Marte A. Ramírez-Ortegón

An optimization algorithm inspired by the States of Matter that improves the balance between exploration and exploitation

The ability of an Evolutionary Algorithm (EA) to find a global optimal solution depends on its capacity to find a good rate between exploitation of found-so-far elements and exploration of the search space. Inspired by natural phenomena, researchers have developed many successful evolutionary algorithms which, at original versions, define operators that mimic the way nature solves complex problems, with no actual consideration of the exploration-exploitation balance. In this paper, a novel nature-inspired algorithm called the States of Matter Search (SMS) is introduced. The SMS algorithm is based on the simulation of the states of matter phenomenon. In SMS, individuals emulate molecules which interact to each other by using evolutionary operations which are based on the physical principles of the thermal-energy motion mechanism. The algorithm is devised by considering each state of matter at one different exploration–exploitation ratio. The evolutionary process is divided into three phases which emulate the three states of matter: gas, liquid and solid. In each state, molecules (individuals) exhibit different movement capacities. Beginning from the gas state (pure exploration), the algorithm modifies the intensities of exploration and exploitation until the solid state (pure exploitation) is reached. As a result, the approach can substantially improve the balance between exploration–exploitation, yet preserving the good search capabilities of an evolutionary approach. To illustrate the proficiency and robustness of the proposed algorithm, it is compared to other well-known evolutionary methods including novel variants that incorporate diversity preservation schemes. The comparison examines several standard benchmark functions which are commonly considered within the EA field. Experimental results show that the proposed method achieves a good performance in comparison to its counterparts as a consequence of its better exploration–exploitation balance.

Circle detection using discrete differential evolution optimization

This paper introduces a circle detection method based on differential evolution (DE) optimization. Just as circle detection has been lately considered as a fundamental component for many computer vision algorithms, DE has evolved as a successful heuristic method for solving complex optimization problems, still keeping a simple structure and an easy implementation. It has also shown advantageous convergence properties and remarkable robustness. The detection process is considered similar to a combinational optimization problem. The algorithm uses the combination of three edge points as parameters to determine circle candidates in the scene yielding a reduction of the search space. The objective function determines if some circle candidates are actually present in the image. This paper focuses particularly on one DE-based algorithm known as the discrete differential evolution (DDE), which eventually has shown better results than the original DE in particular for solving combinatorial problems. In the DDE, suitable conversion routines are incorporated into the DE, aiming to operate from integer values to real values and then getting integer values back, following the crossover operation. The final algorithm is a fast circle detector that locates circles with sub-pixel accuracy even considering complicated conditions and noisy images. Experimental results on several synthetic and natural images with varying range of complexity validate the efficiency of the proposed technique considering accuracy, speed, and robustness.

Unsupervised Evaluation Methods Based on Local Gray-Intensity Variances for Binarization of Historical Documents

We attempt to evaluate the efficacy of six unsupervised evaluation method to tune Sauvolas threshold in optical character recognition (OCR) applications. We propose local implementations of well-known measures based on gray-intensity variances. Additionally, we derive four new measures from them using the unbiased variance estimator and gray-intensity logarithms. In our experiment, we selected the well binarized images, according each measure, and computed the accuracy of the recognized text of each. The results show that the weighted and uniform variance (using logarithms) are suitable measures for OCR applications.

Transition Thresholds for Binarization of Historical Documents

This paper extends the transition method for binarization based on transition pixels, a generalization of edge pixels. This method originally computes transition thresholds using the quantile thresholding algorithm, that has a critical parameter. We achieved an automatic version of the transition method by computing the transition thresholds with the Rosins algorithm. We experimentally tested four variants of the transition method combining the density and cumulative distribution functions of transition values, with gray-intensity thresholds based on the normal and lognormal density functions. The results of our experiments show that these unsupervised methods yields superior binarization compared with top-ranked algorithms.

POLYNOMIAL TRAJECTORY ALGORITHM FOR A BIPED ROBOT

Building trajectories for biped robot walking is a complex task considering all degrees of freedom (DOFs) commonly bound within the mechanical structure. A typical problem for such robots is the instability produced by violent transitions between walking phases in particular when a swinging leg impacts the surface. Although extensive research on novel efficient walking algorithms has been conducted, falls commonly appear as the walking speed increases or as the terrain condition changes. This paper presents a polynomial trajectory generation algorithm (PTA) to implement the walking on biped robots following the cubic Hermitian polynomial interpolation between initial and final conditions. The proposed algorithm allows smooth transitions between walking phases, significantly reducing the possibility of falling. The algorithm has been successfully tested by generating walking trajectories under different terrain conditions on a biped robot of 10 DOFs. PTA has shown to be simple and suitable to generate real time walking trajectories, despite reduced computing resources of a commercial embedded microcontroller. Experimental evidence and comparisons to other state-of-the-art methods demonstrates a better performance of the proposed method in generating walking trajectories under different ground conditions.

A model for the gray-intensity distribution of historical handwritten documents and its application for binarization

In this article, our goal is to describe mathematically and experimentally the gray-intensity distributions of the fore- and background of handwritten historical documents. We propose a local pixel model to explain the observed asymmetrical gray-intensity histograms of the fore- and background. Our pixel model states that, locally, the gray-intensity histogram is the mixture of gray-intensity distributions of three pixel classes. Following our model, we empirically describe the smoothness of the background for different types of images. We show that our model has potential application in binarization. Assuming that the parameters of the gray-intensity distributions are correctly estimated, we show that thresholding methods based on mixtures of lognormal distributions outperform thresholding methods based on mixtures of normal distributions. Our model is supported with experimental tests that are conducted with extracted images from DIBCO 2009 and H-DIBCO 2010 benchmarks. We also report results for all four DIBCO benchmarks.

An Objective Method to Evaluate Stroke-Width Measures for Binarized Documents

In this article, we propose an objective method to evaluate stroke-width measures. With this aim, we discuss the relevance of features based on the stroke width for document analysis. Then, we point out that most of the consulted references have a vague definition of stroke width. Because of this, we propose a formal definition of the stroke-width and remark the linearity of the stroke-width as an important property. Inspired by these ideas, we propose a measure together with a dataset to evaluate the linearity of the measurements of the stroke width and conduct an evaluation for seven well-known stroke-width methods. Our experiments have interesting results, like the fact that the most popular method is the one with the worst performance and that the best method is the easiest to implement. We hope that our objective evaluation assists further authors to choose suitable stroke-width methods for their applications.

Hands-on experiments on intelligent behavior for mobile robots

In recent years, Artificial Intelligence (AI) techniques have emerged as useful non-traditional tools for solving various engineering problems. AI has thus become an important subject in the engineering curriculum. However, the design of a balanced AI course is not a trivial task as its concepts commonly overlap with many other disciplines relating a wide number of subjects and ranging from applied approaches to more formal mathematical issues. This paper presents the use of a simple robotic platform to assist the learning of basic AI concepts. The study is guided by simple experimentation on autonomous mobile robots. The presented material has been successfully tested as an AI teaching aid in the University of Guadalajaras robotics group, yielding motivation to students, increasing enrolment and retention on robotics-related courses and eventually contributing to the development of competent computer engineers.

ROBUST FUZZY CORNER DETECTOR

Abstract Reliable corner detection is an important task in detemvning the shape of different regions within an image. Real-life image data are always imprecise due to inherent uncertainties that may arise from the imaging process such as defocusing, illumination changes, noise, eta Therefore, the localization and detection of corners has become a difficult task to accomplish under such imperfect situations. On the other hand, Fuzzy systems are well known for their efficient handling of impreciseness and incompleteness, which make them inherently suitable for modelling corner properties by means of a rule-based fuzzy system. The paper presents a corner detection algorithm which employs such fuzzy reasoning. The robustness of the proposed algorithm is compared to well-known conventional corner detectors and its performance is also tested over a number of benchmark images to illustrate the efficiency of the algorithm under uncertainty.