In several scientific areas, data are sampled irregularly and insufficiently due to practical and economical limitations. the use of such data in applications results in some artifacts and poor spatial resolution. the...
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ISBN:
(纸本)9781424442195
In several scientific areas, data are sampled irregularly and insufficiently due to practical and economical limitations. the use of such data in applications results in some artifacts and poor spatial resolution. therefore, before being used, the data are to be interpolated onto a regular grid. One of the methods achieving this objective is based on the Fourier reconstruction, which deals withthe under-determined system of equations. the Stagewise Orthogonal Matching Pursuit (StOMP) is a recently proposed greedy algorithm. Compared to the other recent algorithms like l(1) minimization techniques, StOMP admits certain promising features such as faster and simpler implementation even in large scale settings. the present work applies StOMP to the Fourier-based interpolation problem for the signals that have sparse Fourier spectra. the basic objective is to verify empirically the performance of the algorithm if and how far the measurement coordinates can be shifted from uniform distribution on the continuous interval. Taking kurtosis as a quantifier for the deviation of distribution from being uniform, we show numerically that the measurement coordinates can be significantly shifted from uniform distribution.
this paper presents a switched predictive coding method for lossless compression of video. In the proposed method, a set of switched predictors is found by a training process that uses only a small number of successiv...
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ISBN:
(纸本)9781424442195
this paper presents a switched predictive coding method for lossless compression of video. In the proposed method, a set of switched predictors is found by a training process that uses only a small number of successive frames of a video and then the trained predictors are used with a large number of the frames of the video. To find the predictors, the pixels of the successive frames are first classified based on an estimate of activity level in their neighbouring pixels and then LS based feedback type of predictors are estimated for all the pixels belonging to each of the classes. We propose a total of 21 classes, which are obtained by combining the seven slope bins of Gradient Adjusted Predictor (GAP) and three classified temporal contexts. After collecting the predictors for pixels belonging to each of the 21 classes, the best predictor in terms of minimum zero-order entropy, is chosen to represent the various classes. Simulation results show that the application of the set of the predictors results in competitive performance withthe LOPT - one of the best methods in terms of achievable compression ratio. Our method and LOPT has same order of coding complexity while our decoder is computationally very simple as against high complexity of LOPT based decoder
this paper presents a system for unconstrained handwritten Odia text recognition using Hidden Markov Model (HMM) framework. Existing literature for Odia text recognition works primarily with individual isolated charac...
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ISBN:
(纸本)9781467385640
this paper presents a system for unconstrained handwritten Odia text recognition using Hidden Markov Model (HMM) framework. Existing literature for Odia text recognition works primarily with individual isolated characters. In this study we introduce a Odia dataset of word samples collected from different professionals. Concavity feature from each word image is extracted in our approach. Next, the features are fed to HMM-based sequential classifier for recognition. the experiment has been performed on a large dataset consisting of 4000 words and results obtained are encouraging.
In this paper, we propose two designs of redundant finer directional wavelet transform (FiDWT) and explain its application to image denoising. 2-channel perfect reconstruction (PR) checkerboard-shaped filter bank (CSF...
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ISBN:
(纸本)9781479915880
In this paper, we propose two designs of redundant finer directional wavelet transform (FiDWT) and explain its application to image denoising. 2-channel perfect reconstruction (PR) checkerboard-shaped filter bank (CSFB) is at the core of the designs. the 2-channel CSFB, uses 2-D nonseparable analysis and synthesis filter responses without downsampling/upsampling matrices resulting in redundancy factor of 2. Boththese designs have two lowpass and six highpass directional subbands. the hard-thresholding results for image denoising using proposed designs clearly shows improvement in PSNR as well as visual quality of the denoised images. Using the Bayes least squares-Gaussian scale mixture (BLS-GSM), a current state-of-the-art wavelet-based image denoising technique withthe proposed two times redundant FiDWT design indicates encouraging results on textural images with much less computational cost.
Diabetic retinopathy is one of the major causes of blindness. However diabetic retinopathy does not usually cause a loss of sight until it has reached an advanced stage. the earliest sign of the disease are microaneur...
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ISBN:
(纸本)9781424442195
Diabetic retinopathy is one of the major causes of blindness. However diabetic retinopathy does not usually cause a loss of sight until it has reached an advanced stage. the earliest sign of the disease are microaneurysms (MA) which appear as small red dots on retinal fundus images. Various screening programmes have been established in the UK and other countries to collect and assess images on a regular basis, especially in the diabetic population. A considerable amount of time and money is spent in manually grading these images, a large percentage of which are normal. By automatically identifying the normal images, the manual workload and costs could be reduced greatly while increasing the effectiveness of the screening programmes. A novel method of microaneurysm detection from digital retinal screening images is proposed. It is based on filtering using complex-valued circular-symmetric filters, and an eigen-image, morphological analysis of the candidate regions to reduce the false-positive rate. We detail the imageprocessing algorithms and present results on a typical set of 89 image from a published database. Our method is shown to have a best operating sensitivity of 82.6% at a specificity of 80.2% which makes it viable for screening. We discuss the results in the context of a model of visual search and the ROC curves that it can predict.
the popular techniques to eliminate temporal redundancy in video sequences are Motion Estimation and Motion Compensation. these techniques have also been used in popular H.264, MPEG-2 and MPEG-4 video coding standards...
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ISBN:
(纸本)9781479915880
the popular techniques to eliminate temporal redundancy in video sequences are Motion Estimation and Motion Compensation. these techniques have also been used in popular H.264, MPEG-2 and MPEG-4 video coding standards. Conventional fast Block Matching Algorithms (BMA) perform exhaustive search between the current and the reference frame. Although BMA technique gives the exact result but it is computationally very expensive. Another drawback of this method is that it easily gets trapped into the local minima which eventually lead to degradation of the video quality. the proposed Motion Estimation Technique exploits the fact that the human eyes are incapable of detecting different frames when they are run at particular frame rate. the experimental results on various video sequences demonstrate that the proposed technique has outperformed all the existing conventional motion estimation techniques.
In recent years the need of a robust facial component tracking especially lip tracking algorithm has increased dramatically. We implement an active contour (snake) model inspired by human perception for lip tracking. ...
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ISBN:
(纸本)9781479915880
In recent years the need of a robust facial component tracking especially lip tracking algorithm has increased dramatically. We implement an active contour (snake) model inspired by human perception for lip tracking. In addition to the conventional energy terms for tension, rigidity (internal energy) and gradient magnitude (external energy) we propose to include energy terms from domain knowledge for lip shape constraint and local region profile constraint. Generalized deterministic annealing (GDA) update of the energy functional helps the solution to escape suboptimal local minima in the energy space and give better tracking result. Experimental results show that the proposed method efficiently adapts to the highly deformable lip boundaries even for lips with indistinct edges and colored (adorned) lips where gradient magnitude based or local region based tracking methods respectively fail. We have done a number of experiments to evaluate the performance of our method in comparison withthe existing state-of-the-art methods.
In this paper, a complete database of handwritten atomic Odia characters is suggested. the first version of the database has been modeled and named OHCSv1.0 (Odia handwritten character set). the database comprises of ...
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ISBN:
(纸本)9781467385640
In this paper, a complete database of handwritten atomic Odia characters is suggested. the first version of the database has been modeled and named OHCSv1.0 (Odia handwritten character set). the database comprises of 17,100 transcribed characters, each collected twice from 150 unique people at different point of time. Each character has 300 number of occurrences. the character images are standardized to a size of 6 4 x 6 4 pixels. A novel framework for perceiving transcribed Odia characters from this database has also been proposed. the character images are gathered into various groups in view of their shape components utilizing an incremental spectral clustering algorithm. During testing, affinity of probe character to a cluster is first decided. Subsequently, the trained classifier recognizes the character inside the cluster. Suitable simulation has been carried out to validate the scheme.
We have developed a novel method for image abstraction which preserves more details present in the salient regions and removes details present in the non- salient regions from the given image of a natural scene. We de...
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ISBN:
(纸本)9781467385640
We have developed a novel method for image abstraction which preserves more details present in the salient regions and removes details present in the non- salient regions from the given image of a natural scene. We define a region to be salient based on the saliency measure estimated in the region. We propose to preserve details in salient regions by dividing them into smaller groups of pixels and remove details from non- salient regions by dividing them in larger group of pixels. We achieve this kind of grouping by guiding an over- segmentation algorithm with spatially varying block size depending on the saliency measure. the adaptive image abstraction goal is finally achieved using a novel brush called point spread brush which is used to reproduce the action of brush with a varying spatial spread.
this paper proposes a novel recommendation engine to suggest coordinated outfits to the users that complements each other. the proposed recommendation model encodes subjective knowledge of clothing experts in Multimed...
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ISBN:
(纸本)9781467385640
this paper proposes a novel recommendation engine to suggest coordinated outfits to the users that complements each other. the proposed recommendation model encodes subjective knowledge of clothing experts in Multimedia Web Ontology Language (MOWL) and makes use of evidential and causal reasoning scheme to deal withthe media properties of concepts. Our approach automatically identifies the user visual personality and interprets the contextual meaning of media features of the garments in the context of input query image. As a result, personalized complementary garments based on occasion of wear are recommended to the user. We have validated our approach with garment preferences of various models with a large collection of shirts and trousers, collected from various websites.
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