Advance three-dimensional (3D) scanning devices can create very detail complex 3D polygonal models. Though the random access memory (RAM) and parallel computing in graphics card are enormously improved, many modelers still confront with intricacy of slow computing due to exponential increase of number of polygons for realistic look of surface models. In this paper, a simple and fast triangular mesh simplification method based on half-edge collapsed scheme is proposed. The Euclidean distance of each triangle edges and the disparity between two adjacent triangles are computed for decimation validation. The to-be-collapsed vertices and edges are conducted in priority queue data structure. The empirical results show that the proposed simplified model shape is well-preserved and is computation efficient. Two existing popular methods (FMLOD and FSIMP) are to be compared with the proposed method and the result demonstrates a rational outcome and is comparable in visual quality.
Emotion is a scorching topic in the recent years due to the critical unseen stress incurred during the pandemic and post-pandemic. This is worsening with the recent economy’s inflation and increase of living cost, many employees are seriously affected and drawn forth many families saddened cases and tremendous drop of working performance. The increasing stress brings a lot of harm not only to the individual but to the company’s and country’s growth. To recognize emotion through a single model is less accurate, however, recruiting multiple-models may lead to latency in data processing and possibly misleading results if the input models data are not properly filtered and segmented. This paper will review, analyze and theoretically compare 15 facial expression methods and 17 voice methods of emotion recognition research works. It will outline the pros and cons of each method and discuss the accuracy of some of the standalone and hybrid emotion recognition methods. Some of the methods (such as CNN, KNN and SVM) can span over multiple-models, but reveal different level of strengths. This is very important to discover, so that one may replace or enhance the weaker level if applying the same method across the multiple-models. This paper will also illustrate different levels of popularity of the methods in each model for visual comparison in ease. Hopefully, it can cater the new researchers a quick identification on the most suitable method for recognizing the emotion through facial expression and/or voice.
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