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

3D object reconstruction using computer vision : reconstruction and characterization applications for external human anatomical structures

Tese de doutoramento. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 201

Depth Enhancement and Surface Reconstruction with RGB/D Sequence

Surface reconstruction and 3D modeling is a challenging task, which has been explored for decades by the computer vision, computer graphics, and machine learning communities. It is fundamental to many applications such as robot navigation, animation and scene understanding, industrial control and medical diagnosis. In this dissertation, I take advantage of the consumer depth sensors for surface reconstruction. Considering its limited performance on capturing detailed surface geometry, a depth enhancement approach is proposed in the first place to recovery small and rich geometric details with captured depth and color sequence. In addition to enhancing its spatial resolution, I present a hybrid camera to improve the temporal resolution of consumer depth sensor and propose an optimization framework to capture high speed motion and generate high speed depth streams. Given the partial scans from the depth sensor, we also develop a novel fusion approach to build up complete and watertight human models with a template guided registration method. Finally, the problem of surface reconstruction for non-Lambertian objects, on which the current depth sensor fails, is addressed by exploiting multi-view images captured with a hand-held color camera and we propose a visual hull based approach to recovery the 3D model

3D surface reconstruction for lower limb prosthetic model using modified radon transform

Computer vision has received increased attention for the research and innovation on three-dimensional surface reconstruction with aim to obtain accurate results. Although many researchers have come up with various novel solutions and feasibility of the findings, most require the use of sophisticated devices which is computationally expensive. Thus, a proper countermeasure is needed to resolve the reconstruction constraints and create an algorithm that is able to do considerably fast reconstruction by giving attention to devices equipped with appropriate specification, performance and practical affordability. This thesis describes the idea to realize three-dimensional surface of the residual limb models by adopting the technique of tomographic imaging coupled with the strategy based on multiple-views from a digital camera and a turntable. The surface of an object is reconstructed from uncalibrated two-dimensional image sequences of thirty-six different projections with the aid of Radon transform algorithm and shape-from-silhouette. The results show that the main objective to reconstruct three-dimensional surface of lower limb model has been successfully achieved with reasonable accuracy as the starting point to reconstruct three-dimensional surface and extract digital reading of an amputated lower limb model where the maximum percent error obtained from the computation is approximately 3.3 % for the height whilst 7.4%, 7.9% and 8.1% for the diameters at three specific heights of the objects. It can be concluded that the reconstruction of three-dimensional surface for the developed method is particularly dependent to the effects the silhouette generated where high contrast two-dimensional images contribute to higher accuracy of the silhouette extraction. The advantage of the concept presented in this thesis is that it can be done with simple experimental setup and the reconstruction of three-dimensional model neither involves expensive equipment nor require any service by an expert to handle sophisticated mechanical scanning system

Characterizing Objects in Images using Human Context

Humans have an unmatched capability of interpreting detailed information about existent objects by just looking at an image. Particularly, they can effortlessly perform the following tasks: 1) Localizing various objects in the image and 2) Assigning functionalities to the parts of localized objects. This dissertation addresses the problem of aiding vision systems accomplish these two goals. The first part of the dissertation concerns object detection in a Hough-based framework. To this end, the independence assumption between features is addressed by grouping them in a local neighborhood. We study the complementary nature of individual and grouped features and combine them to achieve improved performance. Further, we consider the challenging case of detecting small and medium sized household objects under human-object interactions. We first evaluate appearance based star and tree models. While the tree model is slightly better, appearance based methods continue to suffer due to deficiencies caused by human interactions. To this end, we successfully incorporate automatically extracted human pose as a form of context for object detection. The second part of the dissertation addresses the tedious process of manually annotating objects to train fully supervised detectors. We observe that videos of human-object interactions with activity labels can serve as weakly annotated examples of household objects. Since such objects cannot be localized only through appearance or motion, we propose a framework that includes human centric functionality to retrieve the common object. Designed to maximize data utility by detecting multiple instances of an object per video, the framework achieves performance comparable to its fully supervised counterpart. The final part of the dissertation concerns localizing functional regions or affordances within objects by casting the problem as that of semantic image segmentation. To this end, we introduce a dataset involving human-object interactions with strong i.e. pixel level and weak i.e. clickpoint and image level affordance annotations. We propose a framework that utilizes both forms of weak labels and demonstrate that efforts for weak annotation can be further optimized using human context

Global optimization methods for full-reference and no-reference motion estimation with applications to atherosclerotic plaque motion and strain imaging

Pixel-based motion estimation using optical flow models has been extensively researched during the last two decades. The driving force of this research field is the amount of applications that can be developed with the motion estimates. Image segmentation, compression, activity detection, object tracking, pattern recognition, and more recently non-invasive biomedical applications like strain imaging make the estimation of accurate velocity fields necessary. The majority of the research in this area is focused on improving the theoretical and numerical framework of the optical flow models. This effort has resulted in increased method complexity with an increasing number of motion parameters. The standard approach of heuristically setting the motion parameters has become a major source of estimation error. This dissertation is focused in the development of reliable motion estimation based on global parameter optimization methods. Two strategies have been developed. In full-reference optimization, the assumption is that a video training set of realistic motion simulations (or ground truth) are available. Global optimization is used to calculate the best motion parameters that can then be used on a separate set of testing videos. This approach helps provide bounds on what motion estimation methods can achieve. In no-reference optimization, the true displacement field is not available. By optimizing for the agreement between different motion estimation techniques, the no-reference approach closely approximates the best (optimal) motion parameters. The results obtained with the newly developed global no-reference optimization approach agree closely with those produced with the full-reference approach. Moreover, the no-reference approach calculates velocity fields of superior quality than published results for benchmark video sequences. Unreliable velocity estimates are identified using new confidence maps that are associated with the disagreement between methods. Thus, the no-reference global optimization method can provide reliable motion estimation without the need for realistic simulations or access to ground truth. The methods developed in this dissertation are applied to ultrasound videos of carotid artery plaques. The velocity estimates are used to analyze plaque motion and produce novel non-invasive elasticity maps that can help in the identification of vulnerable atherosclerotic plaques

Robust surface modelling of visual hull from multiple silhouettes

Reconstructing depth information from images is one of the actively researched themes in computer vision and its application involves most vision research areas from object recognition to realistic visualisation. Amongst other useful vision-based reconstruction techniques, this thesis extensively investigates the visual hull (VH) concept for volume approximation and its robust surface modelling when various views of an object are available. Assuming that multiple images are captured from a circular motion, projection matrices are generally parameterised in terms of a rotation angle from a reference position in order to facilitate the multi-camera calibration. However, this assumption is often violated in practice, i.e., a pure rotation in a planar motion with accurate rotation angle is hardly realisable. To address this problem, at first, this thesis proposes a calibration method associated with the approximate circular motion. With these modified projection matrices, a resulting VH is represented by a hierarchical tree structure of voxels from which surfaces are extracted by the Marching cubes (MC) algorithm. However, the surfaces may have unexpected artefacts caused by a coarser volume reconstruction, the topological ambiguity of the MC algorithm, and imperfect image processing or calibration result. To avoid this sensitivity, this thesis proposes a robust surface construction algorithm which initially classifies local convex regions from imperfect MC vertices and then aggregates local surfaces constructed by the 3D convex hull algorithm. Furthermore, this thesis also explores the use of wide baseline images to refine a coarse VH using an affine invariant region descriptor. This improves the quality of VH when a small number of initial views is given. In conclusion, the proposed methods achieve a 3D model with enhanced accuracy. Also, robust surface modelling is retained when silhouette images are degraded by practical noise

Robust surface modelling of visual hull from multiple silhouettes

Reconstructing depth information from images is one of the actively researched themes in computer vision and its application involves most vision research areas from object recognition to realistic visualisation. Amongst other useful vision-based reconstruction techniques, this thesis extensively investigates the visual hull (VH) concept for volume approximation and its robust surface modelling when various views of an object are available. Assuming that multiple images are captured from a circular motion, projection matrices are generally parameterised in terms of a rotation angle from a reference position in order to facilitate the multi-camera calibration. However, this assumption is often violated in practice, i.e., a pure rotation in a planar motion with accurate rotation angle is hardly realisable. To address this problem, at first, this thesis proposes a calibration method associated with the approximate circular motion. With these modified projection matrices, a resulting VH is represented by a hierarchical tree structure of voxels from which surfaces are extracted by the Marching cubes (MC) algorithm. However, the surfaces may have unexpected artefacts caused by a coarser volume reconstruction, the topological ambiguity of the MC algorithm, and imperfect image processing or calibration result. To avoid this sensitivity, this thesis proposes a robust surface construction algorithm which initially classifies local convex regions from imperfect MC vertices and then aggregates local surfaces constructed by the 3D convex hull algorithm. Furthermore, this thesis also explores the use of wide baseline images to refine a coarse VH using an affine invariant region descriptor. This improves the quality of VH when a small number of initial views is given. In conclusion, the proposed methods achieve a 3D model with enhanced accuracy. Also, robust surface modelling is retained when silhouette images are degraded by practical noise

Automatic 3D human modeling: an initial stage towards 2-way inside interaction in mixed reality

3D human models play an important role in computer graphics applications from a wide range of domains, including education, entertainment, medical care simulation and military training. In many situations, we want the 3D model to have a visual appearance that matches that of a specific living person and to be able to be controlled by that person in a natural manner. Among other uses, this approach supports the notion of human surrogacy, where the virtual counterpart provides a remote presence for the human who controls the virtual character\u27s behavior. In this dissertation, a human modeling pipeline is proposed for the problem of creating a 3D digital model of a real person. Our solution involves reshaping a 3D human template with a 2D contour of the participant and then mapping the captured texture of that person to the generated mesh. Our method produces an initial contour of a participant by extracting the user image from a natural background. One particularly novel contribution in our approach is the manner in which we improve the initial vertex estimate. We do so through a variant of the ShortStraw corner-finding algorithm commonly used in sketch-based systems. Here, we develop improvements to ShortStraw, presenting an algorithm called IStraw, and then introduce adaptations of this improved version to create a corner-based contour segmentatiuon algorithm. This algorithm provides significant improvements on contour matching over previously developed systems, and does so with low computational complexity. The system presented here advances the state of the art in the following aspects. First, the human modeling process is triggered automatically by matching the participant\u27s pose with an initial pose through a tracking device and software. In our case, the pose capture and skeletal model are provided by the Microsoft Kinect and its associated SDK. Second, color image, depth data, and human tracking information from the Kinect and its SDK are used to automatically extract the contour of the participant and then generate a 3D human model with skeleton. Third, using the pose and the skeletal model, we segment the contour into eight parts and then match the contour points on each segment to a corresponding anchor set associated with a 3D human template. Finally, we map the color image of the person to the 3D model as its corresponding texture map. The whole modeling process only take several seconds and the resulting human model looks like the real person. The geometry of the 3D model matches the contour of the real person, and the model has a photorealistic texture. Furthermore, the mesh of the human model is attached to the skeleton provided in the template, so the model can support programmed animations or be controlled by real people. This human control is commonly done through a literal mapping (motion capture) or a gesture-based puppetry system. Our ultimate goal is to create a mixed reality (MR) system, in which the participants can manipulate virtual objects, and in which these virtual objects can affect the participant, e.g., by restricting their mobility. This MR system prototype design motivated the work of this dissertation, since a realistic 3D human model of the participant is an essential part of implementing this vision