Towards an efficient and robust foot classification from pedobarographic images

O documento em anexo encontra-se na versão post-print (versão corrigida pelo editor).This paper presents a new computational framework for automatic foot classification from digital plantar pressure images. It classifies the foot as left or right and simultaneously calculates two well-known footprint indices: the Cavanagh's arch index and the modified arch index. The accuracy of the framework was evaluated using a set of plantar pressure images from two common pedobarographic devices. The results were outstanding, since all feet under analysis were correctly classified as left or right and no significant differences were observed between the footprint indices calculated using the computational solution and the traditional manual method. The robustness of the proposed framework to arbitrary foot orientations and to the acquisition device was also tested and confirmed

Spatio-temporal alignment of pedobarographic image sequences

O documento em anexo encontra-se na versão post-print (versão corrigida pelo editor).This paper presents a methodology to align plantar pressure image sequences simultaneously in time and space. The spatial position and orientation of a foot in a sequence are changed to match the foot represented in a second sequence. Simultaneously with the spatial alignment, the temporal scale of the first sequence is transformed with the aim of synchronizing the two input footsteps. Consequently, the spatial correspondence of the foot regions along the sequences as well as the temporal synchronizing is automatically attained, making the study easier and more straightforward. In terms of spatial alignment, the methodology can use one of four possible geometric transformation models: rigid, similarity, affine or projective. In the temporal alignment, a polynomial transformation up to the 4th degree can be adopted in order to model linear and curved time behaviors. Suitable geometric and temporal transformations are found by minimizing the mean squared error (MSE) between the input sequences. The methodology was tested on a set of real image sequences acquired from a common pedobarographic device. When used in experimental cases generated by applying geometric and temporal control transformations, the methodology revealed high accuracy. Additionally, the intra-subject alignment tests from real plantar pressure image sequences showed that the curved temporal models produced better MSE results (p<0.001) than the linear temporal model. This paper represents an important step forward in the alignment of pedobarographic image data, since previous methods can only be applied on static images

Alinhamento computacional de imagens de pedobarografia estática e dinâmica

O alinhamento de imagens, isto é, o processo de transformação de uma imagem de modo que as estruturas representadas nessa imagem passem a estar ajustadas às estruturas homólogas representadas numa segunda imagem, é uma área de grande investigação em Visão Computacional. Na área médica, por exemplo, o alinhamento de imagens tem aplicações no auxílio ao diagnóstico, na fusão de informação contida em imagens obtidas por diferentes modalidades de imagem, monitorização temporal de órgãos e patologias, em cirurgia assistida por computador, etc.Na área da pedobarografia, o alinhamento de imagens é uma ferramenta relevante para clínicos e investigadores. Pois, após o adequado alinhamento computacional das imagens estáticas ou dinâmicas, tarefas como análise da distribuição da pressão plantar, comparação de imagens de um dado caso clínico com as imagens de casos previamente estudados, identificação automática de regiões, entre outras, ficam facilitadas e podem ser realizadas de forma automática.Nesta apresentação serão introduzidas três metodologias computacionais automáticas de alinhamento de imagens de pedobarografia estática (alinhamento de um par de imagens) e uma metodologia de alinhamento de sequências de imagens de pedobarografia dinâmica (associadas a passadas completa). Nos ensaios realizados envolvendo imagens de diferentes pessoas e obtidas por distintos equipamentos de pedobarografia, as referidas metodologias, descritas a seguir de forma resumida, revelaram elevada precisão e robustez, além de extrema rapidez de execução

Registration of plantar pressure images

We present an analysis of four different algorithms used to register plantar pressure images: a first one based on the matching of the external contours of the feet, a second algorithm based on the technique of phase correlation, a third one based on the direct optimization of the cross-correlation (CC) and using the Fourier transform, and a fourth and last algorithm that is based on the iterative optimization of an intensity (dis)similarity measure. In terms of accuracy, the later algorithm achieved the best registration results; on the other hand, the algorithm based on the matching of contours was the fastest, but its accuracy was inferior to the accuracy of the remaining algorithms

Tracking moving objects in image sequences

The computational movement analysis of objects in temporal image sequences is very changeling,given that it usually involves tasks for image enhancement, features segmentation, objects matchingand registration, features tracking and motion analysis. Notwithstanding the difficulties, thiscomputational analysis has a wide range of prominent applications; for instance, in engineering,medicine, virtual reality, biology and sports.Difficulties that frequently appear while tracking moving objects include the simultaneous trackingof manifold objects, objects temporary occlusion or definitively disappearance, variations of theviewpoints considered in the imaging acquisition or of the illumination conditions, or even nonrigiddeformations or topological alterations that objects may undergo.In this presentation, we are going to introduce and discuss methods often considered incomputational movement analysis of objects in image sequences; in particularly, for theirsegmentation, tracking and matching in images, and for estimation of the deformation involvedamong images