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Validation Of Stimulus For EEG Signal Based Cognitive Load Analysis 5

Validation Of Stimulus For EEG Signal Based Cognitive Load A...

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Sinha, Aniruddha Chatterjee, Debatri Saha, Sanjoy Kumar Basu, Anupam Tata Consultancy Serv Ltd Innovat Lab Kolkata India Jadavpur Univ Comp Sci & Engn Kolkata India Indian Inst Technol Comp Sci & Engn Kharagpur W Bengal India

ISBN: (纸本)9781467385640

Cognitive load is defined as the mental workload imparted on brain while doing a task. the amount of cognitive load experienced depends on individual's ability of perception, assimilation and response to a task. Real-time measurement of the level of cognitive load using low cost Electroencephalogram (EEG) signal enables understanding of personal cognitive skills. In this paper, we propose a methodology of selecting a reference task whose bio-markers closely match with a given task while probing different cognitive abilities. the benefit of this approach is to have a limited set of training models for the reference tasks related to various cognitive categories and use the same for a variety of unknown tasks. Experiment is performed for two levels of cognitive load with three different tasks namely Stroop color task, logical reasoning task and usage of on-screen keyboards. the training models of the reference tasks, selected by cluster analysis of low and high cognitive levels are used to evaluate an unknown task. Experimental results indicate that the Stroop is a better reference for On-Screen keyboard test compared to the Logical reasoning test. Support vector machine (SVM) and principal component analysis (PCA) followed by SVM (PCASVM) are used as the classifiers for the testing.

关键词： cognitive load EEG clustering

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Offline Handwritten Signature Verification using Zernike Moments 5

Offline Handwritten Signature Verification using Zernike Mom...

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Kaur, Harman Preet Sharma, Anmol DAV Inst Engn & Technol Dept Comp Sci Jalandhar Punjab India DAV Inst Engn & Technol Dept Informat Technol Jalandhar Punjab India

ISBN: (纸本)9781467385640

In this paper, a novel approach for the verification of offline handwritten signatures is proposed. Despite tremendous growth of digital technologies in the last 4 decades, the most used authentication method today remains to be handwritten signature. It is the most natural method of authenticating a person's identity as compared to other biometric and cryptographic forms of authentication. We propose a method for verifying the signatory's identity by using Zernike Moments as global shape descriptors. Zernike Moments are image moments that are rotation invariant. the moments are also orthogonal on a unit circle which ensures minimum redundancy between the features representing the object shape. the features extracted in our approach have a relatively low dimensionality as compared to other studies, while retaining high representation power of the moments. Moreover, the module developed using our approach was able to demonstrate high performance coupled with low computation times in testing phase, making it suitable for real time applications. Experiments show high overall performance of our approach with an equal error rate EER of 13.42% and area under the curve A(z) equal to 0.84 using 1564 images from the NFI SigComp2009 dataset.

关键词： Authentication

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PD-Shift: Patch Detector Shift based Tracker 5

PD-Shift: Patch Detector Shift based Tracker

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Francis, Mathew Rajesh, Ratnakaram Guha, Prithwijit IIT Guwahati Dept EEE Gauhati India

ISBN: (纸本)9781467385640

We identify two important directions of algorithm development in the tracking literature - first, the older and more established mean-shift based approaches;and second, more recent formulations of viewing tracking as detection by parts (aka tracking-learning-detecting). Introductory versions of both these approaches had their own weaknesses due to the main challenges of tracking like illumination variations, abrupt motions, deformations/articulations and occlusions. Both these approaches have developed independently with a number of follow-ups in the literature and even the matured versions were never integrated. We propose to integrate these two different viewpoints of single object tracking where the contribution lies in the construction of the target model as a collection of object part/patch detectors integrated with a kernel. Similarly, the kernel weighted patch detector responses form the candidate model. the Bhattacharyya coefficient based similarity measure between the target and candidate model is maximized in a gradient ascent framework to deduce a mean-shift like centroid update rule to iteratively track the target in the subsequent frames. We call this unified one the Patch Detector Shift based Tracker (PD-Shift). Experimental results of PD-Shift based tracking performance analysis is presented for the BoBoT dataset.

关键词： Iterative methods

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A novel floor segmentation algorithm for mobile robot navigation 5

A novel floor segmentation algorithm for mobile robot naviga...

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Bhowmick, Soumabha Pant, Abhishek Mukherjee, Jayanta Deb, Alok Kanti IIT Kharagpur Adv Technol Dev Ctr Kharagpur W Bengal India IIT Kharagpur Comp Sci & Engn Kharagpur W Bengal India IIT Kharagpur Elect Engn Kharagpur W Bengal India

ISBN: (纸本)9781467385640

the task of detection of floor area for mobile robot navigation has received immense importance over the years. the main challenging problem as long as the path planning of robots is concerned, is the obstacle avoidance. Obstacle detection and avoidance in real time is a complex and computationally expensive process as a result of which the robotics researchers opted to segment out floors, which is comparatively easier process and at the same time very much feasible in real time applications. the proposed floor segmentation algorithm detects floor from a scene irrespective of the change of illumination as well as shadows. A conventional breadth first search based region growing technique has been used with histogram based features in YCbCr color space and floor junction masking to detect the floor pixels from the scene. the algorithm has been compared with various other existing techniques to showcase the improved results of the proposed one.

关键词： Histogram floor junction masking breadth first search

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Greedy Partitioning based Tree Structured Multiclass SVM for Odia OCR 5

Greedy Partitioning based Tree Structured Multiclass SVM for...

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Sahu, Sandeep Kumar Pujari, Arun K. Kumar, Vikas Kagita, Venkateswara Rao Padmanabhan, Vineet Univ Hyderabad Sch Comp & Informat Sci Hyderabad 500046 Andhra Pradesh India

ISBN: (纸本)9781467385640

there have been many proposals to extend the basic two-class SVM classifier for multiclass classification and it is established that among these extensions binary-structured hierarchical SVMs is the most efficient computationally. However, determining an effective binary structure by recursively dividing the classes is a major research issue. We describe a new classifier, GP-SVM, based on greedy partitioning of classes and demonstrate that GP-SVM gives better classification accuracy than all major combinational techniques besides having the computational advantages. the advantages of GP-SVM is better realized when the number of classes is large. We demonstrate this advantage in recognition of printed Odia character. We built a corpus of 10025 tagged Odia aksharas collected over multiple printed documents of different fonts. We used a very modest number of features. GP-SVM with 133 classes yielded 95% accuracy of recognition. During the learning process of GP-SVM, the proposed system could learn the taxonomy of character-shapes of Odia script.

关键词： Optical character recognition

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Kernelization of Locality Preserving Discriminant Projection 5

Kernelization of Locality Preserving Discriminant Projection

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Shikkenawis, Gitam Mitra, Suman K. Dhirubhai Ambani Inst Informat & Commun Technol Gandhinagar 382007 India

ISBN: (纸本)9781467385640

In recent years, non-linear dimensionality reduction approaches have become popular due to their suitability to capture the non-linearities present in the data. However, these are not applicable for recognition related applications as representation of new data points is not defined in the reduced subspace. Hence, to explore the non-linearities of the data, local structure preserving approaches received considerable attention. these approaches keep the local information of the data points intact in the lower dimensional space as well. Locality Preserving Discriminant Projection (LPDP) not only preserves the neighborhood information for each data point, but also tries to discriminate data points from different classes using their class labels. the performance of such neighborhood information preserving dimensionality reduction techniques do not guarantee to capture complex non-linearities present in the data. To address this issue, kernel functions that map the data in a non-linear feature space before applying the dimensionality reduction approach are widely used. this article discusses Kernelization of LPDP which explores complex non-linear changes of face images due to facial expression, illumination, pose and environmental changes. the proposed approach is applied for face recognition on some of the benchmark databases with such variability.

关键词： Face recognition

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Psoriatic Plaque Segmentation in Skin images 5

Psoriatic Plaque Segmentation in Skin Images

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Pal, Anabik Garain, Utpal Chatterjee, Raghunath Senapati, Swapan ISI CVPRU Kolkata 108 W Bengal India ISI HGU Kolkata 108 W Bengal India

ISBN: (纸本)9781467385640

this paper concerns about automatic psoriatic plaque segmentation in skin images. A novel pixel clustering based image segmentation approach is proposed. the clustering approach works with the circular-linear color spaces which remained unexplored because of the unavailability of appropriate mixture models and hence, the traditional clustering algorithms are incapable of handling such color spaces. this paper makes use of a novel Semi-Wrapped Gaussian Mixture Model (SWGMM) based approach for clustering image pixels. the experiment considers CIE Lch color space which is essentially a circularlinear space. the performance of SWGMM-based segmentation is compared to commonly used Fuzzy C-Means(FCM) and Fuzzy Kernel C-Means (FKCM)-based segmentation methods in linear color space. Results on a newly generated dataset show that SWGMM-based segmentation achieves accuracy of 82.84% in CIE Lch;whereas experiments with three linear color spaces namely, RGB, CIE L*u*v* and CIE L*a*b* show that FKCM and FCM achieve best accuracies of 78.80% and 77.79%, respectively in Lu*v* space. For comparing the clustering-based diseased detection result to a supervised pixel classification based approach, a support vector machines (SVM) based classifier is trained with labelled diseased and normal healthy skin pixels. As the number of images is limited for the present problem, SVM based diseased detection achieves 63.56% accuracy which is significantly lower than the accuracies given by the clustering based approaches.

关键词： Support vector machines

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Complexity Assisted Consistent Quality Rate Control for High Resolution H.264 Video Conferencing 5

Complexity Assisted Consistent Quality Rate Control for High...

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Bhattacharyya, Subhomoy Chakrabarti, Indrajit Kolekar, Maheshkumar H. Squid Syst Inc Hyderabad 500062 Andhra Pradesh India Indian Inst Technol Kharagpur 721302 W Bengal India Indian Inst Technol Patna 800013 Bihar India

ISBN: (纸本)9781467385640

Video based multimedia services are showing major growth in recent years. though the video coding recommendation ITU-T H.265/HEVC is in operating state, the majority of real-time video applications including video conferencing and live streaming of events rely mainly on less computationally intensive H.264/AVC coding standard which is well established among video industry. Rate control algorithms are indispensable for delivering superior quality video over limited bandwidth connections. In this paper, we propose a rate control technique which provides consistent quality video output over time in a limited bandwidth video conferencing scenario. Using the concept of video traffic prediction and linear complexity model, we compute the complexity of the video sequence in real time. We present a better estimate of quantization parameter at the GOP (group of picture) layer. Our proposed method maintains similar quality for reconstructed video when compared to rate control scheme of JM 19.0 implemented in real-time video conferencing system with restricted quality variation. On an average, 22% improvement has been observed in terms of standard deviation of frame PSNR values when compared to JM 19.0 rate control scheme implemented in high resolution video conferencing scenario.

关键词： rate control H.264/AVC video traffic prediction consistent quality GOP layer

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Lip Contour Detection for Estimation of Mouth Opening Area 5

Lip Contour Detection for Estimation of Mouth Opening Area

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Jain, Shreyansh Pandey, Prem C. Velmurugan, Rajbabu Indian Inst Technol Dept Elect Engn Bombay 400076 Maharashtra India

ISBN: (纸本)9781467385640

In speech training aids for providing visual feedback of the articulatory efforts, time-varying vocal tract shape during speech production is generally obtained by linear prediction (LP) analysis of the speech signal and assuming a constant area at the glottis end as a reference. Its variation during speech production causes errors in the estimated vocal tract shape. the problem can be overcome by using area of the mouth opening as the reference. this area can be estimated by detecting the inner lip contour from the video recording of speaker's face during speech utterance. A technique for detection of inner lip contour, based on color transformation and template matching, is presented for reducing the errors caused by presence of teeth and tongue. Face detection by Viola-Jones algorithm, localization using a mouth detection technique, and outer lip contour detection are used to narrow down the search region for inner mouth opening. Presence of the teeth is masked by separate color transformations for upper and lower lip segments. For reducing the errors due to visibility of the tongue, which may not have any significant separation from the lips in the color space, a template matching technique is employed. It is used separately for the upper and lower lip segments to obtain the mouth opening area. the technique has been validated against graphically measured values of the mouth opening and found to be successful in estimating the mouth opening area, and it is not affected by skin hue and presence of teeth.

关键词： lip contour mouth opening area speech training aid template matching

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Efficient Face Frontalization in Unconstrained images 5

Efficient Face Frontalization in Unconstrained Images

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Mallikarjun, B. R. Chari, Visesh Jawahar, C. V. IIIT Hyderabad CVIT Hyderabad Andhra Pradesh India

ISBN: (纸本)9781467385640

Face frontalization is the process of synthesizing a frontal view of a face, given its non-frontal view. Frontalization is used in intelligent photo editing tools and also aids in improving the accuracy of face recognition systems. For example, in the case of photo editing, faces of persons in a group photo can be corrected to look into the camera, if they are looking elsewhere. Similarly, even though recent methods in face recognition claim accuracy which surpasses that of humans in some cases, performance of recognition systems degrade when profile view of faces are given as input. One way to address this issue is to synthesize frontal views of faces before recognition. We propose a simple and efficient method to address the face frontalization problem. Our method leverages the fact that faces in general have a definite structure and can be represented in a low dimensional subspace. We employ an exemplar based approach to find the transformation that relates the profile view to the frontal view, and use it to generate realistic frontalizations. Our method does not involve estimating 3D model of the face, which is a common approach in previous work in this area. this leads to an efficient solution, since we avoid the complexity of adding one more dimension to the problem. Our method also retains the structural information of the individual as compared to that of a recent method [4], which assumes a generic 3D model for synthesis. We show impressive qualitative and quantitative results in comparison to the state-of-the-art in this field.

关键词： Face recognition

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