A moving object detection method based on the dense velocity field detection followed by an unsupervised
spatiotemporal clustering by means of the mean shift algorithm is proposed. The method firstly makes use of a novel
imaging system and an algebraic solution to detect the extremely dense velocity vector distribution with a pixel-wise
spatial resolution and a frame-wise temporal resolution. It is based on the complex sinusoidal-modulated imaging with a
three-phase correlation image sensor (3PCIS) and an exact algebraic inversion method based on an equivalent weighted
integral form of the optical flow partial differential equation. Since the inversion method is free from time derivatives,
any limitations on the object velocity and inaccuracies due to approximated time derivatives are thoroughly avoided.
Secondly, in order to segregate dominant velocity regions from noisy background and identify the exact moving object,
the mean shift clustering method is employed to the spatially and temporarily dense velocity vector distribution. To
avoid too many cluster centers or over clustering, the traditional mean shift method is improved by setting an emerging
condition. An experimental system was constructed with a 320×256 pixel 3PCIS device and a standard PC for inversion
operations and display. Several experimental results are shown including an application to dense motion capture of face
and gesture and traffic scene, which including several independent moving objects.
In this paper, a new fault diagnosis approach for the photovoltaic array, which is based on infrared and visible image fusion technique, is proposed. Firstly, the temperature difference and infrared characteristics are analyzed, and then the features of both infrared image and visible image are extracted. By comparing the features of infrared image with those of visible image, the abnormal operating regions covered with something are distinguished. A fuzzy fault diagnosis approach is introduced and implemented for other regions detected in infrared image but none in visible image. Experimental results show that the proposed approach is feasible and effective.
In this paper, we present an image segmentation approach, which is based on the data fusion technique and divided into two steps. At first, we segment the image in a low-resolution to find the coarse contour of regions; then we use the fuzzy-c-means algorithm to process the image in a fine resolution to find its delicate characters. To avoid the large amount of calculation, the results of the first step is fused into the second step. Experimental results show that this technique is effective in improving the quality of segmentation and lessening the calculating time.