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The success of many computer vision tasks lies in the ability to exploit the interdependency between different image modalities such as intensity and depth. Fusing corresponding information can be achieved on several levels, and one promising approach is the integration at a low level. Moreover, sparse signal models have successfully been used in many vision applications. Within this area of research, the so-called co-sparse analysis model has attracted considerably less attention than its well-known counterpart, the sparse synthesis model, although it has been proven to be very useful in various image processing applications. In this paper, we propose a bimodal co-sparse analysis model that is able to capture the interdependency of two image modalities. It is based on the assumption that a pair of analysis operators exists, so that the co-supports of the corresponding bimodal image structures have a large overlap. We propose an algorithm that is able to learn such a coupled pair of operators from registered and noise-free training data. Furthermore, we explain how this model can be applied to solve linear inverse problems in image processing and how it can be used as a prior in bimodal image registration tasks. This paper extends the work of some of the authors by two major contributions. Firstly, a modification of the learning process is proposed that a priori guarantees unit norm and zero-mean of the rows of the operator. This accounts for the intuition that local texture carries the most important information in image modalities independent of brightness and contrast. Secondly, the model is used in a novel bimodal image registration algorithm, which estimates the transformation parameters of unregistered images of different modalities. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]