4,805 research outputs found
Unobtrusive and pervasive video-based eye-gaze tracking
Eye-gaze tracking has long been considered a desktop technology that finds its use inside the traditional office setting, where the operating conditions may be controlled. Nonetheless, recent advancements in mobile technology and a growing interest in capturing natural human behaviour have motivated an emerging interest in tracking eye movements within unconstrained real-life conditions, referred to as pervasive eye-gaze tracking. This critical review focuses on emerging passive and unobtrusive video-based eye-gaze tracking methods in recent literature, with the aim to identify different research avenues that are being followed in response to the challenges of pervasive eye-gaze tracking. Different eye-gaze tracking approaches are discussed in order to bring out their strengths and weaknesses, and to identify any limitations, within the context of pervasive eye-gaze tracking, that have yet to be considered by the computer vision community.peer-reviewe
Vision technology/algorithms for space robotics applications
The thrust of automation and robotics for space applications has been proposed for increased productivity, improved reliability, increased flexibility, higher safety, and for the performance of automating time-consuming tasks, increasing productivity/performance of crew-accomplished tasks, and performing tasks beyond the capability of the crew. This paper provides a review of efforts currently in progress in the area of robotic vision. Both systems and algorithms are discussed. The evolution of future vision/sensing is projected to include the fusion of multisensors ranging from microwave to optical with multimode capability to include position, attitude, recognition, and motion parameters. The key feature of the overall system design will be small size and weight, fast signal processing, robust algorithms, and accurate parameter determination. These aspects of vision/sensing are also discussed
Color image-based shape reconstruction of multi-color objects under general illumination conditions
Humans have the ability to infer the surface reflectance properties and three-dimensional shape of objects from two-dimensional photographs under simple and complex illumination fields. Unfortunately, the reported algorithms in the area of shape reconstruction require a number of simplifying assumptions that result in poor performance in uncontrolled imaging environments. Of all these simplifications, the assumptions of non-constant surface reflectance, globally consistent illumination, and multiple surface views are the most likely to be contradicted in typical environments. In this dissertation, three automatic algorithms for the recovery of surface shape given non-constant reflectance using a single-color image acquired are presented. In addition, a novel method for the identification and removal of shadows from simple scenes is discussed.In existing shape reconstruction algorithms for surfaces of constant reflectance, constraints based on the assumed smoothness of the objects are not explicitly used. Through Explicit incorporation of surface smoothness properties, the algorithms presented in this work are able to overcome the limitations of the previously reported algorithms and accurately estimate shape in the presence of varying reflectance. The three techniques developed for recovering the shape of multi-color surfaces differ in the method through which they exploit the surface smoothness property. They are summarized below:• Surface Recovery using Pre-Segmentation - this algorithm pre-segments the image into distinct color regions and employs smoothness constraints at the color-change boundaries to constrain and recover surface shape. This technique is computationally efficient and works well for images with distinct color regions, but does not perform well in the presence of high-frequency color textures that are difficult to segment.iv• Surface Recovery via Normal Propagation - this approach utilizes local gradient information to propagate a smooth surface solution from points of known orientation. While solution propagation eliminates the need for color-based image segmentation, the quality of the recovered surface can be degraded by high degrees of image noise due to reliance on local information.• Surface Recovery by Global Variational Optimization - this algorithm utilizes a normal gradient smoothness constraint in a non-linear optimization strategy, to iteratively solve for the globally optimal object surface. Because of its global nature, this approach is much less sensitive to noise than the normal propagation is, but requires significantly more computational resources.Results acquired through application of the above algorithms to various synthetic and real image data sets are presented for qualitative evaluation. A quantitative analysis of the algorithms is also discussed for quadratic shapes. The robustness of the three approaches to factors such as segmentation error and random image noise is also explored
Multidimensional imaging for skin tissue surface characterization
Human skin, the outer and largest organ covering our body, can be described in terms of both its 3D spatial topography and its 2D spectral reflectance. Such a characterization normally requires the application of separate procedures using different kinds of equipment, where spectral reflectance can only be obtained from a small patch of the skin surface. This paper investigates the integration of multiple imaging modalities to simultaneously capture both spectral and spatial information from the skin surface over a wide area. By extending the imaging spectrum from the visible to the near-infrared (NIR), we improve general recovery, obtain a more detailed skin profile, and are able to identify the distribution of various principal chromophores within the deeper dermal layers. Experiments show that new dimensions of skin characterization can be generated through the recovered geometrical and spectral information, so that an enhanced visibility of important skin physiological phenomena can be achieved. © 2013 Elsevier B.V. All rights reserved
Proceedings of the 2011 New York Workshop on Computer, Earth and Space Science
The purpose of the New York Workshop on Computer, Earth and Space Sciences is
to bring together the New York area's finest Astronomers, Statisticians,
Computer Scientists, Space and Earth Scientists to explore potential synergies
between their respective fields. The 2011 edition (CESS2011) was a great
success, and we would like to thank all of the presenters and participants for
attending. This year was also special as it included authors from the upcoming
book titled "Advances in Machine Learning and Data Mining for Astronomy". Over
two days, the latest advanced techniques used to analyze the vast amounts of
information now available for the understanding of our universe and our planet
were presented. These proceedings attempt to provide a small window into what
the current state of research is in this vast interdisciplinary field and we'd
like to thank the speakers who spent the time to contribute to this volume.Comment: Author lists modified. 82 pages. Workshop Proceedings from CESS 2011
in New York City, Goddard Institute for Space Studie
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
Towards a complete 3D morphable model of the human head
Three-dimensional Morphable Models (3DMMs) are powerful statistical tools for
representing the 3D shapes and textures of an object class. Here we present the
most complete 3DMM of the human head to date that includes face, cranium, ears,
eyes, teeth and tongue. To achieve this, we propose two methods for combining
existing 3DMMs of different overlapping head parts: i. use a regressor to
complete missing parts of one model using the other, ii. use the Gaussian
Process framework to blend covariance matrices from multiple models. Thus we
build a new combined face-and-head shape model that blends the variability and
facial detail of an existing face model (the LSFM) with the full head modelling
capability of an existing head model (the LYHM). Then we construct and fuse a
highly-detailed ear model to extend the variation of the ear shape. Eye and eye
region models are incorporated into the head model, along with basic models of
the teeth, tongue and inner mouth cavity. The new model achieves
state-of-the-art performance. We use our model to reconstruct full head
representations from single, unconstrained images allowing us to parameterize
craniofacial shape and texture, along with the ear shape, eye gaze and eye
color.Comment: 18 pages, 18 figures, submitted to Transactions on Pattern Analysis
and Machine Intelligence (TPAMI) on the 9th of October as an extension paper
of the original oral CVPR paper : arXiv:1903.0378
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