A new distance measure based on generalized Image Normalized Cross-Correlation for robust video tracking and image recognition

We propose two novel distance measures, normalized between 0 and 1, and based on Normalized Cross-Correlation for image matching. These distance measures explicitly utilize the fact that for natural images there is a high correlation between spatially close pixels. Image matching is used in various computer vision tasks, and the requirements to the distance measure are application dependent. Image recognition applications require more shift and rotation robust measures. In contrast, registration and tracking applications require better localization and noise tolerance. In this paper, we explore different advantages of our distance measures, and compare them to other popular measures, including Normalized Cross-Correlation (NCC) and Image Euclidean Distance (IMED). We show which of the proposed measures is more appropriate for tracking, and which is appropriate for image recognition tasks.

► Two novel distance measures, normalized between 0 and 1, for image matching.
► Advantage: Robustness of our distance measures to translation, scaling, and noise.
► Comparison to other popular measures.
► The first distance measure, IMNCC, is more appropriate for recognition tasks.
► The second distance measure, IMZNCC, is more appropriate for visual tracking.