Introdução à Plataforma de Processamento e Análise de Imagem e Computação Gráfica - CMIS

Desde 1995 tem vindo a ser desenvolvida na FEUP uma plataforma de análise e processamento de imagem e de computação gráfica para sistemas operativos Microsoft Windows designada por CMIS.A linguagem utilizada para a referida plataforma foi a C++ e o ambiente de desenvolvimento foi o Microsoft Visual C++. Para aumentar as suas foram incorporadas algumas bibliotecas de domínio público: para operações com imagens bitmap as bibliotecas Cimage e Jpeglib, para cálculo matricial a biblioteca Newmat, e para objectos gráficos a biblioteca VTK.A filosofia utilizada no desenho da plataforma permite que a mesma seja facilmente configurável a diferentes géneros de utilizadores, e que os mais diversos tipos de investigadores a utilizem sem grande dificuldade no desenvolvimento e ensaio dos seus próprios algoritmos. Actualmente a plataforma é bastante utilizada por vários alunos da FEUP, quer ao nível da Licenciatura quer ao nível de Cursos de Mestrado, para desenvolverem os seus trabalhos, tendo-se verificado as boas condições que a mesma disponibiliza. Deste modo, o número de funções disponíveis na plataforma tem vindo a crescer rapidamente, tendo-se incluindo um grupo relevante de funções adequadas para objectos deformáveis o que também a torna bastante adequada para esse domínio.Neste relatório é realizada uma descrição da plataforma desenvolvida, assim é descrita a filosofia adoptada, indicadas as entidades actualmente suportadas, descritas de forma resumida algumas das funções já incorporadas de índole mais geral, são indicados os algoritmos disponíveis mais específicos aos objectos deformáveis e finalmente, em anexo, é descrito o processo de compilação do projecto CMIS no ambiente de desenvolvimento Microsoft Visual C++.Since 1995 it has been developed, in FEUP, a platform for analysis and image processing and for computer graphics for Microsoft Windows operative systems designed by CMIS.The used language for the platform is the C++ and the development system is the Microsoft Visual C++. To increase the capacities of the platform they were incorporate some public domain libraries: for bitmap images operations the Cimage and Jpeglib, for matrices computation the Newmat, and for graphics objects the VTK.The philosophy used in the platform drawing allows that the same is easily configured to different users, and that several types of investigators use it in the development and rehearsal of its own algorithms without great difficulty. Actually the platform is quite used by several students of FEUP, for their algorithms developing, having been verified the good conditions that the same offers. This way, the number of available functions in the platform has been coming to grow quickly, and an important group of functions adequate for deformable objects also was being included what turns it quite adapted for this domain.In this report a description of the developed platform is accomplished, the adopted philosophy is described, indicated the entities actually supported, briefly described some of the generally functions already incorporated, the available algorithms more suitable for deformable objects are presented and finally, in appendix, the CMIS project compilation process in the Microsoft Visual C++ development system is described

Actividades desenvolvidas e perspectivas futuras na área da Engenharia Biomédica

O LOME (www.fe.up.pt/~inegi/lome) é uma unidade do INEGI (www.inegi.pt) situada no campus da FEUP, criada com subsídios do programa CIÊNCIA (1991-1993), integrando a Unidade de Novos Materiais e Mecânica Experimental classificada como Excelente e financiada pela Fundação para a Ciência e a Tecnologia - FCT. Actualmente, integra seis investigadores Doutorados e vários alunos de Mestrado e de Doutoramento.Em 2001, o Prof. João Manuel R. S. Tavares passou a ser Investigador Sénior e Coordenador de Projecto no LOME, passando a desenvolver várias actividades de investigação em conjunto com diversos colaboradores resumidamente apresentados neste documento

Obtenção de informação 3D a partir de movimento de câmara : calibração, detecção e simplificação de entidades, seguimento temporal, triangulação

A obtenção de informação tridimensional (3D) de objectos apresenta importância extrema em muitas aplicações de Visão Computacional. Como exemplos, podem ser citados:inspecção industrial, guiamento de veículos, reconstrução, seguimento e identificação de objectos. Neste artigo, é apresentada uma metodologia para obter informação 3D a partir do movimento de uma câmara, constituída pelas fases: calibração, detecção e simplificação de entidades, seguimento temporal das mesmas e obtenção de coordenadas 3D

COMPUTATIONAL MODELING OF CELLULAR EFFECTS POST-IRRADIATION WITH LOW- AND HIGH-LET PARTICLES AND DIFFERENT ABSORBED DOSES

The use of computational methods to improve the understanding of biological responses to various types of radiation is an approach where multiple parameters can be modelled and a variety of data is generated. This study compares cellular effects modelled for low absorbed doses against high absorbed doses. The authors hypothesized that low and high absorbed doses would contribute to cell killing via different mechanisms, potentially impacting on targeted tumour radiotherapy outcomes. Cellular kinetics following irradiation with selective low- and high-linear energy transfer (LET) particles were investigated using the Virtual Cell (VC) radiobiology algorithm. Two different cell types were assessed using the VC radiobiology algorithm: human fibroblasts and human crypt cells. The results showed that at lower doses (0.01 to 0.2 Gy), all radiation sources used were equally able to induce cell death (p\u3e0.05, ANOVA). On the other hand, at higher doses (1.0 to 8.0 Gy), the radiation response was LET and dose dependent (p\u3c0.05, ANOVA). The data obtained suggests that the computational methods used might provide some insight into the cellular effects following irradiation. The results also suggest that it may be necessary to re-evaluate cellular radiation-induced effects, particularly at low doses that could affect therapeutic effectiveness

Image segmentation for human motion analysis: methods and applications

Human motion analysis is closely connected with the development of computational techniques capable of automatically identify objects represented in image sequences, track and analyse its movement. Feature extraction is generally the first step in the study of human motion in image sequences which is strictly related to human motion modelling [1]. Next step is feature correspondence, where the problem of matching features between two consecutives image frames is addressed. Finally high level processing can be used in several applications of Computer Vision like, for instance, in the recognition of human movements, activities or poses. This work will focus in the study of image segmentation methods and applications for human motion analysis. Image segmentation methods related to human motion need to deal with several challenges such as: dynamic backgrounds, for instance when the camera is in motion; lighting conditions that can change along the image sequences; occlusion problems, when the subject does not remain inside the workspace; or image sequences with more than one subject in the workspace at the same time. It is not easy to develop methods which can deal with all thes

Image processing and analysis : applications and trends

The computational analysis of images is challenging as it usually involves tasks such as segmentation, extraction of representative features, matching, alignment, tracking, motion analysis, deformation estimation, and 3D reconstruction. To carry out each of these tasks in a fully automatic, efficient and robust manner is generally demanding.The quality of the input images plays a crucial role in the success of any image analysis task. The higher their quality, the easier and simpler the tasks are. Hence, suitable methods of image processing such as noise removal, geometric correction, edges and contrast enhancement or illumination correction are required.Despite the challenges, computational methods of image processing and analysis are suitable for a wide range of applications.In this paper, the methods that we have developed for processing and analyzing objects in images are introduced. Furthermore, their use in applications from medicine and biomechanics to engineering and materials sciences are presented

Methods to automatically build Point Distribution Models for objects like hand palms and faces represented in images

In this work we developed methods to automatically extract significant points of objects like hand palms and faces represented in images that can be used to build Point Distribution Models automatically. These models are further used to segment the modelled objects in new images, through the use of Active Shape Models or Active Appearance Models. These models showed to be efficient in the segmentation of objects, but had as drawback the fact that the labelling of the landmark points was usually manually made and consequently time consuming. Thus, in this paper we describe some methods capable to extract significant points of objects like hand palms and compare the segmentation results in new images