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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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Color feature based approach for determining ink age in printed documents

Color feature based approach for determining ink age in prin...

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International conference on Pattern Recognition

作者： Halder, Biswajit Garain, Utpal Dept. of Information Technology Mallabhum Institute of Technology Bisnupur WB India Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B.T. Road Kolkata 700108 India

ISBN: (纸本)9780769541099

Answering to a query like when a particular document was printed is quite helpful in practice especially forensic purposes. This study attempts to develop a general framework that makes use of image processing and pattern recognition principles for ink age determination in printed documents. The approach, at first, computationally extracts a set of suitable color features and then analyzes them to properly associate them with ink age. Finally, a neural net is designed and trained to determine ages of unknown samples. The dataset used for the present experiment consists of the cover pages of LIFE magazines published in between 1930's and 70's (five decades). Test results show that a viable framework for involving machines in assisting human experts for determining age of printed documents. © 2010 IEEE.

关键词： image processing

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Psychovisual saliency in color images

Psychovisual saliency in color images

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

作者： Soumyajit Gupta Rahul Agrawal Ritwik Layek Jayanta Mukhopadhyay Dept. of Electronics and Electrical Communication Engineering Indian Institute of Technology Kharagpur Dept. of Computer Science and Engineering Indian Institute of Technology Kharagpur

Visual attention is an indispensable component of complex vision tasks. When looking at a complex scene, our ocular perception is confronted with a large amount of data that needs to be broken down for processing by our psychovisual system. Selective visual attention provides a mechanism for serializing the visual data, allowing for sequential processing of the content of the scene. A Bottom-Up computational model is described that simulates the psycho-visual model of saliency based on features of intensity and color. The method gives sequential priorities to objects which other computational models cannot account for. The results demonstrate a fast execution time, full resolution maps and high detection accuracy. The model is applicable on both natural and artificial images.

关键词： image color analysis Visualization Standards Computational modeling Real-time systems image resolution Colored noise

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An Efficient Multi Lingual Optical Character Recognition System for indian Languages Through Use of Bharati Script 4th

An Efficient Multi Lingual Optical Character Recognition Sys...

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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

作者： Vorugunti, Chandra Sekhar Chakravarthy, Srinivasa Pulabaigari, Viswanath Indian Institute of Information Technology-SriCity Chittoor India Indian Institute of Technology-Madras Chennai India

ISBN: (纸本)9789811393600

Optical character recognition performs a critical part in interpreting videos and documents. Document specific issues like low image quality, distortions, composite background, noise etc. and language specific issues like cursive connectivity among the characters etc. makes OCR challenging and erroneous for indian languages. The language specific challenges can be overcome by computing the script-based features and can achieve better accuracy. Computing the script based invariant features and patterns is computationally complex and error prone. In this background, we put forward Bharathi script (*** ) based OCR system in which the inherent drawbacks of indian scripts i.e. Hindi, Tamil, Telugu etc. are eliminated. The proposed OCR model has been tested on a synthetic dataset of documents of Bharathi script (in which Hindi scripts are converted to Bharathi script). Thorough experimental analysis with varied levels of noise confirms the promising results of character recognition accuracy of the proposed OCR model which out-performs the state-of-the-art OCR systems for indian scripts. The proposed model achieves 76.70% with test documents consists of 50% noise and 99.98% with test documents of 0% noise. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Optical character recognition

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How Much Zoom is the Right Zoom from the Perspective of Super-Resolution?

How Much Zoom is the Right Zoom from the Perspective of Supe...

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

作者： Himanshu Arora Anoop M. Namboodiri Center for Visual Information Technology Indian Institute of Information Technology Hyderabad India

Constructing a high-resolution (HR) image from low-resolution (LR) image(s) has been a very active research topic recently with focus shifting from multi-frames to learning based single-frame super-resolution (SR). Multi-frame SR algorithms attempt the exact reconstruction of reality, but are limited to small magnification factors. Learning based SR algorithms learn the correspondences between LR and HR patches. Accurate replacements or revealing the exact underlying information is not guaranteed in many scenarios. In this paper we propose an alternate solution. We propose to capture images at right zoom such that it has just sufficient amount of information so that further resolution enhancements can be easily achieved using any off the shelf single-frame SR algorithm. This is true under the assumption that such a zoom factor is not very high, which is true for most man-made structures. The low-resolution image is divided into small patches and ideal resolution is predicted for every patch. The contextual information is incorporated using a Markov Random Field based prior. Training data is generated from high-quality images and can use any single-frame SR algorithm. Several constraints are proposed to minimize the extent of zoom-in. We validate the proposed approach on synthetic data and real world images to show the robustness.

关键词： Strontium image resolution image generation computer vision image processing image reconstruction Inference algorithms Layout computer graphics Information technology

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Fast, Processor-Cardinality Agnostic PRNG with a Tracking Application

Fast, Processor-Cardinality Agnostic PRNG with a Tracking Ap...

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

作者： Andrew Janowczyk Sharat Chandran Srinivas Aluru ViGIL Department of Computer Science and Engineering Indian Institute of Technology Bombay India Iowa State University USA

As vision algorithms mature with increasing inspiration from the learning community, statistically independent pseudo random number generation (PRNG) becomes increasingly important. At the same time, execution time demands have seen algorithms being implemented on evolving parallel hardware such as GPUs. The Mersenne Twister (MT) has proven to be the current state of the art for generating high quality random numbers, and the Nvidia provided software for parallel MT is in widespread use. While execution time is important, development time is also critical. As processor cardinality changes, a foundation for generating simulations that will vary only in execution time and not in the actual result is useful; otherwise the development time will be impacted. In this paper, we present an implementation of the Lagged Fibonacci Generator (LFG) considered to be of quality equal to MT on the GPU. Unlike MT, LFG has this important processor-cardinality agnostic capability -- that is -- as the number of processing resources changes, the overall sequence of random numbers remains the same. This feature not withstanding, our basic implementation is roughly as fast as the parallel MT; an in-memory version is actually 25% faster in execution time. Both parallel MT as well as parallel LFG show enormous speed up over their sequential counterparts. Finally, a prototype particle filter tracking application shows that our method works not just in parallel computing theory, but also in practice for vision applications, providing a decrease of 60% in execution time.

关键词： Random number generation Principal component analysis Parallel algorithms computer vision Application software Parallel processing computer graphics image processing computer science Hardware

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Cross-view kernel similarity metric learning using pairwise constraints for person re-identification 21

Cross-view kernel similarity metric learning using pairwise ...

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

作者： Ali, T.M. Feroz Chaudhuri, Subhasis Indian Institute of Technology Bombay Maharashtra Mumbai India

ISBN: (纸本)9781450391276

Person re-identification is the task of matching pedestrian images across non-overlapping cameras. In this paper, we propose an efficient kernel based similarity metric learning for learning non-linear features using small scale training data for practical person re-ID systems. The method employs non-linear mappings combined with cross-view discriminative subspace learning and cross-view distance metric learning based on pairwise similarity constraints. It is a natural extension of Cross-view Quadratic Discriminant Analysis (XQDA) from linear to non-linear model using kernels. In addition to outperforming XQDA, the proposed method is computationally very efficient compared to its baselines. Extensive experiments on four benchmark datasets show that our method attains competitive performance against state-of-the-art methods. Our code is available at https://***/ferozalitm/Efficient-Kernel-XQDA. © 2021 ACM.

关键词： Benchmarking

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Unique Identity Generation with Global Features from Multimodal Biometric Data 14

Unique Identity Generation with Global Features from Multimo...

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

作者： Dash, Priyabrata Samanta, Debasis Sarma, Monalisa Indian Institute of Technology Kharagpur West Bengal Kharagpur India

ISBN: (纸本)9798400716256

The patterns in biometric data, such as fingerprints, iris, etc., are random and distinct from one individual to another, making them ideal for generating unique identities suitable for many applications. This work proposes creating users' unique identities based on their fingerprint and iris biometric data. The global feature descriptor, which is invariant to scale, rotation, and translation, is considered in this work. The global feature descriptors are obtained from fingerprint and iris images and fused to create a single feature vector. Next, feature selection based on the Laplacian score is applied to remove irrelevant features. Further, a metric-learning-based mechanism is introduced to enhance the feature vectors' intra-class similarity and inter-class separability. Finally, a novel quantization mechanism is used to generate a unique identity. Experiments with multimodal datasets show that the generated identities are distinct, random, revocable, and unlinkable. This identity generation scheme can be utilized in storage security, digital signature schemes, remote authentication protocols, blockchain security, etc. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Authentication

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Deformable deep networks for instance segmentation of overlapping multi page handwritten documents 21

Deformable deep networks for instance segmentation of overla...

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

作者： Aitha, Sowmya Bollampalli, Sindhu Sarvadevabhatla, Ravi Kiran CVIT IIIT Hyderabad Hyderabad500032 India

ISBN: (纸本)9781450391276

Digitizing via scanning the physical artifact often forms the first primary step in preserving historical handwritten manuscripts. To maximally utilize scanner surface area and minimize manual labor, multiple manuscripts are usually scanned together into a scanned image. Therefore, the first crucial task in manuscript content understanding is to ensure that each of the individual manuscripts within a scanned image can be isolated (segmented) on a per-instance basis. Existing deep network based approaches for manuscript layout understanding implicitly assume a single or two manuscripts per image. Since this assumption may be routinely violated, there is a need for a precursor system which extracts individual manuscripts before downstream processing. Another challenge is the highly curved and deformed boundaries of manuscripts, causing them to often overlap with each other. To tackle such challenges, we introduce a new document image dataset called IMMI (Indic Multi Manuscript images). To improve the efficiency of dataset and aid deep network training, we also propose an approach which generates synthetic images to augment sourced non-synthetic images. We conduct experiments using modified versions of existing document instance segmentation frameworks. The results demonstrate the efficacy of the new frameworks for the task. Overall, our contributions enable robust extraction of individual historical manuscript pages. This in turn, could potentially enable better performance on downstream tasks such as region-level instance segmentation within handwritten manuscripts and optical character recognition. © 2021 ACM.

关键词： image enhancement

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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...

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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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Design of a System and Method for Optimal selection of Tumor Slice using Linear Ultrasound Imaging for Histopathology 13

Design of a System and Method for Optimal selection of Tumor...

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13th indian conference on computer vision, graphics, and image processing, ICVGIP 2022

作者： Kumar, Abhishek Sheet, Debdoot Indian Institute of Technology Kharagpur India

ISBN: (纸本)9781450398237

In excision biopsy, a tumor mass is surgically removed from the body. Subsequently, it is sliced at an appropriate location and investigated microscopically through a process called histopathology. Any bias in tumor slicing severely influences histopathology outcomes, such as if malignant foci do not appear in the sliced location, then, the tumor would be accidentally reported non-malignant. The standard approach adopted to solve this challenge by a histopathologist is to overcome this bias by slicing at multiple locations for their investigation. Till now, this process has been manual, time-consuming, and error-prone. We aim to design a system and develop a data-driven deep learning approach to assist histopathologists by providing them with a representative slice location for reporting to increase their efficiency and accuracy. We have developed a low cost linear gantry scanner that can acquire images and integrated with a deep learning model to predict the optimal slice representative of pathology in a tumor mass. We achieve an F1 score of 0.97 and an accuracy of 97.5% in predicting an optimal slice using this approach. © 2022 ACM.

关键词： Tumors

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