With the rapid development of the computer and sensor field, inertial sensor data have been widely used in human activity recognition. At present, most relevant studies divide human activities into basic actions and transitional actions, in which basic actions are classified by unified features, while transitional actions usually use context information to determine the category. For the existing single method that cannot well realize human activity recognition, this paper proposes a human activity classification and recognition model based on smartphone inertial sensor data. The model fully considers the feature differences of different properties of actions, uses a fixed sliding window to segment the human activity data of inertial sensors with different attributes and, finally, extracts the features and recognizes them on different classifiers. The experimental results show that dynamic and transitional actions could obtain the best recognition performance on support vector machines, while static actions could obtain better classification effects on ensemble classifiers; as for feature selection, the frequency-domain feature used in dynamic action had a high recognition rate, up to 99.35%. When time-domain features were used for static and transitional actions, higher recognition rates were obtained, 98.40% and 91.98%, respectively.
Low-rank decomposition models have potential for fabric defect detection, where a feature matrix is decomposed into a low-rank matrix that corresponding to repeated texture structure and a sparse matrix that represent defective regions. Two limitations, however, still exist. First, previous work might fail to detect some large homogeneous defective block. Second, when the background and defective regions are relatively coherent or the texture of fabric image is complex, it is difficult to use previous methods to separate them. To deal with these problems, a new weighted low-rank decomposition model with Laplace regularization (WLRL) is proposed in this paper: (1) a weighted low-rank decomposition model that can decompose the original image into background and defective regions, and (2) a Laplace regularization that can enlarge the distance between the background and the defective regions. The performance of the proposed method WLRL is evaluated on the box- and star-patterned fabric databases, and superior results are shown compared with state-of-the-art methods, that is, 98.70% ACC (accuracy) and 72.83% TPR (true positive rate) for box-patterned fabrics, 99.09% ACC (accuracy) and 83.63% TPR (true positive rate) for star-patterned fabrics.
Low-rank decomposition model has been widely used in fabric defect detection, where a matrix is decomposed into a low-rank matrix representing the defect-free region (background) of the image and a sparse matrix identifying the defective region (foreground). Two shortcomings, however, still exist. First, the traditional low-rank decomposition model can only deal with Laplacian noise, ignoring Gaussian noise in the image region. Second, when textiles have periodic complex patterns, it is difficult for traditional models to separate the defective regions from the background. To address these problems, we propose a low-rank decomposition model with noise regularization and gradient information (G-NLR): 1) noise regularization, which characterizes the image noise part and enlarges the gaps between defective objects and background in the feature space. 2) The gradient information constrains the noise term adaptively according to the mutation degree of the current pixel point, so as to guide the matrix decomposition and reduce the noise misjudgment. This ensures the accuracy of detection results. We evaluate our model for fabric defect detection on the standard database including box-, star-and dot-patterns, and compare with four recent detection methods. Experimental results show that our method has good results in terms of recall rate and precision.
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