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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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Scene Adaptive Cosmetic Makeup Transfer 18

Scene Adaptive Cosmetic Makeup Transfer

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proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Rishubh Parihar Aniket Dashpute Prem Kalra Indian Institute of Technology Delhi Hauz Khas New Delhi

ISBN: (纸本)9781450366151

Cosmetic makeup is an art in itself which people use to often enhance their beauty and express themselves. Putting on makeup is a tedious task, so who would not love to see themselves in makeup before physically applying it? In this work, we demonstrate the transfer of makeup from a reference makeup image to some subject image. Our technique involves generation and use of 3D face models of these images. Some nice features of our pipeline include: (1) process adapts to the lighting conditions of the subject image. (2) after makeup on subject image, it is possible to view it in different indoor and outdoor lighting conditions. (3) accessories can be added realistically with proper lighting so that they naturally fit in the subject's *** show that these also make our makeup look very realistic and improved compared to many existing *** main advantage of our novel pipeline is the requirement of only the subject and reference images as input. This is unlike other techniques using 3D models which require extensive data collection.

关键词： computer graphics

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Dynamic Gesture Recognition with Pose-based CNN Features derived from videos using LSTM 18

Dynamic Gesture Recognition with Pose-based CNN Features der...

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proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Kankana Roy Rajiv R. Sahay Computational Vision Laboratory Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Computational Vision Laboratory Department of Electrical Engineering Indian Institute of Technology Kharagpur

ISBN: (纸本)9781450366151

In this work, we address the problem of dynamic gesture recognition using a pose based video descriptor. The proposed approach takes as input video frames and extracts pose-specific image regions which are further processed by a pre-trained Convolutional Neural Network (CNN) to derive a pose-based descriptor for each frame. A Long Short Term Memory (LSTM) network is trained from scratch for dynamic gesture classification by learning long-term spatiotemporal relations among features. We also demonstrate that only using video data (RGB frames and optical flow) one can design an effective model for recognizing dynamic gestures. We utilize ChaLearn multi-modal gesture challenge dataset [13] and Cambridge hand gesture dataset [18] for evaluation of the proposed algorithm achieving an accuracy of 91.27% and 96% respectively using only RGB data.

关键词： Long short term memory

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Some applications of active contours using ANNs isomorphic to boundaries

Some applications of active contours using ANNs isomorphic t...

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International conference on Information, Communications and Signal processing

作者： Y.V. Venkatesh N. Rishikesh Computer Vision and Artificial Intelligence Laboratory Department of Electrical Engineering Indian Institute of Science Bangalore India Indian Institute of Science Bangalore Karnataka IN

We present a few applications of the neural active contour model described by Venkatesh Rishikesh (see International conference on Neural Networks (ICNN-97), Houston, Texas, USA, 1997). The model, which is a neural network based on the principles of self-organization, exploits the principles of spatial isomorphism and self-organization in order to create flexible contours for characterizing shapes in images. We illustrate the versatility of the model by presenting its applications to the problems of boundary extraction, stereo vision, biomedical image analysis and digital image libraries. Only the essential ideas are described.

关键词： Active contours image edge detection Shape Deformable models Application software computer vision Artificial neural networks Stereo vision image motion analysis Tracking

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High precision automated face localization in thermal images: Oral cancer dataset as test case

High precision automated face localization in thermal images...

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conference on Medical Imaging - image processing

作者： Chakraborty, M. Raman, S. K. Mukhopadhyay, S. Patsa, S. Anjum, N. Ray, J. G. Indian Inst Technol Kharagpur W Bengal India Natl Inst Technol Tiruchirappalli Tamil Nadu India Dr R Ahmed Dent Coll & Hosp Kolkata India

ISBN: (数字)9781510607125

ISBN: (纸本)9781510607118;9781510607125

Automated face detection is the pivotal step in computer vision aided facial medical diagnosis and biometrics. This paper presents an automatic, subject adaptive framework for accurate face detection in the long infrared spectrum on our database for oral cancer detection consisting of malignant, precancerous and normal subjects of varied age group. Previous works on oral cancer detection using Digital Infrared Thermal Imaging(DITI) reveals that patients and normal subjects differ significantly in their facial thermal distribution. Therefore, it is a challenging task to formulate a completely adaptive framework to veraciously localize face from such a subject specific modality. Our model consists of first extracting the most probable facial regions by minimum error thresholding followed by ingenious adaptive methods to leverage the horizontal and vertical projections of the segmented thermal image. Additionally, the model incorporates our domain knowledge of exploiting temperature difference between strategic locations of the face. To our best knowledge, this is the pioneering work on detecting faces in thermal facial images comprising both patients and normal subjects. Previous works on face detection have not specifically targeted automated medical diagnosis;face bounding box returned by those algorithms are thus loose and not apt for further medical automation. Our algorithm significantly outperforms contemporary face detection algorithms in terms of commonly used metrics for evaluating face detection accuracy. Since our method has been tested on challenging dataset consisting of both patients and normal subjects of diverse age groups, it can be seamlessly adapted in any DITI guided facial healthcare or biometric applications.

关键词： Digital Infrared Thermal Imaging Facial Biometric Facial Healthcare Oral Cancer Thermal Face Detection

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Compressive Sensing Based Privacy for Fall Detection 7th

Compressive Sensing Based Privacy for Fall Detection

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7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Gupta, Ronak Anand, Prashant Chaudhury, Santanu Lall, Brejesh Singh, Sanjay Department of Electrical Engineering Indian Institute of Technology Delhi New Delhi India Indian Institute of Technology Jodhpur Jheepasani India Cognitive Computing Group CSIR-CEERI Pilani India

ISBN: (纸本)9789811586965

Fall detection holds immense importance in the field of healthcare, where timely detection allows for instant medical assistance. In this context, we propose a 3D ConvNet architecture which consists of 3D Inception modules for fall detection. The proposed architecture is a custom version of Inflated 3D (I3D) architecture, that takes compressed measurements of video sequence as spatio-temporal input, obtained from compressive sensing framework, rather than video sequence as input, as in the case of I3D convolutional neural network. This is adopted since privacy raises a huge concern for patients being monitored through these RGB cameras. The proposed framework for fall detection is flexible enough with respect to a wide variety of measurement matrices. Ten action classes randomly selected from Kinetics-400 with no fall examples, are employed to train our 3D ConvNet post compressive sensing with different types of sensing matrices on the original video clips. Our results show that 3D ConvNet performance remains unchanged with different sensing matrices. Also, the performance obtained with Kinetics pre-trained 3D ConvNet on compressively sensed fall videos from benchmark datasets is better than the state-of-the-art techniques. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Compressed sensing

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Performance, Trust, or both? COVID-19 Diagnosis and Prognosis using Deep Ensemble Transfer Learning on X-ray imagesg± 13

Performance, Trust, or both? COVID-19 Diagnosis and Prognosi...

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

作者： Tiwari, Abhishek Singh, Rajeev Kumar Department of Computer Science and Engineering Shiv Nadar University Gautam Buddha Nagar UP Delhi India

ISBN: (纸本)9781450398237

The COVID-19 pandemic still affects most parts of the world today. Despite a lot of research on diagnosis, prognosis, and treatment, a big challenge today is the limited number of expert radiologists who provide diagnosis and prognosis on X-Ray images. Thus, to make the diagnosis of COVID-19 accessible and quicker, several researchers have proposed deep-learning-based Artificial Intelligence (AI) models. While most of these proposed machine and deep learning models work in theory, they may not find acceptance among the medical community for clinical use due to weak statistical validation. For this article, radiologists' views were considered to understand the correlation between the theoretical findings and real-life observations. The article explores Convolutional Neural Network (CNN) classification models to build a four-class viz. "COVID-19", "Lung Opacity", "Pneumonia", and "Normal"classifiers, which also provide the uncertainty measure associated with each class. The authors also employ various pre-processing techniques to enhance the X-Ray images for specific features. To address the issues of over-fitting while training, as well as to address the class imbalance problem in our dataset, we use Monte Carlo dropout and Focal Loss respectively. Finally, we provide a comparative analysis of the following classification models - ResNet-18, VGG-19, ResNet-152, MobileNet-V2, Inception-V3, and EfficientNet-V2, where we match the state-of-the-art results on the Open Benchmark Chest X-ray datasets, with a sensitivity of 0.9954, specificity of 0.9886, the precision of 0.9880, F1-score of 0.9851, accuracy of 0.9816, and receiver operating characteristic (ROC) of the area under the curve (AUC) of 0.9781 (ROC-AUC score). © 2022 ACM.

关键词： Coronavirus

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CDA-Net: computer vision based Automatic Car Damage Analysis 23

CDA-Net: Computer Vision based Automatic Car Damage Analysis

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

作者： Iyshwarya Ratthi Kannan Yogameena Balasubramanian Saravana Perumaal Subramanian Menaka Kandhasamy Sivasankari Ramesh Electronics and Communication Engineering Thiagarajar College of Engineering India Mechanical Engineering National Institute of Technical Teachers Training and Research (NITTTR) India Velammal Institute of Technology India

ISBN: (纸本)9798400716256

Car insurance claims are rising in tandem with the rising tide of car users. Every insurance claim requires an engineer’s manual assessment and a surveyor’s actual examination. This procedure can last anywhere from a few days to several weeks. Current deep-learning techniques have paved the way for this type of mechanization. Both the business and the client would benefit from a comprehensive system. To assess the damage cost for the insurance claim process, the make and model of the car, damaged parts, damage type, and severity of the damage are important parameters. We introduced two datasets, a piqued car make and model (CMM) dataset containing images of the most popular 23 car makes and 148 vehicle models available in the indian automotive market. The second dataset consists of 11,380 images collected from insurance offices and web resources of different types of car damage, including annotations. In addition, it provides an assessment of the damage to each part, the severity of the damage (dents, scratches, bent, broken, cracks, smashed, punched, and pushed), and the location of the damage (front, back, side). The assessment helps estimate the damage cost when combined with structured data. The proposed CDA_YOLOv5 car damage assessment framework outperformed existing state-of-the-art one-stage models with an average per-class accuracy of 87.36% and an overall accuracy of 90.45%.

关键词： Car Parts detection

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Optimizing Knowledge Transfer in Sequential Models: Leveraging Residual Connections in Flow Transfer Learning for Lung Cancer Classification 23

Optimizing Knowledge Transfer in Sequential Models: Leveragi...

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

作者： Richa Sharma Santosh Kumar Abhishek Shrivastava Tejasv Bhatt Computer Science and Engineering SSTC Bhilai IN Computer Science and Engineering International Institute of Information Technology (IIIT)-Naya Raipur IN Computer Science and Engineering United College of Engineering & Research (UCER) Prayajraj Uttar Pradesh IN

ISBN: (纸本)9798400716256

Lung cancer is a prevalent and life-threatening disease characterized by abnormal cell growth in lung tissue. Early detection and accurate classification of lung cancer are crucial for timely treatment and improved patient outcomes. Our work proposes, a novel framework for accurate multi-class categorization of lung cancer using deep learning. The proposed framework uses a customized Densenet-201 model to leverage the knowledge of transfer learning, which acts as a parent architecture, enhanced by a residual structure as the child architecture. The performance of our model was examined by conducting experiments on the LCS25000 data set. The results demonstrate the outstanding accuracy of our proposed framework, achieving a remarkable accuracy of 95% on the test data set. This signifies the model’s ability to accurately classify different types of lung cancer based on histopathology images. Our model also achieved remarkably well results in the TCGA lung cancer dataset, hence proving its generalization. These findings have important repercussions for improving pulmonary pathology diagnostic abilities and hold promise for enhancing patient care in the field of lung cancer diagnosis.

关键词： Deep-learning

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Writer Identification from offline isolated Bangla characters and numerals 13

Writer Identification from offline isolated Bangla character...

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13th International conference on Document Analysis and Recognition, ICDAR 2015

作者： Adak, Chandranath Chaudhuri, Bidyut B. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata700108 India

ISBN: (纸本)9781479918058

Writer identification is an essential component in computational forensic. In this paper, we attempt to do this job based only on isolated characters and numerals. For that, at first, some points of interest (keypoints) on the image are detected by structural analysis and SIFT based detector. Then we calculate a set of features within a certain neighborhood of the keypoint and employ fusion rule on multiple probabilistic SVM classifiers output for writer identification. For experimental analysis, a database containing 212,300 isolated Bangla orthosyllabic characters and numerals are generated with the help of 100 writers. We obtain fairly good result to identify a writer. We also try to find a small set of highly discriminative characters storing extra information about the writing style of an individual. © 2015 IEEE.

关键词： handwriting recognition natural language processing probability support vector machines Bangla orthosyllabic characters SIFT based detector computational forensic isolated Bangla characters isolated Bangla numerals isolated characters multiple probabilistic SVM classifiers structural analysis writer identification Chlorine Discriminative Bangla character Handwriting analysis Writer identification Numerals handwriting recognition structural analysis Structural analysis Support Vector Network Natural Language processing Writers Chlorine

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Complexity assisted consistent quality rate control for high resolution H.264 video conferencing

Complexity assisted consistent quality rate control for high...

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

作者： Subhomoy Bhattacharyya Indrajit Chakrabarti Maheshkumar H. Kolekar Squid Systems Inc. Hyderabad Indian Institute of Technology Kharagpur Kharagpur Indian Institute of Technology Patna Patna

Video based multimedia services are showing major growth in recent years. Though the video coding recommendation ITU-T H.265/HEVC is in operating state, the majority of real-time video applications including video conferencing and live streaming of events rely mainly on less computationally intensive H.264/AVC coding standard which is well established among video industry. Rate control algorithms are indispensable for delivering superior quality video over limited bandwidth connections. In this paper, we propose a rate control technique which provides consistent quality video output over time in a limited bandwidth video conferencing scenario. Using the concept of video traffic prediction and linear complexity model, we compute the complexity of the video sequence in real time. We present a better estimate of quantization parameter at the GOP (group of picture) layer. Our proposed method maintains similar quality for reconstructed video when compared to rate control scheme of JM 19.0 implemented in real-time video conferencing system with restricted quality variation. On an average, 22% improvement has been observed in terms of standard deviation of frame PSNR values when compared to JM 19.0 rate control scheme implemented in high resolution video conferencing scenario.

关键词： Streaming media Complexity theory Encoding Real-time systems Standards Bandwidth Predictive models

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