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For displaying high-dynamic-range images acquired by thermal camera systems, 14-bit raw infrared data should map into 8-bit gray values. This paper presents a new method for detail enhancement of infrared images to display the image with a relatively satisfied contrast and brightness, rich detail information, and no artifacts caused by the image processing. We first adopt a propagated image filter to smooth the input image and separate the image into the base layer and the detail layer. Then, we refine the base layer by using modified histogram projection for compressing. Meanwhile, the adaptive weights derived from the layer decomposition processing are used as the strict gain control for the detail layer. The final display result is obtained by recombining the two modified layers. Experimental results on both cooled and uncooled infrared data verify that the proposed method outperforms the method based on log-power histogram modification and bilateral filter-based detail enhancement in both detail enhancement and visual effect.

This work adopts sparse coding and spatial pyramid matching to classify the vehicle images. The targets of interest, vehicles in the images, are always degraded in the complex circumstance. Hence, it seems difficult to carry out the classification task by the methods combined gray feature and traditional classifiers. Considering the vehicle image without assignment and complex influence caused by weather, this paper proposes a vehicle classification method based on sparse coding and spatial pyramid matching. First, the proposed method extracts a patch-based sparse feature computed with a discriminate dictionary. With dualizing the sparse feature, the spatial pyramid model is employed to generate a long but sparse feature. At last, SVM with the histogram intersection kernel finishes the ultimate classification task. Diverse from the traditional bag of features model employed to compute the histogram in each level of the spatial pyramid, this paper codes the image patch with a fine learned and discriminate dictionary for a better representation than the gradient-based feature extraction. Fast iteration method on computing the sparse feature ensures the real-time need. Experimental results on the vehicle datasets includes sedan, taxi, van, and truck show the efficiency and accuracy of the proposed method for vehicle classification in practice.

Vehicle image classification can describe the visual vehicle with a semantically meaningful category directly. Motivated by its importance, this paper proposes a fast vehicle image classification based on binary coding. As for the vehicle image classification, this paper focuses on the image obtained from the video via analyzing the moving object near the key frames. The proposed method extracts a dense boosting binary feature computed with a boosted binary hash function, and then pools the features in different resolutions. At last, the SVM with spatial pyramid kernel fmishes the classification task. In this work, 8 bytes for the feature computed with a hash function that ensures the real time need. Experimental results on the vehicle datasets includes sedan, taxi, van, and truck show the efficiency and accuracy of the proposed method for vehicle classification in practice.Index Terms-binary coding, spatial pyramid matching, vehicle image classification.