Color Edge Detection Using Multidirectional Sobel Filter and Fuzzy Fusion

A new model is proposed in this paper on color edge detection that uses the second derivative operators and data fusion mechanism.The secondorder neighborhood shows the connection between the current pixel and the surroundings of this pixel.This connection is for each RGB component color of the input image.Once the image edges are detected for the three primary colors:red,green,and blue,these colors are merged using the combination rule.Then,the final decision is applied to obtain the segmentation.This process allows different data sources to be combined,which is essential to improve the image information quality and have an optimal image segmentation.Finally,the segmentation results of the proposed model are validated.Moreover,the classification accuracy of the tested data is assessed,and a comparison with other current models is conducted.The comparison results show that the proposed model outperforms the existing models in image segmentation.

TFN-FICFM:sEMG-Based Gesture Recognition Using Temporal Fusion Network and Fuzzy Integral-based Classifier Fusion

Surface electromyography(sEMG)-based gesture recognition is a key technology in the field of human–computer interaction.However,existing gesture recognition methods face challenges in effectively integrating discriminative temporal feature representations from sEMG signals.In this paper,we propose a deep learning framework named TFN-FICFM comprises a Temporal Fusion Network(TFN)and Fuzzy Integral-Based Classifier Fusion method(FICFM)to improve the accuracy and robustness of gesture recognition.Firstly,we design a TFN module,which utilizes an attention-based recurrent multi-scale convolutional module to acquire multi-level temporal feature representations and achieves deep fusion of temporal features through a feature pyramid module.Secondly,the deep-fused temporal features are utilized to generate multiple sets of gesture category prediction confidences through a feedback loop.Finally,we employ FICFM to perform fuzzy fusion on prediction confidences,resulting in the ultimate decision.This study conducts extensive comparisons and ablation studies using the publicly available datasets Ninapro DB2 and DB5.Results demonstrate that the TFN-FICFM model outperforms state-of-the-art methods in classification performance.This research can serve as a benchmark for sEMG-based gesture recognition and related deep learning modeling.