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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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8th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2023

8th National Conference on Computer Vision, Pattern Recognit...

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8th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2023

ISBN: (纸本)9789819752119

The proceedings contain 18 papers. The special focus in this conference is on computer vision, Pattern Recognition, image processing, and graphics. The topics include: RGB to Chlorophyll Fluorescence image Reconstruction of Maize for Plant Phenotyping;novel Unsupervised Disease Segmentation Framework Based on Clustering;YOLOv5-MasselNet: In-Field Maize Tassel Detection and Density Estimation Using YOLOv5;MuSiC V1.0: A Software Solution for Automated Mustard Silique Count Using YOLOv5;taPaSe: Tanjore Paddy Seed Dataset;processing 3D Point Clouds for High-Throughput Plant Phenotyping;a Deep Learning-Driven Contactless Finger Recognition System;deep Features for Contactless Fingerprint Presentation Attack Detection: Can They Be Generalized?;unlocking Communication: Exploring Hand Gesture-Based Interaction in a Code-Mixed Conversational Speech-to-indian-Sign-Language Translator;a Touchless Palm-Photo Recognition System for Mobile and Handheld Devices;detection of Palmprint from Contactless Smartphone-Based Video Hand Dataset;improved Localization of Knuckle Regions for Contactless Acquisition;NCVPRIPG 2023 Summer Challenge on Writer Verification;C4MTS: Challenge on Categorizing Missing Traffic Signs from Contextual Cues;dePondFi’23 Challenge Summary;a Challenge on 3D Reconstruction and Restoration of indian Heritage from Partial and Noisy Scans.

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NCVPRIPG 2023 Summer Challenge on Writer Verification 8th

NCVPRIPG 2023 Summer Challenge on Writer Verification

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8th National conference on computer vision, Pattern Recognition, image processing and graphics-NCVPRIPG

作者： Prabhat, Gyan Shahid, Abu Gatti, Prajwal Shah, Manan Chutia, Girin Sharma, Arvind Kumar Mishra, Anand Indian Inst Technol Jodhpur Jodhpur Rajasthan India Cashfree Payments India Pvt Ltd Bengaluru India HCL Technol Ltd Chennai Tamil Nadu India

ISBN: (纸本)9789819752119;9789819752126

This report presents the outcomes of the Summer Challenge on Writer Verification, hosted as part of the Eighth National conference on computer vision, Pattern Recognition, image processing, and graphics (NCVPRIPG) held at IIT Jodhpur on July 21-23, 2023. This challenge introduces a novel dataset comprising images of handwritten text contributed by 1,352 unique writers. Predominantly, these images feature handwritten Hindi text, but they also encompass a variety of elements such as numbers, mathematical symbols, and English text. Participants were tasked with developing a model capable of automatically determining whether the text in a given pair of images is authored by the same writer or different writers. The primary objective of this challenge was to advance research in the realm of handwritten text recognition. Throughout the competition, we registered 108 teams, with 18 teams submitting results for the validation dataset. Out of these, 13 teams provided submissions for the semi-final dataset. The top six teams from the semi-finals were subsequently invited to compete in the finals. Additional details about the challenge can be found at https://***/challenges/wv2023/.

关键词： Writer verification Handwritten text Indic language Document analysis Handwritten text recognition

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A Challenge on 3D Reconstruction and Restoration of indian Heritage from Partial and Noisy Scans 8th

A Challenge on 3D Reconstruction and Restoration of Indian H...

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8th National conference on computer vision, Pattern Recognition, image processing and graphics-NCVPRIPG

作者： Pandey, Atharva Yadav, Vishal Karthikeyan, Akash Solanki, Deepanshu Nagar, Rajendra IIT Jodhpur Jodhpur Rajasthan India Thiagarajar Coll Engn Madurai Tamil Nadu India

ISBN: (纸本)9789819752119;9789819752126

The problem of 3D Reconstruction and restoration of indian heritage sites is a classical 3D computer vision problem which has been addressed in the indian vision and graphics research communities. The major focus of these approaches has been to reconstruct a point cloud model of the heritage sites from multiple colored images captured from different viewpoints. This essentially requires solving the structure from motion problem to be solved. This becomes a challenging problem to solve for large scale architectural sites. With the emergence of 3D scanners, we can obtain partial 3D scans of a site from different viewpoints. Then, themain problem to solve is to fuse these noisy and partial 3Dscans to obtain the complete 3D model of the underlying surface of the heritage site. The problem of reconstructing the complete 3D model from the partial 3D scan can be solved using classical approaches as well as learning based approaches. Using machine learning based approaches in the context of indian heritage may suffer from the limited training dataset availability. Also, the order of points representing the 3D model of such sites can be in millions. Therefore, reconstructing such high resolution 3D models using neural network may demand high-end parallel computing facility.

关键词： Heritage site restoration Point cloud registration Feature based registration

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Novel Unsupervised Disease Segmentation Framework Based on Clustering 8th

Novel Unsupervised Disease Segmentation Framework Based on C...

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8th National conference on computer vision, Pattern Recognition, image processing and graphics-NCVPRIPG

作者： Singh, Rama Kant Aggarwal, Monika Lall, Brejesh Indian Inst Technol Delhi Bharti Sch New Delhi India Indian Inst Technol Delhi CARE New Delhi India Indian Inst Technol Delhi Elect Engg Dept New Delhi India

ISBN: (纸本)9789819752119;9789819752126

Plant disease is a major limiting factor in yield production. Traditionally, biologists rely on manual screening (localisation) of disease symptoms exhibited on plant leaves. However, this is a highly subjective and error-prone analysis. With the advancement of imaging devices, image-based analysis permits high-throughput disease localisation. In this context, state-of-the-art supervised deep learning-based methods have been presented. However, annotation of disease symptoms is time-consuming and is a current bottleneck. To relieve this limitation, we propose a novel unsupervised framework that utilises Cluster weight as well as Group-local Feature-weight learning using iteration in Fuzzy C-Means (CGFFCM) guided via differentiable clustering algorithm for accurate disease localisation. It is to be noted that the proposed framework does not rely on annotated image data. In this paper, we also performed an exhaustive comparison based on different clustering methods to show the efficacy of the proposed framework.

关键词： Disease phenotyping Unsupervised segmentation Differentiable clustering Fuzzy C-means CGFFCM

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Unlocking Communication: Exploring Hand Gesture-Based Interaction in a Code-Mixed Conversational Speech-to-indian-Sign-Language Translator 8th

Unlocking Communication: Exploring Hand Gesture-Based Intera...

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8th National conference on computer vision, Pattern Recognition, image processing and graphics-NCVPRIPG

作者： Saini, Neelam Mishra, Anand Indian Inst Technol Jodhpur Jodhpur Rajasthan India

ISBN: (纸本)9789819752119;9789819752126

Communication plays a vital role in society and is considered an essential life skill. However, individuals with hearing disabilities often encounter challenges when interacting with others, leading to feelings of isolation and reduced self-confidence. Sign language, a combination of manual gestures and facial expressions, serves as a means of communication for individuals with hearing and speaking disorders. Ordinary individuals are often unaware of sign language, making communication with them difficult. Additionally, the increasing prevalence of Code-Mixed languages, where multiple languages are used within a single sentence, poses a challenge for existing speech-to-sign translating systems to accurately recognize the code-mixed languages and generate the corresponding sign language. In this paper, we propose a novel system that facilitates communication between individuals with hearing impairments and those who can speak, specifically targeting code-mixed indian-English languages. Our system initially converts code-mixed speech to text and subsequently maps the generated text to the appropriate indian Sign Language, thereby enabling effective communication between these two user groups.

关键词： Speech-to-sign language conversion indian sign language Code-mixing Multilingual speech Hand-gesture interaction

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Detection of Palmprint from Contactless Smartphone-Based Video Hand Dataset 8th

Detection of Palmprint from Contactless Smartphone-Based Vid...

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8th National conference on computer vision, Pattern Recognition, image processing and graphics-NCVPRIPG

作者： Chodvadiya, Charulkumar Singh, Kinshuk Gaurav Vyas, Ritesh Pandit Deendayal Energy Univ Gandhinagar Gujarat India

ISBN: (纸本)9789819752119;9789819752126

A fast-expanding topic is the study of palmprint biometric identification in contactless scenario, which uses techniques from computer vision and machine learning to identify and authenticate people. In this study, we utilized a handcrafted video dataset with 60 distinct classes, each labelled as either a left or right hand, to investigate palmprint detection and matching tasks. The dataset showcases various variations in palmprint patterns, like distance from the sensor, orientation, finger positioning, and deformation, making it an ideal candidate for the development of robust and accurate palmprint recognition models. The major goal of the study is to identify palmprints in the video collection and match them with the right class or pattern. To accomplish this task, different machine learning (ML) and deep learning (DL) models were trained and evaluated. To find the best method for palmprint identification in a contactless manner, the accuracy of each model was tested. In conclusion, our study adds to the expanding body of knowledge on biometric palmprint identification and introduces a fresh handmade video dataset that can be used to compare the effectiveness of various models.

关键词： Palmprint biometric identification computer vision Deep learning model Palmprint patterns Transfer learning

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RGB to Chlorophyll Fluorescence image Reconstruction of Maize for Plant Phenotyping 8th

RGB to Chlorophyll Fluorescence Image Reconstruction of Maiz...

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8th National conference on computer vision, Pattern Recognition, image processing and graphics-NCVPRIPG

作者： Shukla, Ankit Upadhyay, Avinash Sharma, Manoj Saini, Anil Bennett Univ Greater Noida India CSIR CEERI Pilani Rajasthan India

ISBN: (纸本)9789819752119;9789819752126

The advancement in imaging devices has permitted high-throughput study of plant traits also termed as plant phenotyping. In this context, among the different imaging modalities employed in plant phenotyping, chlorophyll fluorescence imaging provides accurate characterisation of crucial plant traits such as photosynthetic efficiency, water use efficiency and nutrients use efficiency to name a few. However, chlorophyll fluorescence imaging is complex and expensive in contrast to widely adopted visible (RGB) images. This limits the reliable study of the aforementioned plant traits. To relieve this limitation, chlorophyll fluorescence image reconstruction algorithms from widely available visible images are a prime solution. Thus in this paper, we propose a domain translation-based approach using cycle consistency loss for chlorophyll fluorescence image reconstruction from RGB images. The experimental results on the maize dataset demonstrate the efficiency of our approach with respect to this novel application.

关键词： Chlorophyll fluorescence image GAN Deep learning Domain translation Plant phenotyping Maize

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Cross-domain image Translation and Feature Extraction for Large FoV Aerial Crop NIR image Creation 8th

Cross-domain Image Translation and Feature Extraction for La...

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8th National conference on computer vision, Pattern Recognition, image processing and graphics-NCVPRIPG

作者： Rathore, Sudeep Shukla, Ankit Upadhyay, Avinash Sharma, Manoj Yadav, Ajay Bennett Univ Greater Noida UP India

ISBN: (纸本)9789819752119;9789819752126

Near-Infrared Spectroscopy (NIR) is valuable for getting thorough information the human eye cannot capture in agricultural operations and investigations. A special multispectral camera is required to get NIR spectral data, which provides low-resolution images compared to RGB (Red-Green-Blue) data. NIR spectral data contains low contrast and no colour information making it difficult to identify and match features in the NIR domain. We require more features for image stitching to achieve large FoV (Field of View) NIR images. We use Cycle Generative Adversarial Network (CycleGAN) for image-to-image translation to synthesise an RGB image from a NIR spectral band image to take advantage of both RGB and NIR bands for the feature extraction. Features are extracted from NIR and synthetic RGB images. Then, we stitch the different NIR views using concatenated features of both domains to create large Field of View (FoV) NIR images. This augmented feature set yields superior stitching results compared to NIR features alone. The effectiveness of the proposed framework is demonstrated through experiments, showcasing the improvements in stitching quality.

关键词： NIR image stitching CycleGAN Deep learning Feature matching image blending

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processing 3D Point Clouds for High-Throughput Plant Phenotyping 8th

Processing 3D Point Clouds for High-Throughput Plant Phenoty...

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8th National conference on computer vision, Pattern Recognition, image processing and graphics-NCVPRIPG

作者： Dagar, Preety Arora, Alka Kumar, Sudhir Marwaha, Sudeep Jain, Rajni Chaurasia, Himanshushekhar Chinnusamy, Vishwanathan ICAR IASRI Div Comp Applicat Library Ave New Delhi 110012 India IARI Pusa ICAR IARI Div Plant Physiol New Delhi 110012 India ICAR CIRCOT Mumbai 400019 Maharashtra India

ISBN: (纸本)9789819752119;9789819752126

In recent years, measuring three-dimensional (3D) surface information has gained a great interest in plant phenotyping because it can represent the nature of plant architecture better than conventional 2D images. This paper presents an approach for processing 3D point clouds converted from 2D RGB images in the context of high-throughput plant phenotyping. High-resolution RGB multi-view imagery of a Chickpea plant was collected using a high-end camera. Based on these image sequences, 3D point cloud reconstruction of the canopy was conducted and analyzed. Later sophisticated 3D operations were performed on these images including 3D downsampling and after that clustering was performed on the processed point cloud. The information generated can help in the evaluation of crop traits and provide accurate statistics for the assessment of their growth parameters.

关键词： 3D image processing 2D to 3D image conversion Open3D 3D image processing for plant phenotyping

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A Deep Learning-Driven Contactless Finger Recognition System 8th

A Deep Learning-Driven Contactless Finger Recognition System

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8th National conference on computer vision, Pattern Recognition, image processing and graphics-NCVPRIPG

作者： Attris, Aman Bharat, Nagasai Anand, Vijay Kanhangad, Vivek Indian Inst Technol Indore Discipline Elect Engn Indore 453552 India

ISBN: (纸本)9789819752119;9789819752126

Fingerprint is one of the most widely explored biometric traits. Specifically, contact-based fingerprint recognition systems reign supreme due to their effectiveness, portability, and research advancements in this field. However, these are issues such as hygiene concerns, sensor degradation due to constant physical contact, and latent fingerprint threats. In this paper, we present the details of a fingerprint recognition system that captures finger photos without contact. The captured finger photos are then processed to obtain global and local (minutiae-based) features. Specifically, a Siamese convolutional neural network is designed to extract global features from a given finger photo. Finally, the matching scores computed using the global and local features are fused to generate the final matching score between the probe and the reference fingerprint templates. We have developed the proposed system using the Nvidia Jetson Nano development kit. The developed system performs contactless fingerprint recognition in real time with minimum latency and acceptable matching accuracy. The performance of the proposed system is evaluated on an in-house IITI contactless fingerprint dataset (IITI-CFD) containing 105 train and 100 test subjects. The proposed system achieves an equal error rate of 2.19% on IITI-CFD.

关键词： Contactless fingerprint Fingerprint recognition Siamese CNN

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