Ma. de Guadalupe García-Hernández

Image enhancement using Bi-Histogram Equalization with adaptive sigmoid functions

Among the contrast-enhancement methods, histogram equalization is the most popular. However, its major drawback is that it over-enhances the image and shifts its mean brightness and, consequently, it creates an unnatural look. In this paper, we propose a method that overcomes this problem by splitting the image histogram into two sub-histograms, using the mean as a threshold, and replacing their cumulative distribution functions with two smooth sigmoids with their origins placed on the median of the sub-histograms. Our method has been tested on gray scale images taken from the USC-SIPI database. Experimental results have shown that the proposed method outperforms other state-of-the-art methods in terms of contrast-enhancement and brightness-preservation.

Automatic Image Segmentation Using Active Contours with Univariate Marginal Distribution

This paper presents a novel automatic image segmentation method based on the theory of active contour models and estimation of distribution algorithms. The proposed method uses the univariate marginal distribution model to infer statistical dependencies between the control points on different active contours. These contours have been generated through an alignment process of reference shape priors, in order to increase the exploration and exploitation capabilities regarding different interactive segmentation techniques. This proposed method is applied in the segmentation of the hollow core in microscopic images of photonic crystal fibers and it is also used to segment the human heart and ventricular areas from datasets of computed tomography and magnetic resonance images, respectively. Moreover, to evaluate the performance of the medical image segmentations compared to regions outlined by experts, a set of similarity measures has been adopted. The experimental results suggest that the proposed image segmentation method outperforms the traditional active contour model and the interactive Tseng method in terms of segmentation accuracy and stability.

Unsupervised cardiac image segmentation via multiswarm active contours with a shape prior.

This paper presents a new unsupervised image segmentation method based on particle swarm optimization and scaled active contours with shape prior. The proposed method uses particle swarm optimization over a polar coordinate system to perform the segmentation task, increasing the searching capability on medical images with respect to different interactive segmentation techniques. This method is used to segment the human heart and ventricular areas from datasets of computed tomography and magnetic resonance images, where the shape prior is acquired by cardiologists, and it is utilized as the initial active contour. Moreover, to assess the performance of the cardiac medical image segmentations obtained by the proposed method and by the interactive techniques regarding the regions delineated by experts, a set of validation metrics has been adopted. The experimental results are promising and suggest that the proposed method is capable of segmenting human heart and ventricular areas accurately, which can significantly help cardiologists in clinical decision support.

New prioritized value iteration for Markov decision processes

The problem of solving large Markov decision processes accurately and quickly is challenging. Since the computational effort incurred is considerable, current research focuses on finding superior acceleration techniques. For instance, the convergence properties of current solution methods depend, to a great extent, on the order of backup operations. On one hand, algorithms such as topological sorting are able to find good orderings but their overhead is usually high. On the other hand, shortest path methods, such as Dijkstra’s algorithm which is based on priority queues, have been applied successfully to the solution of deterministic shortest-path Markov decision processes. Here, we propose an improved value iteration algorithm based on Dijkstra’s algorithm for solving shortest path Markov decision processes. The experimental results on a stochastic shortest-path problem show the feasibility of our approach.

Adaptive sigmoid function bihistogram equalization for image contrast enhancement

Abstract. Contrast enhancement plays a key role in a wide range of applications including consumer electronic applications, such as video surveillance, digital cameras, and televisions. The main goal of contrast enhancement is to increase the quality of images. However, most state-of-the-art methods induce different types of distortion such as intensity shift, wash-out, noise, intensity burn-out, and intensity saturation. In addition, in consumer electronics, simple and fast methods are required in order to be implemented in real time. A bihistogram equalization method based on adaptive sigmoid functions is proposed. It consists of splitting the image histogram into two parts that are equalized independently by using adaptive sigmoid functions. In order to preserve the mean brightness of the input image, the parameter of the sigmoid functions is chosen to minimize the absolute mean brightness metric. Experiments on the Berkeley database have shown that the proposed method improves the quality of images and preserves their mean brightness. An application to improve the colorfulness of images is also presented.

Reduction of temporal complexity in Markov decision processes

In this paper we propose a new algorithm in order to reduce temporal complexity in Markov decision processes. Value iteration is a classical algorithm for solving them, but this algorithm and its variants are quite slow for discount factors close to one and their convergence properties depend to a great extent on a good state update order. It has been shown that improved topological value iteration presents a good convergence speed thanks to the use of an improved topological ordering. Nevertheless, its drawback is due to high memory requirements. So, our algorithm obtains the optimal state backup order with less memory requirements. Experimental results on stochastic shortest-path problems (highly cyclic) are presented. Our approach obtained a considerable reduction in temporal complexity with respect to other variants of value iteration. For instance, the experiments showed in one test a reduction of six times with respect to asynchronous value iteration.

Combination of acceleration procedures for solving stochastic shortest-path Markov decision processes

In this paper we propose the combination of accelerated variants of value iteration with improved prioritized sweeping for the solution of stochastic shortest path Markov decision processes. For the fastest solution, asynchronous updates, prioritization and prioritized sweeping have been tested. A topological reordering algorithm was also compared with a static reordering algorithm. Experimental results obtained on afinite state and action-space stochastic shortest path problem are presented.

An integrated and flexible architecture for planning and scheduling

In this paper we propose an integrated architecture to cope with real-world problems of planning and scheduling. Our approach consists of two specialised processes, one for planning and one for scheduling, which work together in a strong-coupled manner to deal with complex problems that involve time and resource constraints. Besides describing the elements of this approach, we also present its functional behaviour together with an example to show its applicability to real problems.

A Hybrid Method to Design Wire Antennas: Design and optimization of antennas using artificial intelligence.

Antenna design involves a set of complex requirements that may conflict. Some of these requirements are frequently expressed in terms of antenna parameters, such as radiation pattern, voltage standing wave ratio (VSWR) or impedance. In general, antenna requirements are specified for a frequency band or a set of frequencies. In some cases, the designer may create an antenna draft by trial and error until some performance goals are reached. Occasionally, this process may be simple, but in others, the number of variables makes it difficult to easily meet all of the goals. Alternatively, antenna design can be seen as an intricate error surface in an N-dimensional space. In this space, moving the solution over its surface in one direction may be good for some of the goals, but bad for others. Regularly, this error surface is difficult to travel through for most optimization algorithms. In previous works, genetic algorithms (GAs) have been used to assist the designer in this complicated process. This article proposes a hybrid method using artificial intelligence to design and optimize antennas. First, simulated annealing (SA) is used to draft the antenna. Second, the draft is improved until most of the performance goals are achieved.

Human Heart Segmentation Based on Differential Evolution and Active Contours with Shape Prior

Active contour model is an image segmentation technique that uses the evaluation of internal and external forces to be attracted towards the edge of a target object. In this paper a novel image segmentation method based on differential evolution and active contours with shape prior is introduced. In the proposed method, the initial active contours have been generated through an alignment process of reference shape priors, and differential evolution is used to perform the segmentation task over a polar coordinate system. This method is applied in the segmentation of the human heart from datasets of Computed Tomography images. To assess the segmentation results compared to those outlined by experts and by different segmentation techniques, a set of similarity measures has been adopted. The experimental results suggest that by using differential evolution, the proposed method outperforms the classical active contour model and the interactive Tseng method in terms of efficiency and segmentation accuracy.