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Automatic image colorization is one of the attractive research topics in image processing. The most crucial task in this field is how to design an algorithm to define appropriate color from the reference image(s) for propagating to the target image. In other words, we need to determine whether two pixels in reference and target images have similar color. In previous methods, many approaches have been introduced mostly based on local feature matching algorithms. However, they still have some defects as well as time-consuming. In this paper, we will present a novel automatic image colorization method based on Feature Lines. Feature Lines is our new concept, which enhances the concept of Color Lines. It represents the distribution of each pixel feature vector as being elongated around the lines so that we are able to assemble the similar feature pixels into one feature line. By introducing this new technique, pixel matching between reference and target images performs precisely. The experimental achievements show our proposed method achieves smoother, evener and more natural color assignment than the previous methods.

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3D object retrieval system is a system where a similar or the same object in the database should be retrieved given a 2D query image (sketches or photographs). Unfortunately, as the appearance of 3D object might vary depending on the viewing directions, a vast amount of 2D rendered images must be processed (matched) to solve this problem. In this paper, we present a novel method called skewness map to relieve this problem. Skewness map can estimate the orientation of the object and select a few representative images accurately from the database; therefore matching every image in the database can be avoided. Experimental results show the retrieval system becomes much faster (14 times faster in matching time) and accurate in estimating the object orientation (less than one-degree error in average).