Dalong Li

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This paper describes an algorithm based on constrained variance maximization for the restoration of a blurred image. Blurring is a smoothing process by definition. Accordingly, the deblurring filter shall be able to perform as a high pass filter, which increases the variance. Therefore, we formulate a variance maximization object function for the(More)
Plan-view projection of real-time depth imagery can improve the statistics of its intrinsic 3D data, and allows for cleaner separation of occluding and closely-interacting people. We build a probabilistic, real-time multi-person tracking system upon a plan-view image substrate that well preserves both shape and size information of foreground objects. The(More)
Support vector regression has been proposed in a number of image processing tasks including blind image deconvolution, image denoising and single frame super-resolution. As for other machine learning methods, the training is slow. In this paper, we attempt to address this issue by reducing the feature dimensionality through Principal Component Analysis(More)
In this paper, we describe an algorithm for identifying a parametrically described blur based on kurtosis minimization. Using different choices for the parameters of the blur, the noisy blurred image is restored using Wiener filter. We use the kurtosis as a measurement of the quality of the restored image. From the set of the candidate deblurred images, the(More)
In this paper, an example-based image denoising algorithm is introduced. Image denoising is formulated as a regression problem, which is then solved using support vector regression (SVR). Using noisy images as training sets, SVR models are developed. The models can then be used to denoise different images corrupted by random noise at different levels.(More)
Since abnormal control chart patterns (CCPs) are indicators of production processes being out-of-control, it is a critical task to recognize these patterns effectively based on process measurements. Most methods on CCP recognition assume that the process data only suffers from single type of unnatural pattern. In reality, the observed process data could be(More)