Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image selective smoothing and edge detection by nonlinear diffusion
SIAM Journal on Numerical Analysis
International Journal of Computer Vision
Relations Between Regularization and Diffusion Filtering
Journal of Mathematical Imaging and Vision
Enhanced and Synthetic Vision, 1999
Enhanced and Synthetic Vision, 1999
IEEE Computer Graphics and Applications
Combination of Tangent Vectors and Local Representations for Handwritten Digit Recognition
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Efficient and reliable schemes for nonlinear diffusion filtering
IEEE Transactions on Image Processing
Fast natural color mapping for night-time imagery
Information Fusion
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A natural color mapping method has been previously proposed that matches the statistical properties (mean and standard deviation) of night-vision (NV) imagery to those of a daylight color image (manually selected as the ''target'' color distribution). Thus the rendered NV image appears to resemble the natural target image in terms of color appearance. However, in this prior method (termed ''global-coloring'') the colored NV image may appear unnatural if the target image's ''global'' color statistics are different from that of the night-vision scene (e.g., it would appear to have too much green if much more vegetation was contained in the target image). Consequently, a new ''local-coloring'' method is presented that functions to render the NV image segment-by-segment by taking advantage of image segmentation, pattern recognition, histogram matching and image fusion. Specifically, a false-color image (source image) is formed by assigning multi-band NV images to three RGB (red, green and blue) channels. A nonlinear diffusion filter is then applied to the false-colored image to reduce the number of colors. The final grayscale segments are obtained by using clustering and merging techniques. With a supervised nearest-neighbor paradigm, a segment can be automatically associated with a known ''color scheme''. The statistic-matching procedure is merged with the histogram-matching procedure to enhance the color mapping effect. Instead of extracting the color set from a single target image, the mean, standard deviation and histogram distribution of the color planes from a set of natural scene images are used as the target color properties for each color scheme. The target color schemes are grouped by their scene contents and colors such as plants, mountain, roads, sky, water, etc. In our experiments, five pairs of night-vision images were initially analyzed, and the images that were colored (segment-by-segment) by the proposed ''local-coloring'' method are shown to possess much more natural and realistic coloration when compared with those produced by the previous ''global-coloring'' method.