A Divide-and-conquer Approach to Contour Extraction and Invariant Features Analysis in Spatial Image Processing

This paper presents a novel divide-and-conquer method to analyze spatial information, such as geometric shapes, contours and trajectories extracted as a discrete sequence of points (or pixels) from images or spatial sensors, including GPS or transponders. The method extracts contour point sequences and then uses a scale invariant analysis to extract invariant arc features. The arc feature is a generalization of scale invariant corners used in many object recognition and image matching methods. The method considers detection of corner like features in the presence of slow curvature and sharp noise and discretization (spatial quantization), typical for images obtained by aerial photography, digital map scanning or other GIS image acquisition techniques. The resulting feature vectors can be used for stable and robust object feature analysis and object detection. The developed method is found to be capable of ignoring local sharp noise and detecting globally prevailing sharp features. Experimental analysis confirms the efficiency and robustness of this method using several difficult shapes with considerable noise and ambiguity. The method allows not only stable feature detection but also general shape analysis such as convexity, linearity and curvature.