João Rogério Caldas Pinto

Uncalibrated Eye-to-Hand Visual Servoing Using Inverse Fuzzy Models

A new uncalibrated eye-to-hand visual servoing based on inverse fuzzy modeling is proposed in this paper. In classical visual servoing, the Jacobian plays a decisive role in the convergence of the controller, as its analytical model depends on the selected image features. This Jacobian must also be inverted online. Fuzzy modeling is applied to obtain an inverse model of the mapping between image feature variations and joint velocities. This approach is independent from the robots kinematic model or camera calibration and also avoids the necessity of inverting the Jacobian online. An inverse model is identified for the robot workspace, using measurement data of a robotic manipulator. This inverse model is directly used as a controller. The inverse fuzzy control scheme is applied to a robotic manipulator performing visual servoing for random positioning in the robot workspace. The obtained experimental results show the effectiveness of the proposed control scheme. The fuzzy controller can position the robotic manipulator at any point in the workspace with better accuracy than the classic visual servoing approach.

A new graph-like classification method applied to ancient handwritten musical symbols

Abstract.Several algorithms have been proposed in the past to solve the problem of binary pattern recognition. The problem of finding features that clearly distinguish two or more different patterns is a key issue in the design of such algorithms. In this paper, a graph-like recognition process is proposed that combines a number of different classifiers to simplify the type of features and classifiers used in each classification step. The graph-like classification method is applied to ancient music optical recogniti on, and a high degree of accuracy has been achieved.

Ancient document recognition Using Fuzzy Methods

This paper proposes an optical character recognition system based on fuzzy logic for ancient printed documents. The recognition process consists of two stages: training with collected character image examples and classification of new character images. The proposed OCR builds fuzzy membership functions from oriented features extracted using Gabor filter banks. Results on a significant test led to a character recognition success rate of 88%

Intelligent real-time fabric defect detection

This paper presents real-time fabric defect detection based in intelligent techniques. Neural networks (NN), fuzzy modeling (FM) based on productspace fuzzy clustering and adaptive network based fuzzy inference system (ANFIS) were used to obtain a clearly classification for defect detection. Their implementation requires thresholding its output, and based in previous studies a confusion matrix based optimization is used to obtain the threshold. Experimental results for real fabric defect detection were obtained from the experimental apparatus presented in the paper, that showed the usefulness of the three intelligent techniques, although the NN has a faster performance. Online implementation of the algorithms showed they can be easily implemented with commonly available resources and may be adapted to industrial applications without great effort.

NN automated defect detection based on optimized thresholding

This paper presents a new contribution for the problem of automatic visual inspection. New methods for determining threshold values for fabric defect detection using feedforward neural networks are proposed. Neural networks are one of the fastest most flexible classification systems in use. Their implementation in defect detection, where a clear classification is needed, requires thresholding the output. Two methods are proposed for threshold selection, statistical analysis of the NN output and confusion matrix based optimization. Experimental results obtained from the real fabric defects, for the two approaches proposed in this paper, have confirmed their usefulness.

Visual Based Predictive Control for a Six Degrees of Freedom Robot

A visual servoing architecture for a six degrees of freedom PUMA robot, using predictive control, is presented. Two different approaches, GPC and MPC, are used. A comparison between these two ones and the classical PI controller is performed. The implemented PUMA robot model simulator used as platform for the development of the control algorithms is presented. A control law based on features extracted from camera images is used. Simulation results show that the proposed strategies provide an efficient control system and that visual servoing architectures using predictive control are faster than those using PI control. Experimental results are obtained from an architecture using a XPC Target and Matlab Simulink. Through this technology is possible to create an operative system which allows working in real time robot control.

Combining fuzzy clustering and morphological methods for old documents recovery

In this paper we tackle the specific problem of old documents recovery. Spots, print through, underlines and others ageing features are undesirable not only because they harm the visual appearance of the document, but also because they affect future Optical Character Recognition (OCR). This paper proposes a new method integrating fuzzy clustering of color properties of original images and mathematical morphology. We will show that this technique leads to higher quality of the recovered images and, at the same time, it delivers cleaned binary text for OCR applications. The proposed method was applied to books of XIX Century, which were cleaned in a very effective way.

Comparison of Intelligent Classification Techniques Applied to Marble Classification

Automatic marbles classification based on their visual appearance is an important industrial issue. However, there is no definitive solution to the problem, mainly due to the presence of randomly distributed high number of different colors and due to the subjective evaluation made by human experts. In this paper, we present a study of soft computing classification algorithms, which proved to be a valuable tool to be applied in this type of problems. Fuzzy, neural, simulated annealing, genetic and combinations of these approaches are compared. Color and vein classification of marbles are compared. The combination of fuzzy classifiers optimized by genetic algorithms revealed to be the best classifier for this application.

Ant based fuzzy modeling applied to marble classification

Automatic classification of objects based on their visual appearance is often performed based on clustering algorithms, which can be based on soft computing techniques. One of the most used methods is fuzzy clustering. However, this method can converge to local minima. This problem has been addressed very recently by applying ant colony optimization to tackle this problem. This paper proposed the use of this fuzzy-ant clustering approach to derive fuzzy models. These models are used to classify marbles based on their visual appearance; color and vein classification is performed. The proposed fuzzy modeling approach is compared to other soft computing classification algorithms, namely: fuzzy, neural, simulated annealing, genetic and combinations of these approaches. Fuzzy-ant models presented higher classification rates than the other soft computing techniques.

An experimental testbed for visual servo control of robotic manipulators

This paper presents an experimental testbed for visual servo control of robotic manipulators. The testbeds flexibility in implementing visual servoing algorithms is tested under image-based visual servoing with P, PD, PI and PDI controllers, when using redundant and non-redundant visual features. A vision system and a 2 degrees of freedom planar robotic manipulator compose the testbed, where the visual feedback loop operates at 50 Hz. The eye-in-hand camera configuration is used, with the camera looking up to a planar target. Also tackled in this work are the correspondence problem and the 50 Hz image acquisition and processing. Experimental results are presented to show the behavior of image-based visual servoing with the different controllers.