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

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

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

Soft tissue structure modelling for use in orthopaedic applications and musculoskeletal biomechanics

We present our methodology for the three-dimensional anatomical and geometrical description of soft tissues, relevant for orthopaedic surgical applications and musculoskeletal biomechanics. The technique involves the segmentation and geometrical description of muscles and neurovascular structures from high-resolution computer tomography scanning for the reconstruction of generic anatomical models. These models can be used for quantitative interpretation of anatomical and biomechanical aspects of different soft tissue structures. This approach should allow the use of these data in other application fields, such as musculoskeletal modelling, simulations for radiation therapy, and databases for use in minimally invasive, navigated and robotic surgery

Benchmark RGB-D Gait Datasets: A Systematic Review

Human motion analysis has proven to be a great source of information for a wide range of applications. Several approaches for a detailed and accurate motion analysis have been proposed in the literature, as well as an almost proportional number of dedicated datasets. The relatively recent arrival of depth sensors contributed to an increasing interest in this research area and also to the emergence of a new type of motion datasets. This work focuses on a systematic review of publicly available depth-based datasets, encompassing human gait data which is used for person recognition and/or classification purposes. We have conducted this systematic review using the Scopus database. The herein presented survey, which to the best of our knowledge is the first one dedicated to this type of datasets, is intended to inform and aid researchers on the selection of the most suitable datasets to develop, test and compare their algorithms. (c) Springer Nature Switzerland AG 2019

Evaluation of CNN-Based Human Pose Estimation for Body Segment Lengths Assessment

Human pose estimation (HPE) methods based on convolutional neural networks (CNN) have demonstrated significant progress and achieved state-of-the-art results on human pose datasets. In this study, we aimed to assess the perfor-mance of CNN-based HPE methods for measuring anthropometric data. A Vicon motion analysis system as the reference system and a stereo vision system recorded ten asymptomatic subjects standing in front of the stereo vision system in a static posture. Eight HPE methods estimated the 2D poses which were transformed to the 3D poses by using the stereo vision system. Percentage of correct keypoints, 3D error, and absolute error of the body segment lengths are the evaluation measures which were used to assess the results. Percentage of correct keypoints – the stand-ard metric for 2D pose estimation – showed that the HPE methods could estimate the 2D body joints with a minimum accuracy of 99%. Meanwhile, the average 3D error and absolute error for the body segment lengths are 5 cm

GRIDDS - A Gait Recognition Image and Depth Dataset

Several approaches based on human gait have been proposed in the literature, either for medical research reasons, smart surveillance, human-machine interaction, or other purposes, whose validation highly depends on the access to common input data through available datasets, enabling a coherent performance comparison. The advent of depth sensors leveraged the emergence of novel approaches and, consequently, the usage of new datasets. In this work we present the GRIDDS - A Gait Recognition Image and Depth Dataset, a new and publicly available gait depth-based dataset that can be used mostly for person and gender recognition purposes. (c) Springer Nature Switzerland AG 2019

Connections between Operator-splitting Methods and Deep Neural Networks with Applications in Image Segmentation

Deep neural network is a powerful tool for many tasks. Understanding why it is so successful and providing a mathematical explanation is an important problem and has been one popular research direction in past years. In the literature of mathematical analysis of deep deep neural networks, a lot of works are dedicated to establishing representation theories. How to make connections between deep neural networks and mathematical algorithms is still under development. In this paper, we give an algorithmic explanation for deep neural networks, especially in their connection with operator splitting and multigrid methods. We show that with certain splitting strategies, operator-splitting methods have the same structure as networks. Utilizing this connection and the Potts model for image segmentation, two networks inspired by operator-splitting methods are proposed. The two networks are essentially two operator-splitting algorithms solving the Potts model. Numerical experiments are presented to demonstrate the effectiveness of the proposed networks

Speckle ultrasound image filtering: Performance analysis and comparison

This paper compiles and compares well-known techniques mostly used in the smoothing or suppression of speckle noise in ultrasound images. A comparison of the methods studied is done based on an experiment, using quality metrics, texture analysisand interpretation of row profiles to evaluate their performance and show the benefits each one can contribute to denoising and feature preservation. To test the methods, a noise-free image of a kidney is used and then the Field II program simulates a B-mode ultrasound image. This way, the smoothing techniques can be compared using numeric metrics, taking the noise-free image as a reference. In this study, a total of seventeen different speckle reduction algorithms have been documented based on spatial filtering, diffusion filtering and wavelet filtering, with fifteen qualitative metrics estimation. We use the tendencies observed in our study in real images. This work was carried out in collaboration with S. Teotonio—Viseu (Portugal) Hospital. A new evaluation metric is proposed to evaluate the despeckling results.info:eu-repo/semantics/publishedVersio