Abstract :
In this paper, a continuous valued measure for local color symmetry is introduced. The new algorithm is an extension of the successful gray value-based symmetry map proposed by Reisfeld et al. The use of color facilitates the detection of focus points (FPs) on objects that are difficult to detect using gray-value contrast only. The detection of FPs is aimed at guiding the attention of an object recognition system; therefore, FPs have to fulfill three major requirements: stability, distinctiveness, and usability. The proposed algorithm is evaluated for these criteria and compared with the gray value-based symmetry measure and two other methods from the literature. Stability is tested against noise, object rotation, and variations of lighting. As a measure for the distinctiveness of FPs, the principal components of FP-centered windows are compared with those of windows at randomly chosen points on a large database of natural images. Finally, usability is evaluated in the context of an object recognition task.
Keywords :
image colour analysis; object detection; object recognition; stability; color symmetry; focus point detection; focus-of-attention; gray value based symmetry map; lighting noise; lighting object rotation; natural image database; object recognition system; stability; Focusing; Image databases; Image edge detection; Image processing; Nominations and elections; Object detection; Object recognition; Stability; Testing; Usability; Focus-of-attention; color vision; object recognition.; saliency maps; symmetry; Algorithms; Artificial Intelligence; Attention; Biomimetics; Color; Colorimetry; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Pattern Recognition, Automated; Visual Perception;
