Three Dimensional Fast Exact Euclidean Distance (3D-FEED) Maps

In image and video analysis, distance maps are frequently used. They provide the (Euclidean) distance (ED) of background pixels to the nearest object pixel. Recently, the Fast Exact Euclidean Distance (FEED) transformation was launched. In this paper, we present the three dimensional (3D) version of FEED. 3D-FEED is compared with four other methods for a wide range of 3D test images. 3D-FEED proved to be twice as fast as the fastest algorithm available. Moreover, it provides true exact EDs, where other algorithms only approximate the ED. This unique algorithm makes the difference, especially there where time and precision are of importance

Video Surveillance using Distance Maps

Human vigilance is limited; hence, automatic motion and distance detection is one of the central issues in video surveillance. Hereby, many aspects are of importance, this paper specially addresses: efficiency, achieving real-time performance, accuracy, and robustness against various noise factors. To obtain fully controlled test environments, an artificial development center for robot navigation is introduced in which several parameters can be set (e.g., number of objects, trajectories and type and amount of noise). In the videos, for each following frame, movement of stationary objects is detected and pixels of moving objects are located from which moving objects are identified in a robust way. An Exact Euclidean Distance Map (E2DM) is utilized to determine accurately the distances between moving and stationary objects. Together with the determined distances between moving objects and the detected movement of stationary objects, this provides the input for detecting unwanted situations in the scene. Further, each intelligent object (e.g., a robot), is provided with its E2DM, allowing the object to plan its course of action. Timing results are specified for each program block of the processing chain for 20 different setups. So, the current paper presents extensive, experimentally controlled research on real-time, accurate, and robust motion detection for video surveillance, using E2DMs, which makes it a unique approach

Improving active mealy machine learning for protocol conformance testing

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