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检索条件"任意字段=International Conference on Computer Vision and Image Processing, CVIP 2016"

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A novel visual secret sharing scheme using affine cipher and image interleaving

A novel visual secret sharing scheme using affine cipher and...

引用

international conference on computer vision and image processing, cvip 2016

作者： Kaur, Harkeerat Ojha, Aparajita PDPM Indian Institute of Information Technology Design and Manufacturing JabalpurMadhya Pradesh India

ISBN: (纸本)9789811021060

Recently an interesting image sharing method for gray level images using Hill Cipher and RG-method has been introduced by Chen [1]. The method does not involve pixel expansion and image recovery is lossless. However, use of Hill Cipher requires a2 × 2integermatrixwhose inverse should also be an integer matrix. Further, to extend the method for multi-secret sharing, one requires higher order integer matrices. This needs heavy computation and the choice of matrices is also very restricted, due to integer entry constraints. In the present paper we introduce an RG-based Visual Secret Sharing Scheme (VSS) scheme using image interleaving and affine cipher. Combined effect of image interleaving and affine transformation helps in improving the security of the secret images. Parameters of the affine cipher serve as keys and the random grid and encrypted image form the shares. No one can reveal the secret unless the keys and both the shares are known. Further, as opposed to the method in [1], the present scheme does not require invertible matrix with integer inverse. The scheme is also extended for multi-secret sharing. © Springer Science+Business Media Singapore 2017.

关键词： Pixels

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Face expression recognition using histograms of oriented gradients with reduced features

Face expression recognition using histograms of oriented gra...

引用

international conference on computer vision and image processing, cvip 2016

作者： Kar, Nikunja Bihari Babu, Korra Sathya Jena, Sanjay Kumar Department of Computer Science and Engineering National Institute of Technology Rourkela769008 India

ISBN: (纸本)9789811021060

Facial expression recognition has been an emerging research area in last two decades. This paper proposes a new hybrid system for automatic facial expression recognition. The proposed method utilizes histograms of oriented gradients (HOG) descriptor to extract features from expressive facial images. Feature reduction techniques namely principal component analysis (PCA) and linear discriminant analysis (LDA) are applied to obtain the most important discriminant features. Finally, the discriminant features are fed to the back-propagation neural network (BPNN) classifier to determine the underlying emotions from expressive facial images. The Extended Cohn-Kanade dataset (CK+) is used to validate the proposed method. Experimental results indicate that the proposed system provides the better result as compared to state-of-the-art methods in terms of accuracy with the substantially lesser number of features. © Springer Science+Business Media Singapore 2017.

关键词： Hybrid systems

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Palmprint recognition based on minutiae quadruplets

Palmprint recognition based on minutiae quadruplets

引用

international conference on computer vision and image processing, cvip 2016

作者： Rao, A. Tirupathi Ramaiah, N. Pattabhi Mohan, C. Krishna Department of Computer Science and Engineering Indian Institute of Technology Hyderabad Hyderabad502205 India

ISBN: (纸本)9789811021060

Palmprint recognition is a variant of fingerprint matching as both the systems share almost similar matching criteria and the minutiae feature extraction methods. However, there is a performance degradation with palmprint biometrics because of the failure of extracting genuine minutia points from the region of highly distorted ridge information with huge data. In this paper, we propose an efficient palmprint matching algorithm using nearest neighbor minutiae quadruplets. The representation of minutia points in the form of quadruplets improves the matching accuracy at nearest neighbors by discarding scope of the global matching on false minutia points. The proposed algorithm is evaluated on publicly available high resolution palmprint standard databases, namely, palmprint benchmark data sets (FVC ongoing) and Tsinghua palmprint database (THUPALMLAB). The experimental results demonstrate that the proposed palmprint matching algorithm achieves the state-of-the-art performance. © Springer Science+Business Media Singapore 2017.

关键词： Palmprint recognition

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Support vector machine based extraction of crime information in human brain using ERP image

Support vector machine based extraction of crime information...

引用

international conference on computer vision and image processing, cvip 2016

作者： Kolekar, Maheshkumar H. Dash, Deba Prasad Patil, Priti N. Indian Institute of Technology Patna Bihta India Netaji Subhash Institute of Technology Amhara Patna India

ISBN: (纸本)9789811021060

Event related potential (ERP) is a non-invasive way to measure person’s cognitive ability or any neuro-cognitive disorder. Familiarity with any stimulus can be indicated by the brain’s instantaneous response to that particular stimulus. In this research work ERP based eye witness identification system is proposed. Electroencephalogram (EEG) signal was passed through butterworth band-pass filter and EEG signal was segmented based on marker. EEG segments were averaged and ERP was extracted from EEG signal. Grey incidence degree based wavelet denoising was performed. ERP was converted to image formand structural similarity index feature was extracted. Radial basis function kernel based support vector machine classifier was used to classify a person with or without crime information. The observed accuracy of proposed approach was 87.50 %. © Springer Science+Business Media Singapore 2017.

关键词： Electroencephalography

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Motion estimation from image sequences: A fractional order total variation model

Motion estimation from image sequences: A fractional order t...

引用

international conference on computer vision and image processing, cvip 2016

作者： Kumar, Pushpendra Raman, Balasubramanian Department of Mathematics Indian Institute of Technology Roorkee Roorkee247667 India Department of Computer Science & Engineering Indian Institute of Technology Roorkee Roorkee247667 India

ISBN: (纸本)9789811021060

In this paper, a fractional order total variation model is introduced in the estimation of motion field. In particular, the proposed model generalizes the integer order total variation models. The motion estimation is carried out in terms optical flow. The presented model is made using a quadratic and total variation terms. This mathematical formulation makes the model robust against outliers and preserves discontinuities. However, it is difficult to solve the presented model due to the non-differentiability nature of total variation term. For this purpose, the Grünwald- Letnikov derivative is used as a discretization scheme to discretize the fractional order derivative. The resulting formulation is solved by using a more efficient algorithm. Experimental results on various datasets verify the validity of the proposed model. © Springer Science+Business Media Singapore 2017.

关键词： Optical flows

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Content Based image Retrieval with Hadoop 2nd

引用

2nd international conference on Advanced Intelligent Systems and Informatics (AISI)

作者： Gaber, Heba Marey, Mohammed Amin, Safaa E. Tolba, Mohamed F. Ain Shams Univ Comp & Informat Sci Cairo Egypt

ISBN: (纸本)9783319483085;9783319483078

Hadoop has become a widely used open source framework for large scale data processing. MapReduce is the core component of Hadoop. It is this programming paradigm that allows for massive scalability across hundreds or thousands of servers in a Hadoop cluster. It allows processing of extremely large video files or image files on data nodes. This can be used for implementing Content Based image Retrieval (CBIR) algorithms on Hadoop to compare and match query images to the previously stored terabytes of an image descriptors databases. This work presents the implementation for one of the well-known CBIR algorithms called Scale Invariant Feature Transformation (SIFT) for image features extraction and matching using Hadoop platform. It gives focus on utilizing the parallelization capabilities of Hadoop MapReduce to enhance the CBIR performance and decrease data input\output operations through leveraging Partitioners and Combiners. Additionally, image processing and computer vision tools such as Hadoop image processing (HIPI) and Open computer vision (OpenCV) are integration is shown.

关键词： Hadoop HIPI OpenCV Big data Crowd sourcing MapReduce Combiners Partitioners

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Unsupervised learning from video to detect foreground objects in single images 16

Unsupervised learning from video to detect foreground object...

引用

16th IEEE international conference on computer vision (ICCV)

作者： Croitoru, Ioana Bogolin, Simion-Vlad Leordeanu, Marius Romanian Acad Inst Math Bucharest Romania Univ Politehn Bucuresti Bucharest Romania

ISBN: (纸本)9781538610329

Unsupervised learning from visual data is one of the most difficult challenges in computer vision. It is essential for understanding how visual recognition works. Learning from unsupervised input has an immense practical value, as huge quantities of unlabeled videos can be collected at low cost. Here we address the task of unsupervised learning to detect and segment foreground objects in single images. We achieve our goal by training a student pathway, consisting of a deep neural network that learns to predict, from a single input image, the output of a teacher pathway that performs unsupervised object discovery in video. Our approach is different from the published methods that perform unsupervised discovery in videos or in collections of images at test time. We move the unsupervised discovery phase during the training stage, while at test time we apply the standard feed-forward processing along the student pathway. This has a dual benefit: firstly, it allows, in principle, unlimited generalization possibilities during training, while remaining fast at testing. Secondly, the student not only becomes able to detect in single images significantly better than its unsupervised video discovery teacher, but it also achieves state of the art results on two current benchmarks, YouTube Objects and Object Discovery datasets. At test time, our system is two orders of magnitude faster than other previous methods.

关键词： image segmentation Unsupervised learning Training Principal component analysis computer vision Visualization

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Depth image super-resolution: A review and wavelet perspective

Depth image super-resolution: A review and wavelet perspecti...

引用

international conference on computer vision and image processing, cvip 2016

作者： Balure, Chandra Shaker Ramesh Kini, M. Surathkal Mangalore India

ISBN: (纸本)9789811021060

We propose an algorithm which utilizes the Discrete Wavelet Transform (DWT) to super-resolve the low-resolution (LR) depth image to a high-resolution (HR) depth image. Commercially available depth cameras capture depth images at a very low-resolution as compared to that of the optical cameras. Having an highresolution depth camera is expensive because of the manufacturing cost of the depth sensor element. In many applications like robot navigation, human-machine interaction (HMI), surveillance, 3D viewing, etc. where depth images are used, the LR images from the depth cameras will restrict these applications, thus there is a need of a method to produce HR depth images from the available LR depth images. This paper addresses this issue using DWT method. This paper also contributes to the compilation of the existing methods for depth image super-resolution with their advantages and disadvantages, along with a proposed method to super-resolve depth image using DWT. Haar basis for DWT has been used as it has an intrinsic relationship with super-resolution (SR) for retaining the edges. The proposed method has been tested on Middlebury and Tsukuba dataset and compared with the conventional interpolation methods using peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) performance metrics. © Springer Science+Business Media Singapore 2017.

关键词： Discrete wavelet transforms

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images: A dataset and texture-feature based performance evaluation

Images: A dataset and texture-feature based performance eval...

引用

international conference on computer vision and image processing, cvip 2016

作者： Verma, Manisha Sood, Nitakshi Roy, Partha Pratim Raman, Balasubramanian Mathematics Department IIT Roorkee Roorkee India University Institute of Engineering and Technology Panjab University Chandigarh India Computer Science and Engineering Department IIT Roorkee Roorkee India

ISBN: (纸本)9789811021060

Recognizing text with occlusion and perspective distortion in natural scenes is a challenging problem. In this work, we present a dataset of multi-lingual scripts and performance evaluation of script identification in this dataset using texture features. A ‘Station Signboard’ database that contains railway sign-boards written in 5 different Indic scripts is presented in this work. The images contain challenges like occlusion, perspective distortion, illumination effect, etc. We have collected a total of 500 images and corresponding ground-truths are made in semiautomatic way. Next, a script identification technique is proposed for multi-lingual scene text recognition. Considering the inherent problems in scene images, local texture features are used for feature extraction and SVMclassifier, is employed for script identification. From the preliminary experiment, the performance of script identification is found to be 84% using LBP feature with SVM classifier. © Springer Science+Business Media Singapore 2017.

关键词： Local binary pattern

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DEEP BLIND image QUALITY ASSESSMENT BY EMPLOYING FR-IQA 24

DEEP BLIND IMAGE QUALITY ASSESSMENT BY EMPLOYING FR-IQA

引用

24th IEEE international conference on image processing (ICIP)

作者： Kim, Jongyoo Lee, Sanghoon Yonsei Univ Seoul South Korea

ISBN: (纸本)9781509021758

In this paper, we propose a convolutional neural network (CNN)-based no-reference image quality assessment (NR-IQA). Though deep learning has yielded superior performance in a number of computer vision studies, applying the deep CNN to the NR-IQA framework is not straightforward, since we face a few critical problems: 1) lack of training data;2) absence of local ground truth targets. To alleviate these problems, we employ the full-reference image quality assessment (FR-IQA) metrics as intermediate training targets of the CNN. In addition, we incorporate the pooling stage in the training stage, so that the whole parameters of the model can be optimized in an end-to-end framework. The proposed model, named as a blind image evaluator based on a convolutional neural network (BIECON), achieves state-of-the-art prediction accuracy that is comparable with that of FR-IQA methods.

关键词： Convolutional neural network deep learning image quality assessment no-reference image quality assessment

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