A new method for localising and recognising hand poses and objects in real-time is presented. this problem is important in vision-driven applications where it is natural for a user to combine hand gestures and real ob...
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ISBN:
(纸本)9781424411795
A new method for localising and recognising hand poses and objects in real-time is presented. this problem is important in vision-driven applications where it is natural for a user to combine hand gestures and real objects when interacting with a machine. Examples include using a real eraser to remove words from a document displayed on an electronic surface. In this paper the task of simultaneously recognising object classes, hand gestures and detecting touch events is cast as a single classification problem. A random forest algorithm is employed which adaptively selects and combines a minimal set of appearance, shape and stereo features to achieve maximum class discrimination for a given image. this minimal set leads to both efficiency at run time and good generalisation. Unlike previous stereo works which explicitly construct disparity maps, here the stereo matching costs are used directly as visual cue and only computed on-demand, i.e. only for pixels where they are necessary for recognition. this leads to improved efficiency. the proposed method is assessed on a database of a variety of objects and hand poses selected for interacting on a flat surface in an office environment.
We present a new approach to reconstruct the shape of a 3D object or scene from a set of calibrated images. the central idea of our method is to combine the topological flexibility of a point-based geometry representa...
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ISBN:
(纸本)9781424411795
We present a new approach to reconstruct the shape of a 3D object or scene from a set of calibrated images. the central idea of our method is to combine the topological flexibility of a point-based geometry representation withthe robust reconstruction properties of scene-aligned planar primitives. this can be achieved by approximating the shape with a set of surface elements (surfels) in the form of planar disks which are independently fitted such that their footprint in the input images matches. Instead of using an artificial energy functional to promote the smoothness of the recovered surface during fitting, we use the smoothness assumption only to initialize planar primitives and to check the feasibility of the fitting result. After an initial disk has been found, the recovered region is iteratively expanded by growing further disks in tangent direction. the expansion stops when a disk rotates by more than a given threshold during the fitting step. A global sampling strategy guarantees that eventually the whole surface is covered. Our technique does not depend on a shape prior or silhouette information for the initialization and it can automatically and simultaneously recover the geometry, topology, and visibility information which makes it superior to other state-of-theart techniques. We demonstrate with several high-quality reconstruction examples that our algorithm performs highly robustly and is tolerant to a wide range of image capture modalities.
Registering consecutive images from an airborne sensor into a mosaic is an essential tool for image analysts. Strictly local methods tend to accumulate errors, resulting in distortion. We propose here to use a referen...
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ISBN:
(纸本)9781424411795
Registering consecutive images from an airborne sensor into a mosaic is an essential tool for image analysts. Strictly local methods tend to accumulate errors, resulting in distortion. We propose here to use a reference image (such as a high resolution map image) to overcome this limitation. In our approach, we register a frame in an image sequence to the map using both frame-to-frame registration and frame-to-map registration iteratively. In frame-to-frame registration, a frame is registered to its previous frame. With its previous frame been registered to the map in the previous iteration, we can derive an estimated transformation from the frame to the map. In frame-to-map registration, we warp the frame to the map by this transformation to compensate for scale and rotation difference and then perform an area based matching using Mutual Information to find correspondences between this warped frame and the map. these correspondences together withthe correspondences in previous frames could be regarded as correspondences between the partial local mosaic and the map. By registering the partial local mosaic to the map, we derive a transformation from the frame to the map. Withthis two-step registration, the errors between each consecutive frames are not accumulated We then extend our approach to synchronize multiple image sequences by tracking moving objects in each image sequence, and aligning the frames based on the object's coordinates in the reference image.
Cognitive patternrecognition has two basic research problem, one is to understand principle of human patternrecognition, and the other is to develop computerrecognition system which has certain learning ability and...
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ISBN:
(纸本)9787811240559
Cognitive patternrecognition has two basic research problem, one is to understand principle of human patternrecognition, and the other is to develop computerrecognition system which has certain learning ability and adaptive ability based on principle of human patternrecognition. Some achievement of patternrecognition in cognitive science was present, the frame of tradition machine patternrecognition was described. How to apply achievement of cognitive science to traditional machine patternrecognition by combining with characteristic of machine patternrecognition was discussed. recognition of printed digit character was performed according to frame of cognitive patternrecognition, and the frame is supported by the result of experiment.
Tracking over a long period of time is challenging as the appearance, shape and scale of the object in question may vary. We propose a paradigm of tracking by repeatedly segmenting figure from background. Accurate spa...
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this paper presents a new method for object tracking in video sequences that is especially suitable in very noisy environments. In such situations, segmented images from one frame to the next one are usually so differ...
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