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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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Synthetic handwritten Odia numeral database: From shallow hundreds to comprehensive thousands

Synthetic handwritten Odia numeral database: From shallow hu...

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

作者： Kalyan S Dash N B Puhan Ganapati Panda School of Electrical Sciences Indian Institute of Technology Bhubaneswar India

Comprehensive database that contains all possible variations of handwriting is crucial for training and recognition. The primary challenge for an optical character recognizer (OCR) is that a number of interclass characters bear structural resemblance whereas images within a class render much dissimilarity. Acquisition of such a large database that ensures robust training of the recognizer is a painstaking task. Therefore, recent research interests have been to create, from a few samples of handwriting, a comprehensive synthetic database which not only ensures naturalness, but provides much needed pattern variability. In this paper, we propose a new approach of synthetic handwritten numeral generation for Odia language using interclass deformation. We experimentally evaluate the generated databases using the state-of-the-art recognition systems. The recognition results are compared on two benchmark databases (ISI Kolkata and IIT Bhubaneswar Odia numeral) as well as two newly created synthetic databases. The Odia numeral database sizes are increased by 20-fold each using our proposed approach. The introduction of nonlinear pattern variance because of interclass deformation is proved to pose better challenge to conventional recognizers. We also experimented on a mixture of original and synthetic database for training the OCR to achieve robustness and higher accuracy.

关键词： Databases Handwriting recognition Training Shape Optical character recognition software Robustness

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Contrastive Multi-View Textual-Visual Encoding: Towards One Hundred Thousand-Scale One-Shot Logo Identificationg 13

Contrastive Multi-View Textual-Visual Encoding: Towards One ...

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

作者： Sharma, Nakul Penamakuri, Abhirama Subramanyam V B Mishra, Anand Indian Institute of Technology Rajasthan Jodhpur India

ISBN: (纸本)9781450398237

In this paper, we study the problem of identifying logos of business brands in natural scenes in an open-set one-shot setting. This problem setup is significantly more challenging than traditionally-studied gclosed-set' and glarge-scale training samples per category' logo recognition settings. We propose a novel multi-view textual-visual encoding framework that encodes text appearing in the logos as well as the graphical design of the logos to learn robust contrastive representations. These representations are jointly learned for multiple views of logos over a batch and thereby they generalize well to unseen logos. We evaluate our proposed framework for cropped logo verification, cropped logo identification, and end-to-end logo identification in natural scene tasks;and compare it against state-of-the-art methods. Further, the literature lacks a gvery-large-scale' collection of reference logo images that can facilitate the study of one-hundred thousand-scale logo identification. To fill this gap in the literature, we introduce Wikidata Reference Logo Dataset (WiRLD), containing logos for 100K business brands harvested from Wikidata. Our proposed framework that achieves an area under the ROC curve of 91.3% on the QMUL-OpenLogo dataset for the verification task, outperforms state-of-the-art methods by 9.1% and 2.6% on the one-shot logo identification task on the Toplogos-10 and the FlickrLogos32 datasets, respectively. Further, we show that our method is more stable compared to other baselines even when the number of candidate logos is on a 100K scale. © 2022 ACM.

关键词： Encoding (symbols)

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An Algorithm to Calculate Retinal Vessel Diameter in Fundus images 23

An Algorithm to Calculate Retinal Vessel Diameter in Fundus ...

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proceedings of the fourteenth indian conference on computer vision, graphics and image processing

作者： Yoga Sri Varshan V Subin Sahayam Dr.Umarani Jayaraman Computer Science and Engineering Indian Institute of Information Technology Design and Manufacturing Kancheepuram IN

ISBN: (纸本)9798400716256

Retinal blood vessels are the arteries and veins that supply blood to the human eye. Fundus images obtained through a fundus camera capture retinal information like, the macula, optic disc, cup, fovea, retinal blood vessels, and abnormalities. The retinal blood vessels are miniature and are usually measured in micrometers. It is difficult and time-consuming to study retinal vessels from a fundus image. Generally, a detailed vessel study requires costly equipment like Ocular Coherence Tomography (OCT) and is comparatively less available at hospitals than a fundus camera. Blood vessels are connected throughout the body. Studying retinal blood vessel health can help ophthalmologists and doctors understand the overall health of blood vessels across the body non-invasively. Narrowing of blood vessels can make a person prone to many diseases, like hypertension, cardiovascular disease, and stroke. A few methods aim to automate the task using computer Aided Diagnosis (CAD). The work proposes a novel and relatively less complex algorithm for calculating the diameter of a given retinal blood vessel at a given distance from the optic disc center in a fundus image. The approach aims to compute the artery-to-vein diameter ratio of retinal blood vessels at a given distance from the center of the optic disc. The proposed method has been tested on the LES-AV 2020 dataset and it achieved an approximate artery-to-vein diameter ratio (AVR) of 2:3, which is in line with the average healthy AVR in the literature. Therefore, the proposed method is accurate and can be extended to find AVR in various clinical scenarios to detect and study any abnormalities.

关键词： AV Ratio Artery-Vein Ratio Medical Imaging Retinal Blood Vessels Vessel Thickness

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Identification of indian Medicinal Plants from Leaves using Transfer Learning Approach 5

Identification of Indian Medicinal Plants from Leaves using ...

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5th International conference on Trends in Electronics and Informatics, ICOEI 2021

作者： Arunaggiri Pandian, K. Sai Kumar, T.S. Thabasum Aara, S. Prabalakshmi, A. Thiagarajar College of Engineering Dept of Electronics and Communication Engineering Madurai India

ISBN: (纸本)9781665415712

India is well-known for providing the optimal habitat for a wide variety of medicinal plants. Numerous parts of the plants are utilized as crucial elements for the creation of natural medications. Utilization of image processing and computer vision strategies for the distinguishing proof of the medicinal plants are extremely urgent as a large number of these plants are under extinction according to the IUCN (International Union for Conservation of Nature) records. Subsequently, the digitization of helpful therapeutic plants is urgent for the protection of biodiversity. This paper examines Convolutional Neural Network (CNN) based methodologies for distinguishing indian leaf species. In recent times, numerous Deep Learning structures have been utilized in the distinguishing proof and characterization of a wide assortment of plants. This research chiefly centers around identifying the therapeutic plants that are accessible in rustic territories. To do so, three notable pre-trained CNN architectures namely ResNet101, InceptionV3, and VGG16 that were trained for the imageNet database were chosen by actualizing the Transfer Learning technique. These models were inspected with their pre-trained weights for the Ayur Bharat dataset that was created utilizing 10 unique classes of medicinal plants that sum up to a total of 10000 images. The performance of these models was improved via the Canny edge detection method and a comparison was carried out between these three architectures that were trained, both without and with, preprocessing techniques. The best classification performance for the Ayur Bharat dataset was attained by the InceptionV3 architecture trained with the Canny edge detection pre-processing technique, with the finest validation accuracy and F1-score of 0.9732 and 0.9653 respectively. © 2021 IEEE.

关键词： Convolutional neural networks

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Emotion recognition from geometric facial features using self-organizing map

Emotion recognition from geometric facial features using sel...

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作者： Majumder, Anima Behera, Laxmidhar Subramanian, Venkatesh K. Department of Electrical Engineering Indian Institute of Technology Kanpur Kanpur 208016 India

This paper presents a novel emotion recognition model using the system identification approach. A comprehensive data driven model using an extended Kohonen self-organizing map (KSOM) has been developed whose input is a 26 dimensional facial geometric feature vector comprising eye, lip and eyebrow feature points. The analytical face model using this 26 dimensional geometric feature vector has been effectively used to describe the facial changes due to different expressions. This paper thus includes an automated generation scheme of this geometric facial feature vector. The proposed non-heuristic model has been developed using training data from MMI facial expression database. The emotion recognition accuracy of the proposed scheme has been compared with radial basis function network, multi-layered perceptron model and support vector machine based recognition schemes. The experimental results show that the proposed model is very efficient in recognizing six basic emotions while ensuring significant increase in average classification accuracy over radial basis function and multi-layered perceptron. It also shows that the average recognition rate of the proposed method is comparatively better than multi-class support vector machine. © 2013 Elsevier Ltd. All rights reserved.

关键词： Support vector machines

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An IDE-agnostic system to capture reading behaviour of C++ programs using eye-gaze tracker

An IDE-agnostic system to capture reading behaviour of C++ p...

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

作者： Sayani Mondal Partha Pratim Das Rajendra Mishra School of Engineering Entrepreneurship Indian Institute of Technology Kharagpur India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India

ISBN: (纸本)9781467385657

Over 70% of software development effort is spent in software maintenance comprising bug fixes and version updates. These activities involve fast comprehension of large codebases authored by multiple developers. Developers are mostly trained to write code while in practice they often need to read code. Hence effective and efficient systems are needed to assess the code reading ability of developers. While there have been some studies on program comprehension, they do not deliver a working system. They use an IDE (Integrated Development Environment) that more suits the needs of program comprehension system and caters less to the needs of program development or developers. Importantly, to measure the productivity of a developer, we need to use the same IDE which the developer regularly uses and is comfortable with. In short, such systems need to be IDE-agnostic. In this paper, we build a front-end for a program comprehension assessment system using Tobii X2-30 eye-gaze tracker. The system comprises a flat screen monitor fitted with X2-30 and the developer seated within a specific distance from it. As the developer reads the C++ program, her eye movements are captured by X2-30 at 30 samples / sec. Once the reading is over, the X2-30 data stream is analysed off-line along with the program text to generate the reading sequence of the tokens as read by the developer. We use image analysis to extract regions from the displayed program text corresponding to token lexicons in C++. The lexical tokens are extracted separately from the program text. Finally we stitch the image regions, gaze points in and around the regions, and the tokens to generate the reading streams. We use several heuristics to compress the stream for semantic clarity. Several experiments have been conducted to estimate the accuracy of the generated sequences. In future, we plan to mine these reading sequences for extracting the comprehension features and develop metrics for reading skills.

关键词： Streaming media image segmentation Tracking Feature extraction Solids Semantics Visualization

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Psoriatic plaque segmentation in skin images

Psoriatic plaque segmentation in skin images

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

作者： Anabik Pal Utpal Garain Raghunath Chatterjee Swapan Senapati CVPRU ISI Kolkata West Bengal India Indian Statistical Institute Kolkata West Bengal IN (Consultant Dermatologist) Uttarpara Hooghly West Bengal India

This paper concerns about automatic psoriatic plaque segmentation in skin images. A novel pixel clustering based image segmentation approach is proposed. The clustering approach works with the circular-linear color spaces which remained unexplored because of the unavailability of appropriate mixture models and hence, the traditional clustering algorithms are incapable of handling such color spaces. This paper makes use of a novel Semi-Wrapped Gaussian Mixture Model (SWGMM) based approach for clustering image pixels. The experiment considers CIE Lch color space which is essentially a circular-linear space. The performance of SWGMM-based segmentation is compared to commonly used Fuzzy C-Means(FCM) and Fuzzy Kernel C-Means (FKCM)-based segmentation methods in linear color space. Results on a newly generated dataset show that SWGMM-based segmentation achieves accuracy of 82.84% in CIE Lch; whereas experiments with three linear color spaces namely, RGB, CIE L*u*v* and CIE L*a*b* show that FKCM and FCM achieve best accuracies of 78.80% and 77.79%, respectively in Lu*v* space. For comparing the clustering-based diseased detection result to a supervised pixel classification based approach, a support vector machines (SVM) based classifier is trained with labelled diseased and normal healthy skin pixels. As the number of images is limited for the present problem, SVM based diseased detection achieves 63.56% accuracy which is significantly lower than the accuracies given by the clustering based approaches.

关键词： image color analysis Skin Clustering algorithms image segmentation Kernel Support vector machines Colored noise

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Real-time monocular fixation control using the log-polar transformation and a confidence-based similarity measure

Real-time monocular fixation control using the log-polar tra...

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International conference on Pattern Recognition

作者： I. Ahrns H. Neumann Research and Technology Daimler Benz Aerospace Ulm Germany Department of Neural Information Processing University of Ulm (EBS) Ulm Germany

A new technique for the fixation task is proposed. The proposed technique is view-based and uses a gradient descent technique in order to maximize an intensity-based similarity measure between the reference image and the currently grabbed image during control time. A key constraint for the development of computational mechanisms is the achievement of real-time capability on low-cost hardware. We utilize the complex-logarithmic mapping of input images into log-polar space. In addition to a high compression rate this also supports better fixation performance using a space-variant image representation of the image. We further suggest a confidence based similarity measure to overcome the problem of distortions and partial occlusions during the movement of the camera which is the main problem of view-based approaches. We demonstrate the practicability of our method in a real world experiment which solves the fixation task at a frame rate of 16 Hz on an Intel P200 architecture without any special image processing hardware.

关键词： Cameras Hardware Distortion measurement Optical distortion image representation Target tracking Time measurement computer vision Motion estimation image processing

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Telugu Word Segmentation Using Fringe Maps 4th

Telugu Word Segmentation Using Fringe Maps

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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： Devarapalli, Koteswara Rao Negi, Atul Department of Computer Science and Engineering Mahatma Gandhi Institute of Technology Hyderabad500075 India School of Computer and Information Sciences University of Hyderabad Gachibowli Hyderabad500046 India

ISBN: (纸本)9789811393600

In this paper, we propose a word segmentation method that is based on fringe maps on Telugu script. Our objective is to create a data set of word images for enabling direct training for recognition on those. The standard methods employed for the task of word segmentation in Telugu OCR systems are projection profiles and run-length smearing. However those methods have their limitations. In this work a different application of fringe maps is shown for line segmentation into words. Fringes were previously applied successfully for carrying out classification and line segmentation. Telugu script, which has consonant modifiers that are usually placed below or below-right to the base consonants. This kind of orthographic property leads to characters that may touch each other. One way to deal with touched characters is to make use of segmentation free methods, which do not need prior segmentation of word images into characters or connected components. The novelty of our method is that we analyze fringe maps of document images to find an appropriate fringe value threshold and apply it for word segmentation of Telugu documents. Encouraging results are observed with our fringe value threshold based word segmentation. We observe that choosing higher threshold fringe values leads to under-segmentation of words, whereas lower values cause over-segmentation of words. Our word segmentation approach is successfully compared with the widely used projection profiles based word segmentation method. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Optical character recognition

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Face verification using correlation filters and autoassociative neural networks

Face verification using correlation filters and autoassociat...

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International conference on Intelligent Sensing and Information processing (ICISIP)

作者： Anil Kumar Sao B. Yegnanarayana Department of Computer Science and Engineering Speech and Vision Laboratory Indian Institute of Technology Madras Chennai India Department of Computer Science and Engineering Speech and Vision Laboratory Indian Institute of Technology Madras India

Face verification is the process of accepting or rejecting the identity claim of a person using information from his/her face. Representation of the face is an important issue in face verification. This paper propose edge gradient-based representation of face, for correlation-based face verification. The edge gradient based representation of face is obtained using one-dimensional (1-D) processing of the image, which has the advantage of providing multiple partial evidences for a given image. This representation of face is used to recognize the faces, which is performed by a specific type of correlation filter called minimum average correlation energy (MACE). Separate correlation filters are employed for each partial evidence. A method is proposed to combine the output of the filter using an auto-associative neural network (AANN) model to arrive at a decision to accept or reject the claim.

关键词： Neural networks Face detection Face recognition Matched filters image databases Multimedia databases Discrete Fourier transforms Speech processing Laboratories computer science

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