.Abstract: Aiming at the complex and diverse interference of image moir é patterns, the difficulty in fully preserving texture details and edge information, and the poor performance and high computational complexity of existing moir é pattern removal models, a new moir é pattern removal network model based on UNet network is proposed, which integrates weighte...Learn More
Abstract: University major evaluation is a systematic evaluation of the professional construction, teaching quality, and talent training in colleges and universities, aiming to promote construction of professional connotation and improve the quality of talent training. The evaluation is usually organized by government education authorities, third-party institutions, or colleges and universities independently, adopting combination of quantitative and qualitative methods, mainly based on a professional evaluation system that relies on static analysis. The social participation is relatively low. With the comprehensive and in-depth integration of-education integration and science-education integration into the teaching and scientific research work of colleges and universities, the professional construction and development of colleges and universities need more participation from various sectors of. In order to better verify the school-running strength and school-running level of colleges and universities, it is very necessary to construct a professional evaluation system with the participation of subjects, such as government education authorities, colleges and universities, third-party evaluation institutions, and industry enterprises. This kind of multi-party collaborative working mode is a very complex systematic, which not only involves the acquisition of various different information resources and massive data storage and computing, but also needs a complete set of data classification processing and information processing technology. By using "AI + Big Data" technology and adopting a multi-source distributed parallel computing model, it can effectively solve the storage and computing of various information data and greatly avoid the intervention of human in the evaluation process, which is a powerful guarantee for the extensive participation in the evaluation process and the fairness and justice of the evaluation results.Abstract: University major evaluation is a systematic evaluation of the professional construction, teaching quality, and talent training in colleges and universities, aiming to promote construction of professional connotation and improve the quality of talent training. The evaluation is usually organized by government education authorities, third-party insti...Learn More
