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检索条件"任意字段=Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing"

共 1497 条记录，以下是41-50 订阅

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Multi-Scale Transformer-CNN Network for Brain Tumor Segmentation and Survival Prediction 24

Multi-Scale Transformer-CNN Network for Brain Tumor Segmenta...

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15th indian conference on computer vision graphics and image processing

作者： Mazumdar, Indrajit Mukhopadhyay, Jayanta Indian Inst Technol Kharagpur Dept Comp Sci & Engn Kharagpur W Bengal India

ISBN: (纸本)9798400710759

Accurately segmenting brain tumors and predicting survival is crucial for diagnostic and personalized treatment plans. Recently, convolutional neural networks (CNNs) based on U-Net architecture have been extensively used to segment brain tumors. It is essential to capture both local and global dependencies to segment brain tumors accurately. Therefore, several studies have considered U-Net variants by combining a CNN and a Transformer. However, the skip connections in these Transformer-based U-Net variants do not incorporate the features from multiple scales available at the encoder. To address this issue, we propose a new 3D U-Net variant called Multi-Scale Transformer-CNN Network (MTC-Net) that incorporates a Multi-Scale Transformer Convolution (MTC) block into the skip connections. The MTC block extracts local multi-scale features from the CNN encoder blocks and global features using Swin Transformer blocks to handle the wide variability in tumor size and enhance the feature representation capability. The segmentation mask predicted by the proposed MTC-Net is subsequently used to predict a patient's overall survival time. Most studies on overall survival prediction have used handcrafted radiomic features that lack the ability to fully model complex tumor patterns. In contrast, deep features are specifically adapted to brain tumors. We observed that instead of using only a CNN, combining it with a Transformer produces deep features that provide more accurate predictions. For this purpose, we extract deep features using our Transformer-based network MTC-Net to construct a regression model for predicting overall survival. Comprehensive experimentation on the BraTS 2020 and 2021 benchmark datasets proved the efficacy of the proposed components. MTC-Net outperformed the CNN- and Transformer-based state-of-the-art segmentation networks. Moreover, our approach outperformed the state-of-the-art survival prediction systems.

关键词： Brain tumor segmentation Deep learning CNN Transformer Survival prediction Machine learning

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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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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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MuSiC V1.0: A Software Solution for Automated Mustard Silique Count Using YOLOv5 8th

MuSiC V1.0: A Software Solution for Automated Mustard Siliqu...

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

作者： Deb, Chandan Kumar Das, Madhurima Kumar, Mahesh Kumar, Sudhir Haque, Md. Ashraful Arora, Alka Marwaha, Sudeep Singh, Biswabiplab Raju, Dhandapani Chinnusamy, Viswanathan ICAR Indian Agr Stat Res Inst New Delhi India

ISBN: (纸本)9789819752119;9789819752126

Object detection is a challenging and important field in the domain of computer vision. Automated detection and counting of the number of siliques non-destructively, which is the most important yield component trait in indian mustard (B. juncea), which is important as well as widely cultivated oilseed in India, is done here. We have used Deep Learning based one-stage object detection model, YOLOv5 in the counting of mustard siliques from the images captured by a DSLR camera (SONY Alpha 7iii, 24.2MP) manually mounted on a tripod. A GUI-based desktop software, MuSiC v1.0 is developed using Tkinter, Python backed by YOLOv5 model developed from annotated training dataset using Roboflow annotator. Approximately 22,800 annotations were used to train the deep learning YOLOv5 model and the whole dataset was partitioned into training, testing and validation in the ratio of 8:1:1. The Confusion matrix, F1 score and mAP showed a moderate result of 66%, 0.51 and 0.38, respectively, with the handcrafted ground truth data. The deployed model in MuSiC v1.0 showed a promising result of approximately good accuracy for silique count. This is the first initiative of creation of a desktop application for mustard silique count non-destructively.

关键词： Silique count Non-destructive YOLOv5 Object detection Deep learning Mustard

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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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CHAPVIDMR: Chapter-based Video Moment Retrieval using Natural Language Queries 24

CHAPVIDMR: Chapter-based Video Moment Retrieval using Natura...

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15th indian conference on computer vision graphics and image processing

作者： Agarwal, Uday Kumar, Yogesh Shahid, Abu Gatti, Prajwal Gupta, Manish Mishra, Anand IIT Jodhpur Jodhpur Rajasthan India Univ Bristol Bristol Avon England MicrosoftIndia Hyderabad Andhra Pradesh India Indian Inst Technol Jodhpur CSE Jodhpur Rajasthan India

ISBN: (纸本)9798400710759

Video Moment Retrieval (VMR) is the task of linking a query with a relevant moment from a video. Although, recently, there has been work on the VMR task where a query is linked to a single moment, the corresponding task where the query needs to be linked to multiple moments has been understudied. In this paper, we aim to work on the VMR task primarily by leveraging chapters of YouTube videos, i.e., video segments. YouTube chapters provide a meaningful segmentation of videos annotated by content authors. These annotated segmented regions help the viewer to navigate to a specific segment of the long video in which the user is interested. We present the CHAPVIDMR (Chapter-based Video Moment Retrieval) dataset, containing 10.8K user queries (obtained using GPT4) formed using multiple chapter names and other metadata extracted from videos using the YouTube API. Furthermore, we benchmark the proposed dataset on two VMR tasks: chapter classification-based VMR and segmentation-based VMR. In the chapter classification-based VMR task, the model classifies which of the chapters associated with a video are most relevant to the query. We represent a chapter by exploiting text (subtitles), audio, and visual (captions, video) modalities using state-of-the-art feature representation techniques and experiment with an exhaustive ablation for each modality. In segmentation-based VMR, the video is divided into various segments, and the segments most likely to answer the query are identified and returned. We benchmark our dataset on the state-of-the-art methods for segmentation tasks. We find that for the chapter classification-based VMR task, Sentence-BERT with subtitles and visual captions leads to the best results, while for the segmentation-based VMR, UniVTG is the most accurate. We make our code and data publicly available(1).

关键词： video moment retrieval CHAPVIDMR VMR chapter-based retrieval

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TaPaSe: Tanjore Paddy Seed Dataset 8th

TaPaSe: Tanjore Paddy Seed Dataset

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

作者： Sasithradevi, A. Vijayalakshmi, M. Varsini, S. R. Gabriel, Joshua R. Roomi, S. Mohamed Mansoor Prakash, P. Vellore Inst Technol Ctr Adv Data Sci Chennai Tamil Nadu India Vellore Inst Technol Sch Elect Engn Chennai Tamil Nadu India Vellore Inst Technol Sch Comp Sci & Engn Chennai Tamil Nadu India Thiagarajar Coll Engn Dept Elect & Commun Engn Madurai Tamil Nadu India Madras Inst Technol Dept Elect Engn Chennai Tamil Nadu India

ISBN: (纸本)9789819752119;9789819752126

Rice is a popular staple diet in India, and its demand has recently increased. Thanjavur, located in the Cauvery Delta region, is known as the rice granary of South India. Due to recent technological advancements, digital farming and globalization have significantly impacted the agricultural industry. It is crucial to differentiate between types of rice grains to prevent fraudulent labeling during import and export. To achieve this, a dataset, namely "TaPaSe Dataset", comprising five varieties of rice, including MTU 1010, MTU 1290, Narmadha, Pacha Ponni, and Sonna Masur, which are mainly cultivated in Thanjavur, has been collected. We designed an image acquisition system to capture the aforementioned varieties in real time. The captured paddy rice images are highly challenging in the sense that all the images are captured under illumination and scale variations. We evaluated existing deep learning models to understand their ability to classify paddy seed varieties. The existing pre-trained models attain remarkable recognition rates on the proposed paddy seed varieties dataset.

关键词： image classification Deep learning Pre-trained models

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Maize leaf disease image classification based on ResNet18 image classification model

Maize leaf disease image classification based on ResNet18 im...

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2023 International conference on computer graphics, Artificial Intelligence, and Data processing, ICCAID 2023

作者： Wu, Haoyan School of Information and Communication Studies University College Dublin Stillorgan Rd 4 Stillorgan Rd Belfield Dublin 4 Ireland

ISBN: (纸本)9781510677685

This paper introduces an effective algorithm for classifying maize leaf diseases using a publicly available image dataset. While the dataset itself, consisting of maize leaf disease images, was developed by indian researchers for agricultural applications, this study's contribution is the development and optimization of an image classification algorithm tailored for this dataset. The paper details the preprocessing and data augmentation techniques applied, such as size adjustment, normalization, and enhancement to ensure data uniformity and diversity. These methods significantly improve the model's accuracy and generalization capabilities. The study employs a deep learning model based on resnet18, dividing the dataset into training, validation, and test sets in a 6:2:2 ratio. The training process is analyzed through loss and accuracy curves, revealing a steady decrease in loss and increase in accuracy over time. The model achieves 98% accuracy, 95% Precision, 95% Recall, and a 95% F1-score, indicating high effectiveness in classifying maize leaf diseases. A confusion matrix analysis confirms the model's precision across most image types. The research demonstrates the algorithm's strong performance in classifying various types of maize leaf diseases, highlighting its potential to aid farmers in disease identification and treatment, thereby improving agricultural productivity and quality. © 2024 SPIE.

关键词： image classification

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

12th Indian Conference on Computer Vision, Graphics, and Ima...

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

ISBN: (纸本)9789811941351

The proceedings contain 17 papers. The special focus in this conference is on computer vision, graphics, and image processing. The topics include: The Ikshana Hypothesis of Human Scene Understanding;worst-Case Adversarial Perturbation and Effect of Feature Normalization on Max-Margin Multi-label Classifiers;Catch Me if You Can: A Novel Task for Detection of Covert Geo-Locations (CGL);MATIC: Memory-Guided Adaptive Transformer for image Captioning;Semantic Map Injected GAN Training for image-to-image Translation;textGen3D: A Real-Time 3D-Mesh Generation with Intersecting Contours for Text;comparative Analysis of Neural Architecture Search Methods for Classification of Cultural Heritage Sites;heritage Representation of Kashi Vishweshwar Temple at Kalabgoor, Telangana with Augmented Reality Application Using Photogrammetry;augmented Data as an Auxiliary Plug-In Toward Categorization of Crowdsourced Heritage Data;evolution of Bagbazar Street Through Visibility Graph Analysis (1746–2020);mapping Archaeological Remains of 14th Century Fort of Jahanpanah Using Geospatial Analysis;spatial Analysis and 3d Mapping Historic Landscapes—Implications of Adopting an Integrated Approach in Simulation and Visualization of Landscapes;HSADML: Hyper-Sphere Angular Deep Metric Based Learning for Brain Tumor Classification;model Compression Based Lightweight Online Signature Verification Framework.

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Parallelization Algorithm of computer vision for Autonomous Vehicles 2

Parallelization Algorithm of Computer Vision for Autonomous ...

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2nd IEEE International conference on vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Kaniskaa, M.S. Manimegalai, R. Noor Mahammad, S.K. PSG Institute of Technology and Applied Research India Indian Institute of Information Technology Design and Manufacturing Kancheepuram India

ISBN: (纸本)9798350347982

The important features that enable computer vision in autonomous vehicle technology and infotainment function are image processing and object identification. image segmentation is the preliminary step of any image processing algorithm. This work uses the Union-Find algorithm to segment images on a grey scale. It is a simple yet effective algorithm for intense applications. A review of the image segmentation algorithms and parallelization techniques is presented in this paper. Initially, three different profiling techniques are applied in order to identify the hot-spots, i.e. most time-consuming parts of the code. Parallelization techniques are applied to the regions of the hot-spots identified during profiling. The Union-Find algorithm is parallelized using OpenMP, MPI, and CUDA. The execution time decreases with the increase in the number of threads till a certain optimal value of threads. The optimal number of threads is found for the respective parallelization techniques. © 2023 IEEE.

关键词： image segmentation

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