Published: Apr 1, 2015
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DOI: 10.4018/ijsda.20150401pre
Volume 4
Nilanjan Dey, Vikrant Bhateja, Jitendra Virmani
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MLA
Dey, Nilanjan, et al. "Special Issue on Advances in Machine Vision, Image Processing, and Pattern Analysis, Part 1." IJSDA vol.4, no.2 2015: pp.4-6. http://doi.org/10.4018/ijsda.20150401pre
APA
Dey, N., Bhateja, V., & Virmani, J. (2015). Special Issue on Advances in Machine Vision, Image Processing, and Pattern Analysis, Part 1. International Journal of System Dynamics Applications (IJSDA), 4(2), 4-6. http://doi.org/10.4018/ijsda.20150401pre
Chicago
Dey, Nilanjan, Vikrant Bhateja, and Jitendra Virmani. "Special Issue on Advances in Machine Vision, Image Processing, and Pattern Analysis, Part 1," International Journal of System Dynamics Applications (IJSDA) 4, no.2: 4-6. http://doi.org/10.4018/ijsda.20150401pre
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Published: Apr 1, 2015
Converted to Gold OA:
DOI: 10.4018/ijsda.2015040101
Volume 4
Suranjan Ganguly, Debotosh Bhattacharjee, Mita Nasipuri
In this paper the pivotal contribution of the authors is to recognize the 3D face images from range images in the unconstrained environment i.e. under varying illumination, pose as well as occlusion...
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In this paper the pivotal contribution of the authors is to recognize the 3D face images from range images in the unconstrained environment i.e. under varying illumination, pose as well as occlusion that are considered to be the most challenging task in the domain of face recognition. During this investigation, face images have been normalized in terms of pose registration as well as occlusion restoration using ERFI (Energy Range Face Image) model. 3D face images are inherently illumination invariant due its point-based representation of data along three axes. Here, other than quantitative analysis, a subjective analysis is also carried out. However, synthesized datasets have been accomplished to investigate the performance of recognition rate from Frav3D and Bosphorus databases using SIFT and SURF like features. Moreover, weighted fusion of these individual feature sets is also done. Later these feature sets have been classified by K-NN and Sequence Matching Technique and achieved maximum recognition rates of 99.17% and 98.81% for Frav3D and GavabDB databases respectively.
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Ganguly, Suranjan, et al. "Illumination, Pose and Occlusion Invariant Face Recognition from Range Images Using ERFI Model." IJSDA vol.4, no.2 2015: pp.1-20. http://doi.org/10.4018/ijsda.2015040101
APA
Ganguly, S., Bhattacharjee, D., & Nasipuri, M. (2015). Illumination, Pose and Occlusion Invariant Face Recognition from Range Images Using ERFI Model. International Journal of System Dynamics Applications (IJSDA), 4(2), 1-20. http://doi.org/10.4018/ijsda.2015040101
Chicago
Ganguly, Suranjan, Debotosh Bhattacharjee, and Mita Nasipuri. "Illumination, Pose and Occlusion Invariant Face Recognition from Range Images Using ERFI Model," International Journal of System Dynamics Applications (IJSDA) 4, no.2: 1-20. http://doi.org/10.4018/ijsda.2015040101
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Published: Apr 1, 2015
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DOI: 10.4018/ijsda.2015040102
Volume 4
Satya P Singh, Shabana Urooj
In this paper, the authors propose a method to analyze and capture the information from texture regardless their geometric deformation. Input image is transformed to radon space and multiresolution...
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In this paper, the authors propose a method to analyze and capture the information from texture regardless their geometric deformation. Input image is transformed to radon space and multiresolution is achieved within the radon space using Gaussian derivative wavelet. The transformed image is applied to the polar harmonic transform (PHT). The proposed method is tested against additive Gaussian noise and impulse noise with different rotations. A k- nearest neighbor classifier is employed to classify the texture. To test and evaluate correct classification percentage of the method, several sets of texture are evaluated with different rotation angle under different noisy condition. Experimental results show superiority of method in comparison to recent invariant texture analysis method.
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Singh, Satya P., and Shabana Urooj. "Localized Radon Polar Harmonic Transform (LRPHT) Based Rotation Invariant Analysis of Textured Images." IJSDA vol.4, no.2 2015: pp.21-41. http://doi.org/10.4018/ijsda.2015040102
APA
Singh, S. P. & Urooj, S. (2015). Localized Radon Polar Harmonic Transform (LRPHT) Based Rotation Invariant Analysis of Textured Images. International Journal of System Dynamics Applications (IJSDA), 4(2), 21-41. http://doi.org/10.4018/ijsda.2015040102
Chicago
Singh, Satya P., and Shabana Urooj. "Localized Radon Polar Harmonic Transform (LRPHT) Based Rotation Invariant Analysis of Textured Images," International Journal of System Dynamics Applications (IJSDA) 4, no.2: 21-41. http://doi.org/10.4018/ijsda.2015040102
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Published: Apr 1, 2015
Converted to Gold OA:
DOI: 10.4018/ijsda.2015040103
Volume 4
L. Balaji, K.K. Thyagharajan, A. Dhanalakshmi
H.264 / AVC expansion is H.264 / SVC which is applicable in environments that demand video streaming. This paper delivers an algorithm to shorten computational complexity and extend coding...
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H.264 / AVC expansion is H.264 / SVC which is applicable in environments that demand video streaming. This paper delivers an algorithm to shorten computational complexity and extend coding efficiency by determining the mode speedily. In this writing, the authors talk a fast mode resolution algorithm with less complexity unlikely the traditional joint scalable video model (JSVM). Their algorithm end mode hunt by a probability model defined. This model is address for both intra-mode and inter-mode predictions of base layer and enhancement layers in a macro block (MB). The estimated rate distortion cost (RDC) for modes among layers is custom to determine the best mode of each MB. The experimental results show that the authors' algorithm realizes 26.9% of encoding time when compared with the JSVM reference software with smallest reduction in peak signal to noise ratio (PSNR).
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Balaji, L., et al. "An Efficient Block Mode Detection Algorithm for Scalable Video Coding using Probability Model." IJSDA vol.4, no.2 2015: pp.42-55. http://doi.org/10.4018/ijsda.2015040103
APA
Balaji, L., Thyagharajan, K., & Dhanalakshmi, A. (2015). An Efficient Block Mode Detection Algorithm for Scalable Video Coding using Probability Model. International Journal of System Dynamics Applications (IJSDA), 4(2), 42-55. http://doi.org/10.4018/ijsda.2015040103
Chicago
Balaji, L., K.K. Thyagharajan, and A. Dhanalakshmi. "An Efficient Block Mode Detection Algorithm for Scalable Video Coding using Probability Model," International Journal of System Dynamics Applications (IJSDA) 4, no.2: 42-55. http://doi.org/10.4018/ijsda.2015040103
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Published: Apr 1, 2015
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DOI: 10.4018/ijsda.2015040104
Volume 4
Suresh Chandra Raikwar, Charul Bhatnagar, Anand Singh Jalal
The key frame extraction, aimed at reducing the amount of information from a surveillance video for analysis by human. The key frame is an important frame of a video to provide an overview of the...
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The key frame extraction, aimed at reducing the amount of information from a surveillance video for analysis by human. The key frame is an important frame of a video to provide an overview of the video. Extraction of key frames from surveillance video is of great interest in effective monitoring and later analysis of video. The computational cost of the existing methods of key frame extraction is very high. The proposed method is a framework for Key frame extraction from a long surveillance video with significantly reduced computational cost. The proposed framework incorporates human intelligence in the process of key frame extraction. The results of proposed framework are compared with the results of IMARS (IBM multimedia analysis and retrieval system), results of the key frame extraction methods based on entropy difference method, spatial color distribution method and edge histogram descriptor method. The proposed framework has been objectively evaluated by fidelity. The experimental results demonstrate evidence of the effectiveness of the proposed approach.
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Raikwar, Suresh Chandra, et al. "A Novel Framework for Efficient Extraction of Meaningful Key Frames from Surveillance Video." IJSDA vol.4, no.2 2015: pp.56-73. http://doi.org/10.4018/ijsda.2015040104
APA
Raikwar, S. C., Bhatnagar, C., & Jalal, A. S. (2015). A Novel Framework for Efficient Extraction of Meaningful Key Frames from Surveillance Video. International Journal of System Dynamics Applications (IJSDA), 4(2), 56-73. http://doi.org/10.4018/ijsda.2015040104
Chicago
Raikwar, Suresh Chandra, Charul Bhatnagar, and Anand Singh Jalal. "A Novel Framework for Efficient Extraction of Meaningful Key Frames from Surveillance Video," International Journal of System Dynamics Applications (IJSDA) 4, no.2: 56-73. http://doi.org/10.4018/ijsda.2015040104
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Published: Apr 1, 2015
Converted to Gold OA:
DOI: 10.4018/ijsda.2015040105
Volume 4
Pawan Kumar Singh, Ram Sarkar, Mita Nasipuri
Script identification is an appealing research interest in the field of document image analysis during the last few decades. The accurate recognition of the script is paramount to many...
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Script identification is an appealing research interest in the field of document image analysis during the last few decades. The accurate recognition of the script is paramount to many post-processing steps such as automated document sorting, machine translation and searching of text written in a particular script in multilingual environment. For automatic processing of such documents through Optical Character Recognition (OCR) software, it is necessary to identify different script words of the documents before feeding them to the OCR of individual scripts. In this paper, a robust word-level handwritten script identification technique has been proposed using texture based features to identify the words written in any of the seven popular scripts namely, Bangla, Devanagari, Gurumukhi, Malayalam, Oriya, Telugu, and Roman. The texture based features comprise of a combination of Histograms of Oriented Gradients (HOG) and Moment invariants. The technique has been tested on 7000 handwritten text words in which each script contributes 1000 words. Based on the identification accuracies and statistical significance testing of seven well-known classifiers, Multi-Layer Perceptron (MLP) has been chosen as the final classifier which is then tested comprehensively using different folds and with different epoch sizes. The overall accuracy of the system is found to be 94.7% using 5-fold cross validation scheme, which is quite impressive considering the complexities and shape variations of the said scripts. This is an extended version of the paper described in (Singh et al., 2014).
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MLA
Singh, Pawan Kumar, et al. "Word-Level Script Identification Using Texture Based Features." IJSDA vol.4, no.2 2015: pp.74-94. http://doi.org/10.4018/ijsda.2015040105
APA
Singh, P. K., Sarkar, R., & Nasipuri, M. (2015). Word-Level Script Identification Using Texture Based Features. International Journal of System Dynamics Applications (IJSDA), 4(2), 74-94. http://doi.org/10.4018/ijsda.2015040105
Chicago
Singh, Pawan Kumar, Ram Sarkar, and Mita Nasipuri. "Word-Level Script Identification Using Texture Based Features," International Journal of System Dynamics Applications (IJSDA) 4, no.2: 74-94. http://doi.org/10.4018/ijsda.2015040105
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