Swarm Intelligence uses a set of agents which are able to move and gather local information in a search space and utilize communication, limited memory, and intelligence for problem solving. In this work, we present an agent-based algorithm which is specifically tailored to detect contours in images. Following a novel movement and communication scheme, the agents are able to position themselves distributed over the entire image to cover all important image positions. To generate global contours, the agents examine the local windowed image information, and based on a set of fitness functions and via communicating with each other, they establish connections. Instead of a centralized paradigm, the global solution is discovered by some principal rules each agent is following. The algorithm is independent of object models or training steps. In our evaluation we focus on boundary detection as a major step towards image segmentation. We therefore evaluate our algorithm using the Berkeley Segmentation Dataset (BSDS) and compare its performance to existing methods via the BSDS benchmark and Pratt's Figure of Merit.
In this work, we use the principles of Swarm Intelligence to establish a novel algorithm for detecting and describing straight edges in images. The algorithm uses a set of individual mobile agents with limited cognitive possibilities. Using their memory and communication abilities, the agents can establish fast and robust solutions. The agents initially move randomly in a two dimensional space defined by an arbitrary input image or image sequence. In every time step, each agent calculates the derivative values in x and y direction at its current position and thresholds these values subsequently. If an agent discovers an edge or respectively a straight edge, it follows this straight edge and stores its start point. When it reaches the straight edge's end, it marks its last position as its stop point. As a kind of indirect communication between the agents, each of them leaves important information at each new position discovered. Thus each agent can benefit from the calculations any other agent has done before, which speeds up the algorithm. This new approach is a fast alternative to classical line finding operation like e.g. the Hough Transform.
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