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检索条件"任意字段=Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing"

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A hybrid deep architecture for face recognition in real-life scenario 10

A hybrid deep architecture for face recognition in real-life...

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10th indian conference on computer vision, graphics and image processing, ICVGIP 2016

作者： Sanya, A. Bhattacharya, U. Parui, S.K. Department of CSE Indian Institute of Technology Kanpur India CVPR Unit Indian Statistical Institute Kolkata India

This article describes our recent study of a real-life face recognition problem using a hybrid architecture consisting of a very deep convolution neural network (CNN) and a support vector machine (SVM). The novel aspects of this study include (i) implementation of a really deep CNN architecture consisting of 11 layers to study the effect of increasing depth on recognition performance by a subsequent SVM, and (ii) verification of the recognition performance of this hybrid classifier trained by samples of a certain standard size on test face images of smaller sizes reminiscent to various real-life scenarios. Results of the present study show that the features computed at various shallow levels of a deep architecture have identical or at least comparable performances and are more robust than the deepest feature computed at the inner most sub-sampling layer. We have also studied a simple strategy of recognizing face images of very small sizes using this hybrid architecture trained by standard size face images and the recognition performance is reported. We obtained simulation results using the cropped images of the standard extended Yale Face Database which show an interesting characteristic of the proposed architecture with respect to face images captured in a very low intensity lighting condition. © Springer International Publishing AG 2017.

关键词： Face recognition

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Tracking of retinal microsurgery tools using late fusion of responses from convolutional neural network over pyramidally decomposed frames 10

Tracking of retinal microsurgery tools using late fusion of ...

引用

10th indian conference on computer vision, graphics and image processing, ICVGIP 2016

作者： Mishra, Kaustuv Sathish, Rachana Sheet, Debdoot Indian Institute of Technology Kharagpur Kharagpur India

computer vision and robotic assistance are increasingly being used to improve the quality of surgical interventions. Tool tracking becomes critical in interventions viz. endoscopy, laparoscopy and retinal microsurgery (RM) where unlike open surgery the surgeons do not have direct visual and physical access to the surgical site. RM is performed using miniaturized tools and requires careful observation through a surgical microscope by the surgeon. Tracking of surgical tools primarily provides robotic assistance during surgery and also serves as a means to assess the quality of surgery, which is extremely useful during surgical training. In this paper we propose a deep learning based visual tracking of surgical tool using late fusion of responses from convolutional neural network (CNN) which comprises of 3 steps: (i) training of CNN for localizing the tool tip on a frame (ii) coarsely estimating the tool tip region using the trained CNN and (iii) a finer search around the estimated region to accurately localize the tool tip. Scale invariant tracking of tool is ensured by incorporating multi-scale late fusion where the CNN responses are obtained at each level of the Gaussian scale decomposition pyramid. Performance of the proposed method is experimentally validated on the publicly available retinal microscopy instrument tracking (RMIT) dataset (https://***. com/site/sznitr/code-and-datasets). Our method tracks tools with a maximum accuracy of 99.13% which substantiates the efficacy of the proposed method in comparison to existing approaches. © Springer International Publishing AG 2017.

关键词： Convolution

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Simultaneous reconstruction of multiple hand shredded content-less pages using graph-based global reassembly 10

Simultaneous reconstruction of multiple hand shredded conten...

引用

10th indian conference on computer vision, graphics and image processing, ICVGIP 2016

作者： Lalitha, K.S. Das, Sukhendu Menon, Arun Varghese, Koshy Department of Computer Science and Engineering IIT Madras Chennai India Department of Civil Engineering IIT Madras Chennai India

Hand shredded content-less pages reassembly is a challenging task. This has applications in forensics and fun games. The process is even more tedious when the number of pages from which the fragments are obtained is unknown. An iterative framework to solve the jigsaw puzzles of multiple hand shredded content-less pages has been proposed in this paper. This framework makes use of the shape-based information alone to solve the puzzle. All pairs of fragments are matched using the normalized shape-based features. Then, incorrect matches between the fragments are pruned using three scores that measure the goodness of the alignment. Finally, a graph-based technique is used to densely arrange the fragments for the global reassembly of the page(s). Experimental evaluation of our proposed framework on an annotated dataset of shredded documents shows the efficiency in the reconstruction of multiple content-less pages from arbitrarily torn fragments and performance metrics have been proposed to numerically evaluate the reassembly. © Springer International Publishing AG 2017.

关键词： Iterative methods

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MR imaging via reduced generalized autocalibrating partially parallel acquisition compressed sensing 10

MR imaging via reduced generalized autocalibrating partially...

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10th indian conference on computer vision, graphics and image processing, ICVGIP 2016

作者： Islam, Sheikh Rafiul Maity, Seba Maity, Santi P. Ray, Ajoy Kumar Kolkata743368 India College of Engineering and Management Kolaghat721171 India Indian Institute of Engineering Science and Technology Shibpur Howrah711103 India

Magnetic Resonance Imaging (MRI) system in recent times demands a high rate of acceleration in data acquisition to reduce the scanning time. The data acquisition rate can be accelerated to a significant order through Parallel MRI (pMRI) approach. An additional improvement in low sensing time for data acquisition can be achieved using Compressed Sensing (CS) or Compressive Sampling that enables reconstruction of a sparse signal from sub-sample (incomplete) measurements. This paper proposes an efficient pMRI scheme by combining CS with Generalized Auto-calibrating Partially Parallel Acquisitions (GRAPPA) to produce an MR image at high data acquisition rate. A kernel of reduced size is used within GRAPPA for estimating the unobserved encoded samples. Instead of all the unobserved samples, a certain number of the same are estimated randomly. Now, an l1-minimization based CS reconstruction technique is used in which the observed and the estimated unobserved samples are taken as measurements to reconstruct the final MR images. Extensive simulation results show that a significant reduction in artifacts and thereby consequent visual improvement in the reconstructed MRIs are achieved even when a high rate of acceleration factor is used. Simulation results also demonstrate that the proposed method outperforms some state-of-art pMRI methods, both in terms of subjective and objective quality assessment for the reconstructed images. © Springer International Publishing AG 2017.

关键词： Compressed sensing

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Local Gabor Wavelet-Based Feature Extraction and Evaluation 3rd

Local Gabor Wavelet-Based Feature Extraction and Evaluation

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3rd International conference on Advanced Computing, Networking, and Informatics (ICACNI)

作者： Malathi, T. Bhuyan, M. K. Indian Inst Technol Dept Elect & Elect Engn Gauhati 781039 India

ISBN: (纸本)9788132225386;9788132225379

Feature extraction is an essential step in many image processing and computer vision applications. It is quite desirable that the extracted features can effectively represent an image. Furthermore, the dominant information visually perceived by human beings should be efficiently represented by the extracted features. Over the last few decades, different algorithms are proposed to address the major issues of image representations by the efficient features. Gabor wavelet is one of the most widely used filters for image feature extraction. Existing Gabor wavelet-based feature extraction methodologies unnecessarily use both the real and the imaginary coefficients, which are subsequently processed by dimensionality reduction techniques such as PCA, LDA etc. This procedure ultimately affects the overall performance of the algorithm in terms of memory requirement and the computational complexity. To address this particular issue, we proposed a local image feature extraction method by using a Gabor wavelet. In our method, an image is divided into overlapping image blocks, and subsequently each of the image blocks are separately filtered out by Gabor wavelet. Finally, the extracted coefficients are concatenated to get the proposed local feature vector. The efficacy and effectiveness of the proposed feature extraction method is evaluated using the estimation of mean square error (MSE), peak signal-to-noise ratio (PSNR), and the correlation coefficient (CC) by reconstructing the original image using the extracted features, and compared it with the original input image. All these performance evaluation measures clearly show that real coefficients of the Gabor filter alone can effectively represent an image as compared to the methods which utilize either the imaginary coefficients or the both. The major novelty of our method lies on our claim-capability of the real coefficients of a Gabor filter for image representation.

关键词： image feature extraction Gabor filter image reconstruction

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SPODS: A dataset of color-official documents and detection of logo, stamp, and signature 10

SPODS: A dataset of color-official documents and detection o...

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10th indian conference on computer vision, graphics and image processing, ICVGIP 2016

作者： Nandedkar, Amit Vijay Mukherjee, Jayanta Sural, Shamik Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India

Office automation is an active area of research. It involves archival and retrieval of official documents. For developing a system for this purpose, it is necessary to have an extensive benchmark dataset consisting various types of official documents. However, it is hard to make available real world official documents as they are mostly confidential. In the absence of such benchmark datasets, it is difficult to evaluate newly developed algorithms. Hence, efforts have been made to build dataset consisting of different categories of documents that resemble real world official documents. In this work, we present a dataset called as scanned pseudo-official data-set (SPODS) which is created by us and made available online. Official documents are usually distinguished by presence of logo, stamp, signature, date, etc. The paper also presents a new approach for the detection of logo, stamp, and signature using spectral filtering and part based features. A comparative study on performances of the proposed method and existing algorithms on the SPODS dataset demonstrates the effectiveness of the proposed technique. © Springer International Publishing AG 2017.

关键词： Office automation

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Message from the program co-chairs

ACM International Conference Proceeding Series

引用

ACM International conference Proceeding Series 2016年

作者： Batra, Dhruv Natarajan, Vijay Brown, Michael Georgia Institute of Technology United States Indian Institute of Science Bangalore India York University Canada

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Deep automatic license plate recognition system 16

Deep automatic license plate recognition system

引用

proceedings of the tenth indian conference on computer vision, graphics and image processing

作者： Vishal Jain Zitha Sasindran Anoop Rajagopal Soma Biswas Harish S Bharadwaj K R Ramakrishnan Indian Institute of Science Myntra Designs

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： automatic license plate recognition convolution neural network image transformation pursuit spatial transformer network

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Shearlet based medical image fusion using pulse-coupled neural network with fuzzy memberships 10

Shearlet based medical image fusion using pulse-coupled neur...

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10th indian conference on computer vision, graphics and image processing, ICVGIP 2016

作者： Mishra, Niladri Shekhar Das, Sudeb Chakrabarti, Amlan Department of Electronics and Communication Engineering Netaji Subhash Engineering College Kolkata700152 India Videonetics Technology Private Limited B.P. Block Sector V Salt Lake City Kolkata700091 India A.K. Choudhury School of Information Technology University of Calcutta Kolkata700009 India

In this article, we propose a novel multimodal Medical image Fusion (MIF) method based on a neuro-fuzzy technique in the transform (Non-Subsampled Shearlet Transform (NSST)) domain for spatially registered, multi-modal medical images. The source medical images are first decomposed by NSST. The low-frequency subbands (LFSs) are fused using the Max-selection rule. Fuzzy triangular memberships are derived from a specific neighborhood-region of each high-frequency coefficient. Then they (high-frequency subbands, HFSs) are fused using a biologically inspired neural network (Pulse Coupled Neural Network (PCNN)) according to our newly proposed rule. Then inverse NSST (INSST) is applied to the fused coefficients to get the fused image. Visual and quantitative analysis and comparisons with state-of-the-art MIF techniques show the effectiveness of the proposed scheme in fusing multimodality medical images. © Springer International Publishing AG 2017.

关键词： image fusion

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Flash/No-flash image Fusion using Dictionary Learning 5

Flash/No-flash Image Fusion using Dictionary Learning

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

作者： Samani, Ekta Gupta, Vikas Raman, Shanmuganathan Indian Inst Technol Gandhinagar India

ISBN: (纸本)9781467385640

An image captured in dark environment usually has ambient illumination, but the image looks dark and noisy. However, the use of flash can introduce unwanted artifacts such as sharp shadows at silhouettes, red eyes, and non-uniform brightness in the image. We propose a new framework to enhance photographs captured in dark environments by combining the best features from a flash and a no-flash image. We use sparse and redundant dictionary learning based approach to denoise the no-flash image. A weighted least squares framework is used to transfer sharp details from the flash image into the no-flash image. We show that our approach is simple and able to generate better images than that of the state-of-the-art flash/no-flash fusion method.

关键词： image fusion

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