Multiple sclerosis (MS) is a chronic autoimmune inflammatory disease that damages the central nervous system by causing small lesions in the brain. In this study, we present the fusion of four features extraction methods such as the 3D Local Binary Pattern (3D-LBP), 3D Decimal Descriptor Patterns (3D-DDP), Local Binary Pattern from Three Orthogonal Planes (LBP-TOP) and Decimal Descriptor Patterns from Three Orthogonal Planes (DDP-TOP) with Convolutional Neural Network (CNN) for MS classification using three 3D MRI sequences datasets T1, T2 and PD from 3D BrainWeb dataset. We implement twelve CNN models and apply each method with each of the CNN models on T1, T2 then PD MRI sequences. The experimental results demonstrate that 3D-DDP and DDP-TOP methods are the most robust and, for the contrast change effect of MRI sequences on the classification results, T2 yields the best performance.
This paper introduces a novel approach for human activities recognition (HAR) based on body articulations (joints) that represent the connection between bones in the human body which join the skeletal system such as the knee, shoulder and hand, and which are made to allow different degrees and types of movement. To implement our system, we used PoseNet to extract articulation points, which will be classified employing transfer learning approach to recognize the activity. The created system will be named in the rest of the paper (PTLHAR). The experimental results show that the proposed approach provides a significant improvement over state-of-the-art methods.
Human gait is an attractive modality for recognizing people at a distance. Gait recognition systems aims to identify people by studying their manner of walking. In this paper, we contribute by the creation of a new approach for gait recognition based on fast wavelet network (FWN) classifier. To guaranty the effectiveness of our gait recognizer, we have employed both static and dynamic gait characteristics. So, to extract the static features (dimension of the body part), model based method was employed. Thus, for the dynamic features (silhouette appearance and motion), model free method was used. The combination of these two methods aims at strengthens the WN classification results. Experimental results employing universal datasets show that our new gait recognizer performs better than already established ones.
This paper presents a novel hand posture recognizer based on separator wavelet networks (SWNs). Aiming at creating a robust and rapid hand posture recognizer, we have contributed by proposing a new training algorithm for the wavelet network classifier based on fast wavelet transform (FWN). So, the contribution resides in reducing the number of WNs modeling training data. To make that, inspiring from the adaboost feature selection method, we thought to create SWNs (n-1 WNs for n classes) instead of modeling each training sample by its wavelet network (WN). By proposing the new training algorithm, the recognition phase will be positively influenced. It will be more rapid thanks to the reduction of the number of comparisons between test images WNs and training WNs. Comparisons with other works, employing universal hand posture datasets are presented and discussed. Obtained results have shown that the new hand posture recognizer is comparable to previously established ones.
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