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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"

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proceedings of International conference on Computational Intelligence 1

引用

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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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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A novel framework for multi-focus image fusion

A novel framework for multi-focus image fusion

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

作者： Gaurav Bhatnagar Q.M. Jonathan Wu Indian Institute of Technology Jodhpur Jodhpur Rajasthan India University of Windsor Windsor ON Canada

One of the foremost requisite for human perception and computer vision task is to get an image with all objects in focus. The image fusion process, as one of the solutions, allows getting a clear fused image from several images acquired with different focus levels of a scene. In this paper, a novel framework for multi-focus image fusion is proposed, which is computationally simple since it realizes only in the spatial domain. The proposed framework is based on the fractal dimensions of the images into the fusion process. The extensive experiments on different multi-focus image sets demonstrate that it is consistently superior to the conventional image fusion methods in terms of visual and quantitative evaluations.

关键词： image fusion Fractals Measurement Wavelet transforms Principal component analysis image quality

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VLSI Implementation of Fast Connected Component Labeling Using Finite State Machine Based Cell Network

VLSI Implementation of Fast Connected Component Labeling Usi...

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

作者： Pradipta Roy Prabir Kumar Biswas Optronics Centre Integrated Test Range Chandipur India Department of E&ECE IIT Kharagpur India

Connected component labeling of a binary image is an indispensable task for image segmentation and analysis. For real time video object segmentation, total processing time to label all the objects of an entire image is critical as it is constrained by inter frame temporal difference. Parallel dedicated hardware is necessary to solve this problem in real time. In this paper we have proposed a parallel VLSI architecture for fast connected component labeling of a binary video frame image. We have adopted a seeded region-growing algorithm, which is implemented in a state machine based cell network. The design is verified in XILINX FPGA with real time video image data containing different objects with different shapes and sizes. The worst-case labeling time for a full video frame is 5 ms (using a 32 MHz clock), which is well below the required inter frame timing interval of 40 ms.

关键词： Very large scale integration Labeling Automata image segmentation image analysis Object segmentation Hardware Field programmable gate arrays Shape Clocks

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Off-line skilled forgery detection on handwritten Devanagri script

Off-line skilled forgery detection on handwritten Devanagri ...

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

作者： Nivedita Yadav Santanu Chaudhury Prem Kalra Amarnath and Shashi Khosla School of IT Indian Institute of Technology Delhi India Dept of Electrical Engineering Indian Institute of Technology Delhi India Dept of Computer Science and Engineering Indian Institute of Technology Delhi India

This paper is aimed at exploring the potential of using discriminatory primitives containing words for the task of detecting skilled forgeries. We consider handwritten Devanagri documents for this work. We have obtained experimental handwriting data from subjects who have contributed handwriting samples in their natural handwriting. Other authors are asked to imitate the writing style of the subjects to produce a skilled forgery sample. Most of the literature dealing with writer recognition focus on signatures and very few reports have addressed the problem of detecting forgeries for handwritten indian scripts. We also use multiple words based classification for the targeted task of forgery detection. Our experiments show encouraging results.

关键词： Forgery Shape Feature extraction Histograms Forensics Accuracy Writing

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TransDocUNet: A Transformer-based UNet Architecture for Degraded Document image Binarization 14

TransDocUNet: A Transformer-based UNet Architecture for Degr...

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

作者： Biswas, Risab Sarkhel, Soumik Roy, Swalpa Kumar Pal, Umapada Optiks Innovations Pvt. Ltd. Maharashtra Mumbai India Alipurduar Government Engineering and Management College West Bengal India Indian Statistical Institute West Bengal Kolkata India

ISBN: (纸本)9798400716256

The enhancement of historical document images is critical for improving the quality and legibility of scanned or captured document images. Convolutional-based techniques previously generated competitive results for document image binarization, however, due to their inherent locality, these models are often limited in explicitly expressing long-range dependency. Transformers (ViT) have evolved as an alternative design with a global self-attention mechanism to tackle this issue, however, they can result in restricted localization capabilities due to a lack of low-level details. To address this problem, we propose TransDocUNet, a CNN-Transformer hybrid UNet architecture for document image binarization that merits both attention and convolution capabilities in a U-Net architecture and serves as a strong alternative to the existing solutions. The experimental results, obtained using the DIBCO/H-DIBCO datasets, highlight that our proposed method outperforms all the existing competing methods in terms of both objective quality metrics and visual quality assessment, achieving state-of-the-art performance in document image binarization. In addition, we undertake an ablation study to understand the role of dilation in the CNN to capture feature dependencies while reducing the computational cost as well. The findings helped us arrive at the final model and provide valuable insights into the importance of acquiring both global and local contextual information for tasks like enhancing document images. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： image enhancement

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Enhancing label transfer in non-parametric scene parsing by superpixel-based dense alignment 21

Enhancing label transfer in non-parametric scene parsing by ...

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12th indian conference on computer vision, graphics and image processing, ICVGIP 2021

作者： Bhowmick, Alexy Saharia, Sarat Hazarika, Shyamanta M. Dept. of Computer Science and Engineering Assam Don Bosco University Assam Guwahati781017 India Dept. of Computer Science and Engineering Tezpur University Assam Tezpur784028 India Biomimetic Robotics and Artificial Intelligence Lab Dept. of Mechanical Engineering IIT Guwahati Assam Guwahati781039 India

ISBN: (纸本)9781450391276

Contemporary (parametric) scene parsing methods are learning-based and mostly operate in a closed-universe scenario. We introduce a non-parametric scene parsing framework that is model-free, data-driven, and scales naturally to growing data. The scene parsing performance in the non-parametric approach depends on reliable dense correspondence or alignment across scenes for label transfer. Incorrect correspondence is known to affect the scene parsing results adversely. We propose a label transfer approach that relies on the dense correspondence of super-pixel pairs (in a query and candidate image) matched by a homogeneous kernel map to guide semantic label transfer. The aggregation (fusing) of multiple labels is done through a simple heuristic aggregation scheme (simple majority voting). The Markov Random Field (MRF) provides a principled probabilistic framework for combining the disparate information in the smoothing stage and ensures plausible labeling results. Evaluation results show that our non-parametric system obtains competitive scene parsing performance on the standard SIFT Flow and MSRC-21 datasets. © 2021 ACM.

关键词： Superpixels

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Real-Time Painterly Rendering of Terrains

Real-Time Painterly Rendering of Terrains

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

作者： Shiben Bhattacharjee P.J. Narayanan Center for Visual Information Technology International Institute of Information Technology Hyderabad India

We present a non-photo realistic, real-time painterly rendering technique for terrains. The painterly appearance and the impression of terrains is created by effectively rendering several brush strokes. The strokes have fixed locations on the surfaces of the terrain during animation to enable frame to frame coherency. The strokes are rendered as alpha blended sprites in two-dimensions and are oriented along the slope of terrain analogous to the way artists paint on canvas. By exploiting the regular nature of terrain data, we create pre-decided rendering depth orders for primitives for any camera orientation. With this, we avoid the necessity of sorting the primitives of sprites required for alpha blending. We use DirectX10/SM4.0 based shaders to render strokes to improve performance. Being distributed on terrain, strokes get cluttered when they are closely located on screen. We follow a level of detail scheme that maintains a uniform stroke density in screen space. Various styles can be achieved with different stroke variations. Phong shading the rendered output in real-time is possible for more varied styles. We achieve painterly rendering in real-time with a combination of object space positioning and image space rendering of strokes. We illustrate our method with images and performance results.

关键词： Rendering (computer graphics) Animation Painting computer graphics Information technology Sprites (computer) Sorting Layout Hardware computer vision

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Perceptual video hashing based on the Achlioptas's random projections

Perceptual video hashing based on the Achlioptas's random pr...

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

作者： Sandeep R. Prabin K. Bora Department of EEE Indian Institute of Technology Guwahati Guwahati India

A perceptual video hashing function maps the perceptual content of a video into a fixed-length binary string called the perceptual hash. Perceptual hashing is a promising solution to the content-identification and the content-authentication problems. The projections of image and video data onto a subspace have been exploited in the literature to get a compact hash function. We propose a new perceptual video hashing algorithm based on the Achlioptas's random projections. Simulation results show that the proposed perceptual hash function is robust to common signal and image processing attacks.

关键词： Robustness Vectors Signal processing algorithms image processing Approximation algorithms Algorithm design and analysis Video sequences

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Object Category Recognition with Projected Texture

Object Category Recognition with Projected Texture

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

作者： Avinash Sharma Anoop Namboodiri International Institute of Information Technology Center for Visual Information Technology Hyderabad India

Recognition of object categories from their images is extremely challenging due to the large intra-class variations, and variations in pose, illumination and scale, in addition to lack of depth information of the object. Recovering the depth information from multiple images or from image cues such as variations in illumination or focus, is both computationally intensive and error prone. In contrast, the appearance based approaches are more robust and computationally efficient. However, they lack the potential accuracy of 3D feature based approaches due to the lack of shape information. We propose the use of structured lighting patterns projected on the object, which gets deformed according to the shape of the object for recognition. Since our goal is object classification and not shape recovery, we characterize the deformations using simple texture measures, thus avoiding depth recovery step. Moreover, the shape information present in the deformations is implicitly used for classification. We show that the information thus derived can significantly improve the accuracy of object category recognition from arbitrary-pose images.

关键词： Robustness image recognition Lighting Focusing Information technology Pattern recognition Shape measurement Cameras Object recognition computer vision

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