Basic phenomenology of human color vision has been widely taken as an inspiration to devise explicit color correction algorithms. The behavior of these models in terms of significative image features (such as, e.g., contrast and dispersion) can be difficult to characterize. To cope with this, we propose to use a variational formulation of color contrast enhancement that is inspired by the basic phenomenology of color perception. In particular, we devise a set of basic requirements to be fulfilled by an energy to be considered as 'perceptually inspired', showing that there is an explicit class of functionals satisfying all of them. We single out three explicit functionals that we consider of basic interest, showing similarities and differences with existing models. The minima of such functionals is computed using a gradient descent approach. We also present a general methodology to reduce the computational cost of the algorithms under analysis from O(N2) to O(N logN), being N the number of pixels of the input image.

No abstract

Color cast cancellation and local contrast enhancement are very important problems in computer vision. In this paper we review the algorithm proposed by Palma-Amestoy et al. Source CodeThe code implements the algorithm presented by Palma-Amestoy et al. [7], with a precomputation of the polynomials. These polynomials are inputted from text files which are also provided.The reviewed source code and documentation for this algorithm are available from the web page of this article 1 . Compilation and usage instruction are included in the README.txt file of the archive. Supplementary MaterialAs supplementary material, a reference dataset is provided, and the Matlab code to generate the polynomial approximation, although it is not necessary to execute it to test the algorithm.

A real-time hybrid control architecture for biped humanoid robots is proposed. The architecture is modular and hierarchical. The main robot’s functionalities are organized in four parallel modules: perception, actuation, world-modeling, and hybrid control. Hybrid control is divided in three behavior-based hierarchical layers: the planning layer, the deliberative layer, and the reactive layer, which work in parallel and have very different response speeds and planning capabilities. The architecture allows: (1) the coordination of multiple robots and the execution of group behaviors without disturbing the robot’s reactivity and responsivity, which is very relevant for biped humanoid robots whose gait control requires real-time processing. (2) The straightforward management of the robot’s resources using resource multiplexers. (3) The integration of active vision mechanisms in the reactive layer under control of behavior-dependant value functions from the deliberative layer. This adds flexibility in the implementation of complex functionalities, such as the ones required for playing soccer in robot teams. The architecture is validated using simulated and real Nao humanoid robots. Passive and active behaviors are tested in simulated and real robot soccer setups. In addition, the ability to execute group behaviors in realtime is tested in international robot soccer competitions.This research was partially funded by FONDECYT (CHILE) under Project Number 1090250

No abstract