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Growing numbers of aging people result in great demand for social robots and virtual humans to support health care and independent life for a long time at home. We provide an overview of various actual potential applications at home in the literature concerning social robots and virtual humans. In addition, we report our latest research progress in our institute, i.e., Social communication, falling-down...
In this paper we propose a robust sparse based visual tracking method by exploiting local representations in a particle filter framework. We construct a Multi-level Local Dictionary which consists of positive templates and negative templates for discriminative model, Which divide the positive and negative dictionary into two levels called static templates and dynamic templates, respectively, thus...
Object recognition is a wide applied task in computer vision. Many fine-grained object recognition approaches are proposed in recent years to detect the same species objects effectively at subordinate-level. In this paper, we present a novel fine-grained vehicle recognition by utilizing collaborative feedback scheme of detection-classification-tracking in surveillance video. We collect a labeled data...
Although a lot of works have been done in the domain of hand tracking, it is still a challenge to robustly tracking hand motion. Traditional Cam shift algorithm which can efficiently tracking object in a simple scene is sensitive to the changing of background, other variant such as Camshift&Kalman tracking is still not robust enough to give a reliable result. This paper proposes a real-time...
In recent years, Sparse Representation based classification (SRC) has made great progress in Face Recognition. However, SRC is only efficient and effective when the noise is sparse. The recognition rate of SRC decreases when the noise is non-Gaussian, for example, the light on the face is quite various or the face is covered in part by a mask. In this paper, we propose a robust l2,1-norm Sparse Representation...
Principal component analysis (PCA) is an effective statistical technique for face recognition because it can reduce the dimensions of a given unlabeled high-dimensional dataset while keeping its spatial characteristics as much as possible. However, since PCA only explains the covariance structure of all the data its most expressive components, it cannot represent the most important discriminant directions...
Human face recognition technology is one of the hottest research in the field of pattern recognition at present. In this paper, the principle component analysis (PCA) and bidirectional principle component analysis (BDPCA) methods are proposed to recognize a grayscale face image, for which the size of the spatial distribution is 64 × 64. At first, the main part of the face is extracted to form the...
Most edge detection methods are based on first-order or second-order differential. These are local methods. Using Hausdorff distance to quantify the strength of the edge is a method with a holistic property. Firstly, down sample the image, and split the image into two sets. Secondly, get the feature image by assigning a value for each point using the scalar field map constructed by Hausdorff distance...
Connected components of edge pixels are valuable for many applications, e.g., Image segmentation, contour extraction, and text detection. However, a connected component of edge pixels may not form a simple curve due to the existence of bifurcation pixels, and thus limits its application in shape modeling. In this paper, we propose an approach to remove bifurcation pixels in an edge image, which let...
Concave region partitioning is valuable for object modeling and recognition. A key issue to the design of an efficient partitioning method is on the selection of cut points. In this paper, we propose to use imbalanced points in a region to characterize cut points in the contour of the region, motivated by the good corner nature of imbalanced points. Specifically, we formulate a concave region as a...
Semantic feature extraction technique for human bodies is essential for some application, such as reverse engineering, mannequin dimensions extraction and parametric design of mannequins. This paper present a method on 2D orthographic projection to extract the semantic feature of 3D human model. Firstly, we get the human models orthographic projection and use a new intelligent contour tracking algorithm...
In this paper we proposed a dictionary learning and dimensionality reduction (DLDR) scheme for image steganalysis. We construct a structural discriminative dictionary which is learned from the reduced dimension space and exploit the discriminative information in stego-images. Simulation results verify the effectiveness of the proposed approach and the performance is considerable. Both the dictionary...
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