An Information Theory framework for two-stage binary image operator design

The design of translation invariant and locally defined binary image operators over large windows is made difficult by decreased statistical precision and increased training time. We present a complete framework for the application of stacked design, a recently proposed technique to create two-stage operators that circumvents that difficulty. We propose a novel algorithm, based on Information Theory, to find groups of pixels that should be used together to predict the Output Value. We employ this algorithm to automate the process of creating a set of first-level operators that are later combined in a global operator. We also propose a principled way to guide this combination, by using feature selection and model comparison. Experimental results Show that the proposed framework leads to better results than single stage design. (C) 2009 Elsevier B.V. All rights reserved.

Plane detection from monocular image sequences

AIRES, Kelson R. T. ; ARAÚJO, Hélder J. ; MEDEIROS, Adelardo A. D. . Plane Detection from Monocular Image Sequences. In: VISUALIZATION, IMAGING AND IMAGE PROCESSING, 2008, Palma de Mallorca, Spain. Proceedings..., Palma de Mallorca: VIIP, 2008

Image analysis for assessing broiler breeder behavior response to thermal environment

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Pattern recognition theory in nonlinear signal processing

A body of research has developed within the context of nonlinear signal and image processing that deals with the automatic, statistical design of digital window-based filters. Based on pairs of ideal and observed signals, a filter is designed in an effort to minimize the error between the ideal and filtered signals. The goodness of an optimal filter depends on the relation between the ideal and observed signals, but the goodness of a designed filter also depends on the amount of sample data from which it is designed. In order to lessen the design cost, a filter is often chosen from a given class of filters, thereby constraining the optimization and increasing the error of the optimal filter. To a great extent, the problem of filter design concerns striking the correct balance between the degree of constraint and the design cost. From a different perspective and in a different context, the problem of constraint versus sample size has been a major focus of study within the theory of pattern recognition. This paper discusses the design problem for nonlinear signal processing, shows how the issue naturally transitions into pattern recognition, and then provides a review of salient related pattern-recognition theory. In particular, it discusses classification rules, constrained classification, the Vapnik-Chervonenkis theory, and implications of that theory for morphological classifiers and neural networks. The paper closes by discussing some design approaches developed for nonlinear signal processing, and how the nature of these naturally lead to a decomposition of the error of a designed filter into a sum of the following components: the Bayes error of the unconstrained optimal filter, the cost of constraint, the cost of reducing complexity by compressing the original signal distribution, the design cost, and the contribution of prior knowledge to a decrease in the error. The main purpose of the paper is to present fundamental principles of pattern recognition theory within the framework of active research in nonlinear signal processing.

An image analysis study of pit formation on Ti-6Al-4V

Metallographic techniques and digital image processing have been used to investigate heat-treated Ti-6Al-4V pitting corrosion, often used as aircraft components. LM and SEM metallography of 'as received', annealed (heating up to 800 degreesC/30 min and cooling furnace) and aged (heating up to 900 degreesC/30 min, quenching in water, heating up to 540 degreesC/240 min and again water-quenched) microstructures reveal pitting sites at primary and secondary alpha/beta interfaces. Microstructural arrangements influence and corrosive environment association on pit morphology could be demonstrated by digital image analysis and results statistical treatment. (C) 2002 Elsevier B.V. B.V. All rights reserved.

Image registration and image interpolation in volume rendering of cross sections

We outline a method for registration of images of cross sections using the concepts of The Generalized Hough Transform (GHT). The approach may be useful in situations where automation should be a concern. To overcome known problems of noise of traditional GHT we have implemented a slight modified version of the basic algorithm. The modification consists of eliminating points of no interest in the process before the application of the accumulation step of the algorithm. This procedure minimizes the amount of accumulation points while reducing the probability of appearing of spurious peaks. Also, we apply image warping techniques to interpolate images among cross sections. This is needed where the distance of samples between sections is too large. Then it is suggested that the step of registration with GHT can help the interpolation automation by simplifying the correspondence between points of images. Some results are shown.

A method for the digital image analysis of ceramic grains based on shape factor segmentation

A very simple and robust method for ceramics grains quantitative image analysis is presented. Based on the use of optimal imaging conditions for reflective light microscopy of bulk samples, a digital image processing routine was developed for shading correction, noise suppressing and contours enhancement. Image analysis was done for grains selected according to their concavities, evaluated by perimeter ratio shape factor, to avoid consider the effects of breakouts and ghost boundaries due to ceramographic preparation limitations. As an example, the method was applied for two ceramics, to compare grain size and morphology distributions. In this case, most of artefacts introduced by ceramographic preparation could be discarded due to the use of perimeter ratio exclusion range.

Road segment delineation using active testing principle and an edge-based post-processing strategy

This paper presents a semi-automated method for extracting road segments from medium-resolution images based on active testing and edge analysis. The method is based on two sequential and independent stages. Firstly, an active testing method is used to extract an approximated road centreline which is based on a sequential and local exploitation of the image. Secondly, an iterative strategy based on edge analysis and the approximated centreline is used to measure precisely the road centreline. Based on the results obtained using medium-resolution test images, the method seems to be very promising. In general, the method proved to be very accurate whenever the roads are characterized by two well-defined anti-parallel edges and robust even in the presence of larger obstacles such as trees and shadows.

Morphometric study by image analysis of Ag-stained nucleoli in thyroids bearing proliferating lesions

The Ag-NOR staining technique and image analysis were used to evaluate morphological parameters (area, perimeter and axis ratio) in nucleoli from normal thyroids and from thyroids bearing proliferating lesions (carcinomas, adenomas and hyperplasias). Regions with normal appearance located close to adenomatous and carcinomatous regions, in the thyroid of every patient, were also analyzed for comparison with the respective pathological regions and with normal thyroids. Statistical analysis of data for the nucleolar area and perimeter allowed the separation of adenomas and carcinomas from hyperplasias and normal tissue but not the two components in each of these two groups. However, if we look at the numbers, a sequence of increasing nucleolar mean areas in the order: normal, hyperplasia, adenoma and carcinoma may be observed, indicating the sequence of increasing rRNA requirements in these different kinds of cells. The axis ratio that denotes the nucleolar shape (round or oblong) did not show significant differences among tissues, suggesting that shape is not important in the characterization of these pathologies. Differences in nucleolar areas and perimeter between normal and affected regions from each patient were statistically significant for adenomas and carcinomas. When these normal regions were compared with the normal thyroids, significant differences were not obtained in the three evaluated parameters. The observations and their importance for histopathological diagnosis are discussed.

A photogrammetric method for single image orientation and measurement

The aim of this paper is to present a photogrammetric method for determining the dimensions of flat surfaces, such as billboards, based on a single digital image. A mathematical model was adapted to generate linear equations for vertical and horizontal lines in the object space. These lines are identified and measured in the image and the rotation matrix is computed using an indirect method. The distance between the camera and the surface is measured using a lasermeter, providing the coordinates of the camera perspective center. Eccentricity of the lasermeter center related to the camera perspective center is modeled by three translations, which are computed using a calibration procedure. Some experiments were performed to test the proposed method and the achieved results are within a relative error of about 1 percent in areas and distances in the object space. This accuracy fulfills the requirements of the intended applications. © 2005 American Society for Photogrammetry and Remote Sensing.

JSEG-based image segmentation in computer vision for agricultural mobile robot navigation

This project aims to apply image processing techniques in computer vision featuring 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 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.

A regularized nonlinear diffusion approach for texture image denoising

In this paper a new partial differential equation based method is presented with a view to denoising images having textures. The proposed model combines a nonlinear anisotropic diffusion filter with recent harmonic analysis techniques. A wave atom shrinkage allied to detection by gradient technique is used to guide the diffusion process so as to smooth and maintain essential image characteristics. Two forcing terms are used to maintain and improve edges, boundaries and oscillatory features of an image having irregular details and texture. Experimental results show the performance of our model for texture preserving denoising when compared to recent methods in literature. © 2009 IEEE.

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. © 2010 IEEE.

Image categorization through optimum path forest and visual words

Different from the first attempts to solve the image categorization problem (often based on global features), recently, several researchers have been tackling this research branch through a new vantage point - using features around locally invariant interest points and visual dictionaries. Although several advances have been done in the visual dictionaries literature in the past few years, a problem we still need to cope with is calculation of the number of representative words in the dictionary. Therefore, in this paper we introduce a new solution for automatically finding the number of visual words in an N-Way image categorization problem by means of supervised pattern classification based on optimum-path forest. © 2011 IEEE.

Improving image classification through descriptor combination

The efficiency in image classification tasks can be improved using combined information provided by several sources, such as shape, color, and texture visual properties. Although many works proposed to combine different feature vectors, we model the descriptor combination as an optimization problem to be addressed by evolutionary-based techniques, which compute distances between samples that maximize their separability in the feature space. The robustness of the proposed technique is assessed by the Optimum-Path Forest classifier. Experiments showed that the proposed methodology can outperform individual information provided by single descriptors in well-known public datasets. © 2012 IEEE.