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Don't See Me, Just Filter Me: Towards secure cloud based filtering using Shamir's secret sharing and POB number system 16

Don't See Me, Just Filter Me: Towards secure cloud based fil...

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10th indian conference on computer vision, graphics and image processing (icvgip)

作者： Singh, Priyanka Agarwal, Nishant Raman, Balasubramanian Indian Inst Technol Roorkee Uttar Pradesh India Natl Inst Technol Kurukshetra Kurukshetra Haryana India

ISBN: (纸本)9781450347532

Cloud computing paradigm is attracting individuals as well as organizations all round the globe due to its multiple facilities of powerful resources, storage hubs, computational power and cost effective solutions. However, distributed cloud data centers pose the threat of security breaches to the privacy of the data and pose high risks. A secured image data sharing scheme over cloud domain based on the Shamir's secret sharing and permutation ordered binary number system has been proposed here. It distributes the image information into multiple random shares that reveal no information and thus can be stored securely over the distributed cloud data centers. Different image operations could be applied in the encrypted domain over the cloud servers itself. this approach reduces the threat to security which is a major hindrance in utilizing the facilities of the cloud based architecture. Only the authentic owner possessing the secret keys could restore back the original image information from the random shares. Comparative results for both the plain domain as well as the encrypted domain have been presented to validate the efficiency of using the various image filtering operations like motion blur, unsharp masking, weiner, gaussian etc in the encrypted domain over cloud.

关键词： Cloud Computing Shamir's Secret Sharing Permutation Ordered Binary Number System Encrypted Domain

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Pap Smear image Classification Using Convolutional Neural Network 16

Pap Smear Image Classification Using Convolutional Neural Ne...

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10th indian conference on computer vision, graphics and image processing (icvgip)

作者： Bora, Kangkana Chowdhury, Manish Mahanta, Lipi B. Kundu, Malay K. Das, Anup K. Inst Adv Study Sci & Technol Cent Computat & Numer Studies Gauhati Assam India KTH Sch Technol & Hlth Stockholm Sweden Indian Stat Inst Machine Intelligence Unit Kolkata India Ayursundra Healthcare Pvt Ltd Dept Pathol Gauhati Assam India

ISBN: (纸本)9781450347532

this article presents the result of a comprehensive study on deep learning based computer Aided Diagnostic techniques for classification of cervical dysplasia using Pap smear images. All the experiments are performed on a real indigenous image database containing 1611 images, generated at two diagnostic centres. Focus is given on constructing an effective feature vector which can perform multiple level of representation of the features hidden in a Pap smear image. For this purpose Deep Convolutional Neural Network is used, followed by feature selection using an unsupervised technique with Maximal Information Compression Index as similarity measure. Finally performance of two classifiers namely Least Square Support Vector Machine (LSSVM) and Softmax Regression are monitored and classifier selection is performed based on five measures along with five fold cross validation technique. Output classes reflects the established Bethesda system of classification for identifying pre-cancerous and cancerous lesion of cervix. the proposed system is also compared with two existing conventional systems and also tested on a publicly available database. Experimental results and comparison shows that proposed system performs efficiently in Pap smear classification.

关键词： Pap smear image Deep learning LSSVM Softmax Regression

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Bangla Online Handwriting Recognition Using Recurrent Neural Network Architecture 16

Bangla Online Handwriting Recognition Using Recurrent Neural...

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10th indian conference on computer vision, graphics and image processing (icvgip)

作者： Chakraborty, Bappaditya Mukherjee, Partha Sarathi Bhattacharya, Ujjwal Univ Calcutta AKCSIT Kolkata W Bengal India Indian Stat Inst CVPR Unit Kolkata W Bengal India

ISBN: (纸本)9781450347532

Recognition of unconstrained handwritten texts is always a difficult problem, particularly if the style of handwriting is a mixed cursive one. Among various indian scripts, only Bangla has this additional difficulty of tackling mixed cursiveness of its handwriting style in the pipeline of a method towards its automatic recognition. A few other common recognition difficulties of handwriting in an indian script include the large size of its alphabet and the extremely cursive nature of the shapes of its alphabetic characters. these are among the reasons of achieving only limited success in the study of unconstrained handwritten Bangla text recognition. Artificial Neural Network (ANN) models have often been used for solving difficult real-life pattern recognition problems. Recurrent Neural Network models (RNN) have been studied in the literature for modeling sequence data. In this study, we consider Long Short Term Memory (LSTM) network model, a useful member of this family. In fact, Bidirectional Long Short-Term Memory (BLSTM) neural networks is a special kind of RNN and have recently attracted special attention in solving sequence labelling problems. In this article, we present a BLSTM architecture based approach for unconstrained online handwritten Bangla text recognition.

关键词： RNN BLSTM-CTC Online Handwriting Recognition

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Spatio-temporal Weighted Histogram based Mean Shift for Illumination Robust Target Tracking 16

Spatio-temporal Weighted Histogram based Mean Shift for Illu...

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10th indian conference on computer vision, graphics and image processing (icvgip)

作者： Deopujari, Kalyani Velmurugan, Rajbabu Tiwari, Kanchan MES Coll Engn Pune Maharashtra India Indian Inst Technol Bombay Maharashtra India

ISBN: (纸本)9781450347532

this paper proposes a simple method to handle illumination variation in a video. the proposed method is based on generative mean shift tracker, which uses energy compaction property of discrete Cosine transform (DCT) to handle illumination variation within and across frames. the proposed method uses spatial and temporal DCT coefficient based approach to assign weights to target and candidate histograms in mean shift. the proposed weighing factor takes care of changes in illumination within a frame i.e., illumination change of the target with respect to background and also across the frames i.e., varying illumination between the consecutive time instances. the algorithm was tested using VOT2015 challenge dataset and also on sequences from OTB and CAVIAR datasets. the proposed method was also tested rigorously for illumination attribute. the qualitative and quantitative evaluation process of the proposed method was twofold. First, the tracker was compared with existing DCT coefficient based method and showed improved results. Secondly, the proposed algorithm was compared with other state of the art trackers. the results show that the proposed algorithm outperformed some state-of-the-art trackers while with others it showed comparable performance.

关键词： Mean Shift Discrete Cosine Transform DC Coefficient Illumination Variation

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A Framework to assess Sun Salutation videos 16

A Framework to assess Sun Salutation videos

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10th indian conference on computer vision, graphics and image processing (icvgip)

作者： Jain, Hiteshi Harit, Gaurav Indian Inst Technol Jodhpur Dept Comp Sci & Engn Jodhpur 342011 Rajasthan India

ISBN: (纸本)9781450347532

there are many exercises which are repetitive in nature and are required to be done with perfection to derive maximum benefits. Sun Salutation or Surya Namaskar is one of the oldest yoga practice known. It is a sequence of ten actions or 'asanas' where the actions are synchronized with breathing and each action and its transition should be performed with minimal jerks. Essentially, it is important that this yoga practice be performed with Grace and Consistency. In this context, Grace is the ability of a person to perform an exercise with smoothness i.e. without sudden movements or jerks during the posture transition and Consistency measures the repeatability of an exercise in every cycle. We propose an algorithm that assesses how well a person practices Sun Salutation in terms of grace and consistency. Our approach works by training individual HMMs for each asana using STIP features[11] followed by automatic segmentation and labeling of the entire Sun Salutation sequence using a concatenated-HMM. the metric of grace and consistency are then laid down in terms of posture transition times. the assessments made by our system are compared with the assessments of the yoga trainer to derive the accuracy of the system. We introduce a dataset for Sun Salutation videos comprising 30 sequences of perfect Sun Salutation performed by seven experts and used this dataset to train our system. While Sun Salutation can be judged on multiple parameters, we focus mainly on judging Grace and Consistency.

关键词： Sun Salutation HMM STIP grace consistency

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Event Recognition in Egocentric Videos Using a Novel Trajectory Based Feature 16

Event Recognition in Egocentric Videos Using a Novel Traject...

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10th indian conference on computer vision, graphics and image processing (icvgip)

作者： Buddubariki, Vinodh Tulluri, Sunitha Gowd Mukherjee, Snehasis IIIT Chittoor Sricity India

ISBN: (纸本)9781450347532

this paper proposes an approach for event recognition in Egocentric videos using dense trajectories over Gradient Flow - Space Time Interest Point (GF-STIP) feature. We focus on recognizing events of diverse categories (including indoor and outdoor activities, sports and social activities and adventures) in egocentric videos. We introduce a dataset with diverse egocentric events, as all the existing egocentric activity recognition datasets consist of indoor videos only. the dataset introduced in this paper contains 102 videos with 9 different events (containing indoor and outdoor videos with varying lighting conditions). We extract Space Time Interest Points (STIP) from each frame of the video. the interest points are taken as the lead pixels and Gradient-Weighted Optical Flow (GWOF) features are calculated on the lead pixels by multiplying the optical flow measure and the magnitude of gradient at the pixel, to obtain the GF-STIP feature. We construct pose descriptors with the GF-STIP feature. We use the GF-STIP descriptors for recognizing events in egocentric videos with three different approaches: following a Bag of Words (BoW) model, implementing Fisher Vectors and obtaining dense trajectories for the videos. We show that the dense trajectory features based on the proposed GF-STIP descriptors enhance the efficacy of the event recognition system in egocentric videos.

关键词： GWOF Dense Trajectories Event Recognition First Person Egocentric

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Deep Automatic Licence Plate Recognition system 10

Deep Automatic Licence Plate Recognition system

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10th indian conference on computer vision, graphics and image processing (icvgip)

作者： Jain, Vishal Sasindran, Zitha Rajagopal, Anoop Biswas, Soma Bharadwaj, Harish S. Ramakrishnan, K. R. Indian Inst Sci Bengaluru Karnataka India Myntra Designs Bengaluru Karnataka India

ISBN: (纸本)9781450347532

Automatic License Plate Recognition (ALPR.) has important applications in traffic surveillance. It is a challenging problem especially in countries like in India where the license plates have varying sizes, number of lines, fonts etc. the difficulty is all the more accentuated in traffic videos as the cameras are placed high and most plates appear skewed. this work aims to address ALPR. using Deep CNN methods for real-time traffic videos. We first extract license plate candidates from each frame using edge information and geometrical properties, ensuring high recall. these proposals are fed to a CNN classifier for License Plate detection obtaining high precision. We then use a CNN classifier trained for individual characters along with a spatial transformer network (STN) for character recognition. Our system is evaluated on several traffic videos with vehicles having different license plate formats in terms of tilt, distances, colors, illumination, character size, thickness etc. Results demonstrate robustness to such variations and impressive performance in both the localization and recognition. We also make available the dataset for further research on this topic.

关键词： Convolution Neural Network Spatial Transformer Network image Transformation Pursuit Automatic License Plate Recognition

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Generic TV Advertisement Detection using Progressively Balanced Perceptron Trees 16

Generic TV Advertisement Detection using Progressively Balan...

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10th indian conference on computer vision, graphics and image processing (icvgip)

作者： Kannao, Raghvendra Guha, Prithwijit IIT Guwahati Dept EEE Gauhati 781039 Assam India

ISBN: (纸本)9781450347532

Automatic detection of TV advertisements is of paramount importance for various media monitoring agencies. Existing works in this domain have mostly focused on news channels using news specific features. Most commercial products use near copy detection algorithms instead of generic advertisement classification. A generic detector needs to handle inter-class and intra-class imbalances present in data due to variability in content aired across channels and frequent repetition of advertisements. Imbalances present in data make classifiers biased towards one of the classes and thus require special treatment. We propose to use tree of perceptrons to solve this problem. the training data available for each perceptron node is balanced using cluster based over sampling and TOMEK link cleaning as we traverse the tree downwards. the trained perceptron node then passes the original unbalanced data to its children. this process is repeated recursively till we reach the leaf nodes. We call this new algorithm as "Progressively Balanced Perceptron Tree". We have also contributed a TV advertisements dataset consisting of 250 hours of videos recorded from five non-news TV channels of different genres. Experimentations on this dataset have shown that the proposed approach has comparatively superior and balanced performance with respect to six baseline methods. Our proposal generalizes well across channels, with varying training data sizes and achieved a top Fl-score of 97% in detecting advertisements.

关键词： Trees (mathematics)

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Robust Registration of Mouse Brain Slices with Severe Histological Artifacts 16

Robust Registration of Mouse Brain Slices with Severe Histol...

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10th indian conference on computer vision, graphics and image processing (icvgip)

作者： Agarwal, Nitin Xu, Xiangmin Gopi, M. Univ Calif Irvine Dept Comp Sci Irvine CA 92697 USA Univ Calif Irvine Dept Anat Neurobiol Irvine CA USA

ISBN: (纸本)9781450347532

Brain mapping research is facilitated by first aligning digital images of mouse brain slices to standardized atlas framework such as the Allen Reference Atlas (ARA). However, conventional processing of these brain slices introduces many histological artifacts such as tears and missing regions in the tissue, which make the automatic alignment process extremely challenging. We present an end-to-end fully automatic registration pipeline for alignment of digital images of mouse brain slices that may have histological artifacts, to a standardized atlas space. We use a geometric approach where we first align the bounding box of convex hulls of brain slice contours and atlas template contours, which are extracted using a variant of Canny edge detector. We then detect the artifacts using Constrained Delaunay Triangulation (CDT) and remove them from the contours before performing global alignment of points using iterative closest point (ICP). this is followed by a final non-linear registration by solving the Laplace's equation with Dirichlet boundary conditions. We tested our algorithm on 200 mouse brain slice images including slices acquired from conventional processing techniques having major histological artifacts, and from serial two-photon tomography (STPT) with no major artifacts. We show significant improvement over other registration techniques, both qualitatively and quantitatively, in all slices especially on slices with significant histological artifacts.

关键词： Non-Linear Registration image Artifacts Histology Mouse Brain Allen Reference Atlas Point Correspondences

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Learning to Hash-tag Videos with Tag2Vec 16

Learning to Hash-tag Videos with Tag2Vec

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10th indian conference on computer vision, graphics and image processing (icvgip)

作者： Singh, Aditya Saini, Saurabh Shah, Rajvi Narayanan, P. J. IIIT Hyderabad KCIS CVIT Hyderabad Telangana India

ISBN: (纸本)9781450347532

User-given tags or labels are valuable resources for semantic understanding of visual media such as images and videos. Recently, a new type of labeling mechanism known as hash tags have become increasingly popular on social media sites. In this paper, we study the problem of generating relevant and useful hash-tags for short video clips. Traditional data driven approaches for tag enrichment and recommendation use direct visual similarity for label transfer and propagation. We attempt to learn a direct low-cost mapping from video to hash-tags using a two step training process. We first employ a natural language processing (NLP) technique, skip-gram models with neural network training to learn a low-dimensional vector representation of hash-tags (Tag2Vec) using a corpus of similar to 10 million hash-tags. We then train an embedding function to map video features to the low-dimensional Tag2vec space. We learn this embedding for 29 categories of short video clips with hash-tags. A query video without any tag-information can then be directly mapped to the vector space of tags using the learned embedding and relevant tags can be found by performing a simple nearest-neighbor retrieval in the Tag2Vec space. We validate the relevance of the tags suggested by our system qualitatively and quantitatively with a user study.

关键词： Tag2Vec Video Tagging Hash-tag recommendation

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