POSE ESTIMATION FOR ROBOTIC DISASSEMBLY USING RANSAC WITH LINE FEATURES

In this thesis, a new technique to recognize and estimate the pose of a given 3-D object from a single real image provided known prior knowledge of its approximate structure is proposed. Metrics to evaluate the correctness of a calculated pose are presented and analyzed. The traditional and the more recent approaches used in solving this problem are explored and the various methodologies adopted are discussed. The first step in disassembling a given assembly from its image is to recognize the attitude and translation of each of its constituent components - a fundamental problem which is being addressed in this work. The proposed algorithm does not depend on uniquely identifiable 3D model surface features for its operation - this makes it ideally suited for object recognition for assemblies. The algorithm works well even for low-resolution occluded object images taken under variable illumination conditions and heavy shadows and performs markedly better when these factors are removed. The algorithm uses a combination of various computer vision concepts such as segmentation, corner detection and camera calibration, and subsequently adopts a line-based object pose estimation technique (originally based on the RANSAC algorithm) to settle on the best pose estimate. The novelty of the proposed technique lies in the specific way in which the poses are evaluated in the RANSAC-like algorithm. In particular, line-based pose evaluation is adopted where the line chamfer image is used to evaluate the error distance between the projected model line and the image edges. The correctness of the computed pose is determined based on the number of line matches computed using this error distance. As opposed to the RANSAC algorithm where the search process is pseudo-random, we do an exhaustive pose search instead. Techniques to reduce the search space by a large amount are discussed and implemented. The algorithm was used to estimate the pose of 28 objects in 22 images, where some images contain multiple objects. The algorithm has been found to work with a 3-D mismatch error of less than 2.5cm in 90% of the cases and less than 1cm error in 53% of the cases in the dataset used

Weed Classification for Site-Specific Weed Management Using an Automated Stereo Computer-Vision Machine-Learning System in Rice Fields

Producción CientíficaSite-specific weed management and selective application of herbicides as eco-friendly techniques are still challenging tasks to perform, especially for densely cultivated crops, such as rice. This study is aimed at developing a stereo vision system for distinguishing between rice plants and weeds and further discriminating two types of weeds in a rice field by using artificial neural networks (ANNs) and two metaheuristic algorithms. For this purpose, stereo videos were recorded across the rice field and different channels were extracted and decomposed into the constituent frames. Next, upon pre-processing and segmentation of the frames, green plants were extracted out of the background. For accurate discrimination of the rice and weeds, a total of 302 color, shape, and texture features were identified. Two metaheuristic algorithms, namely particle swarm optimization (PSO) and the bee algorithm (BA), were used to optimize the neural network for selecting the most effective features and classifying different types of weeds, respectively. Comparing the proposed classification method with the K-nearest neighbors (KNN) classifier, it was found that the proposed ANN-BA classifier reached accuracies of 88.74% and 87.96% for right and left channels, respectively, over the test set. Taking into account either the arithmetic or the geometric means as the basis, the accuracies were increased up to 92.02% and 90.7%, respectively, over the test set. On the other hand, the KNN suffered from more cases of misclassification, as compared to the proposed ANN-BA classifier, generating an overall accuracy of 76.62% and 85.59% for the classification of the right and left channel data, respectively, and 85.84% and 84.07% for the arithmetic and geometric mean values, respectively

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Ensembles of segmentation algorithms for figure-ground segmentation.

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Ensembles of segmentation algorithms for figure-ground segmentation.

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