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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1091-1100 订阅

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Fine-grain Cluster Estimation of Land Cover Classes using Landsat 8 Multi-spectral images 14

Fine-grain Cluster Estimation of Land Cover Classes using La...

引用

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

作者： Pal, Shankho Subhra Chatterjee, Ankita Mukhopadhyay, Jayanta Sarkar, Sudeshna Das, Partha Pratim Indian Institute of Technology Kharagpur West Bengal Kharagpur India Ashoka University Haryana Sonipat India

ISBN: (纸本)9798400716256

Earth observation satellites provide us with ample amount of raw data for land cover analysis. However, annotating these data is a cumbersome process, subjected to human error which compel us to shift from supervised to unsupervised techniques. Although clustering methods are being widely used for the past few years in the field of remote sensing, identifying the number of fine-grain classes present in a region remain a challenging problem. Therefore, we propose a rule-based and neural-network learning technique that can divide the pixels into three standard classes, water bodies, vegetation and vegetation-void. These classes are easier to identify and is not region-specific. Later we apply fine-grain clustering on each of these classes to segregate them into finer groups. Our clustering method identifies the appropriate number of fine-grain classes present in a specific region. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Landsat

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Handwritten text recognition in Odia script using Hidden Markov Model

Handwritten text recognition in Odia script using Hidden Mar...

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

作者： Suman Bhoi D. P. Dogra P. P. Roy School of Electrical Sciences Indian Institute of Technology Bhubaneswar India Department of Computer Science and Engineering Indian Institute of Technology Roorkee India

This paper presents a system for unconstrained handwritten Odia text recognition using Hidden Markov Model (HMM) framework. Existing literature for Odia text recognition works primarily with individual isolated characters. In this study we introduce a Odia dataset of word samples collected from different professionals. Concavity feature from each word image is extracted in our approach. Next, the features are fed to HMM-based sequential classifier for recognition. The experiment has been performed on a large dataset consisting of 4000 words and results obtained are encouraging.

关键词： Text recognition Hidden Markov models Character recognition Feature extraction Training image recognition Handwriting recognition

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Objects from Animacy: Joint Discovery in Shape and Haar Feature Space

Objects from Animacy: Joint Discovery in Shape and Haar Feat...

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： Sudipto Nandi Prithwijit Guha K.S. Venkatesh Department of Electrical Engineering Indian Institute of Technology Kanpur India

We propose that appearance descriptors derived from the complete animacy of an object during its scene presence more comprehensively capture the essence of an object than descriptors that merely encode uncorrelated sets of its instantaneous appearances. During its frame presence, an object presents itself in many poses with differing frequencies, thus generating multiple modes of varying strengths in the appearance feature space. Further, we utilize tracking information to extract the set of all appearances of the object, while excluding those intervals where the object is partly or fully occluded by other objects or background entities. This allows for completely unsupervised computation of the descriptors that consist of time-indexed vectors from shape and Haar feature templates which are then clustered to obtain appearance modes. These lead to the construction of object-animacy models as probability distributions over the space of co-occurrent shape and Haar templates. These object models are clustered further in an unsupervised manner by using different spatial clustering algorithms with a Bhattacharya distance metric between object models. Unsupervised categorization results on simple (PETS2000) and complex traffic scenes consisting of a wide variety of objects show robust performance of the proposed approach.

关键词： Shape Object detection Layout Clustering algorithms Robustness computer vision Space technology Probability distribution Traffic control computer graphics

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Adaptive Nonlinear image Denoising and Restoration Using a Cooperative Bayesian Estimation Approach

Adaptive Nonlinear Image Denoising and Restoration Using a C...

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： Akshaya. K. Mishra Alexander Wong David. A. Clausi Paul. W. Fieguth

A novel nonlinear cooperative approach to image denoising and restoration is presented. Samples from the image field with similar characteristics are first grouped into clusters by first performing image decomposition based on the Mumford-Shah model using a total variational framework and performing fuzzy c-means clustering within each image partition. Samples within each cluster are then aggregated using an cooperative Bayesian estimation method based on information from all the samples to provide a nonlinear estimate of the original image. The proposed method exploits information redundancy within each cluster to denoise and restore the original image. Furthermore, the proposed cooperative Bayesian estimation method is capable of suppressing noise and reducing image degradation while preserving image detail by utilizing intra-cluster statistics. The experimental results using different types of images demonstrate that the proposed algorithm provides state-of-the-art image denoising performance in terms of both peak signal-to-noise ratio (PSNR) and subjective visual quality.

关键词： image denoising image restoration Bayesian methods Signal to noise ratio Noise reduction Degradation Discrete wavelet transforms PSNR Noise shaping Filtering

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A Fast Interactive image Segmentation to Locate Multiple Similar-colored Objects

A Fast Interactive Image Segmentation to Locate Multiple Sim...

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

作者： Bibhas Chandra Dhara Bhabatosh Chanda Department of Information Technology Jadavpur University Kolkata India Electronics and Communication Sciences Unit Indian Statistical Institute Kolkata India

A fundamental problem in image processing is image segmentation. The conventional image segmentation methods, to some extent, all suffe from the problem of inaccurate segmentation. A slightly easier and more approachable problem - interactive segmentation - has also received a lot of attentions over the years. In this paper, we propose a fast and simple interactive image segmentation technique. This segmentation process is conducted in two modules. First, the original image is pre-segmented by a low level segmentation method. Second, some portion of the object-of-interest is marked. The proposed method identifies all the similar-colored objects present in the image. In this work, color feature is used to measure the closeness between two regions and accordingly the label of the unmarked region is decided.

关键词： image segmentation Merging computer vision image color analysis Histograms Object recognition

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proceedings of International conference on Computational Intelligence 1

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丛书名： Algorithms for Intelligent Systems

1000年

作者： Ritu Tiwari Apoorva Mishra Neha Yadav Mario Pavone

ISBN: (数字)9789811638022

ISBN: (纸本)9789811638015;9789811638046

The book presents high quality research papers presented at International conference on Computational Intelligence (ICCI 2020) held at indian Institute of Information Technology, Pune, India during 12–13 December, 2020. The topics covered are artificial intelligence, neural network, deep learning techniques, fuzzy theory and systems, rough sets, self-organizing maps, machine learning, chaotic systems, multi-agent systems, computational optimization ensemble classifiers, reinforcement learning, decision trees, support vector machines, hybrid learning, statistical learning. metaheuristics algorithms: evolutionary and swarm-based algorithms like genetic algorithms, genetic programming, differential evolution, particle swarm optimization, whale optimization, spider monkey optimization, firefly algorithm, memetic algorithms. And also machine vision, Internet of Things, image processing, image segmentation, data clustering, sentiment analysis, big data, computer networks, signal processing,supply chain management, web and text mining, distributed systems, bioinformatics, embedded systems, expert system, forecasting, pattern recognition, planning and scheduling, time series analysis, human-computer interaction, web mining, natural language processing, multimedia systems, and quantum computing.

关键词： Computational Intelligence Artificial Intelligence Cyber-physical systems, IoT Professional Computing Algorithms

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Human tracking using distributed vision systems

Human tracking using distributed vision systems

引用

International conference on Pattern Recognition

作者： A. Nakazawa H. Kato S. Inokuchi Department of Systems and Human Science Graduate School of Engineering Science Osaka University Japan

We present a wide area human tracking method using distributed computer vision systems. Each vision system consists of a camera and an image processor and they are all connected through a computer network. In this paper, we propose a method for human tracking and for coordination of all the vision systems. The human tracking method works on each vision system and uses a type of model based template matching to track moving people at 15 frame/sec on a standard personal computer. Coordination between the vision systems is necessary to achieve consistent wide area tracking. We use a state transition map and several action rules to synchronize the image processing between systems. All the vision systems share the state transition map jointly and decide their own actions according to the action rules. We describe experimental results that show the validity of our approach.

关键词： Machine vision Cameras computer vision image processing Broadcasting computer networks Surveillance Application software Human computer interaction Biological system modeling

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Data Analytics and Learning: proceedings of DAL 2018

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2018年

作者： P Nagabhushan Devanur S Guru B H Shekar Y H Sharath Kumar

ISBN: (纸本)9789811325137

This book presents new theories and working models in the area of data analytics and learning. The papers included in this volume were presented at the first International conference on Data Analytics and Learning (DAL 2018), which was hosted by the Department of Studies in computer Science, University of Mysore, India on 3031 March 2018. The areas covered include pattern recognition, image processing, deep learning, computer vision, data analytics, machine learning, artificial intelligence, and intelligent systems. As such, the book offers a valuable resource for researchers and practitioners alike.

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Segmentation of lumen and external elastic laminae in intravascular ultrasound images using ultrasonic backscattering physics initialized multiscale random walks 10

Segmentation of lumen and external elastic laminae in intrav...

引用

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

作者： China, Debarghya Mitra, Pabitra Sheet, Debdoot Advanced Technology Development Centre Indian Institute of Technology Kharagpur Kharagpur India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India Department of Electrical Engineering Indian Institute of Technology Kharagpur Kharagpur India

Coronary artery disease accounts for a large number of deaths across the world and clinicians generally prefer using x-ray computed tomography or magnetic resonance imaging for localizing vascular pathologies. Interventional imaging modalities like intravascular ultrasound (IVUS) are used to adjunct diagnosis of atherosclerotic plaques in vessels, and help assess morphological state of the vessel and plaque, which play a significant role for treatment planning. Since speckle intensity in IVUS images are inherently stochastic in nature and challenge clinicians with accurate visibility of the vessel wall boundaries, it requires automation. In this paper we present a method for segmenting the lumen and external elastic laminae of the artery wall in IVUS images using random walks over a multiscale pyramid of Gaussian decomposed frames. The seeds for the random walker are initialized by supervised learning of ultrasonic backscattering and attenuation statistical mechanics from labelled training samples. We have experimentally evaluated the performance using 77 IVUS images acquired at 40 MHz that are available in the IVUS segmentation challenge dataset (http://***. ***/IVUSchallenge2011/***.) to obtain a Jaccard score of 0.89 ± 0.14 for lumen and 0.85 ± 0.12 for external elastic laminae segmentation over a 10-fold cross-validation study. © Springer International Publishing AG 2017.

关键词： Statistical mechanics

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Semantic Event Detection and Classification in Cricket Video Sequence

Semantic Event Detection and Classification in Cricket Video...

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： M.H. Kolekar K. Palaniappan S. Sengupta Department of Computer Science University of Missouri Columbia MO USA Department of Electronics and ECE Indian Institute of Technology Kharagpur West Bengal India

In this paper, we present a novel hierarchical framework and effective algorithms for cricket event detection and classification. The proposed scheme performs a topdown video event detection and classification using hierarchical tree which avoids shot detection and clustering. In the hierarchy, at level-1, we use audio features, to extract excitement clips from the cricket video. At level-2, we classify excitement clips into real-time and replay segments. At level-3, we classify these segments into field view and non-field view based on dominant grass color ratio. At level-4a, we classify field view into pitch-view, long-view, and boundary view using motion-mask. At level-4b, we classify non-field view into close-up and crowd using edge density feature. At level-5a, we classify close-ups into the three frequently occurring classes batsman, bowler/fielder, umpire using jersey color feature. At level-5b, we classify crowd segment into the two frequently occurring classes spectator and playerspsila gathering using color feature. We show promising results, with correctly classified cricket events, enabling structural and temporal analysis, such as highlight extraction, and video skimming.

关键词： Event detection Video sequences Layout Classification tree analysis TV broadcasting Digital video broadcasting Games computer vision computer graphics image processing

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