Removal of specular reflections from image sequences using feature correspondences

The presence of specular highlights can hide underlying features of a scene within an image and can be problematic in many application scenarios. In particular, this poses a significant challenge for applications where image stitching is used to create a single static image of a scene from inspection footage of pipes, gas tubes, train tracks and concrete structures. Furthermore, they can hide small defects in the images causing them to be missed during inspection. We present a method which exploits additional information in neighbouring frames from video footage to reduce specularity from each frame. The technique first automatically determines frames which contain overlapping regions before the relationship that exists between them is exploited in order to suppress the effects of specular reflections. This results in an image that is free from specular highlights provided there is at least one frame present in the sequence where a given pixel is present in a diffuse form. The method is shown to work well on greyscale as well as colour images and effectively reduces specularity and significantly improves the quality of the stitched image, even in the presence of noise. While applied to the challenge of reducing specularity in inspection videos, the method improves upon the state-of-the-art in specularity removal, and, its applications are wider ranging as a general purpose pre-processing tool

Point and line feature-based observer design on SL(3) for Homography estimation and its application to image stabilization

This paper presents a new algorithm for online estimation of a sequence of homographies applicable to image sequences obtained from robotic vehicles equipped with a monocular camera. The approach taken exploits the underlying Special Linear group SL(3) structure of the set of homographies along with gyrometer measurements and direct point-and line-feature correspondences between images to develop temporal filter for the homography estimate. Theoretical analysis and experimental results are provided to demonstrate the robustness of the proposed algorithm. The experimental results show excellent performance even in the case of very fast camera motion (relative to frame rate), and in presence of severe occlusion, specular reflection, image blur, and light saturation

Robust Specularity Removal from Hand-held Videos

Specular reflection exists when one tries to record a photo or video through a transparent glass medium or opaque surfaces such as plastics, ceramics, polyester and human skin, which can be well described as the superposition of a transmitted layer and a reflection layer. These specular reflections often confound the algorithms developed for image analysis, computer vision and pattern recognition. To obtain a pure diffuse reflection component, specularity (highlights) needs to be removed. To handle this problem, a novel and robust algorithm is formulated. The contributions of this work are three-fold.;First, the smoothness of the video along with the temporal coherence and illumination changes are preserved by reducing the flickering and jagged edges caused by hand-held video acquisition and homography transformation respectively.;Second, this algorithm is designed to improve upon the state-of-art algorithms by automatically selecting the region of interest (ROI) for all the frames, reducing the computational time and complexity by utilizing the luminance (Y) channel and exploiting the Augmented Lagrange Multiplier (ALM) with Alternating Direction Minimizing (ADM) to facilitate the derivation of solution algorithms.;Third, a quantity metrics is devised, which objectively quantifies the amount of specularity in each frame of a hand-held video. The proposed specularity removal algorithm is compared against existing state-of-art algorithms using the newly-developed quantity metrics. Experimental results validate that the developed algorithm has superior performance in terms of computation time, quality and accuracy

Surgical Guidance for Removal of Cholesteatoma Using a Multispectral 3D-Endoscope

We develop a stereo-multispectral endoscopic prototype in which a filter-wheel is used for surgical guidance to remove cholesteatoma tissue in the middle ear. Cholesteatoma is a destructive proliferating tissue. The only treatment for this disease is surgery. Removal is a very demanding task, even for experienced surgeons. It is very difficult to distinguish between bone and cholesteatoma. In addition, it can even reoccur if not all tissue particles of the cholesteatoma are removed, which leads to undesirable follow-up operations. Therefore, we propose an image-based method that combines multispectral tissue classification and 3D reconstruction to identify all parts of the removed tissue and determine their metric dimensions intraoperatively. The designed multispectral filter-wheel 3D-endoscope prototype can switch between narrow-band spectral and broad-band white illumination, which is technically evaluated in terms of optical system properties. Further, it is tested and evaluated on three patients. The wavelengths 400 nm and 420 nm are identified as most suitable for the differentiation task. The stereoscopic image acquisition allows accurate 3D surface reconstruction of the enhanced image information. The first results are promising, as the cholesteatoma can be easily highlighted, correctly identified, and visualized as a true-to-scale 3D model showing the patient-specific anatomy.Peer Reviewe

Reconstruction of Specular Surfaces from Reflectance Correspondences

Image-based reconstruction of specular surfaces usually requires dense correspondences between image features and points in the environment. In natural environments, these points are usually unknown and correspondences often exist only sparsely between pairs of images. These assumptions complicate the reconstruction problem by introducing many ambiguities which can often only be resolved using regularization of the surface. Only very recently, work has been presented which is able to reconstruct specular surfaces using different kinds of algorithms. This thesis gives an introduction to the different types of ambiguities and presents a framework which tries to resolve these through regularization using a multi-view approach in combination with a low-parametric surface. The reconstruction method is modeled as an iterative optimization in order to achieve specular consistency. This consistency is based on the laws of reflection applied to the viewing rays which are given by image-to-image features. The framework is capable of processing different kinds of additional input data, e.g. known environmental features or boundary points on the surface. Synthetic and real-world experiments were executed using both known and unknown feature positions. Results on synthetic datasets show accurate reconstructions even in the presence of specular consistent ambiguities. An adapted outlier removal for feature matching on image series of specular objects was applied to real-wold input data. The results show that it is possible to reconstruct the surface of mirroring objects even with sparse input data

Multiple View Geometry For Video Analysis And Post-production

Multiple view geometry is the foundation of an important class of computer vision techniques for simultaneous recovery of camera motion and scene structure from a set of images. There are numerous important applications in this area. Examples include video post-production, scene reconstruction, registration, surveillance, tracking, and segmentation. In video post-production, which is the topic being addressed in this dissertation, computer analysis of the motion of the camera can replace the currently used manual methods for correctly aligning an artificially inserted object in a scene. However, existing single view methods typically require multiple vanishing points, and therefore would fail when only one vanishing point is available. In addition, current multiple view techniques, making use of either epipolar geometry or trifocal tensor, do not exploit fully the properties of constant or known camera motion. Finally, there does not exist a general solution to the problem of synchronization of N video sequences of distinct general scenes captured by cameras undergoing similar ego-motions, which is the necessary step for video post-production among different input videos. This dissertation proposes several advancements that overcome these limitations. These advancements are used to develop an efficient framework for video analysis and post-production in multiple cameras. In the first part of the dissertation, the novel inter-image constraints are introduced that are particularly useful for scenes where minimal information is available. This result extends the current state-of-the-art in single view geometry techniques to situations where only one vanishing point is available. The property of constant or known camera motion is also described in this dissertation for applications such as calibration of a network of cameras in video surveillance systems, and Euclidean reconstruction from turn-table image sequences in the presence of zoom and focus. We then propose a new framework for the estimation and alignment of camera motions, including both simple (panning, tracking and zooming) and complex (e.g. hand-held) camera motions. Accuracy of these results is demonstrated by applying our approach to video post-production applications such as video cut-and-paste and shadow synthesis. As realistic image-based rendering problems, these applications require extreme accuracy in the estimation of camera geometry, the position and the orientation of the light source, and the photometric properties of the resulting cast shadows. In each case, the theoretical results are fully supported and illustrated by both numerical simulations and thorough experimentation on real data

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions