10,968 research outputs found
Improved depth recovery in consumer depth cameras via disparity space fusion within cross-spectral stereo.
We address the issue of improving depth coverage in consumer depth cameras based on the combined use of cross-spectral stereo and near infra-red structured light sensing. Specifically we show that fusion of disparity over these modalities, within the disparity space image, prior to disparity optimization facilitates the recovery of scene depth information in regions where structured light sensing fails. We show that this joint approach, leveraging disparity information from both structured light and cross-spectral sensing, facilitates the joint recovery of global scene depth comprising both texture-less object depth, where conventional stereo otherwise fails, and highly reflective object depth, where structured light (and similar) active sensing commonly fails. The proposed solution is illustrated using dense gradient feature matching and shown to outperform prior approaches that use late-stage fused cross-spectral stereo depth as a facet of improved sensing for consumer depth cameras
Cross-calibration of Time-of-flight and Colour Cameras
Time-of-flight cameras provide depth information, which is complementary to
the photometric appearance of the scene in ordinary images. It is desirable to
merge the depth and colour information, in order to obtain a coherent scene
representation. However, the individual cameras will have different viewpoints,
resolutions and fields of view, which means that they must be mutually
calibrated. This paper presents a geometric framework for this multi-view and
multi-modal calibration problem. It is shown that three-dimensional projective
transformations can be used to align depth and parallax-based representations
of the scene, with or without Euclidean reconstruction. A new evaluation
procedure is also developed; this allows the reprojection error to be
decomposed into calibration and sensor-dependent components. The complete
approach is demonstrated on a network of three time-of-flight and six colour
cameras. The applications of such a system, to a range of automatic
scene-interpretation problems, are discussed.Comment: 18 pages, 12 figures, 3 table
Stereo study as an aid to visual analysis of ERTS and Skylab images
The author has identified the following significant results. The parallax on ERTS and Skylab images is sufficiently large for exploitation by human photointerpreters. The ability to view the imagery stereoscopically reduces the signal-to-noise ratio. Stereoscopic examination of orbital data can contribute to studies of spatial, spectral, and temporal variations on the imagery. The combination of true stereo parallax, plus shadow parallax offer many possibilities to human interpreters for making meaningful analyses of orbital imagery
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
Meshed Up: Learnt Error Correction in 3D Reconstructions
Dense reconstructions often contain errors that prior work has so far
minimised using high quality sensors and regularising the output. Nevertheless,
errors still persist. This paper proposes a machine learning technique to
identify errors in three dimensional (3D) meshes. Beyond simply identifying
errors, our method quantifies both the magnitude and the direction of depth
estimate errors when viewing the scene. This enables us to improve the
reconstruction accuracy.
We train a suitably deep network architecture with two 3D meshes: a
high-quality laser reconstruction, and a lower quality stereo image
reconstruction. The network predicts the amount of error in the lower quality
reconstruction with respect to the high-quality one, having only view the
former through its input. We evaluate our approach by correcting
two-dimensional (2D) inverse-depth images extracted from the 3D model, and show
that our method improves the quality of these depth reconstructions by up to a
relative 10% RMSE.Comment: Accepted for the International Conference on Robotics and Automation
(ICRA) 201
An application driven comparison of depth perception on desktop 3D displays.
Desktop 3D displays vary in their optical design and this results in a significant variation in the way in which stereo images are physically displayed on different 3D displays. When precise depth judgements need to be made these differences may become critical to task performance. Applications where this is a particular issue include medical imaging, geoscience and scientific visualization. We investigate perceived depth thresholds for four classes of desktop 3D display; full resolution, row interleaved, column interleaved and colour-column interleaved. Given the same input image resolution we calculate the physical view resolution for each class of display to geometrically predict its minimum perceived depth threshold. To verify our geometric predictions we present the design of a task where viewers are required to judge which of two neighboring squares lies in front of the other. We report results from a trial using this task where participants are randomly asked to judge whether they can perceive one of four levels of image disparity (0,2,4 and 6 pixels) on seven different desktop 3D displays. The results show a strong effect and the task produces reliable results that are sensitive to display differences. However, we conclude that depth judgement performance cannot always be predicted from display geometry alone. Other system factors, including software drivers, electronic interfaces, and individual participant differences must also be considered when choosing a 3D display to make critical depth judgements
Assistive technology design and development for acceptable robotics companions for ageing years
© 2013 Farshid Amirabdollahian et al., licensee Versita Sp. z o. o. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs license, which means that the text may be used for non-commercial purposes, provided credit is given to the author.A new stream of research and development responds to changes in life expectancy across the world. It includes technologies which enhance well-being of individuals, specifically for older people. The ACCOMPANY project focuses on home companion technologies and issues surrounding technology development for assistive purposes. The project responds to some overlooked aspects of technology design, divided into multiple areas such as empathic and social human-robot interaction, robot learning and memory visualisation, and monitoring persons’ activities at home. To bring these aspects together, a dedicated task is identified to ensure technological integration of these multiple approaches on an existing robotic platform, Care-O-Bot®3 in the context of a smart-home environment utilising a multitude of sensor arrays. Formative and summative evaluation cycles are then used to assess the emerging prototype towards identifying acceptable behaviours and roles for the robot, for example role as a butler or a trainer, while also comparing user requirements to achieved progress. In a novel approach, the project considers ethical concerns and by highlighting principles such as autonomy, independence, enablement, safety and privacy, it embarks on providing a discussion medium where user views on these principles and the existing tension between some of these principles, for example tension between privacy and autonomy over safety, can be captured and considered in design cycles and throughout project developmentsPeer reviewe
Data association and occlusion handling for vision-based people tracking by mobile robots
This paper presents an approach for tracking multiple persons on a mobile robot with a combination of colour and thermal vision sensors, using several new techniques. First, an adaptive colour model is incorporated into the measurement model of the tracker. Second, a new approach for detecting occlusions is introduced, using a machine learning classifier for pairwise comparison of persons (classifying which one is in front of the other). Third, explicit occlusion handling is incorporated into the tracker. The paper presents a comprehensive, quantitative evaluation of the whole system and its different components using several real world data sets
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