this paper introduces a novel technique of computational art with mandala-an iconic heritage of indian folk art. Its novelty lies in several fundamental steps. the first one is fixing the asymmetries and the imperfect...
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Logo/brand name detection and recognition in unstructured and highly unpredictable natural images has always been a challenging problem. We notice that in most natural images logos are accompanied with associated text...
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In today's world, with advanced technology and easily available portable gadgets as enabler, video has become an important medium of communication. While, the standard H.264/AVC has produced extremely good results...
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ISBN:
(纸本)1595930361
In today's world, with advanced technology and easily available portable gadgets as enabler, video has become an important medium of communication. While, the standard H.264/AVC has produced extremely good results, it has not been suitable for this application domain as it is computationally intensive and unsuitable for low-power resources. In distributed video coding (DVC), an encoder requires less computation than the decoder, which usually runs at sites of higher computational resources. Our approach is to find out a novel approach in DVC, to reduce encoder complexity, using local rank transform (LRT). LRT relies on the relative ordering of local intensity values for application on visual correspondence problem. Use of LRT in DVC, to the best of our knowledge, has not been reported in literature before. First, we have developed techniques for image reconstruction using LRT and then design a DVC codec using it. We show analytically and by experimental results, that, in power-rate-distortion model, LRT encoder outperforms standard encoder (LDPCA in Stanford architecture) specially in low bit rate condition. Copyright 2014 ACM.
We report here on the problem of estimating a smooth planar curve(a) gamma : [0,T] --> R-2 and its derivatives from an ordered sample of interpolation points {gamma(t(0)),gamma(t(1)),..., gamma(t(i-1)), gamma(t(i))...
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ISBN:
(纸本)3540437444
We report here on the problem of estimating a smooth planar curve(a) gamma : [0,T] --> R-2 and its derivatives from an ordered sample of interpolation points {gamma(t(0)),gamma(t(1)),..., gamma(t(i-1)), gamma(t(i)),..., gamma(t(m-1)), gamma(t(m))}, where 0 = t(0) < t(1) <... < t(i-1) < t(i) <... < t(m-i) < t(m) = T, and the ti are not known precisely for 0 < i < m. Such situtation may appear while searching for the boundaries of planar objects or tracking the mass center of a rigid body with no times available. In this paper we assume that the distribution of ti coincides with more-or-less uniform sampling. A fast algorithm, yielding quartic convergence rate based on 4-point piecewise-quadratic interpolation is analysed and tested. Our algorithm forms a substantial improvement (with respect to the speed of convergence) of piecewise 3-point quadratic Lagrange intepolation [19] and [20]. Some related work can be found in [7]. Our results may be of interest in computervision and digital imageprocessing [5], [8], [13], [141, [17] or [24], computergraphics [1], [4], [9], [10], [21] or [23], approximation and complexity theory [3], [6], [16], [22], [26] or [27], and digital and computational geometry [2] and [15].
this paper presents a new method to detect space time interest point (STIP) from video data. We use three dimensional facet model to detect STIP and call it as facet space-time interest point or FaSTIP. the proposed a...
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this book constitutes the refereed proceedings of the 6th National conference on computervision, Pattern Recognition, imageprocessing, and graphics, NCVPRIPG 2017, held in Mandi, India, in December 2017. the 48 revi...
ISBN:
(数字)9789811300202
ISBN:
(纸本)9789811300196
this book constitutes the refereed proceedings of the 6th National conference on computervision, Pattern Recognition, imageprocessing, and graphics, NCVPRIPG 2017, held in Mandi, India, in December 2017. the 48 revised full papers presented in this volume were carefully reviewed and selected from 147 submissions. the papers are organized in topical sections on video processing; image and signal processing; segmentation, retrieval, captioning; pattern recognition applications.
A neuromorphic camera is an image sensor that emulates the human eyes capturing only changes in local brightness levels. they are widely known as event cameras, silicon retinas or dynamic vision sensors (DVS). DVS rec...
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Gait is an important biometric modality for recognizing humans. Unlike other biometrics, human gait can be captured at a distance which makes it an unobtrusive method for recognition. In this paper, an unrestricted ga...
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An automated system for crowd behaviour analysis has gained significance in the context of surveillance and public management. Detecting the changes in the crowd behaviour demarcates one activity or event from another...
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Artificial neural network (ANN) introduced in the 1950s, is a machine learning framework inspired by the functioning of human neurons. However, for a long time the ANN remained inadequate in solving real problems, bec...
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ISBN:
(数字)9783030371883
ISBN:
(纸本)9783030371883;9783030371876
Artificial neural network (ANN) introduced in the 1950s, is a machine learning framework inspired by the functioning of human neurons. However, for a long time the ANN remained inadequate in solving real problems, because of - the problems of overfitting and vanishing gradient while training a deep architecture, dearth of computation power, and non-availability of enough data for training the framework. this concept has lately re-emerged, in the form of Deep Learning (DL) which initially developed for computervision and became immensely popular in several other domains. It gained traction in late 2012, when a DL approach i.e. convolutional neural network won in the imageNet Classification - an acclaimed worldwide computervision competition. thereafter, researchers in practically every domain, including medical imaging, started vigorously contributing in the massively progressing field of DL. the success of DL methods can be owed to the availability of data, boosted computation power provided by the existing graphicsprocessing units (GPUs), and ground-breaking training algorithms. In this paper, we have overviewed the area of DL in medical imaging, including (1) machine learning and DL basics, (2) cause of power of DL, (3) common DL models, (4) their applications to medical imaging and (5) challenges and future work in this field.
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