This paper proposes a novel template-based correction (TC) method for the defect detection on images with periodic structures. In this method, a fabric image is segmented into lattices according to variation regularity, and correction is applied to reduce the effect of misalignment among lattices. Also, defect-free lattices are chosen for establishing an average template as a uniform reference. Furthermore, the defect detection procedure is composed of two steps, namely, defective lattices locating and defect shape outlining. Defective lattices locating is based on classification for defect-free and defective patterns, which involves an improved E-V method with template-based correction and centralized processing, while defect shape outlining provides pixel-level results by threshold segmentation. In this paper we also present some experiments on fabric defect detection. Experimental results show that the proposed method is effective.

No abstract

No abstract

Human action recognition plays a key role in human-computer interaction in complex environments. However, similar actions will lead to poor feature sequence extraction and result in a reduction in recognition accuracy. This paper proposes a method (Action-Fusion: Multi-label subspace Learning (MLSL)) from depth maps called Depth Sequential Information Entropy Maps (DSIEM) and skeleton data for human action recognition in multiple modal features. The DSIEM describe the spatial information of human motion with information entropy, and describe the temporal information through stitching. DSIEM can reduce the redundancy of depth sequences and effectively capture spatial motion states. MLSL studies the relationship between different modalities and the inherent connection between different labels. The method is evaluated on three public datasets: Microsoft action 3D dataset (MSR Action3D), University of Texas at Dallas-multimodal human action dataset (UTD-MHAD), UTD MHAD-Kinect Version-2 (UTD-MHAD-Kinect V2). Experimental results show that the proposed MLSL model obtains new state-of-the-art results, including achieving the average rate of the MSR Action3D to 93.55%, the average rate of the UTD-MHAD to 88.37% and the average rate of the UTD-MHAD-Kinect V2 to 90.66%.

The traditional face alignment approaches based on cascade regression have achieved satisfactory result on the frontal face, but for the face with large changes in posture and expression, a single initial shape will lead to the result falling into local optimum. In order to solve this problem, a two‐stage cascade regression model for face alignment is proposed, which generates coarse initial shape from the aligned salient shape. The first stage is used to align the salient shape that contains some prominent landmarks. To enhance the robustness of authors' method, the fusion subspace is used to divide the samples, and each subset trains cascade regression model separately. The alignment results of the first stage are used to generate the coarse initial shapes for the second stage through 3D fitting. The second stage is still based on cascade regression, which is used to further predict the full shape. The experimental results demonstrate the proposed method can achieve state‐of‐art performance, especially in unconstrained conditions with various poses.