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.
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.
For information lost phenomenon on RS image, we present a robust completion algorithm based on a single image
instead of adopting image placement measure. In order to avoid the occurrence of visually inconsistent results caused by
greedy patch-by-patch manner, we creatively introduce GIS accessorial data to guide structure completion and pose this
task in the form of a discrete global optimization problem. In the process of implementing this optimization scheme, our
method integrates exemplar-based texture synthesis techniques and Dynamic programming algorithm. By this way,
Information Compensation is separated into two independent processes to deal with. Comparative Experiments is
conducted and prove the efficiency of our method successfully.
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