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检索条件"任意字段=6th Indian Conference on Computer Vision, Graphics and Image Processing"

共 2011 条记录，以下是21-30 订阅

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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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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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Leveraging Auxiliary Classification for Rib Fracture Segmentation 24

Leveraging Auxiliary Classification for Rib Fracture Segment...

引用

15th indian conference on computer vision graphics and image processing

作者： Harini, G. Farooq, Aiman Mishra, Deepak Indian Inst Technol Jodhpur Jodhpur Rajasthan India

ISBN: (纸本)9798400710759

thoracic trauma often results in rib fractures, which demand swift and accurate diagnosis for effective treatment. However, detecting these fractures on rib CT scans poses considerable challenges, involving the analysis of many image slices in sequence. Despite notable advancements in algorithms for automated fracture segmentation, the persisting challenges stem from the diverse shapes and sizes of these fractures. To address these issues, this study introduces a sophisticated deep-learning model with an auxiliary classification task designed to enhance the accuracy of rib fracture segmentation. the auxiliary classification task is crucial in distinguishing between fractured ribs and negative regions, encompassing non-fractured ribs and surrounding tissues, from the patches obtained from CT scans. By leveraging this auxiliary task, the model aims to improve feature representation at the bottleneck layer by highlighting the regions of interest. Experimental results on the RibFrac dataset demonstrate significant improvement in segmentation performance.

关键词： Rib Fracture Attention Weak Supervision Segmentation

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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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Pick and Pack: revitalizing mandalas through digital artistry 24

Pick and Pack: revitalizing mandalas through digital artistr...

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15th indian conference on computer vision graphics and image processing

作者： Sarkar, Tusita Abhishek, Tushar Bhowmick, Partha Indian Inst Technol Kharagpur Dept Comp Sci & Engn Kharagpur W Bengal India

ISBN: (纸本)9798400710759

this paper presents a novel approach to computational art focusing on mandalas-an iconic heritage of indian art that has proliferated significantly in recent times. Our innovative software allows users to input a handcrafted mandala and select specific motifs for error rectification. the rectification leverages vector information for geometric discretization alongside a simple GCD rule, adhering to traditional mandala principles. the rectified image, being in vector form, allows various real-time operations in the vector space, such as insertion, deletion, or modification of motifs or layers, facilitating enhancements, enlargements, or creation of new compositions. We demonstrate the software's merit and versatility through various examples, highlighting its potential for special applications such as digital artistry with mandalas, including teaching and training. this work not only advances the field of computational art but also promises to preserve and enhance the rich tradition of mandala art through modern technology.

关键词： Art and culture art and psychology computational art digital art indian art mandala

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Enhancing Visual Question Answering through Bi-Modal Feature Fusion: Performance Analysis 24

Enhancing Visual Question Answering through Bi-Modal Feature...

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6th International conference on image processing and Machine vision (IPMV)

作者： Mao, Keyu Fudan Univ Sch Data Sci Shanghai Peoples R China

ISBN: (纸本)9798400708473

With the significant development in deep learning within the domains of computer vision and natural language processing, the research involving the multimodal aspects of Visual Question Answering (VQA) has also reached a pivotal turning point in recent years. throughout prior investigations, scholars have consistently emphasized feature extraction from images and text. Numerous models have been applied in this context, ranging from the initial breakthroughs of Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN), to the momentary prominence of Dynamic Memory Networks, and subsequently, the rise of transformers in recent times. Nonetheless, it is imperative to recognize that beyond the ambit of feature extraction models, the fusion of bi-modal features assumes pivotal significance. this paper builds upon SOAT model from the previous work, serving as the baseline, and meticulously scrutinizes its performance across distinct fusion methodologies. Various notable fusion strategies, such as MUTAN and BLOCK, are considered. Notably, the most adept model achieves an impressive 65.74% accuracy on the VQA v2 dataset, outperforming established benchmarks. this outcome robustly substantiates the premise that fusion techniques exert tangible influence over the ultimate research outcomes.

关键词： VQA Natural language processing computer vision Fusion Bilinear models

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Driver Drowsiness Detection Using vision Transformer 8th

Driver Drowsiness Detection Using Vision Transformer

引用

8th International conference on computer vision and image processing (CVIP)

作者： Usmani, Shaheen Chandwani, Bharat Sadhya, Debanjan ABV Indian Inst Informat Technol & Management Gwa Gwalior India

ISBN: (纸本)9783031581809;9783031581816

the issue of driver drowsiness poses a considerable safety risk, which can result in accidents and loss of life. To confront this issue, reliable and user-friendly systems that can detect early signs of drowsiness are needed. vision-based drowsiness detection (DDD) systems have emerged as a promising approach to achieve this goal. DDD systems combine image processing methods with artificial intelligence to monitor the driver's eyes and facial expressions and identify signs of drowsiness. In this work, we present a DDD approach using vision transformers (ViT). the attention mechanism in our proposed method focuses on the specific patches of the input image which contribute to the drowsiness phenomenon. Empirical results on the UTA-RLDD dataset show that our proposed model achieves a high level of test accuracy of 98.10 and an average prediction time of 17 ms per frame. Hence, our approach offers a solution for detecting early signs of drowsiness, providing drivers with enough time to take corrective action and reduce accident risks.

关键词： Driver Drowsiness Detection(DDD) image processing Vison Transformers(ViT)

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GraphVL: Graph-Enhanced Semantic Modeling via vision-Language Models for Generalized Class Discovery 24

GraphVL: Graph-Enhanced Semantic Modeling via Vision-Languag...

引用

15th indian conference on computer vision graphics and image processing

作者： Solanki, Bhupendra Nair, Ashwin R. Singha, Mainak Mukhopadhyay, Souradeep Jha, Ankit Banerjee, Biplab Indian Inst Technol Mumbai Maharashtra India Indian Inst Sci Educ & Res Thiruvananthapuram Thiruvananthapuram Kerala India Indian Inst Sci Bangalore Karnataka India LNM Inst Informat Technol Jaipur Rajasthan India

ISBN: (纸本)9798400710759

Generalized Category Discovery (GCD) aims to cluster unlabeled images into known and novel categories using labeled images from known classes. To address the challenge of transferring features from known to unknown classes while mitigating model bias, we introduce GraphVL, a novel approach for vision-language modeling in GCD, leveraging CLIP. Our method integrates a graph convolutional network (GCN) with CLIP's text encoder to preserve class neighborhood structure. We also employ a lightweight visual projector for image data, ensuring discriminative features through margin-based contrastive losses for image-text mapping. this neighborhood preservation criterion effectively regulates the semantic space, making it less sensitive to known classes. Additionally, we learn textual prompts from known classes and align them to create a more contextually meaningful semantic feature space for the GCN layer using a contextual similarity loss. Finally, we represent unlabeled samples based on their semantic distance to class prompts from the GCN, enabling semi-supervised clustering for class discovery and minimizing errors. Our experiments on seven benchmark datasets consistently demonstrate the superiority of GraphVL when integrated with the CLIP backbone.

关键词： Class Discovery Contrastive Learning Graph Convolutional Networks Unsupervised learning vision-Language models

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MoCoMER: A Self Supervised Representation Learning for Handwritten Mathematical Expression Recognition 24

MoCoMER: A Self Supervised Representation Learning for Handw...

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15th indian conference on computer vision graphics and image processing

作者： Pramanik, Sandip Mitra, Shree Das, Nibaran Jadavpur Univ Dept CSE Kolkata W Bengal India Indian Inst Informat Technol Guwahati Gauhati India

ISBN: (纸本)9798400710759

this paper presents a self-supervised learning (SSL) based framework, specifically designed for Handwritten Mathematical Expression Recognition (HMER). the proposed approach incorporates a momentum encoding technique and a non-linear projection head into the image encoder to effectively address the common issue of dimensional collapse in self-supervised learning (SSL) methods. Our approach consists of two main steps: first, we use self-supervised pre-training to train the image encoder to obtain strong feature representations from HME images. Subsequently, we fine-tune the model using a Transformer network to predict LaTeX sequences from HME images. the assessment demonstrates that our SSL framework surpasses other existing SSL frameworks as well as several supervised methods in terms of performance. the findings indicate that our approach is highly advanced, emphasizing its potential to enhance the robustness and efficiency of feature representations in HMER tasks. the integration of momentum encoding and a non-linear projection head in the image encoder is shown to enhance the durability and effectiveness of feature representations, leading to superior performance in HMER tasks. Our experiments reveal that our approaches achieve an expression recognition rate (ExpRate) of 62. 17%, 61. 03%, 64. 8% on the CROHME 2014,2016,2019 test datasets respectively. the CROHME 2019 test data achieves the highest ExpRate, which is state-of-the-art (SOTA). this success is achieved by overcoming the challenges of dimensional collapse and leveraging the advantages of both self-supervised and supervised learning.(1)

关键词： HMER SSL Contrastive Learning Momentum Contrast (MoCo) Non-linear projection head Transformer

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Addressing Diffusion Model Based Counter-Forensic image Manipulation for Synthetic image Detection 24

Addressing Diffusion Model Based Counter-Forensic Image Mani...

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15th indian conference on computer vision graphics and image processing

作者： Herur, Aryan N. Santhosh, Vaibhav Shetty, Nishanth Seelamantula, Chandra Sekhar Natl Inst Technol Karnataka Surathkal Elect & Commun Engn Mangalore Karnataka India Indian Inst Sci Elect Engn Bangalore Karnataka India IISc Bangalore Elect Engn Bangalore Karnataka India

ISBN: (纸本)9798400710759

With the rapid development of modern generative models, the need for an automated synthetic image detection process has never been greater. Recent works in the field of synthetic image detection focus on improving out-of-distribution (OoD) classification performance and robustness to common image pre-processing techniques. However, in this work, we intend to explore the nature of an intricate counter-forensic attack, i.e., the reconstruction of real images with Diffusion Model autoencoders, which could be used to adversely affect the performance of modern synthetic image detection algorithms. We present a variety of experiments to study the nature of this counter-forensic attack and use the inferences from these experiments to develop multiple algorithms to detect such reconstructed images while attempting to detect real and purely synthetic images accurately. To do so, we make use of trained classifiers that can detect real images, autoencoder-reconstructed images, and purely synthetic images. Furthermore, we combine these techniques to build a novel ensemble algorithm that competes with state-of-the-art (SoTA) algorithms in the 'Real vs. Fake' image detection task, while detecting autoencoder reconstructed images accurately, attaining an accuracy of 99.2% in the multiclass setting.

关键词： image Generative Models Convolutional Neural Networks (CNNs) Contrastive Language image Pretraining (CLIP)

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