In recent years, the Earth-observing satellites have obtained the ability to capture city-scale videos, which enable potential vehicle monitoring. Because of the broad field-of-view, the moving vehicles in satellite videos are very small, making it difficult to differentiate true objects from noise. This paper proposes a terse framework that can effectively suppress false targets while keeping a high detection ratio. The framework first applies the K-nearest neighbor (KNN) background subtraction model to produce preliminary detection results at high recall but with low accuracy, and then uses a shallow convolutional neural network (CNN) to eliminate false targets, increasing the detection accuracy. The experiments and evaluations demonstrate that our method can largely improve the accuracy at the expense of a slight reduction of recall.
Image segmentation is the technology that separating the image into several characteristic areas and is very important to
image analysis. In this paper, we propose a new segmentation method based on rival penalized controlled competitive
learning (RPCCL) and watershed transform. We apply watershed transform and RPCCL clustering algorithm separately
on input image, and then combine the two results of them. Compared to traditional watershed segmentation, our method
can avoid over-segmentation and obtain better results.
KEYWORDS: Image restoration, Magnetorheological finishing, Algorithms, Probability theory, Data modeling, Visual process modeling, Digital imaging, Image processing, Image filtering, Roads
The goal of image inpainting is to restore the damaged or missing pixels on images and it is an active research topic in
image engineering. In order to restore narrow gaps on damaged images, we propose a type of anisotropic inpainting
model based on Markov Random Fields. The inpainting model can preserve the edges and orientational texture. We
implement our method using Simulated Annealing algorithm. Experiments show that the proposed method can obtain
satisfying results and is practical in applications.
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