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检索条件"任意字段=Proceedings of the 11th Indian Conference on Computer Vision, Graphics and Image Processing"

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A computer vision Framework on Biomechanical Analysis of Jump Landings 24

A Computer Vision Framework on Biomechanical Analysis of Jum...

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15th indian conference on computer vision graphics and image processing

作者： Sharma, Srishti U. Divakaran, Srikrishnan Kaya, Tolga Raval, Mehul S. Ahmedabad Univ Sch Engn & Appl Sci Amdavad Gujarat India Krea Univ Sri City India Sacred Heart Univ Fairfield CT USA Ahmedabad Univ Amdavad India

ISBN: (纸本)9798400710759

High-intensity activities in sports like basketball can result in fatigue without proper recovery. this study introduces a collaborative framework that leverages computer vision (CV) and Machine Learning for evaluating jump landings and predicting athletic readiness by modelling Countermovement Jumps (CMJs) biomechanical aspects. Seventeen female collegiate basketball athletes of Sacred Heart University (SHU), CT, USA, participated in weekly CMJs over a 26-week season. through CV-driven semantic analysis of videos, the framework identifies the crucial initial contact and maximum flexion point during jump landings and extracts kinetic and kinematic features of the lower extremities. Next, an inferential analysis is conducted to understand the relationship between these features and the CMJ-driven reactive strength indexmodified (RSImod) score, which measures fatigue and athletic readiness. An XGBoost regressor, trained on the past week's data, then predicted the RSImod score for the following week, which resulted in an MSE of 0.020 and an R-2 of 0.892. Using SHapley Additive exPlanations (SHAP), the framework offers interpretable feedback, aiding coaches in creating personalised training programs and optimising athletic performance while minimising injury risks.

关键词： Athletes basketball computer vision countermovement jumps sports analytics

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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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Leveraging Auxiliary Classification for Rib Fracture Segmentation 24

Leveraging Auxiliary Classification for Rib Fracture Segment...

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

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

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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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Enhancing Generalization Ability in Deepfake Detection via Continual Learning 24

Enhancing Generalization Ability in Deepfake Detection via C...

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15th indian conference on computer vision graphics and image processing

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

ISBN: (纸本)9798400710759

Deepfake technology generates counterfeit facial images by replicating the characteristics of genuine ones. Sophisticated manipulation tactics have enabled criminals to incite social panic or accrue illegal gains by creating deceptive media, such as forged facial images. Consequently, numerous techniques for detecting deepfakes have been developed to evaluate the authenticity of images. the existing deepfake detection techniques perform well if training and testing datasets exhibit similarity. However, then they do not provide good results if training and testing datasets are dissimilar, thereby not providing generalization ability. In this work, we have proposed the Continual Learning Based Enhanced vision Transformer (CLEViT) model for deepfake detection. the CLEViT model leverages the continual learning mechanism to enhance the model's generalization ability. Continual learning enables deepfake detection models to continuously adapt and update their knowledge based on new data, allowing them to identify manipulation techniques effectively. After that, a vision transformer is used to extract rich contextual information from images using self-attention mechanisms. To validate the efficacy of the model, we have created a multi-task dataset comprising FakeAVCeleb, RFF, RFFD, and the DFDC datasets. the proposed CLEViT model outperforms existing deepfake detection techniques, yielding superior results and simultaneously providing generalization ability.

关键词： Continual learning vision transformer Deepfake Convolutional neural network

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Semi-Decoupled Distillation for Brain Tumor Segmentation using multimodal MRI Scans 24

Semi-Decoupled Distillation for Brain Tumor Segmentation usi...

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15th indian conference on computer vision graphics and image processing

作者： Kundu, Surajit Chatterjee, Ankita Mukhopadhyay, Jayanta Chakravorty, Nishant Indian Inst Technol kharagpur Kharagpur W Bengal India

ISBN: (纸本)9798400710759

Brain tumour segmentation is a fundamental task in medical image analysis where each year a number of deep learning models are introduced to delineate the tumour regions with high precision. However, most of these works rely on large number of parameters and higher computational cost, thus, rendering them ineffective in real world application. therefore, it is important to devise efficient models that can be easily deployed on resource-constrained devices and perform at par with the existing large models. In this paper, we propose a semi-decoupled distillation technique which trains a lightweight "student" model using the features extracted from the decoder of the nnU-Net "teacher" model and its predictions on these features using a single point-wise convolution layer. the final classification layer remains the same for both the models and is kept frozen while training the "student" network. Our approach follows a two stage training procedure where the tumour regions are detected and extracted in the first step, and then sent for second stage training to segment fine-grain classes, including edema, enhanced tumour and tumour core. Our extensive experimentation shows that a lightweight distilled model performs competitively with large models on brain tumour segmentation.

关键词： Glioblastoma Knowledge Distillation Brain Tumor Segmentation nnU-Net

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