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

共 1495 条记录，以下是1231-1240 订阅

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Tri-modal biometric fusion for human authentication by tracking Differential Code Pattern

Tri-modal biometric fusion for human authentication by track...

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

作者： Aditya Nigam Phalguni Gupta School of Computing and Electrical Engineering Indian Institute of Technology Mandi India CSE Department NITTTR Kolkata Salt Lake India

Human authentication can now be seen as a crucial social problem. In this paper a multimodal authentication system is presented which is highly reliable and fuses iris, finger-knuckle-print and palmprint image matching scores. Segmented ROI are preprocessed using DCP (Differential Code Pattern) to obtain robust corner features. Later they are matched using the GOF (Global Optical Flow) based dissimilarity measure. The proposed system has been tested on Casia Interval and Lamp iris, PolyU finger-knuckle-print and PolyU and Casia palmprint, public databases. The proposed system has shown good performance over all unimodal databases while over multimodal (fusion of all three) databases it has shown perfect performance (i.e. CRR = 100% with EER = 0%).

关键词： Databases Iris recognition Optical imaging Iris Feature extraction Biomedical optical imaging

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Combining NL processing and video data to query American Football

Combining NL processing and video data to query American Foo...

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International conference on Pattern Recognition

作者： M. Lazarescu S. Venkatesh G. West T. Caelli Curtin University of Technology Perth WA Australia Ohio State Uinversity Columbus OH USA

We explore the use of natural language understanding and image processing to index and query American Football tapes. We present a model for representing spatio-temporal characteristics of multiple objects in dynamic ... 详细信息

关键词： Natural languages Electrical capacitance tomography image processing Layout Artificial intelligence computer vision image sequences Software libraries Information retrieval image retrieval

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STAR: A Content Based Video Retrieval system for moving camera video shots

STAR: A Content Based Video Retrieval system for moving came...

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

作者： Chiranjoy Chattopadhyay Sukhendu Das Department of Computer Science and Engineering Indian Institute of Technology Madras India

This paper presents the design of STAR (Spatio-Temporal Analysis and Retrieval), an unsupervised Content Based Video Retrieval (CBVR) System. STAR's key insight and primary contribution is that it models video content using a joint spatio-temporal feature representation and retrieves videos from the database which have similar moving object and trajectories of motion. Foreground moving blobs from a moving camera video shot are extracted, along with a trajectory for camera motion compensation, to form the space-time volume (STV). The STV is processed to obtain the EMST-CSS representation, which can discriminate across different categories of videos. Performance of STAR has been evaluated qualitatively and quantitatively using precision-recall metric on benchmark video datasets having unconstrained video shots, to exhibit efficiency of STAR.

关键词： Cameras Feature extraction Trajectory Surface treatment Shape Benchmark testing Vectors

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Place of articulation from direct imaging for validation of its estimation from speech analysis for use in speech training

Place of articulation from direct imaging for validation of ...

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

作者： K. S. Nataraj Prem C. Pandey Department of Electrical Engineering Indian Institute of Technology Bombay Mumbai India

Place of articulation obtained by analysis of the speech signal is useful for visual feedback of articulatory efforts for speech training of hearing impaired children and for improving pronunciation by learners of second languages. Its estimation by direct imaging of the oral cavity is needed for validating the estimation from the speech signal. For such applications, an automated technique is presented for estimating the place of articulation by graphical processing of the upper and lower contours of the oral cavity image. It iteratively estimates the axial curve as an axis of symmetry of the oral cavity, such that the curve approximately bisects the normals to it. Distance between the contours along the normal to the axial curve gives the oral cavity opening and position of the smallest opening provides the place of articulation. The values estimated using the automated technique closely matched those obtained by manual marking of the visually estimated place of maximum constriction for the oral cavity images of vowels, stops, and fricatives, from the XRMB and MRI databases.

关键词： Cavity resonators Speech Magnetic resonance imaging Databases Estimation image segmentation

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Dual Stage Semantic Information Based Generative Adversarial Network For image Super-Resolution 14

Dual Stage Semantic Information Based Generative Adversarial...

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14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Sharma, Shailza Dhall, Abhinav Kumar, Vinay Singh, Vivek Thapar Institute of Engineering and Technology Punjab India IIT Ropar India Oxford Brookes University United Kingdom

ISBN: (纸本)9798400716256

Deep learning methods for the super-resolution problem are showing great performance compared to other traditional techniques. However, these methods are unable to learn complex spatial structures and high frequency details;which leads to over-smooth results. In the present paper, a novel Generative Adversarial Network based architecture named as Residue and Semantic feature based Dual Subpixel Generative Adversarial Network has been proposed for generator and discriminator networks to solve super-resolution problem. The generator network is residue and semantic feature based dual subpixel generative architecture. This architecture is divided into two stages: premier residual stage and deuxieme residual stage. These two stages are concatenated together to form a two stage upsamping process, which enhances the feature learning capability of our model. Inter and intra residual connections are made within these two stages;helping us to sustain the high texture details of images. Semantic based information is implanted in generator to enhance the quality of objects in an image. For embedding semantic information in generator, feature maps extracted from pre-trained model are merged with the input image. To stabilize the training process, we introduced spectral normalization in the discriminator. Visual perception and mean opinion score shows that proposed method outperforms the other state-of-the-art methods. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Generative adversarial networks

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RECAL: Reuse of Established CNN Classifier Apropos Unsupervised Learning Paradigm 7th

RECAL: Reuse of Established CNN Classifier Apropos Unsupervi...

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7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Saha, Jayasree Mukherjee, Jayanta Indian Institute of Technology Kharagpur KharagpurWest Bengal721302 India

ISBN: (纸本)9789811586965

Recently, clustering with a deep network framework has attracted the attention of several researchers in the computer vision community. The Deep framework gains extensive attention due to its efficiency and scalability towards large-scale and high-dimensional data. In this paper, we transform supervised CNN classifier architecture into an unsupervised clustering model, called RECAL (https://***/abs/1906.06480). It jointly learns discriminative embedding subspace and cluster labels. RECAL is made up of feature extraction layers that are convolutional, followed by unsupervised classifier layers which is fully connected. A multinomial logistic regression function (softmax) stacked on top of classifier layers. We train this network using stochastic gradient descent (SGD) optimizer. However, the successful implementation of our model is revolved around the design of loss function. Our loss function uses the heuristics that true partitioning entails lower entropy, given that the class distribution is not heavily skewed. This is a trade-off between the situations of "skewed distribution" and "low-entropy". To handle this, we have proposed classification entropy and class entropy, which are the two components of our loss function. In this approach, the size of the mini-batch should be kept high. Experimental results indicate the consistent and competitive behavior of our model for clustering well-known digit, multi-viewed object, and face datasets. Moreover, we use this model to generate unsupervised patch segmentation for multi-spectral LISS-IV images. We observe that it is able to distinguish built-up areas, wetland, vegetation and water-body from the underlying scene. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Entropy

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Active gaze stabilization 14

Active gaze stabilization

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9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Lesmana, Martin Landgren, Axel Forssén, Per-Erik Pai, Dinesh K. Computer Science U. British Columbia Canada Electrical Engineering Linköping University Sweden

ISBN: (纸本)1595930361

We describe a system for active stabilization of cameras mounted on highly dynamic robots. To focus on careful performance evaluation of the stabilization algorithm, we use a camera mounted on a robotic test platform that can have unknown perturbations in the horizontal plane, a commonly occurring scenario in mobile robotics. We show that the camera can be effectively stabilized using an inertial sensor and a single additional motor, without a joint position sensor. The algorithm uses an adaptive controller based on a model of the vertebrate Cerebellum for velocity stabilization, with additional drift correction. We have also developed a resolution adaptive retinal slip algorithm that is robust to motion blur. We evaluated the performance quantitatively using another high speed robot to generate repeatable sequences of large and fast movements that a gaze stabilization system can attempt to counteract. Thanks to the high-accuracy repeatability, we can make a fair comparison of algorithms for gaze stabilization. We show that the resulting system can reduce camera image motion to about one pixel per frame on average even when the platform is rotated at 200 degrees per second. As a practical application, we also demonstrate how the common task of face detection benefits from active gaze stabilization. Copyright 2014 ACM.

关键词： Stabilization

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DocDescribor: Digits + Alphabets + Math Symbols - A Complete OCR for Handwritten Documents 7th

DocDescribor: Digits + Alphabets + Math Symbols - A Complete...

引用

7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Aggarwal, Ridhi Jain, Hiteshi Harit, Gaurav Tiwari, Anil Kumar Department of Computer Science and Engineering Indian Institute of Technology Jodhpur JodhpurRajasthan India Department of Electrical Engineering Indian Institute of Technology Jodhpur JodhpurRajasthan India

ISBN: (纸本)9789811586965

This paper presents an Optical Character Recognition (OCR) system for documents with English text and mathematical expressions. Neural network architectures using CNN layers and/or dense layers achieve high level accuracy in character recognition task. However, these models require large amount of data to train the network, with balanced number of samples for each class. Recognition of mathematical symbols poses challenges of the imbalance and paucity of training data available. To address this issue, we pose the character recognition problem as a Distance Metric Learning problem. We propose a Siamese-CNN Network that learns discriminative features to identify if the two images in a pair contain similar or dissimilar characters. The network is then used to recognize different characters by character matching where test images are compared to sample images of any target class which may or may not be included during training. Thus our model can scale to new symbols easily. The proposed approach is invariant to author’s handwriting. Our model has been tested over images extracted from a dataset of scanned answer scripts collected by us. It is seen that our approach achieves comparable performance to other architectures using convolutional layers or dense layers while using lesser training data. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Optical character recognition

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A Novel Framework for Robust Fingerprint Representations using Deep Convolution Network with Attention Mechanism 14

A Novel Framework for Robust Fingerprint Representations usi...

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14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Ranjan, Alka Prakash, Nishkal Peddi, Santhoshkumar Samanta, Debasis Indian Institute of Technology Kharagpur West Bengal India

ISBN: (纸本)9798400716256

Fingerprint recognition systems are highly dependent on the quality and accuracy of the fingerprint representation. Traditional fingerprint recognition algorithms often employ handcrafted features designed manually based on domain knowledge and heuristics. However, these methods may struggle to capture the intricate patterns and minute details present in fingerprints, leading to limited discriminatory power and vulnerability to noise and variations in imaging conditions. This paper presents a novel approach for learning robust fingerprint representation by leveraging the power of deep convolution networks (DCN) along with attention mechanisms. The proposed method consists of a dual-module framework. The first module extracts local features, i.e., minutiae-based features, and the second module focuses on extracting global features, i.e., texture-based features. The extracted local and global features are concatenated into a single fixed-length feature vector. It aims to capture fine-grained details and complex patterns present in fingerprint images, thereby enhancing the accuracy and robustness of fingerprint recognition systems. Experimental results show that the proposed method outperforms state-of-the-art (SOTA) fingerprint recognition performances across a variety of benchmark datasets, containing fingerprints from various sensors and having different resolutions and sizes. It harnesses the finer features of fingerprints, as well as efficiently boosts the performance of the framework to progress SOTA. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Convolution

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An IRAM architecture for image analysis and pattern recognition

An IRAM architecture for image analysis and pattern recognit...

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International conference on Pattern Recognition

作者： Yi Kang J. Torrellas T.S. Huang Department of Computer Science and Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign USA

ISBN: (纸本)0818685123

A new intelligent RAM (IRAM) architecture based on embedding DRAM memory and many simple processors on a chip is introduced. While the architecture can perform massively-parallel computation, it can also be used as plain DRAM. Some key parts of the architecture are discussed, and examples of image analysis algorithms that can run on it are given.

关键词： image analysis Pattern recognition Random access memory computer architecture Application software Concurrent computing Engines Read-write memory image processing computer vision

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