The cast shadows in an image provide important information about illumination and geometry. In this paper, we utilize this information in a novel framework in order to jointly recover the illumination environment, a set of geometry parameters, and an estimate of the cast shadows in the scene given a single image and coarse initial 3D geometry. We model the interaction of illumination and geometry in the scene and associate it with image evidence for cast shadows using a higher order Markov Random Field (MRF) illumination model, while we also introduce a method to obtain approximate image evidence for cast shadows. Capturing the interaction between light sources and geometry in the proposed graphical model necessitates higher order cliques and continuous-valued variables, which make inference challenging. Taking advantage of domain knowledge, we provide a two-stage minimization technique for the MRF energy of our model. We evaluate our method in different datasets, both synthetic and real. Our model is robust to rough knowledge of geometry and inaccurate initial shadow estimates, allowing a generic coarse 3D model to represent a whole class of objects for the task of illumination estimation, or the estimation of geometry parameters to refine our initial knowledge of scene geometry, simultaneously with illumination estimation.

Although a lot of research has been performed in the field of reconstructing 3D shape from the shading in an image, only a small portion of this work has examined the association of local shading patterns over image patches with the underlying 3D geometry. Such approaches are a promising way to tackle the ambiguities inherent in the shape-from-shading (SfS) problem, but issues such as their sensitivity to non-lambertian reflectance or photometric calibration have reduced their real-world applicability. In this paper we show how the information in local shading patterns can be utilized in a practical approach applicable to real-world images, obtaining results that improve the state of the art in the SfS problem. Our approach is based on learning a set of geometric primitives, and the distribution of local shading patterns that each such primitive may produce under different reflectance parameters. The resulting dictionary of primitives is used to produce a set of hypotheses about 3D shape; these hypotheses are combined in a Markov Random Field (MRF) model to determine the final 3D shape.

In this paper, we introduce a simple but efficient cue for the extraction of shadows from a single color image, the bright channel cue. We discuss its limitations and offer two methods to refine the bright channel: by computing confidence values for the cast shadows, based on a shadow-dependent feature, such as hue; and by combining the bright channel with illumination invariant representations of the original image in a flexible way using an MRF model. We present qualitative and quantitative results for shadow detection, as well as results in illumination estimation from shadows. Our results show that our method achieves satisfying results despite the simplicity of the approach.

In this paper, we propose a novel framework to jointly recover the illumination environment and an estimate of the cast shadows in a scene from a single image, given coarse 3D geometry. We describe a higher-order Markov Random Field (MRF) illumination model, which combines low-level shadow evidence with high-level prior knowledge for the joint estimation of cast shadows and the illumination environment. First, a rough illumination estimate and the structure of the graphical model in the illumination space is determined through a voting procedure. Then, a higher order approach is considered where illumination sources are coupled with the observed image and the latent variables corresponding to the shadow detection. We examine two inference methods in order to effectively minimize the MRF energy of our model. Experimental evaluation shows that our approach is robust to rough knowledge of geometry and reflectance and inaccurate initial shadow estimates. We demonstrate the power of our MRF illumination model on various datasets and show that we can estimate the illumination in images of objects belonging to the same class using the same coarse 3D model to represent all instances of the class.

Image formation is a function of three components: scene geometry, surface reflectance and illumination. Estimation of one or more of these components from an image gives rise to inverse rendering problems, such as shape reconstruction or illumination estimation, which are the two major problems of interest in this thesis. We formulate such problems in a way that attempts to bridge the gap between low-level approaches based on the physical laws governing image formation and higher-level models that examine images in a statistical way. We take advantage of the powerful formalism offered by graphical models, which lead to modular frameworks and offer powerful discrete optimization techniques. We first focus on the problem of illumination estimation from a single image, utilizing the information in cast shadows. We start by describing a method to extract cast shadows from an image. We then present three approaches to illumination estimation from shadows: The first models illumination as a mixture of distributions to robustly estimate illumination. The second associates illumination not with pixel intensities but with the existence of shadow edges. The third approach unifies the previous ideas in a Markov Random Field (MRF) framework. Such a model is robust to coarse or incomplete knowledge of geometry, while it can also incorporate geometric parameters, allowing us to jointly infer three major components of the problem: the cast shadows, illumination and geometry. Geometry inference from the information contained in cast shadows can only be coarse, however. We subsequently focus on the problem of inferring geometry from the shading variations in an image. We take a data-driven approach, constructing a dictionary of geometric primitives. To reconstruct an image, we combine local hypotheses from this dictionary in an MRF model. We demonstrate that this approach can effectively reconstruct 3D shapes from real photographs, while removing several important assumptions of previous approaches.

We evaluated the efficacy and safety of ‘punctal switch’ grafting, a proposed new technique for permanent punctal occlusion. We prospectively evaluated the results and complications in 22 patients (5 males and 17 females) who underwent the procedure in our department over a period of 3 years. Patients’ ages ranged from 41 to 81 years. The average follow-up was 12.8 months. Subjective and clinical improvement was documented in 91% of the cases with a low rate of recanalization (9%). Only 1 patient developed epiphora postoperatively, and the operation has been successfully reversed in that case. There were no serious complications. As a conclusion, punctal switch grafting is an effective technique for permanent punctal occlusion. The complication rate is low, recanalization is rare and the occlusion can be reversed if symptomatic epiphora develops.

In this paper we discuss illumination estimation from a single image in general scenes and associate it with the existence of shadow edges, avoiding several pitfalls that burden previous illumination estimation approaches, which rely on associating a parametrization of illumination with the per pixel intensity of shadows or shading. We show a way to couple shadow and illumination estimation, relying only on the subset of shadow edges that is relevant to the provided geometry. In our approach, illumination estimation is posed as the minimization of an energy function that penalizes the matching between the expected shadow outline and observed image edges. Minimizing this energy function is strongly tied to selecting the appropriate set of potential shadow edges in the image. Our approach leads to an illumination estimation algorithm that performs on par with or better than the state of the art, even when scene geometry knowledge is limited, while having much lower computational complexity than state-of-the-art methods. We demonstrate the effectiveness of this approach both with quantitative results on synthetic data and qualitative evaluation on real images.

Anterior capsular opacification and contraction syndrome is a well-recognised complication of uneventful phacoemulsification. It often results in a clinically significant reduction in vision secondary to central opacification, intraocular lens decentration and tilt. We report 3 cases of anterior capsular phimosis, which we treated using a new technique, where long, fine pointed scissors are used to cut the anterior capsule radially towards the edge of the lens optic and the edge is then grasped with capsulorrhexis forceps and the anterior capsule is torn away. The tear occurs at the edge of the optic because the anterior and posterior capsules are fused at this point. We believe that our technique offers a superior alternative for the effective, safe and quick management of anterior capsular phimosis, thereby improving the intraoperative fundus view for vitreoretinal surgery or delivery of laser treatment.

Illuminant estimation from shadows typically relies on accurate segmentation of the shadows and knowledge of exact 3D geometry, while shadow estimation is difficult in the presence of texture. These can be onerous requirements; in this paper we propose a graphical model to estimate the illumination environment and detect the shadows of a scene with textured surfaces from a single image and only coarse 3D information. We represent the illumination environment as a mixture of von Mises-Fisher distributions. Then, each shadow pixel becomes the combination of samples generated from this illumination environment. We integrate a number of low-level, illumination-invariant 2D cues in a graphical model to detect and estimate cast shadows on textured surfaces. Both 2D cues and approximate 3D reasoning are combined to infer a set of labels that identify the shadows in the image and estimate the positions, shapes and intensities of the light sources. Our results demonstrate that the probabilistic combination of multiple cues, unlike prior approaches, manages to differentiate both hard and soft shadows from the underlying surface texture even when we can only coarsely anticipate the effect of 3D geometry. We also experimentally demonstrate how correct estimation of the sharpness and shape of the light sources improves the augmented reality results.

Bilateral congenital hamartomas of the retinal pigment epithelium in a patient with Down's syndrome

The majority of the systems and platforms developed for supporting distributed virtual environments are based on the concept of distribution from the early beginning of their development. In this paper we present the migration to a distributed virtual environment from a traditional client-server architecture. In particular, this paper describes the case of EVE, a networked virtual environment originally aimed to support small-scale applications. EVE started as a standard client-multi server architecture, which could support multiple concurrent virtual worlds with a maximum number of seventeen simultaneous participants in each of these worlds. However, the need to support larger-scale applications revealed that the traditional architecture, upon which EVE was based, is insufficient to meet the needs of these applications, which are large both in the sense of virtual space and graphics and in regard to the number of concurrent participants. This paper discusses the migration of EVE to a distributed platform, which is able to support large-scale networked virtual environments. In particular, the paper describes the modifications realized in the architectural model of the initial platform for supporting effectively large-scale applications. The basic entities of the distributed model are presented, their operations, as well as the interconnection among them. In addition, the paper presents an initial approach of the algorithm that is adopted for the efficient partitioning of the virtual world and the assignment of the clients to the entities and resources of the distributed platform. The approach presented is space-object driven, in the sense that both the actual size of the virtual space along with the number of objects with which the user can interact is taken into account during the partitioning

This paper presents the design, implementation and evaluation of EVE Community Prototype, which is an educational virtual community aiming to meet the requirements of a Virtual Collaboration Space and to support e-learning services. Furthermore, this paper describes the design and implementation of an integrated platform for Networked Virtual Environments, called EVE Platform, which supports the afore-mentioned educational community. This platform supports stable event sharing and creation of multi-user three dimensional (3D) places, H.323-based voice over IP services integrated in 3D spaces as well as multiple concurrent virtual worlds.

In this chapter, we present the design and implementation of an integrated platform for Educational Virtual Environments. This platform aims to support an educational community, synchronous online courses in multi-user three-dimensional (3D) environments, and the creation and access of asynchronous courses through a learning content management system. In order to offer synchronous courses, we have implementeda system called EVE-II, which supports stable event sharing for multi-user 3D places, easy creation of multi-user 3D places, H.323-based voice- over IP services fully integrated in a 3D space, as well as many concurrent 3D multi-user spaces.