Optical flow estimation using steered-L1 norm

Motion is a very important part of understanding the visual picture of the surrounding environment. In image processing it involves the estimation of displacements for image points in an image sequence. In this context dense optical flow estimation is concerned with the computation of pixel displacements in a sequence of images, therefore it has been used widely in the field of image processing and computer vision. A lot of research was dedicated to enable an accurate and fast motion computation in image sequences. Despite the recent advances in the computation of optical flow, there is still room for improvements and optical flow algorithms still suffer from several issues, such as motion discontinuities, occlusion handling, and robustness to illumination changes. This thesis includes an investigation for the topic of optical flow and its applications. It addresses several issues in the computation of dense optical flow and proposes solutions. Specifically, this thesis is divided into two main parts dedicated to address two main areas of interest in optical flow. In the first part, image registration using optical flow is investigated. Both local and global image registration has been used for image registration. An image registration based on an improved version of the combined Local-global method of optical flow computation is proposed. A bi-lateral filter was used in this optical flow method to improve the edge preserving performance. It is shown that image registration via this method gives more robust results compared to the local and the global optical flow methods previously investigated. The second part of this thesis encompasses the main contribution of this research which is an improved total variation L1 norm. A smoothness term is used in the optical flow energy function to regularise this function. The L1 is a plausible choice for such a term because of its performance in preserving edges, however this term is known to be isotropic and hence decreases the penalisation near motion boundaries in all directions. The proposed improved L1 (termed here as the steered-L1 norm) smoothness term demonstrates similar performance across motion boundaries but improves the penalisation performance along such boundaries

Automated Analysis of Time-resolved X-ray data using Optical Flow Methods

We develop a general-purpose framework for analysis of time-resolved X-ray data based on optical flow. We perform a systematic evaluation of state-of-the-art optical flow techniques and their components. On the top of motion estimation we provide an extensive data analysis toolkit. All the devised techniques can be applied in 4D (3D + time). The implementation employs advanced numerical schemes and computations on GPU. We present the application of the optical flow methods to a number of scientific problems from various research fields

3D Motion Analysis via Energy Minimization

This work deals with 3D motion analysis from stereo image sequences for driver assistance systems. It consists of two parts: the estimation of motion from the image data and the segmentation of moving objects in the input images. The content can be summarized with the technical term machine visual kinesthesia, the sensation or perception and cognition of motion. In the first three chapters, the importance of motion information is discussed for driver assistance systems, for machine vision in general, and for the estimation of ego motion. The next two chapters delineate on motion perception, analyzing the apparent movement of pixels in image sequences for both a monocular and binocular camera setup. Then, the obtained motion information is used to segment moving objects in the input video. Thus, one can clearly identify the thread from analyzing the input images to describing the input images by means of stationary and moving objects. Finally, I present possibilities for future applications based on the contents of this thesis. Previous work in each case is presented in the respective chapters. Although the overarching issue of motion estimation from image sequences is related to practice, there is nothing as practical as a good theory (Kurt Lewin). Several problems in computer vision are formulated as intricate energy minimization problems. In this thesis, motion analysis in image sequences is thoroughly investigated, showing that splitting an original complex problem into simplified sub-problems yields improved accuracy, increased robustness, and a clear and accessible approach to state-of-the-art motion estimation techniques. In Chapter 4, optical flow is considered. Optical flow is commonly estimated by minimizing the combined energy, consisting of a data term and a smoothness term. These two parts are decoupled, yielding a novel and iterative approach to optical flow. The derived Refinement Optical Flow framework is a clear and straight-forward approach to computing the apparent image motion vector field. Furthermore this results currently in the most accurate motion estimation techniques in literature. Much as this is an engineering approach of fine-tuning precision to the last detail, it helps to get a better insight into the problem of motion estimation. This profoundly contributes to state-of-the-art research in motion analysis, in particular facilitating the use of motion estimation in a wide range of applications. In Chapter 5, scene flow is rethought. Scene flow stands for the three-dimensional motion vector field for every image pixel, computed from a stereo image sequence. Again, decoupling of the commonly coupled approach of estimating three-dimensional position and three dimensional motion yields an approach to scene ow estimation with more accurate results and a considerably lower computational load. It results in a dense scene flow field and enables additional applications based on the dense three-dimensional motion vector field, which are to be investigated in the future. One such application is the segmentation of moving objects in an image sequence. Detecting moving objects within the scene is one of the most important features to extract in image sequences from a dynamic environment. This is presented in Chapter 6. Scene flow and the segmentation of independently moving objects are only first steps towards machine visual kinesthesia. Throughout this work, I present possible future work to improve the estimation of optical flow and scene flow. Chapter 7 additionally presents an outlook on future research for driver assistance applications. But there is much more to the full understanding of the three-dimensional dynamic scene. This work is meant to inspire the reader to think outside the box and contribute to the vision of building perceiving machines.</em

Long-range video motion estimation using point trajectories

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (leaves 97-104).This thesis describes a new approach to video motion estimation, in which motion is represented using a set of particles. Each particle is an image point sample with a long-duration trajectory and other properties. To optimize these particles, we measure point-based matching along the particle trajectories and distortion between the particles. The resulting motion representation is useful for a variety of applications and differs from optical flow, feature tracking, and parametric or layer-based models. We demonstrate the algorithm on challenging real-world videos that include complex scene geometry, multiple types of occlusion, regions with low texture, and non-rigid deformation.by Peter Sand.Ph.D

NOVEL DENSE STEREO ALGORITHMS FOR HIGH-QUALITY DEPTH ESTIMATION FROM IMAGES

This dissertation addresses the problem of inferring scene depth information from a collection of calibrated images taken from different viewpoints via stereo matching. Although it has been heavily investigated for decades, depth from stereo remains a long-standing challenge and popular research topic for several reasons. First of all, in order to be of practical use for many real-time applications such as autonomous driving, accurate depth estimation in real-time is of great importance and one of the core challenges in stereo. Second, for applications such as 3D reconstruction and view synthesis, high-quality depth estimation is crucial to achieve photo realistic results. However, due to the matching ambiguities, accurate dense depth estimates are difficult to achieve. Last but not least, most stereo algorithms rely on identification of corresponding points among images and only work effectively when scenes are Lambertian. For non-Lambertian surfaces, the brightness constancy assumption is no longer valid. This dissertation contributes three novel stereo algorithms that are motivated by the specific requirements and limitations imposed by different applications. In addressing high speed depth estimation from images, we present a stereo algorithm that achieves high quality results while maintaining real-time performance. We introduce an adaptive aggregation step in a dynamic-programming framework. Matching costs are aggregated in the vertical direction using a computationally expensive weighting scheme based on color and distance proximity. We utilize the vector processing capability and parallelism in commodity graphics hardware to speed up this process over two orders of magnitude. In addressing high accuracy depth estimation, we present a stereo model that makes use of constraints from points with known depths - the Ground Control Points (GCPs) as referred to in stereo literature. Our formulation explicitly models the influences of GCPs in a Markov Random Field. A novel regularization prior is naturally integrated into a global inference framework in a principled way using the Bayes rule. Our probabilistic framework allows GCPs to be obtained from various modalities and provides a natural way to integrate information from various sensors. In addressing non-Lambertian reflectance, we introduce a new invariant for stereo correspondence which allows completely arbitrary scene reflectance (bidirectional reflectance distribution functions - BRDFs). This invariant can be used to formulate a rank constraint on stereo matching when the scene is observed by several lighting configurations in which only the lighting intensity varies

Dense Vision in Image-guided Surgery

Image-guided surgery needs an efficient and effective camera tracking system in order to perform augmented reality for overlaying preoperative models or label cancerous tissues on the 2D video images of the surgical scene. Tracking in endoscopic/laparoscopic scenes however is an extremely difficult task primarily due to tissue deformation, instrument invasion into the surgical scene and the presence of specular highlights. State of the art feature-based SLAM systems such as PTAM fail in tracking such scenes since the number of good features to track is very limited. When the scene is smoky and when there are instrument motions, it will cause feature-based tracking to fail immediately. The work of this thesis provides a systematic approach to this problem using dense vision. We initially attempted to register a 3D preoperative model with multiple 2D endoscopic/laparoscopic images using a dense method but this approach did not perform well. We subsequently proposed stereo reconstruction to directly obtain the 3D structure of the scene. By using the dense reconstructed model together with robust estimation, we demonstrate that dense stereo tracking can be incredibly robust even within extremely challenging endoscopic/laparoscopic scenes. Several validation experiments have been conducted in this thesis. The proposed stereo reconstruction algorithm has turned out to be the state of the art method for several publicly available ground truth datasets. Furthermore, the proposed robust dense stereo tracking algorithm has been proved highly accurate in synthetic environment (< 0.1 mm RMSE) and qualitatively extremely robust when being applied to real scenes in RALP prostatectomy surgery. This is an important step toward achieving accurate image-guided laparoscopic surgery.Open Acces

Variationelle 3D-Rekonstruktion aus Stereobildpaaren und Stereobildfolgen

This work deals with 3D reconstruction and 3D motion estimation from stereo images using variational methods that are based on dense optical flow. In the first part of the thesis, we will investigate a novel application for dense optical flow, namely the estimation of the fundamental matrix of a stereo image pair. By exploiting the high interdependency between the recovered stereo geometry and the established image correspondences, we propose a coupled refinement of the fundamental matrix and the optical flow as a second contribution, thereby improving the accuracy of both. As opposed to many existing techniques, our joint method does not solve for the camera pose and scene structure separately, but recovers them in a single optimisation step. True to our principle of joint optimisation, we further couple the dense 3D reconstruction of the scene to the estimation of its 3D motion in the final part of this thesis. This is achieved by integrating spatial and temporal information from multiple stereo pairs in a novel model for scene flow computation.Diese Arbeit befasst sich mit der 3D Rekonstruktion und der 3D Bewegungsschätzung aus Stereodaten unter Verwendung von Variationsansätzen, die auf dichten Verfahren zur Berechnung des optischen Flusses beruhen. Im ersten Teil der Arbeit untersuchen wir ein neues Anwendungsgebiet von dichtem optischen Fluss, nämlich die Bestimmung der Fundamentalmatrix aus Stereobildpaaren. Indem wir die Abhängigkeit zwischen der geschätzten Stereogeometrie in Form der Fundamentalmatrix und den berechneten Bildkorrespondenzen geeignet ausnutzen, sind wir in der Lage, im zweiten Teil der Arbeit eine gekoppelte Bestimmung der Fundamentalmatrix und des optischen Flusses vorzuschlagen, die zur einer Erhöhung der Genauigkeit beider Schätzungen führt. Im Gegensatz zu vielen existierenden Verfahren berechnet unser gekoppelter Ansatz dabei die Lage der Kameras und die 3D Szenenstruktur nicht einzeln, sondern bestimmt sie in einem einzigen gemeinsamen Optimierungsschritt. Dem Prinzip der gemeinsamen Schätzung weiter folgend koppeln wir im letzten Teil der Arbeit die dichte 3D Rekonstruktion der Szene zusätzlich mit der Bestimmung der zugehörigen 3D Bewegung. Dies wird durch die Intergation von räumlicher und zeitlicher Information aus mehreren Stereobildpaaren in ein neues Modell zur Szenenflussschätzung realisiert

Variationelle 3D-Rekonstruktion aus Stereobildpaaren und Stereobildfolgen

This work deals with 3D reconstruction and 3D motion estimation from stereo images using variational methods that are based on dense optical flow. In the first part of the thesis, we will investigate a novel application for dense optical flow, namely the estimation of the fundamental matrix of a stereo image pair. By exploiting the high interdependency between the recovered stereo geometry and the established image correspondences, we propose a coupled refinement of the fundamental matrix and the optical flow as a second contribution, thereby improving the accuracy of both. As opposed to many existing techniques, our joint method does not solve for the camera pose and scene structure separately, but recovers them in a single optimisation step. True to our principle of joint optimisation, we further couple the dense 3D reconstruction of the scene to the estimation of its 3D motion in the final part of this thesis. This is achieved by integrating spatial and temporal information from multiple stereo pairs in a novel model for scene flow computation.Diese Arbeit befasst sich mit der 3D Rekonstruktion und der 3D Bewegungsschätzung aus Stereodaten unter Verwendung von Variationsansätzen, die auf dichten Verfahren zur Berechnung des optischen Flusses beruhen. Im ersten Teil der Arbeit untersuchen wir ein neues Anwendungsgebiet von dichtem optischen Fluss, nämlich die Bestimmung der Fundamentalmatrix aus Stereobildpaaren. Indem wir die Abhängigkeit zwischen der geschätzten Stereogeometrie in Form der Fundamentalmatrix und den berechneten Bildkorrespondenzen geeignet ausnutzen, sind wir in der Lage, im zweiten Teil der Arbeit eine gekoppelte Bestimmung der Fundamentalmatrix und des optischen Flusses vorzuschlagen, die zur einer Erhöhung der Genauigkeit beider Schätzungen führt. Im Gegensatz zu vielen existierenden Verfahren berechnet unser gekoppelter Ansatz dabei die Lage der Kameras und die 3D Szenenstruktur nicht einzeln, sondern bestimmt sie in einem einzigen gemeinsamen Optimierungsschritt. Dem Prinzip der gemeinsamen Schätzung weiter folgend koppeln wir im letzten Teil der Arbeit die dichte 3D Rekonstruktion der Szene zusätzlich mit der Bestimmung der zugehörigen 3D Bewegung. Dies wird durch die Intergation von räumlicher und zeitlicher Information aus mehreren Stereobildpaaren in ein neues Modell zur Szenenflussschätzung realisiert

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