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The estimation of multiple matching models between wide baseline or large angle images is a quite challenging task in image processing. The existing algorithms can be used to estimate multiple matching models and their inliers between images well, but their results are prone to matching pairs mis-classification issues. In order to accurately estimate the multiple matching models and allocate matching pairs, this paper proposed an image multi-matching model estimation algorithm based on the aggregation of matching points of the same model (AMPSM). Firstly, for improve the proportion of correct matching pairs, it filtered out incorrect matching pairs based on the distribution characteristics of correct matching points in the neighboring region. Furthermore, based on the different matching model degrees to which the matching pairs belong, searched for the suspected intersection matching pairs of multiple models, that was interference matching pairs. Meantime, for reducing the impact of interference matching pairs on the accuracy of matching classification, they were removed. Afterwards, for improve the clustering degree of matching points with the co-model, the position was dynamically moved based on the distance between the points within the same model and the center of gravity of the point set during the sampling process. Finally, classifying clustered matching points by Mean Shift to obtain a multi matching model. And the proposed method was compared with classical framework based algorithms RANSAC, PROSAC, MAGSAC++, GMS, AdaLAM, PEARL, MTC, Sequential RANSAC, and deep learning based algorithms SuperGlue, OANet, CLCNet, CONSAC, etc. Results indicate over 30% increase in the inlier rate, 8.39% reduction in the mis-classification rate of multi model estimation. It is concluded that the new algorithm has significant advantages in incorrect matches filtering and multi-model estimation. [ABSTRACT FROM AUTHOR]

To address the problems of inability to capture the impact of environmental factors and interaction with surrounding individuals on emotion recognition in vision-based affective computing, limitations of describing emotions with a single category, and inability to predict future emotions, this paper proposed a visual emotion recognition and prediction method integrating background context features. This method consisted of an emotion recognition model that integrated background context features (Context-ER) and an emotion prediction model based on GRU and continuous emotion dimensions of Valence-Arousal (GRU mapVA). Context-ER combined facial expressions, body posture, and background context (environment, interaction behavior with surrounding people) features to perform multi-label classification for 26 discrete emotion categories and regression for 3 continuous emotion dimensions. GRU mapVA projected the predicted values of Valence-Arousal onto the improved Valence-Arousal model based on the proposed mapping rules, making the differences between sentiment prediction classes more pronounced. Context-ER was tested on the Emotic dataset, and the results show an average precision improvement of over 4% compared to the state-of-the-art methods. GRU-mapVA was tested on three video samples, and the results demonstrate a significant improvement in emotion prediction compared to existing methods. [ABSTRACT FROM AUTHOR]