Seleção seqüencial de descritores por análise da semântica para recuperação de imagens baseada no conteúdo

With the rapid growth of databases of various types (text, multimedia, etc..), There exist a need to propose methods for ordering, access and retrieve data in a simple and fast way. The images databases, in addition to these needs, require a representation of the images so that the semantic content characteristics are considered. Accordingly, several proposals such as the textual annotations based retrieval has been made. In the annotations approach, the recovery is based on the comparison between the textual description that a user can make of images and descriptions of the images stored in database. Among its drawbacks, it is noted that the textual description is very dependent on the observer, in addition to the computational effort required to describe all the images in database. Another approach is the content based image retrieval - CBIR, where each image is represented by low-level features such as: color, shape, texture, etc. In this sense, the results in the area of CBIR has been very promising. However, the representation of the images semantic by low-level features is an open problem. New algorithms for the extraction of features as well as new methods of indexing have been proposed in the literature. However, these algorithms become increasingly complex. So, doing an analysis, it is natural to ask whether there is a relationship between semantics and low-level features extracted in an image? and if there is a relationship, which descriptors better represent the semantic? which leads us to a new question: how to use descriptors to represent the content of the images?. The work presented in this thesis, proposes a method to analyze the relationship between low-level descriptors and semantics in an attempt to answer the questions before. Still, it was observed that there are three possibilities of indexing images: Using composed characteristic vectors, using parallel and independent index structures (for each descriptor or set of them) and using characteristic vectors sorted in sequential order. Thus, the first two forms have been widely studied and applied in literature, but there were no records of the third way has even been explored. So this thesis also proposes to index using a sequential structure of descriptors and also the order of these descriptors should be based on the relationship that exists between each descriptor and semantics of the users. Finally, the proposed index in this thesis revealed better than the traditional approachs and yet, was showed experimentally that the order in this sequence is important and there is a direct relationship between this order and the relationship of low-level descriptors with the semantics of the users

Sistema auxiliar na análise de mamogramas digitais

This work proposes the development of a Computer System for Analysis of Mammograms SCAM, that aids the doctor specialist in the identification and analysis of existent lesions in digital mammograms. The computer system for digital mammograms processing will make use of a group of techniques of Digital Image Processing (DIP), with the purpose of aiding the medical professional to extract the information contained in the mammogram. This system possesses an interface of easy use for the user, allowing, starting from the supplied mammogram, a group of processing operations, such as, the enrich of the images through filtering techniques, the segmentation of areas of the mammogram, the calculation the area of the lesions, thresholding the lesion, and other important tools for the medical professional's diagnosis. The Wavelet Transform will used and integrated into the computer system, with the objective of allowing a multiresolution analysis, thus supplying a method for identifying and analyzing microcalcifications

Posicionamento e movimentação de um robô humanóide utilizando imagens de uma câmera móvel externa

This work proposes a method to localize a simple humanoid robot, without embedded sensors, using images taken from an extern camera and image processing techniques. Once the robot is localized relative to the camera, supposing we know the position of the camera relative to the world, we can compute the position of the robot relative to the world. To make the camera move in the work space, we will use another mobile robot with wheels, which has a precise locating system, and will place the camera on it. Once the humanoid is localized in the work space, we can take the necessary actions to move it. Simultaneously, we will move the camera robot, so it will take good images of the humanoid. The mainly contributions of this work are: the idea of using another mobile robot to aid the navigation of a humanoid robot without and advanced embedded electronics; chosing of the intrinsic and extrinsic calibration methods appropriated to the task, especially in the real time part; and the collaborative algorithm of simultaneous navigation of the robots

Compressão auto-adaptativa de imagens coloridas

Image compress consists in represent by small amount of data, without loss a visual quality. Data compression is important when large images are used, for example satellite image. Full color digital images typically use 24 bits to specify the color of each pixel of the Images with 8 bits for each of the primary components, red, green and blue (RGB). Compress an image with three or more bands (multispectral) is fundamental to reduce the transmission time, process time and record time. Because many applications need images, that compression image data is important: medical image, satellite image, sensor etc. In this work a new compression color images method is proposed. This method is based in measure of information of each band. This technique is called by Self-Adaptive Compression (S.A.C.) and each band of image is compressed with a different threshold, for preserve information with better result. SAC do a large compression in large redundancy bands, that is, lower information and soft compression to bands with bigger amount of information. Two image transforms are used in this technique: Discrete Cosine Transform (DCT) and Principal Component Analysis (PCA). Primary step is convert data to new bands without relationship, with PCA. Later Apply DCT in each band. Data Loss is doing when a threshold discarding any coefficients. This threshold is calculated with two elements: PCA result and a parameter user. Parameters user define a compression tax. The system produce three different thresholds, one to each band of image, that is proportional of amount information. For image reconstruction is realized DCT and PCA inverse. SAC was compared with JPEG (Joint Photographic Experts Group) standard and YIQ compression and better results are obtain, in MSE (Mean Square Root). Tests shown that SAC has better quality in hard compressions. With two advantages: (a) like is adaptive is sensible to image type, that is, presents good results to divers images kinds (synthetic, landscapes, people etc., and, (b) it need only one parameters user, that is, just letter human intervention is required

Desenvolvimento de um Sistema de Visão Global para uma Frota de Mini-Robôs Móveis

Navigation based on visual feedback for robots, working in a closed environment, can be obtained settling a camera in each robot (local vision system). However, this solution requests a camera and capacity of local processing for each robot. When possible, a global vision system is a cheapest solution for this problem. In this case, one or a little amount of cameras, covering all the workspace, can be shared by the entire team of robots, saving the cost of a great amount of cameras and the associated processing hardware needed in a local vision system. This work presents the implementation and experimental results of a global vision system for mobile mini-robots, using robot soccer as test platform. The proposed vision system consists of a camera, a frame grabber and a computer (PC) for image processing. The PC is responsible for the team motion control, based on the visual feedback, sending commands to the robots through a radio link. In order for the system to be able to unequivocally recognize each robot, each one has a label on its top, consisting of two colored circles. Image processing algorithms were developed for the eficient computation, in real time, of all objects position (robot and ball) and orientation (robot). A great problem found was to label the color, in real time, of each colored point of the image, in time-varying illumination conditions. To overcome this problem, an automatic camera calibration, based on clustering K-means algorithm, was implemented. This method guarantees that similar pixels will be clustered around a unique color class. The obtained experimental results shown that the position and orientation of each robot can be obtained with a precision of few millimeters. The updating of the position and orientation was attained in real time, analyzing 30 frames per second

Compressão Seletiva de Imagens Coloridas com Detecção Automática de Regiões de Interesse

There has been an increasing tendency on the use of selective image compression, since several applications make use of digital images and the loss of information in certain regions is not allowed in some cases. However, there are applications in which these images are captured and stored automatically making it impossible to the user to select the regions of interest to be compressed in a lossless manner. A possible solution for this matter would be the automatic selection of these regions, a very difficult problem to solve in general cases. Nevertheless, it is possible to use intelligent techniques to detect these regions in specific cases. This work proposes a selective color image compression method in which regions of interest, previously chosen, are compressed in a lossless manner. This method uses the wavelet transform to decorrelate the pixels of the image, competitive neural network to make a vectorial quantization, mathematical morphology, and Huffman adaptive coding. There are two options for automatic detection in addition to the manual one: a method of texture segmentation, in which the highest frequency texture is selected to be the region of interest, and a new face detection method where the region of the face will be lossless compressed. The results show that both can be successfully used with the compression method, giving the map of the region of interest as an input

Human face verification based on multidimensional Polynomial Powers of Sigmoid (PPS)

In this paper, we described how a multidimensional wavelet neural networks based on Polynomial Powers of Sigmoid (PPS) can be constructed, trained and applied in image processing tasks. In this sense, a novel and uniform framework for face verification is presented. The framework is based on a family of PPS wavelets,generated from linear combination of the sigmoid functions, and can be considered appearance based in that features are extracted from the face image. The feature vectors are then subjected to subspace projection of PPS-wavelet. The design of PPS-wavelet neural networks is also discussed, which is seldom reported in the literature. The Stirling Universitys face database were used to generate the results. Our method has achieved 92 % of correct detection and 5 % of false detection rate on the database.

Comparison of fracture surface and plane section analysis for ceramic grain size characterisation

A method has been developed to obtain quantitative information about grain size and shape from fractured surfaces of ceramic materials. One elaborated a routine to split intergranular and transgranular grains facets of ceramic fracture surfaces by digital image processing. A commercial ceramic (ALCOA A-16, Al2O3-1.5% of CrO) was used to test the proposed method. Microstructural measurements of grain shape and size taken from fracture surfaces have been compared through descriptive statistics of distributions, with the corresponding measurements from polished and etched surfaces. The agreement between results, with the expected bias on grain size values from fractures, obtained for both types of surfaces allowed to infer that this new technique can be used to extract the relevant microstructural information from fractured surfaces, thus minimising the time consuming steps of sample preparation. (C) 2003 Elsevier Ltd. All rights reserved.

Boundary identification criteria and geometry analysis of Al 7050 stretch zone from elevation profiles

This article describes the development of a method for analysis of the shape of the stretch zone surface based on parallax measurement theory and using digital image processing techniques. Accurate criteria for the definition of the boundaries of the stretch zone are established from profiles of fracture surfaces obtained from crack tip opening displacement tests on Al-7050 alloy samples. The elevation profiles behavior analysis is based on stretch zone width and height parameters. It is concluded that the geometry of the stretch zone profiles under plane strain conditions can be described by a semi-parabolic relationship. (C) Elsevier B.V., 1999. All rights reserved.

Úlceras crônicas dos membros inferiores: avaliação pela fotografia digital

OBJETIVO: Avaliar o desempenho da análise de imagem digital na estimativa da área acometida pelas úlceras crônicas dos membros inferiores. MÉTODOS: Estudo prospectivo em que foram mensuradas úlceras empregando o método planimétrico clássico, utilizando desenho dos seus contornos em filme plástico transparente, medida sua área posteriormente por folha milimetrada. Esses valores foram utilizados como padrão para a comparação com a estimativa de área pelas fotografias digitais padronizadas das úlceras e dos desenhos das mesmas em filme plástico. Para criar um referencial de conversão dos pixels em milímetros, foi empregado um adesivo com tamanho conhecido, adjacente à úlcera. RESULTADOS: foram avaliadas 42 lesões em 20 pacientes portadores de úlceras crônicas de membros inferiores. As áreas das úlceras variaram de 0,24 a 101,65cm². Observou-se forte correlação entre as medidas planimétricas e as fotos das úlceras (R²=0,86 p<0,01), porém a correlação das medidas planimétricas com as fotos digitais dos desenhos das úlceras foi ainda maior (R²=0,99 p<0,01). CONCLUSÃO: A fotografia digital padronizada revelou-se método rápido, preciso e não-invasivo capaz de estimar a área afetada por úlceras. A avaliação das medidas fotográficas dos contornos das úlceras deve ser preferida em relação à análise de sua fotografia direta.

Segmentação fuzzy de imagens coloridas com características texturais: uma aplicação a rochas sedimentares

Image segmentation is the process of labeling pixels on di erent objects, an important step in many image processing systems. This work proposes a clustering method for the segmentation of color digital images with textural features. This is done by reducing the dimensionality of histograms of color images and using the Skew Divergence to calculate the fuzzy a nity functions. This approach is appropriate for segmenting images that have colorful textural features such as geological, dermoscopic and other natural images, as images containing mountains, grass or forests. Furthermore, experimental results of colored texture clustering using images of aquifers' sedimentary porous rocks are presented and analyzed in terms of precision to verify its e ectiveness.

Edge detection and noise removal by use of a partial differential equation with automatic selection of parameters

This work deals with noise removal by the use of an edge preserving method whose parameters are automatically estimated, for any application, by simply providing information about the standard deviation noise level we wish to eliminate. The desired noiseless image u(x), in a Partial Differential Equation based model, can be viewed as the solution of an evolutionary differential equation u t(x) = F(u xx, u x, u, x, t) which means that the true solution will be reached when t ® ¥. In practical applications we should stop the time ''t'' at some moment during this evolutionary process. This work presents a sufficient condition, related to time t and to the standard deviation s of the noise we desire to remove, which gives a constant T such that u(x, T) is a good approximation of u(x). The approach here focused on edge preservation during the noise elimination process as its main characteristic. The balance between edge points and interior points is carried out by a function g which depends on the initial noisy image u(x, t0), the standard deviation of the noise we want to eliminate and a constant k. The k parameter estimation is also presented in this work therefore making, the proposed model automatic. The model's feasibility and the choice of the optimal time scale is evident through out the various experimental results.

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.

Automatic absolute orientation of scanned aerial photographs

Image orientation is a basic problem in Digital Photogrammetry. While interior and relative orientations were succesfully automated, the same can not be said about absolute orientation. This process can be automated by using an approach based on relational matching and a heuristic that uses the analytical relation between straight features in the object space and its homologous in the image space. A build-in self-diagnosis is also used in this method, that is based on the implementation of data snooping statistic test in the process of spatial resection, using the Iterated Extended Kalman Filtering (IEKF). The aim of this paper is to present the basic principles of the proposed approach and results based on real data.