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3-D object recognition involves using image-computable features to identify 3-D object. A single view of a 3-D object may not contain sufficient features to recognize it unambigu-ously. One needs to plan different views around the given object in order to recognize it. Such a task involves an active sensor – one whose parameters (external and/or internal)(More)
BACKGROUND Since the end of last century, technology has taken a front seat in dispersion of medical education. Advancements of technology in neurosurgery and traditional training methods are now being challenged by legal and ethical concerns of patient safety, resident work-hour restriction and cost of operating-room time. To supplement the existing(More)
This paper is concerned with the problem of recognition of dynamic hand gestures. We have considered gestures which are sequences of distinct hand poses. In these gestures hand poses can undergo motion and discrete changes. However, continuous deformations of the hand shapes are not permitted. We have developed a recognition engine which can reliably(More)
In this paper we present a simple framework for activity recognition based on a model of multi-layered finite state machines, built on top of a low level image processing module for spatio-temporal detections and limited object identification. The finite state machine network learns, in an unsupervised mode, usual patterns of activities in a scene over long(More)
Most model-based three-dimensional (3-D) object recognition systems use information from a single view of an object. However, a single view may not contain sufficient features to recognize it unambiguously. Further, two objects may have all views in common with respect to a given feature set, and may be distinguished only through a sequence of views. A(More)
In this paper the problem of computing the point correspondences in a sequence of time-varying images of a 3D object undergoing nonrigid (affine) motion is addressed. It is assumed that the images are obtained through affine projections. The correspondences are established only from the analysis of the unknown 3D affine structure of the object, without(More)
We address the problem of super-resolution-obtaining high-resolution images and videos from multiple low-resolution inputs. The increased resolution can be in spatial or temporal dimensions, or even in both. We present a unified framework which uses a generative model of the imaging process and can address spatial super-resolution, space-time(More)
Graph cuts has emerged as a preferred method to solve a class of energy minimization problems in computer vision. It has been shown that graph cut algorithms designed keeping the structure of vision based flow graphs in mind are more efficient than known strongly polynomial time max-flow algorithms based on preflow push or shortest augmenting path paradigms(More)
We address the problem of super resolved generation of novel views of a 3D scene with the reference images obtained from cameras in general positions; a problem which has not been tackled before in the context of super resolution and is also of importance to the field of image based rendering. We formulate the problem as one of estimation of the color at(More)