Do-It-Yourself Single Camera 3D Pointer Input Device

We present a new algorithm for single camera 3D reconstruction, or 3D input for human-computer interfaces, based on precise tracking of an elongated object, such as a pen, having a pattern of colored bands. To configure the system, the user provides no more than one labelled image of a handmade pointer, measurements of its colored bands, and the camera's pinhole projection matrix. Other systems are of much higher cost and complexity, requiring combinations of multiple cameras, stereocameras, and pointers with sensors and lights. Instead of relying on information from multiple devices, we examine our single view more closely, integrating geometric and appearance constraints to robustly track the pointer in the presence of occlusion and distractor objects. By probing objects of known geometry with the pointer, we demonstrate acceptable accuracy of 3D localization.Comment: 8 pages, 6 figures, 2018 15th Conference on Computer and Robot Visio

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 For non-Lambertian surfaces, the brightness constancy assumption is no longer valid.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

Motion segmentation using an occlusion detector

We present a novel method for the detection of motion boundaries in a video sequence based on differential properties of the spatio-temporal domain. Regarding the video sequence as a 3D spatio-temporal function, we consider the second moment matrix of its gradients (averaged over a local window), and show that the eigenvalues of this matrix can be used to detect occlusions and motion discontinuities. Since these cannot always be determined locally (due to false corners and the aperture problem), a scale-space approach is used for extracting the location of motion boundaries. A closed contour is then constructed from the most salient boundary fragments, to provide the final segmentation. The method is shown to give good results on pairs of real images taken in general motion. We use synthetic data to show its robustness to high levels of noise and illumination changes; we also include cases where no intensity edge exists at the location of the motion boundary, or when no parametric motion model can describe the data.

Evaluation of close-range stereo matching algorithms using stereoscopic measurements

The performance of binocular stereo reconstruction is highly dependent on the quality of the stereo matching result. In order to evaluate the performance of different stereo matchers, several quality metrics have been developed based on quantifying error statistics with respect to a set of independent measurements usually referred to as ground truth data. However, such data are frequently not available, particularly in practical applications or planetary data processing. To address this, we propose a ground truth independent evaluation protocol based on manual measurements. A stereo visualization tool has been specifically developed to evaluate the quality of the computed correspondences. We compare the quality of disparity maps calculated from three stereo matching algorithms, developed based on a variation of GOTCHA, which has been used in planetary robotic rover image reconstruction at UCL-MSSL (Otto and Chau, 1989). From our evaluation tests with the images pairs from Mars Exploration Rover (MER) Pancam and the field data collected in PRoViScout 2012, it has been found that all three processing pipelines used in our test (NASA-JPL, JR, UCL-MSSL) trade off matching accuracy and completeness differently. NASA-JPL's stereo pipeline produces the most accurate but less complete disparity map, while JR's pipeline performs best in terms of the reconstruction completeness

Scene reconstruction using accumulated line-of-sight

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (leaves 49-52).by Christopher P. Stauffer.M.S

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

Homography-based ground plane detection using a single on-board camera

This study presents a robust method for ground plane detection in vision-based systems with a non-stationary camera. The proposed method is based on the reliable estimation of the homography between ground planes in successive images. This homography is computed using a feature matching approach, which in contrast to classical approaches to on-board motion estimation does not require explicit ego-motion calculation. As opposed to it, a novel homography calculation method based on a linear estimation framework is presented. This framework provides predictions of the ground plane transformation matrix that are dynamically updated with new measurements. The method is specially suited for challenging environments, in particular traffic scenarios, in which the information is scarce and the homography computed from the images is usually inaccurate or erroneous. The proposed estimation framework is able to remove erroneous measurements and to correct those that are inaccurate, hence producing a reliable homography estimate at each instant. It is based on the evaluation of the difference between the predicted and the observed transformations, measured according to the spectral norm of the associated matrix of differences. Moreover, an example is provided on how to use the information extracted from ground plane estimation to achieve object detection and tracking. The method has been successfully demonstrated for the detection of moving vehicles in traffic environments

Projector-Based Augmentation

Projector-based augmentation approaches hold the potential of combining the advantages of well-establishes spatial virtual reality and spatial augmented reality. Immersive, semi-immersive and augmented visualizations can be realized in everyday environments – without the need for special projection screens and dedicated display configurations. Limitations of mobile devices, such as low resolution and small field of view, focus constrains, and ergonomic issues can be overcome in many cases by the utilization of projection technology. Thus, applications that do not require mobility can benefit from efficient spatial augmentations. Examples range from edutainment in museums (such as storytelling projections onto natural stone walls in historical buildings) to architectural visualizations (such as augmentations of complex illumination simulations or modified surface materials in real building structures). This chapter describes projector-camera methods and multi-projector techniques that aim at correcting geometric aberrations, compensating local and global radiometric effects, and improving focus properties of images projected onto everyday surfaces

Hand tracking and bimanual movement understanding

Bimanual movements are a subset ot human movements in which the two hands move together in order to do a task or imply a meaning A bimanual movement appearing in a sequence of images must be understood in order to enable computers to interact with humans in a natural way This problem includes two main phases, hand tracking and movement recognition. We approach the problem of hand tracking from a neuroscience point ot view First the hands are extracted and labelled by colour detection and blob analysis algorithms In the presence of the two hands one hand may occlude the other occasionally Therefore, hand occlusions must be detected in an image sequence A dynamic model is proposed to model the movement of each hand separately Using this model in a Kalman filtering proccss the exact starting and end points of hand occlusions are detected We exploit neuroscience phenomena to understand the beha\ tour of the hands during occlusion periods Based on this, we propose a general hand tracking algorithm to track and reacquire the hands over a movement including hand occlusion The advantages of the algorithm and its generality are demonstrated in the experiments. In order to recognise the movements first we recognise the movement of a hand Using statistical pattern recognition methods (such as Principal Component Analysis and Nearest Neighbour) the static shape of each hand appearing in an image is recognised A Graph- Matching algorithm and Discrete Midden Markov Models (DHMM) as two spatio-temporal pattern recognition techniques are imestigated tor recognising a dynamic hand gesture For recognising bimanual movements we consider two general forms ot these movements, single and concatenated periodic We introduce three Bayesian networks for recognising die movements The networks are designed to recognise and combinc the gestures of the hands in order to understand the whole movement Experiments on different types ot movement demonstrate the advantages and disadvantages of each network