Mario Luiz Tronco

A genetic programming based system for the automatic construction of image filters

The manual selection of linear and nonlinear operators for producing image filters is not a trivial task in practice, so new proposals that can automatically improve and speed up the process can be of great help. This paper presents a new proposal for constructing image filters using an evolutionary programming approach, which has been implemented as the IFbyGP software. IFbyGP employs a variation of the Genetic Programming algorithm GP and can be applied to binary and gray level image processing. A solution to an image processing problem is represented by IFbyGP as a set of morphological, convolution and logical operators. The method has a wide range of applications, encompassing pattern recognition, emulation filters, edge detection, and image segmentation. The algorithm works with a training set consisting of input images, goal images, and a basic set of instructions supplied by the user, which would be suitable for a given application. By making the choice of operators and operands involved in the process more flexible, IFbyGP searches for the most efficient operator sequence for a given image processing application. Results obtained so far are encouraging and they stress the feasibility of the proposal implemented by IFbyGP. Also, the basic language used by IFbyGP makes its solutions suitable to be directly used for hardware control, in a context of evolutionary hardware. Although the proposal implemented by IFbyGP is general enough for dealing with binary, gray level and color images, only applications using the first two are considered in this paper; as it will become clear in the text, IFbyGP aims at the direct use of induced sequences of operations by hardware devices. Several application examples discussing and comparing IFbyGP results with those obtained by other methods available in the literature are presented and discussed.

Cognitive-Merged Statistical Pattern Recognition Method for Image Processing in Mobile Robot Navigation

In this project, the main focus is to apply image processing techniques in computer vision to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained.

Automatic construction of image operators using a genetic programming approach

This paper presents a methodology for automatic construction of image operators using a linear genetic programming approach, for binary, gray level and color image processing, where the processing solution for a particular application is expressed in terms of the basic morphological operators, dilation and erosion, in conjunction with convolution and logical operators. Genetic Programming (GP), based on concepts of genetics and Darwins principle of natural selection, to genetically breed and evolve computer programs to solve a wide variety of problems, is a branch of evolutionary computation, and it is consolidating as a promising methodology to be used in applications involving pattern recognition, classification problems and modeling of complex systems. Mathematical morphology is based on the set theory (complete lattice), where the notion of order is very important. This processing technique has proved to be a powerful tool for many computer vision tasks. However, the manual design of complex operations involving image operators is not trivial in practice. Thus, the proposed methodology tries to solve these drawbacks. Some examples of applications are presented and the results are discussed and compared with other methods found in the literature.

ANN statistical image recognition method for computer vision in agricultural mobile robot navigation

The main application area in this project, is to deploy image processing and segmentation techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. Thereby, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for image recognition. Hence, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave computational platforms, along with the application of customized Back-propagation Multilayer Perceptron (MLP) algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of segmented images in which reasonably accurate results were obtained.

A Logical Approach to the Management of Object Identifiers in Non-conventional Database Management Systems

Non-conventional database management systems are used to achieve a better performance when dealing with complex data. One fundamental concept of these systems is object identity (OID), because each object in the database has a unique identifier that is used to access and reference it in relationships to other objects. Two approaches can be used for the implementation of OIDs: physical or logical OIDs. In order to manage complex data, was proposed the Multimedia Data Manager Kernel (NuGeM) that uses a logical technique, named Indirect Mapping. This paper proposes an improvement to the technique used by NuGeM, whose original contribution is management of OIDs with a fewer number of disc accesses and less processing, thus reducing management time from the pages and eliminating the problem with exhaustion of OIDs. Also, the technique presented here can be applied to others OODBMSs.

Managing Object Identity with Less I/O Operations

Non-conventional database management systems are used to achieve a better performance when dealing with complex data. One fundamental concept of these systems is object identity (OID). Two techniques can be used for the implementation of OIDs: physical or logical. A logical implementation of OIDs, based on an Indirection Table, is used by NuGeM, a multimedia data manager kernel which is described in this paper. NuGeM Indirection Table allows the relocation of all pages in a database. The proposed strategy modifies the workings of this table so that it is possible to reduce considerably the number of I/O operations during the request and release of pages containing objects and their OIDs. Tests show a reduction of 84% in reading operations and a 67% reduction in writing operations when pages are requested. Although no changes were observed in writing operations during the release of pages, a 100% of reduction in reading operations was obtained.

Computational support for the process of software requirement specification

The software industry has become more and more concerned with the appropriate application of activities that composes requirement engineering as a way to improve the quality of its products. In order to support these activities, several computational tools have been available in the market, although it is still possible to find a lack of resources related to some activities. In this context, this paper proposes the inclusion of a module to aid in the requirements specification to a tool called Requirements Elicitation Support Tool. This module allows to specify requirements in accordance with IEEE 830 standard, thus contributing to the documentation of the requirements established for a software system, besides supporting the learning of concepts related to the requirements specification, which improves the skills of users of the tool.

Pattern recognition structured heuristics methods for image processing in mobile robot navigation

In this project, the main focus is to apply image processing techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained.

Cognitive and Statistical Pattern Recognition Applied in Color and Texture Segmentation for Natural Scenes

In this approach, cognitive and statistical classifiers were implemented in order to verify the estimated and chosen regions on unstructured environments images. As inspection of crops for natural scenes demands and requires complex analysis of image processing and segmen‐ tation algorithms, since these computational methods evaluate and predict environment physical characteristics, such as color elements, complex objects composition, shadows, brightness and inhomogeneous region colors for texture, JSEG segmentation algorithm was approached to segment these ones, and ANN and Bayes recognition models to classify im‐ ages into predetermined classes (e.g. fruits, plants and general crops). The intended ap‐ proach to segment classification deploys a customized MLP topology to classify and characterize the segments, which deals with a supervised learning by error correction – propagation of pattern inputs with changes in synaptic weights in a cyclic processing, with accurate recognition as well as easy parameter adjustment, as an enhancement of iRPROP algorithm (improved resilient back-propagation) (Igel and Husken, 2003) derived from Backpropagation algorithm, which has a faster identification mapping process, that verifies what region maps have similar matches through the explored environment. Bayes statistical mod‐ els had the addiction of process variable as set parameters of predictive error correction.

JSEG Algorithm and Statistical ANN Image Segmentation Techniques for Natural Scenes

Computer vision, when used in open and unstructured environments as in the inspection of crops for natural scenes, demands and requires complex analysis of image processing and segmentation algorithms, since these computational methods evaluate and predict environment physical characteristics, such as color elements, complex objects composition, shadows, brightness and inhomogeneous region colors for texture. Several segmentation algorithms proposed in literature were designed to process images originally characterized by the above-mentioned items. Additionally, agricultural automation may take advantage of computer vision resources, which can be applied to a number of different tasks, such as crops inspection, classification of fruits and plants, estimated production, automated collection and guidance of autonomous machines. Bearing the afore-named in mind, the present chapter aims the use of JSEG unsupervised segmentation algorithm (Deng et al., 1999a), Statistical Pattern Recognition and Artificial Neural Networks (ANN) Multilayer Perceptron (MLP) topology (Haykin, 2008) as merging processing techniques in order to segment and therefore classify images into predetermined classes (e.g. navigable area, planting area, fruits, plants and general crops). The intended approach to segment classification deploys a customized MLP topology to classify and characterize the segments, which deals with a supervised learning by error correction – propagation of pattern inputs with changes in synaptic weights in a cyclic processing, with accurate recognition as well as easy parameter adjustment, as an enhancement of iRPROP algorithm (improved resilient back-propagation) (Igel and Husken, 2003) derived from Backpropagation algorithm, which has a faster identification mapping process, that verifies what region maps have similar matches through the explored environment. To carry through this task, a feature vector is necessary for color channels histograms (layers of primary color in a digital image with a counting graph that measures how many pixels are at each level between black and white). After training process, the mean squared error (MSE), denotes the best results achieved by segment classification to create the image-class map, which represents the segments into distinct feature vectors. Several metrics (vector bundle) can be part of a feature vector, however, a subset of those which describes and evaluates appropriate classes of segments should be chosen.