29 research outputs found

    Improvement of modal matching image objects in dynamic pedobarography using optimization techniques

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    This paper presents an improved approach for matching objects represented in dynamic pedobarography image sequences, based on finite element modeling, modal analysis and optimization techniques. In this work, the determination of correspondences between objects data points is improved by using optimization techniques and, because the number of data points of each object is not necessary the same, a new algorithm to match the excess points is also proposed. This new matching algorithm uses a neighbourhood criterion and can overcome some disadvantages of the usual one to one matching. The considered approach allows the determination of correspondences between 2D or 3D objects data points, and is here apply in dynamic pedobarography images

    Algorithm of dynamic programming for optimization of the global matching between two contours defined by ordered points

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    This paper presents a new assignment algorithm with order restriction. Our optimization algorithm was developed using dynamic programming. It was implemented and tested to determine the best global matching that preserves the order of the points that define two contours to be matched. In the experimental tests done, we used the affinity matrix obtained via the method proposed by Shapiro, based on geometric modeling and modal matching. \newline The proposed algorithm revealed an optimum performance, when compared with classic assignment algorithms: Hungarian Method, Simplex for Flow Problems and LAPm. Indeed, the quality of the matching improved when compared with these three algorithms, due to the disappearance of crossed matching, which is allowed by the conventional assignment algorithms. Moreover, the computational cost of this algorithm is much lower than the ones of other three, leading to enhanced execution times

    Tracking moving objects in image sequences

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    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

    Computer analysis of objects’ movement in image sequences: methods and applications

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    Computer analysis of objects’ movement in image sequences is a very complex problem, considering that it usually involves tasks for automatic detection, matching, tracking, motion analysis and deformation estimation. In spite of its complexity, this computational analysis has a wide range of important applications; for instance, in surveillance systems, clinical analysis of human gait, objects recognition, pose estimation and deformation analysis. Due to the extent of the purposes, several difficulties arise, such as the simultaneous tracking of manifold objects, their possible temporary occlusion or definitive disappearance from the image scene, changes of the viewpoints considered in images acquisition or of the illumination conditions, or even nonrigid deformations that objects may suffer in image sequences. In this paper, we present an overview of several methods that may be considered to analyze objects’ movement; namely, for their segmentation, tracking and matching in images, and for estimation of the deformation involved between images.This paper was partially done in the scope of project “Segmentation, Tracking and Motion Analysis of Deformable (2D/3D) Objects using Physical Principles”, with reference POSC/EEA-SRI/55386/2004, financially supported by FCT -Fundação para a Ciência e a Tecnologia from Portugal. The fourth, fifth and seventh authors would like to thank also the support of their PhD grants from FCT with references SFRH/BD/29012/2006, SFRH/BD/28817/2006 and SFRH/BD/12834/2003, respectively

    Image processing and analysis : applications and trends

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    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

    Towards an efficient and robust foot classification from pedobarographic images

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    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

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    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

    Registration of pedobarographic images

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    In this study, we compare the performance of six fully automatic methods of within-subjects pedobarographic image registration: principal axes, modal matching, min(XOR), min(MSE), contours-based and frequency-based. These algorithms were tested on 30 control image pairs considered in previous studies. The accuracy was assessed by visual inspection and using the image similarity measures: exclusive-or (XOR) and mean squared error (MSE). Visually, we did not find differences in the registration accuracy among the min(XOR), min(MSE), contours-based and frequency-based algorithms. On the other hand, using the similarity measures, we found out that the best XOR value was achieved by the contours-based algorithm, closely followed by the min(XOR). Additionally, the best MSE value was achieved by the min(MSE) algorithm, nearly followed by the frequency-based algorithm. Finally, the algorithms based on principal axes and modal matching revealed low robustness

    Registration of plantar pressure images

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    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
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