Combining 3D and 2D for less constrained periocular recognition

Periocular recognition has recently become an active topic in biometrics. Typically it uses 2D image data of the periocular region. This paper is the first description of combining 3D shape structure with 2D texture. A simple and effective technique using iterative closest point (ICP) was applied for 3D periocular region matching. It proved its strength for relatively unconstrained eye region capture, and does not require any training. Local binary patterns (LBP) were applied for 2D image based periocular matching. The two modalities were combined at the score-level. This approach was evaluated using the Bosphorus 3D face database, which contains large variations in facial expressions, head poses and occlusions. The rank-1 accuracy achieved from the 3D data (80%) was better than that for 2D (58%), and the best accuracy (83%) was achieved by fusing the two types of data. This suggests that significant improvements to periocular recognition systems could be achieved using the 3D structure information that is now available from small and inexpensive sensors.

Generating segmented meshes from textured color images

This paper presents a new framework for generating triangular meshes from textured color images. The proposed framework combines a texture classification technique, called W-operator, with Imesh, a method originally conceived to generate simplicial meshes from gray scale images. An extension of W-operators to handle textured color images is proposed, which employs a combination of RGB and HSV channels and Sequential Floating Forward Search guided by mean conditional entropy criterion to extract features from the training data. The W-operator is built into the local error estimation used by Imesh to choose the mesh vertices. Furthermore, the W-operator also enables to assign a label to the triangles during the mesh construction, thus allowing to obtain a segmented mesh at the end of the process. The presented results show that the combination of W-operators with Imesh gives rise to a texture classification-based triangle mesh generation framework that outperforms pixel based methods. Crown Copyright (C) 2009 Published by Elsevier Inc. All rights reserved.

Automating condition monitoring of vegetation on railway trackbeds and embankments

Vegetation growing on railway trackbeds and embankments present potential problems. The presence of vegetation threatens the safety of personnel inspecting the railway infrastructure. In addition vegetation growth clogs the ballast and results in inadequate track drainage which in turn could lead to the collapse of the railway embankment. Assessing vegetation within the realm of railway maintenance is mainly carried out manually by making visual inspections along the track. This is done either on-site or by watching videos recorded by maintenance vehicles mainly operated by the national railway administrative body. A need for the automated detection and characterisation of vegetation on railways (a subset of vegetation control/management) has been identified in collaboration with local railway maintenance subcontractors and Trafikverket, the Swedish Transport Administration (STA). The latter is responsible for long-term planning of the transport system for all types of traffic, as well as for the building, operation and maintenance of public roads and railways. The purpose of this research project was to investigate how vegetation can be measured and quantified by human raters and how machine vision can automate the same process. Data were acquired at railway trackbeds and embankments during field measurement experiments. All field data (such as images) in this thesis work was acquired on operational, lightly trafficked railway tracks, mostly trafficked by goods trains. Data were also generated by letting (human) raters conduct visual estimates of plant cover and/or count the number of plants, either on-site or in-house by making visual estimates of the images acquired from the field experiments. Later, the degree of reliability of(human) raters’ visual estimates were investigated and compared against machine vision algorithms. The overall results of the investigations involving human raters showed inconsistency in their estimates, and are therefore unreliable. As a result of the exploration of machine vision, computational methods and algorithms enabling automatic detection and characterisation of vegetation along railways were developed. The results achieved in the current work have shown that the use of image data for detecting vegetation is indeed possible and that such results could form the base for decisions regarding vegetation control. The performance of the machine vision algorithm which quantifies the vegetation cover was able to process 98% of the im-age data. Investigations of classifying plants from images were conducted in in order to recognise the specie. The classification rate accuracy was 95%.Objective measurements such as the ones proposed in thesis offers easy access to the measurements to all the involved parties and makes the subcontracting process easier i.e., both the subcontractors and the national railway administration are given the same reference framework concerning vegetation before signing a contract, which can then be crosschecked post maintenance.A very important issue which comes with an increasing ability to recognise species is the maintenance of biological diversity. Biological diversity along the trackbeds and embankments can be mapped, and maintained, through better and robust monitoring procedures. Continuously monitoring the state of vegetation along railways is highly recommended in order to identify a need for maintenance actions, and in addition to keep track of biodiversity. The computational methods or algorithms developed form the foundation of an automatic inspection system capable of objectively supporting manual inspections, or replacing manual inspections.

Uso da análise discriminante regularizada (RDA) no reconhecimento de padrões em imagens digitais hiperespectral de sensoriamento remoto

Em cenas naturais, ocorrem com certa freqüência classes espectralmente muito similares, isto é, os vetores média são muito próximos. Em situações como esta, dados de baixa dimensionalidade (LandSat-TM, Spot) não permitem uma classificação acurada da cena. Por outro lado, sabe-se que dados em alta dimensionalidade [FUK 90] tornam possível a separação destas classes, desde que as matrizes covariância sejam suficientemente distintas. Neste caso, o problema de natureza prática que surge é o da estimação dos parâmetros que caracterizam a distribuição de cada classe. Na medida em que a dimensionalidade dos dados cresce, aumenta o número de parâmetros a serem estimados, especialmente na matriz covariância. Contudo, é sabido que, no mundo real, a quantidade de amostras de treinamento disponíveis, é freqüentemente muito limitada, ocasionando problemas na estimação dos parâmetros necessários ao classificador, degradando portanto a acurácia do processo de classificação, na medida em que a dimensionalidade dos dados aumenta. O Efeito de Hughes, como é chamado este fenômeno, já é bem conhecido no meio científico, e estudos vêm sendo realizados com o objetivo de mitigar este efeito. Entre as alternativas propostas com a finalidade de mitigar o Efeito de Hughes, encontram-se as técnicas de regularização da matriz covariância. Deste modo, técnicas de regularização para a estimação da matriz covariância das classes, tornam-se um tópico interessante de estudo, bem como o comportamento destas técnicas em ambientes de dados de imagens digitais de alta dimensionalidade em sensoriamento remoto, como por exemplo, os dados fornecidos pelo sensor AVIRIS. Neste estudo, é feita uma contextualização em sensoriamento remoto, descrito o sistema sensor AVIRIS, os princípios da análise discriminante linear (LDA), quadrática (QDA) e regularizada (RDA) são apresentados, bem como os experimentos práticos dos métodos, usando dados reais do sensor. Os resultados mostram que, com um número limitado de amostras de treinamento, as técnicas de regularização da matriz covariância foram eficientes em reduzir o Efeito de Hughes. Quanto à acurácia, em alguns casos o modelo quadrático continua sendo o melhor, apesar do Efeito de Hughes, e em outros casos o método de regularização é superior, além de suavizar este efeito. Esta dissertação está organizada da seguinte maneira: No primeiro capítulo é feita uma introdução aos temas: sensoriamento remoto (radiação eletromagnética, espectro eletromagnético, bandas espectrais, assinatura espectral), são também descritos os conceitos, funcionamento do sensor hiperespectral AVIRIS, e os conceitos básicos de reconhecimento de padrões e da abordagem estatística. No segundo capítulo, é feita uma revisão bibliográfica sobre os problemas associados à dimensionalidade dos dados, à descrição das técnicas paramétricas citadas anteriormente, aos métodos de QDA, LDA e RDA, e testes realizados com outros tipos de dados e seus resultados.O terceiro capítulo versa sobre a metodologia que será utilizada nos dados hiperespectrais disponíveis. O quarto capítulo apresenta os testes e experimentos da Análise Discriminante Regularizada (RDA) em imagens hiperespectrais obtidos pelo sensor AVIRIS. No quinto capítulo são apresentados as conclusões e análise final. A contribuição científica deste estudo, relaciona-se à utilização de métodos de regularização da matriz covariância, originalmente propostos por Friedman [FRI 89] para classificação de dados em alta dimensionalidade (dados sintéticos, dados de enologia), para o caso especifico de dados de sensoriamento remoto em alta dimensionalidade (imagens hiperespectrais). A conclusão principal desta dissertação é que o método RDA é útil no processo de classificação de imagens com dados em alta dimensionalidade e classes com características espectrais muito próximas.

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

Técnica de medição de área foliarconsumida por insetos através de digitalizador de imagens

Este trabalho teve por objetivo verificar a viabilidade do uso de digitalizador de imagens manual, acoplado a um microcomputador, para a avaliação do consumo de folhas de soja, por lagartas de 5o instar de Anticarsia gemmatalis Hübner (Lep.: Noctuidae), em comparação com o método de pesagem e do planímetro, baseando-se na eficiência dos métodos e no tempo gasto para a avaliação. Os testes foram realizados utilizando-se folhas de soja `IAC 8' e lagartas criadas em dieta artificial. Foram realizados 2 tipos de teste: 1o) oferecimento de folíolos inteiros de soja às lagartas e, 2o) oferecimento de disco de folhas de área conhecida. No 1o teste comparou-se o método de pesagem com o digitalizador de imagens (scanner); no 2o experimento foram comparados o método do planímetro com o digitalizador de imagens que emprega o programa PCXAREA. Os resultados obtidos demonstraram que não existem diferenças nas medições de folíolos e discos de soja consumidos por A. gemmatalis quando comparados os métodos tradicionais (planímetro e pesagem) e o de digitalização de imagens. A medição com o digitalizador reduziu o tempo de avaliação em 88,5% e 87%, em relação ao planímetro e método de pesagem, respectivamente, sendo plenamente viável a sua utilização.

Rapid prototyping and inclined plane technique in the treatment of maxillofacial malformations in a fox

An approximately 9-month-old fox (Pseudalopex ventulus) was presented With malocclusion and deviation of the lower jaw to the right side. Orthodontic treatment was performed using the inclined plane technique. Virtual 3D models and prototypes of the head were based on computed tomography (CT) image data to assist in diagnosis and treatment.

Rapid Prototyping applied to surgical planning for correcting craniofacial malformations in wild animals. A case study of a brazilian fox

Veterinary surgery for treatment of wild animals is becoming an increasingly demanding task because it involves animals of different anatomy, many of them are already stressed and treatment must be performed to the highest standard in the minimum period of time. Craniofacial alterations may occur for three main reasons: genetic, functional or a combination of both. It is possible to modify the functional cause using intraoral devices like inclined plane. The treatment planning can be made based on virtual 3D models and rapid prototyping. An approximately 9 months old, 3.7 kg male Brazilian fox (Lycalopex vetulus) was referred to the Veterinary Hospital. Physical examination showed malocclusion with a deviation of the mandible to the right side. The virtual 3D model of the head was generated based on CT image data. The 3D models and rapid prototyping opened up new possibilities for the surgical planning and treatment of wild animals.

Foreign Body in Preauricular Region

Foreign bodies, although they are often found throughout the body, to a lesser degree in the face, still constitute a diagnostic challenge for the trauma surgeon. Its removal means danger of damaging important facial anatomic structures, even if its exact position from the image data was known. So, the objective is to describe a clinical report of a patient (42 years of age, male sex) who experienced falling to the ground, attended by the Department of Surgery and Traumatology Bucco-Maxillo-Facial Surgery, Faculty of Dentistry of Aracatuba, São Paulo State University, and 2 days after the trauma, he reported difficulty in mouth opening and pain. After clinical evaluation, we observed the presence of injury in the left preauricular region already in the process of healing. During the intraoral physical examination, a limitation of the mouth opening was noted. Radiographic posteroanterior and profile of the face showed 2 radiopaque foreign bodies in the left side, lying apparently at the region of the mandibular condylar process. Under local anesthesia, foreign body removal was carried from there with access to it through the preexisting facial injury. Further clinical examinations showed an improvement in mouth opening, absence of pain complaints, and/or functional complaints.

Técnicas de correlação no delineamento de feições rodoviárias em imagens de baixa e média resolução

This paper presents a method for the sequential road feature delineation from digital images. It is based on a feedback loop between extrapolation and refinement steps of a given road centerline point, using in both steps correlation techniques. Firstly, a previously extracted road centerline point is linearly extrapolated, resulting in an approximate position. Secondly, this approximate position is corrected by comparing gray level profiles extracted perpendicularly to the extrapolation direction. This strategy is then repeated to allow the entire road centerline to be extracted or a stop point to be found. In order to initialize the extraction process, the operator needs to supply a starting point plus direction and width. Experimental results obtained from the application of the method to real image data are presented and discussed in this paper.

Photogrammetric refinement of laser-based building roof contours

In this paper a photogrammetric method is proposed for refining 3D building roof contours extracted from airborne laser scanning data. It is assumed that laser-derived planar faces of roofs are potentially accurate, while laser-derived building roof contours are not well defined. First, polygons representing building roof contours are extracted from a high-resolution aerial image. In the sequence, straight-line segments delimitating each building roof polygon are projected onto the corresponding laser-derived roof planes by using a new line-based photogrammetric model. Finally, refined 3D building roof contours are reconstructed by connecting every pair of photogrammetrically- projected adjacent straight lines. The obtained results showed that the proposed approach worked properly, meaning that the integration of image data and laser scanning data allows better results to be obtained, when compared to the results generated by using only laser scanning data. © 2013 IEEE.

Desenvolvimento de uma Plataforma de Software para a Modelagem Digital de Terrenos baseada em TIN

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Análise discriminatória de alvos da paisagem urbana em imagens aéreas multiespectrais

Pós-graduação em Ciências Cartográficas - FCT

Extração semi-automática de rodovias em imagens digitais usando técnicas de correlação e o princípio de teste ativo

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Estimação de densidades e aplicação em segmentação de imagens

The aim of this work is to study some of the density estimation tec- niques and to apply to the segmentation of medical images. Medical images are used to help the diagnostic of tumor diseases as well as to plan and deliver treatment. A computer image is an array of values representing colors in some scale. The smallest element of the image to which it is possible to assign a value is called pixel. Segmen- tation is the process of dividing the image in portions through the classi¯cation of each pixel. The simplest way of classi¯cation is by thresholding, given the number of portions and the threshold values. Another method is constructing a histogram of the pixel values and assign a portion to each pike. The threshold is the mean between two pikes. As the histogram does not form a smooth curve it is di±cult to discern between true pikes and random variation. Density estimation methods allow the estimation of a smooth curve. Image data can be considered as mixture of different densities. In this project parametric and nonparametric methods for density estimation will be addressed and some of them are applied to CT image data