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Traditional nonlinear manifold learning methods have achieved great success in dimensionality reduction and feature extraction, most of which are batch modes. However, if new samples are observed, the batch methods need to be calculated repeatedly, which is computationally intensive, especially when the number or dimension of the input samples are large.(More)
Detecting pedestrians in images and videos plays a critically important role in many computer vision applications. Extraction of effective features is the key to this task. Promising features should be discriminative, robust to various variations and easy to compute. In this work, we presents a novel feature, termed pyramid center-symmetric local(More)
—Accurately detecting pedestrians in images plays a critically important role in many computer vision applications. Extraction of effective features is the key to this task. Promising features should be discriminative, robust to various variations and easy to compute. In this work, we present novel features, termed dense center-symmetric local binary(More)
The quality of motion-blurred image restoration is highly dependent on the blur direction identification, which is an important and difficult problem in image restoration. In frequency domain, we summarize four important characteristics of motion-blurred image, and then propose a method to reduce the impact of deficiencies in frequency characteristics by(More)
BACKGROUND USPIO is a contrast agent for MRI that can generate T2W images with low signal intensities. After subcutaneous or intravenous injection of USPIO, normal lymph node tissues uptake these nano-particles, but tumor cells do not. Thus, tumor metastasis can be detected using this contrast agent. OBJECTIVE The aim of this study was to access the(More)
The mean shift algorithm has achieved considerable success in target tracking due to its simplicity and robustness. However, the lack of spatial information may result in its failure to get high tracking precision. This might be even worse when the target is scale variant and the sequences are gray-levels. This paper presents a novel multiple subtemplates(More)
In this paper, we are interested in the problem of motion-blurred image blind restoration. A new method for this ill-posed problem is proposed. We present an adaptive Huber Markov Random Field (HMRF) image prior model as the regularization term, which can be suitable for motion-blurred situation, then turn the ill-posed problem to well-posed. It can(More)