arXiv subjects

Erik Cuevas

Publications and source records attributed to Erik Cuevas.

25 records · Page 2Linked to original sources

arXiv2014

Circle detection on images using Learning Automata

Circle detection over digital images has received considerable attention from the computer vision community over the last few years devoting a tremendous amount of research seeking for an optimal detector. This article presents an algorithm for the automatic detection of circular shapes from complicated and noisy images with no consideration of conventional Hough transform principles. The proposed algorithm is based on Learning Automata (LA) which is a probabilistic optimization method that explores an unknown random environment by progressively improving the performance via a reinforcement signal (objective function). The approach uses the encoding of three non-collinear points as a candidate circle over the edge image. A reinforcement signal (matching function) indicates if such candidate circles are actually present in the edge map. Guided by the values of such reinforcement signal, the probability set of the encoded candidate circles is modified through the LA algorithm so that they can fit to the actual circles on the edge map. Experimental results over several complex synthetic and natural images have validated the efficiency of the proposed technique regarding accuracy, speed and robustness.

cs.CV↗

arXiv2014

Robust Fuzzy corner detector

Reliable corner detection is an important task in determining 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, etc. 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.

cs.CV↗

arXiv2014

Fast algorithm for Multiple-Circle detection on images using Learning Automata

Hough transform (HT) has been the most common method for circle detection exhibiting robustness but adversely demanding a considerable computational load and large storage. Alternative approaches include heuristic methods that employ iterative optimization procedures for detecting multiple circles under the inconvenience that only one circle can be marked at each optimization cycle demanding a longer execution time. On the other hand, Learning Automata (LA) is a heuristic method to solve complex multi-modal optimization problems. Although LA converges to just one global minimum, the final probability distribution holds valuable information regarding other local minima which have emerged during the optimization process. The detection process is considered as a multi-modal optimization problem, allowing the detection of multiple circular shapes through only one optimization procedure. The algorithm uses a combination of three edge points as parameters to determine circles candidates. A reinforcement signal determines if such circle candidates are actually present at the image. Guided by the values of such reinforcement signal, the set of encoded candidate circles are evolved using the LA so that they can fit into actual circular shapes over the edge-only map of the image. The overall approach is a fast multiple-circle detector despite facing complicated conditions.

cs.CV↗

arXiv2014

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.

cs.AI↗

arXiv2014

Multi-ellipses detection on images inspired by collective animal behavior

This paper presents a novel and effective technique for extracting multiple ellipses from an image. The approach employs an evolutionary algorithm to mimic the way animals behave collectively assuming the overall detection process as a multi-modal optimization problem. In the algorithm, searcher agents emulate a group of animals that interact to each other using simple biological rules which are modeled as evolutionary operators. In turn, such operators are applied to each agent considering that the complete group has a memory to store optimal solutions (ellipses) seen so-far by applying a competition principle. The detector uses a combination of five edge points as parameters to determine ellipse candidates (possible solutions) while a matching function determines if such ellipse candidates are actually present in the image. Guided by the values of such matching functions, the set of encoded candidate ellipses are evolved through the evolutionary algorithm so that the best candidates can be fitted into the actual ellipses within the image. Just after the optimization process ends, an analysis over the embedded memory is executed in order to find the best obtained solution (the best ellipse) and significant local minima (remaining ellipses). Experimental results over several complex synthetic and natural images have validated the efficiency of the proposed technique regarding accuracy, speed and robustness.

cs.CV↗

arXiv2014

Opposition Based ElectromagnetismLike for Global Optimization

Electromagnetismlike Optimization (EMO) is a global optimization algorithm, particularly well suited to solve problems featuring nonlinear and multimodal cost functions. EMO employs searcher agents that emulate a population of charged particles which interact to each other according to electromagnetisms laws of attraction and repulsion. However, EMO usually requires a large number of iterations for a local search procedure; any reduction or cancelling over such number, critically perturb other issues such as convergence, exploration, population diversity and accuracy. This paper presents an enhanced EMO algorithm called OBEMO, which employs the Opposition-Based Learning (OBL) approach to accelerate the global convergence speed. OBL is a machine intelligence strategy which considers the current candidate solution and its opposite value at the same time, achieving a faster exploration of the search space. The proposed OBEMO method significantly reduces the required computational effort yet avoiding any detriment to the good search capabilities of the original EMO algorithm. Experiments are conducted over a comprehensive set of benchmark functions, showing that OBEMO obtains promising performance for most of the discussed test problems.

cs.AI↗

arXiv2014

Block matching algorithm based on Differential Evolution for motion estimation

Motion estimation is one of the major problems in developing video coding applications. Among all motion estimation approaches, Block matching (BM) algorithms are the most popular methods due to their effectiveness and simplicity for both software and hardware implementations. A BM approach assumes that the movement of pixels within a defined region of the current frame (Macro-Block, MB) can be modeled as a translation of pixels contained in the previous frame. In this procedure, the motion vector is obtained by minimizing the sum of absolute differences (SAD) produced by the MB of the current frame over a determined search window from the previous frame. The SAD evaluation is computationally expensive and represents the most consuming operation in the BM process. The most straightforward BM method is the full search algorithm (FSA) which finds the most accurate motion vector, calculating exhaustively the SAD values for all elements of the search window. Over this decade, several fast BM algorithms have been proposed to reduce the number of SAD operations by calculating only a fixed subset of search locations at the price of a poor accuracy. In this paper, a new algorithm based on Differential Evolution (DE) is proposed to reduce the number of search locations in the BM process. In order to avoid computing several search locations, the algorithm estimates the SAD values (fitness) for some locations using the SAD values of previously calculated neighboring positions. Since the proposed algorithm does not consider any fixed search pattern or other different assumption, a high probability for finding the true minimum (accurate motion vector) is expected. In comparison to other fast BM algorithms, the proposed method deploys more accurate motion vectors yet delivering competitive time rates.

cs.MM↗