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检索条件"任意字段=6th Indian Conference on Computer Vision, Graphics and Image Processing"

共 2011 条记录，以下是551-560 订阅

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Improved Fuzzy C-means and K-means Algorithms for Texture and Boundary Segmentation 6

Improved Fuzzy C-means and K-means Algorithms for Texture an...

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6th International conference on Control Engineering and Information Technology (CEIT)

作者： Koc, Yunus Olmez, Tamer Istanbul Tech Univ Dept Elect Elect Engn Istanbul Turkey

ISBN: (纸本)9781538676417

image segmentation is one of the most significant and inevitable task in variety areas ranging from face/object/character recognition and medical imaging applications to robotic control and self-driving vehicular systems. Accuracy and processing time of image segmentation processes are also prominent parameters for quality of such computer vision systems. the proposed method incorporates three main pre-processing techniques such as Down Scaling/Sampling, Gamma Correction and Edge Preserving Smoothing so as to achieve accuracy and robustness of the segmentation. Pre-processing techniques are performed for both Fuzzy C-means (FCM) and K-means algorithm and all RGB information of image are taken into consideration while segmenting the image rather than using only gray scale. Performance analysis are performed on real-world images. Experiments show that, our method achieve higher accuracy levels and feasible processing time results compared to conventional FCM and K-means algorithms.

关键词： image segmentation clustering Fuzz C-means K-means edge preserving smoothing spatial filtering illumination/shadow compensation gamma correction

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Intelligent pattern recognition and applications to biometrics in an interactive environment

Intelligent pattern recognition and applications to biometri...

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4th International conference on computer graphics theory and Applications, GRAPP 2009

作者： Wang, Patrick College of Computer and Information Science Northeastern University Boston MA 02115 United States

ISBN: (纸本)9789898111678

this talk deals with some fundamental aspects of biometrics and its applications. It basically includes the following subtopics: (1) Overvievv of Biometric Technology and Applications (2) Importance of Security: A Scenario of Terrorists Attack, (3) What are Biometric Technologies?, (4) Biometrics: Analysis vs Synthesis.(5) Analysis: Interactive Pattern Recognition Concept, (6) Concept of "Semantics" and "Ambiguitv", their Importance and Applications, (7)computer vision (3D) and image processing (2D), (8) image processing & computer graphicsas Reverse Process, (9) thermal ImagingRecognition. (10) Synthesis in biometrics, (11) Modeling and Simulation, and (12) more Examples and Applications in Interactive Environment.

关键词： Biometrics

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image Captioning with Visual Positional Embedding and Bi-linear Pooling 8th

Image Captioning with Visual Positional Embedding and Bi-lin...

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8th International conference on computer vision and image processing (CVIP)

作者： Nair, Sidharth Guha, Prithwijit HCLTech Chennai Tamil Nadu India Indian Insitute Technol Guwahati Dept Elect & Elect Engn Gauhati India

ISBN: (纸本)9783031581809;9783031581816

Recent approaches to image captioning typically follow an encoder-decoder architecture. the feature vectors extracted from the region proposals obtained from an object detector network serve as input to encoder. Without any explicit spatial information about the visual regions, the caption synthesis model is limited to learn relationship from captions only. However, the structure between the semantic units in images and sentences is different. this work introduces a grid based spatial position encoding scheme to learn relationship from both domains. Furthermore, bi-linear pooling is used with attention for exploiting spatial and channel-wise attention distribution to capture second order interaction between multi-modal inputs. these are integrated within the Transformer architecture achieving a competitive CIDEr score.

关键词： Transformer Positional Embedding image Captioning Bi-linear Pooling

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Reconstruction of Sparse-View Tomography via Banded Matrices 10

Reconstruction of Sparse-View Tomography via Banded Matrices

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

作者： Prasad, T. Kumar, P. U. Praveen Sastry, C. S. Jampana, P. V. Indian Inst Technol Hyderabad Dept Math Hyderabad Telangana India Indian Inst Technol Hyderabad Dept Chem Engn Hyderabad Telangana India

ISBN: (纸本)9783319681245;9783319681238

Computed Tomography (CT) is one of the significant research areas in medical image analysis. One of the main aspects of CT that researchers remain focused, is on reducing the dosage as Xrays are generally harmful to human bodies. In order to reduce radiation dosage, compressed sensing (CS) based methodologies appear to be promising. the basic premise is that medical images have inherent sparsity in some transformation domain. As a result, CS provides the possibility of recovering a high quality image from fewer projection data. In general, the sensing matrix in CT is generated from Radon projections by appropriately sampling the radial and angular parameters. In our work, by restricting the number of such parameters, we generate an under-determined linear system involving projection (Radon) data and a sparse sensing matrix, bringing thereby the problem into CS framework. Among various recent solvers, the Split-Bregman iterative scheme has of late become popular due to its suitability for solving a wide variety of optimization problems. Intending to exploit the underlying structure of sensing matrix, the present work analyzes its properties and finds a banded structure for an associated intermediate matrix. Using this observation, we simplify the Split-Bregman solver, proposing thereby a CT-specific solver of low complexity. We also provide the efficacy of proposed method empirically.

关键词： Compressed sensing Split-Bregman Computed tomography Least squares

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Semi-Decoupled Distillation for Brain Tumor Segmentation using multimodal MRI Scans 24

Semi-Decoupled Distillation for Brain Tumor Segmentation usi...

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15th indian conference on computer vision graphics and image processing

作者： Kundu, Surajit Chatterjee, Ankita Mukhopadhyay, Jayanta Chakravorty, Nishant Indian Inst Technol kharagpur Kharagpur W Bengal India

ISBN: (纸本)9798400710759

Brain tumour segmentation is a fundamental task in medical image analysis where each year a number of deep learning models are introduced to delineate the tumour regions with high precision. However, most of these works rely on large number of parameters and higher computational cost, thus, rendering them ineffective in real world application. therefore, it is important to devise efficient models that can be easily deployed on resource-constrained devices and perform at par with the existing large models. In this paper, we propose a semi-decoupled distillation technique which trains a lightweight "student" model using the features extracted from the decoder of the nnU-Net "teacher" model and its predictions on these features using a single point-wise convolution layer. the final classification layer remains the same for both the models and is kept frozen while training the "student" network. Our approach follows a two stage training procedure where the tumour regions are detected and extracted in the first step, and then sent for second stage training to segment fine-grain classes, including edema, enhanced tumour and tumour core. Our extensive experimentation shows that a lightweight distilled model performs competitively with large models on brain tumour segmentation.

关键词： Glioblastoma Knowledge Distillation Brain Tumor Segmentation nnU-Net

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4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, ICVGIP 2018

4th Workshop on Document Analysis and Recognition, DAR 2018,...

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4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, ICVGIP 2018

ISBN: (纸本)9789811393600

the proceedings contain 14 papers. the special focus in this conference is on Document Analysis and Recognition. the topics include: Word-Wise Handwriting Based Gender Identification Using Multi-Gabor Response Fusion;a Secure and Light Weight User Authentication System Based on Online Signature Verification for Resource Constrained Mobile Networks;benchmark Datasets for Offline Handwritten Gurmukhi Script Recognition;benchmark Dataset: Offline Handwritten Gurmukhi City Names for Postal Automation;attributed Paths for Layout-Based Document Retrieval;textual Content Retrieval from Filled-in Form images;A Study on the Effect of CNN-Based Transfer Learning on Handwritten Indic and Mixed Numeral Recognition;symbol Spotting in Offline Handwritten Mathematical Expressions;online Handwritten Bangla Character Recognition Using Frechet Distance and Distance Based Features;an Efficient Multi Lingual Optical Character Recognition System for indian Languages through Use of Bharati Script;telugu Word Segmentation Using Fringe Maps;an Efficient Character Segmentation Algorithm for Connected Handwritten Documents.

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Design of Manipuri Keywords Spotting System using HMM

Design of Manipuri Keywords Spotting System using HMM

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2013 4th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2013

作者： Rahul, Laishram Nandakishor, Salam Joyprakash Singh, L. Dutta, S.K. Speech and Image Processing Laboratory Department of ECE NEHU Shillong - 793 022 India

ISBN: (纸本)9781479915880

this paper aims to discuss the implementation of phoneme based Manipuri Keyword Spotting System (MKWSS). Manipuri is a scheduled indian language of Tibeto- Burman origin. Around 5 hours of read speech are collected from 4 male and 6 female speakers for development of database of MKWSS. the symbols of International Phonetic Alphabet (IPA)(revised in 2005) are used during the transcription of the data. A five state left to right Hidden Markov Model (HMM) with 32 mixture continuous density diagonal covariance Gaussian Mixture Model (GMM) per state is used to build a model for each phonetic unit. We have used HMM tool kit (HTK), version 3.4 for modeling the system. the system can recognize 29 phonemes and a non-speech event (silence) and will detect the present keywords formed by these phonemes. Continuous Speech data have been collected from 5 males and 8 females for analysing the performance of the system. the performance of the system depends on the ability of detection of the keywords. An overall performance of 65.24% is obtained from the phoneme based MKWSS. © 2013 IEEE.

关键词： Hidden Markov models

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COMA-Boost: Co-Operative Multi Agent AdaBoost 16

COMA-Boost: Co-Operative Multi Agent AdaBoost

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

作者： Lahiri, Avisek Paria, Biswajit Biswas, Prabir Kumar IIT Kharagpur Dept E&ECE Kharagpur W Bengal India IIT Kharagpur Dept CSE Kharagpur W Bengal India

ISBN: (纸本)9781450347532

A Abstract : Multi feature space representation is a common practise in computer vision applications. Traditional features such as HOG, SIFT, SURF etc., individually encapsulates certain discriminative cues for visual classification. On the other hand, each layer of a deep neural network generates multi ordered representations. In this paper we present a novel approach for such multi feature representation learning using Adaptive Boosting (AdaBoost). General practise in AdaBoost [8] is to concatenate components of feature spaces and train base learners to classify examples as correctly/incorrectly classified. We posit that multi feature space learning should be viewed as a derivative of cooperative multi agent learning. To this end, we propose a mathematical framework to leverage performance of base learners over each feature space, gauge a measure of "difficulty" of training space and finally make soft weight updates rather than strict binary weight updates prevalent in regular AdaBoost. this is made possible by periodically sharing of response states by our learner agents in the boosting framework. theoretically, such soft weight update policy allows in finite combinations of weight updates on training space compared to only two possibilities in AdaBoost. this opens up the opportunity to identify 'more difficult' examples compared to 'less difficult' examples. We test our model on traditional multi feature representation of MNIST handwritten character dataset and 100-Leaves classification challenge. We consistently outperform traditional and variants of multi view boosting in terms of accuracy while margin analysis reveals that proposed method fosters formation of more con fident ensemble of learner agents. As an application of using our model in conjecture with deep neural network, we test our model on the challenging task of retinal blood vessel segmentation from fundus images of DRIVE dataset by using kernel dictionaries from layers of unsupervised trained stac

关键词： AdaBoost multi view learning deep learning

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Computational Forensics 1

引用

丛书名： Lecture Notes in computer Science

1000年

作者： Utpal Garain Faisal Shafait

ISBN: (数字)9783319201252

ISBN: (纸本)9783319201245

this book constitutes the refereed post-conference proceedings of the 5th and 6th International Workshops on Computational Forensics, IWCF 2012 and IWCF 2014, held in Tsukuba, Japan, in November 2010 and August 2014. the 16 revised full papers and 1 short paper were carefully selected from 34 submissions during a thorough review process. the papers are divided into three broad areas namely biometrics; document image inspection; and applications.

关键词： Pattern Recognition image processing and computer vision Artificial Intelligence Natural Language processing (NLP)

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Hidden Markov model-unscented Kalman filter contour tracking: A multi-cue and multi-resolution approach

Hidden Markov model-unscented Kalman filter contour tracking...

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Iranian conference on Machine vision and image processing

作者： Moayedi, Fatemeh Kazemi, Alireza Azimifar, Zohreh Computer Vision and Pattern Recognition Group School of Electrical and Computer Engineering Shiraz University shiraz Iran

ISBN: (纸本)9781424497065

this paper present a novel attempt to introduce an HMM-based multi-resolution and multi-cue segmentation in combination with the unscented Kalman filter tracking method. It combines multiple features distribution and multiple resolutions to facilitate 2D video tracking. the advantages of this method lie in its speed and its robustness. Speed is dramatically improved by taking into account multiple resolutions which reduce number of measurement points (number of HMM states) while keeping its quality. Robustness is achieved by using multiple cues. We propose an algorithm to find an optimal operating point for a tracker in terms of the image scale. Furthermore, we propose a faster multi-scale (spatial) tracker based on a minimum acceptable performance limit. the proposed method is demonstrated on human head tracking with a non-stationary camera. Visual tests indicate that the optimized algorithms produce qualitatively better results. Results show that we are able to maintain real-time processing on quite generous video resolutions. therefore it will be shown that our approach is faster and more efficient than conventional UKF and UKF with multi-cue. © 2010 IEEE.

关键词： Hidden Markov models

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