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

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

Modelling and segmentation of the vocal track during speech production by using deformable models in magnetic resonance images

The first and second authors would like to thank the support of the PhD grants with references SFRH/BD/28817/2006 and SFRH/PROTEC/49517/2009, respectively, from Fundação para a Ciência e Tecnol ogia (FCT). This work was partially done in the scope of the project “Methodologies to Analyze Organs from Complex Medical Images – Applications to Fema le Pelvic Cavity”, wi th reference PTDC/EEA- CRO/103320/2008, financially supported by FCT.Since ancient times, speech production has attracted particularly interest aiming at reaching a deeper understanding of the mechanisms involved by considering both morphological and speech acoustic aspects. The central anatomical aspects and the physiology of the human vocal tract are common to all individuals. However, speech production is an exceptionally complex and individualistic process. Therefore, the modelling of the mechanisms involved in speech production implies the enclosing of adequate flexibility in order to consider individual variations accurately. In this work, the shape of vocal tract in the articulation of some European Portuguese (EP) sounds is evaluated by using deformable models applied in Magnetic Resonance (MR) images. Additionally, the deformable models built are afterwards used to automatically segment the modelled vocal tract in MR images. From the imaging modalities that have been take n into consideration in order to study the vocal tract shape and articulators, Magnetic Resonance Imaging (MRI) has been the most commonly accepted. Actually, the use of MRI allows the study of the entire human vocal tract and, in addition, the quality and resolution of soft-tissues and the use of non-ionizing radiation are key advantages presented by MRI. The deformable model used, commonly known as Point Distribution Model (PDM), was built from a set of training images acquired du ring artificially sustained articulations of 21 EP sounds. In a brief review, one can assert that PDM’s are obtained by a statistical analysis done on the co-ordinates of landmark points that represent the shape to be modelled: after aligning the training shapes, a Principal Component Analysis is performed in order to obtain the model mean shape and the modes of variation relatively to this mean shap e. Combining the geometrical information of the PDM with the grey levels of the landmark points us ed in its building one can build the Active Shape Models (ASM) and the Active Appearance Models (AAM). With these enhanced models is possible to segment the modelled shape in new images in a fully automated way. From the experimental results obtained in this work, one may conclude that the PDM built could efficiently characterize the behaviour of the voca l tract shape during the production of the EP sounds studied with MRI. Furthermore, one can ve rify that the ASM and the AAM built could be used to segment the modelled vocal tract in MR images in a successful manner. Therefore, the deformable models built should be considered towards the efficient and automatic study of the vocal tract during speech production with MRI, in particular for enhanced speech production simulation and speech rehabilitation therapies.Fundação para a Ciência e Tecnologia (FCT

Human motion segmentation using active shape models

Human motion analysis in images is thoroughly related with the developmentof computational techniques capable of automatically identify, track andanalyze relevant structures of the body. In fact, in any system designed for humanmotion analysis from image sequences, the first processing step concerns the identificationof the structures to be analyzed in each of the sequence images, beingthis step commonly referred as image segmentation. Here, a widely used database,the CASIA Gait Database, is used to build Point Distribution Models (PDMs) ofthe human silhouette, including specific joints. The training image dataset used includes14 subjects walking in four different directions, and each shape of the trainingset was represented by a set of labeled landmark points. The contours of thesilhouettes were obtained with the purpose of automatically extract 100 silhouettepoints together with additional 13 anatomic joint points, such as elbows, knees andfeet, to be used as landmarks. In order to obtain the mean shape of the silhouetteas well as its admissible shape variations PDMs for each direction were built. ThePDMs built were finally used in the construction of Active Shape Models (ASMs),which combine the shape model with grey level profiles, with the purpose of furthersegment the modeled silhouettes in new images. The referred technique is aniterative optimization scheme for PDMs allowing initial estimates of pose, scaleand shape of an object to be refined in a new image. The experiments conductedusing this segmentation technique has revealed very encouraging results

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

Leveraging deep neural networks for automatic and standardised wound image acquisition

Wound monitoring is a time-consuming and error-prone activity performed daily by healthcare professionals. Capturing wound images is crucial in the current clinical practice, though image inadequacy can undermine further assessments. To provide sufficient information for wound analysis, the images should also contain a minimal periwound area. This work proposes an automatic wound image acquisition methodology that exploits deep learning models to guarantee compliance with the mentioned adequacy requirements, using a marker as a metric reference. A RetinaNet model detects the wound and marker regions, further analysed by a post-processing module that validates if both structures are present and verifies that a periwound radius of 4 centimetres is included. This pipeline was integrated into a mobile application that processes the camera frames and automatically acquires the image once the adequacy requirements are met. The detection model achieved [email protected] values of 0.39 and 0.95 for wound and marker detection, exhibiting a robust detection performance for varying acquisition conditions. Mobile tests demonstrated that the application is responsive, requiring 1.4 seconds on average to acquire an image. The robustness of this solution for real-time smartphone-based usage evidences its capability to standardise the acquisition of adequate wound images, providing a powerful tool for healthcare professionals.info:eu-repo/semantics/publishedVersio