Semiautomatic dental recognition using a graph-based segmentation algorithm and teeth shapes features

Dental recognition is very important for forensic human identification, mainly regarding the mass disasters, which have frequently happened due to tsunamis, airplanes crashes, etc. Algorithms for automatic, precise, and robust teeth segmentation from radiograph images are crucial for dental recognition. In this work we propose the use of a graph-based algorithm to extract the teeth contours from panoramic dental radiographs that are used as dental features. In order to assess our proposal, we have carried out experiments using a database of 1126 tooth images, obtained from 40 panoramic dental radiograph images from 20 individuals. The results of the graph-based algorithm was qualitatively assessed by a human expert who reported excellent scores. For dental recognition we propose the use of the teeth shapes as biometric features, by the means of BAS (Bean Angle Statistics) and Shape Context descriptors. The BAS descriptors showed, on the same database, a better performance (EER 14%) than the Shape Context (EER 20%). © 2012 IEEE.

IFTrace: Video segmentation of deformable objects using the Image Foresting Transform

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

A graph-based framework for thermal faceprint characterization

Thermal faceprint has been paramount in the last years. Since we can handle with face recognition using images acquired in the infrared spectrum, an unique individual's signature can be obtained through the blood vessels network of the face. In this work, we propose a novel framework for thermal faceprint extraction using a collection of graph-based techniques, which were never used to this task up to date. A robust method of thermal face segmentation is also presented. The experiments, which were conducted over the UND Collection C dataset, have showed promising results. © 2011 Springer-Verlag.

Image segmentation through combined methods: watershed transform, unsupervised distance learning and normalized cut

Research on image processing has shown that combining segmentation methods may lead to a solid approach to extract semantic information from different sort of images. Within this context, the Normalized Cut (NCut) is usually used as a final partitioning tool for graphs modeled in some chosen method. This work explores the Watershed Transform as a modeling tool, using different criteria of the hierarchical Watershed to convert an image into an adjacency graph. The Watershed is combined with an unsupervised distance learning step that redistributes the graph weights and redefines the Similarity matrix, before the final segmentation step using NCut. Adopting the Berkeley Segmentation Data Set and Benchmark as a background, our goal is to compare the results obtained for this method with previous work to validate its performance.

Effects of feed restriction on yield, retail cuts and tissue composition of carcass of Saanen kids

Com o objetivo de estudar o efeito da restrição alimentar sobre as características de carcaça de caprinos leiteiros, realizou-se um experimento utilizando 27 cabritos machos Saanen distribuídos nos tratamentos 0 (alimentação à vontade), 30 ou 60% de restrição. Os animais apresentavam 5 kg de PV inicial e foram abatidos quando atingiram 20 kg de PV. Foram avaliados os rendimentos comercial e biológico, os cortes comerciais, a composição tecidual da perna, a área de olho-de-lombo (AOL) e a compacidade da carcaça. Utilizaram-se o delineamento inteiramente ao acaso e a análise de regressão em função da restrição alimentar. A restrição alimentar provocou redução do peso da carcaça e dos cortes comerciais, aumento da proporção do pescoço e diminuição da proporção do lombo. A proporção de ossos aumentou e a do tecido muscular e da gordura total diminuiu com o aumento da restrição. A proporção de gordura subcutânea diminuiu com o aumento da restrição alimentar. A área de olho-de-lombo (AOL) e a compacidade da carcaça foram afetadas pela restrição alimentar, mas ambas as medidas podem ser utilizadas para predizer a proporção de músculo da carcaça. O tratamento com 30% de restrição alimentar não prejudica a qualidade da carcaça de cabritos leiteiros.

Relative development of tissues, commercial meat cuts and live weight components in Saanen goats

Objetivando avaliar o desenvolvimento relativo dos componentes do peso vivo (PV), dos cortes comerciais e dos tecidos da carcaça, utilizaram-se 40 cabritos Saanen. Os animais foram abatidos ao atingir 5,0; 12,5; 20,0; 27,5 e 35,0 kg de PV e a carcaça foi seccionada em paleta, pescoço, 1ª a 5ª costelas, 6ª a 13ª costelas, peito/fralda, lombo e perna. A perna foi dissecada em ossos, músculos e gordura. Utilizou-se a equação alométrica Y=aXb para estimar o desenvolvimento relativo. O crescimento do tecido ósseo foi precoce, o do tecido muscular intermediário e o da gordura crescimento tardio, uma vez que a gordura subcutânea é depositada mais tardiamente. Os cortes comerciais apresentaram coeficiente de alometria isogônico, com exceção do corte da 6ª a 13ª costelas e do peito/fralda. O desenvolvimento da carcaça e dos não-componentes da carcaça acompanhou o peso de corpo vazio. Cabritos com 35 kg de PV possuem proporção de músculos e relação músculo:osso adequadas, mas apresentam proporção de gordura maior que a observada nos animais abatidos com 20 kg de PV.

Dimensões geométricas das fibras do músculo sóleo de ratos exercitados em esteira rolante: a importância da análise por meio de imagens digitalizadas

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

Recursive hierarchic segmentation analysis of bone mineral density changes on digital panoramic images

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Effects of meal size and proximal-distal segmentation on gastric activity

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

Anatomicosurgical arterial segmentation of the cat lungs (Felis catus domesticus, L., 1758)

O objetivo desta pesquisa foi estabelecer os segmentos anátomo-cirúrgicos arteriais, através da lobação e ramificação intralobar arterial, em pulmões de gato. Após a dissecção de vinte pulmões, notou-se que a artéria pulmonar direita geralmente emite um ramo para o lobo cranial e um ramo para o lobo médio, sendo originados juntos em um tronco. Um grande ramo irriga o lobo caudal na maioria dos casos. Dois ramos com origem comum no ramo arterial do lobo caudal irrigam o lobo acessório. A artéria pulmonar esquerda origina um tronco que, na maioria dos casos, emite um ramo para a porção cranial e um ramo para a porção caudal do lobo cranial esquerdo. Pode-se concluir que o pulmão direito é formado por quatro e o esquerdo por dois lobos, ocorrendo variações na ramificação arterial pulmonar.

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.

Segmentação de dados de perfilamento a laser em áreas urbanas utilizando uma abordagem Bayesiana

In this paper is presented a region-based methodology for Digital Elevation Model segmentation obtained from laser scanning data. The methodology is based on two sequential techniques, i.e., a recursive splitting technique using the quad tree structure followed by a region merging technique using the Markov Random Field model. The recursive splitting technique starts splitting the Digital Elevation Model into homogeneous regions. However, due to slight height differences in the Digital Elevation Model, region fragmentation can be relatively high. In order to minimize the fragmentation, a region merging technique based on the Markov Random Field model is applied to the previously segmented data. The resulting regions are firstly structured by using the so-called Region Adjacency Graph. Each node of the Region Adjacency Graph represents a region of the Digital Elevation Model segmented and two nodes have connectivity between them if corresponding regions share a common boundary. Next it is assumed that the random variable related to each node, follows the Markov Random Field model. This hypothesis allows the derivation of the posteriori probability distribution function whose solution is obtained by the Maximum a Posteriori estimation. Regions presenting high probability of similarity are merged. Experiments carried out with laser scanning data showed that the methodology allows to separate the objects in the Digital Elevation Model with a low amount of fragmentation.

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.

Fast automatic microstructural segmentation of ferrous alloy samples using optimum-path forest

In this work we propose a novel automatic cast iron segmentation approach based on the Optimum-Path Forest classifier (OPF). Microscopic images from nodular, gray and malleable cast irons are segmented using OPF, and Support Vector Machines (SVM) with Radial Basis Function and SVM without kernel mapping. Results show accurate and fast segmented images, in which OPF outperformed SVMs. Our work is the first into applying OPF for automatic cast iron segmentation. © 2010 Springer-Verlag.