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Advances in computer vision and image processing technology have led to great success in image recognition when the images are clear. However, in real-world applications, images are often blurred due to factors such as atmospheric turbulence, object-camera relative motion, and focus. This imposes a great challenge on practical image recognition tasks. To improve the performance of burred image recognition,...
Image blurring significantly degrades the image recognition performance. In this paper, we novelly address the blurred image recognition task from the perspective of domain adaptation (DA). The scenario is that, the training set (source domain) only comprises of the labelled clear images, and the test set (target domain) is composed of the unlabelled blurred images. DA is executed to eliminate the...
Blurred image recognition is still remaining as a challenging task, while with the wide applications. One principal way for solving this problem is to extract the blur-invariant visual descriptor. To this end, local phase quantization (LPQ) was ever proposed, and achieved promising results. In this paper, to further enhance LPQ's performance, we propose to apply Fisher Vector (FV) encoding approach...
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