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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211-220 订阅

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Towards the development of personalized and generalized interfaces for brain signals across different styles of meditation 22

Towards the development of personalized and generalized inte...

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

作者： Shruti Singh Pankaj Pandey Shivam Chaudhary Krishna P Miyapuram James Lomas Indian Institute of Technology Gandhinagar IN Indian Institute of Technology Gandhinagar India TU Delft NL

ISBN: (纸本)9781450398220

Human-computer interaction investigates how people learn from technology, and how they use technology in everyday life. Researchers have used brain-computer interfaces to understand how technology can be designed to support human cognition and behavior. The most famous and consumer-friendly approach to measuring brain signals is electroencephalography (EEG) due to its non-invasive, portable, relatively inexpensive, and high temporal resolution. In this study, we develop machine learning models to distinguish between the neural oscillations of meditators and non-meditators. Previous studies have used power spectrum density, entropy, and functional connectivity to distinguish various meditation traditions. We use EEG data set comprising neural activity of expert meditators of Himalayan Yoga (HYT), Vipassana (VIP), Isha Shoonya (SYN), and non-expert control subjects (CTR). We analyze the data using 13 different machine learning models for within-subject and cross-subject. We present the results for six classification conditions for both meditation and mind-wandering. Features extracted from the mean of 64 EEG time series are fed into machine learning classifiers during training. We obtain maximum accuracy for within-subject classification in both meditation and mind-wandering. In cross-subject analysis, we obtained 18.3% above chance level in meditation between control and Isha Shoonya, and similarly above 18% chance level in mind-wandering between control and Vipassana. We discuss the implications of this result for the emerging consumer EEG headset facilitating meditation practice. Our results indicate that personalized models (within-subject) and generalized models (cross-subject) could guide naive (beginner) practitioners to meditate and aim to modulate brain signals by practicing to reach the expert level.

关键词： Brain-computer Interface

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Interpretive self-supervised pre-training: Boosting performance on visual medical data 21

Interpretive self-supervised pre-training: Boosting performa...

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

作者： Manna, Siladittya Bhattacharya, Saumik Pal, Umapada Indian Statistical Institute Kolkata West Bengal Kolkata India Indian Institute of Technology West Bengal Kharagpur India

ISBN: (纸本)9781450391276

Self-supervised learning algorithms have become one of the best tools for unsupervised representation learning. Although self- supervised algorithms have achieved state-of-the-art performance for classification tasks in the case of natural image data, their application on medical data has been limited. In this work, we have proposed a novel loss function and derive it's asymptotic lower bound. We have also shown that self-supervised pre-training with the proposed loss function helps in surpassing the supervised baseline on the downstream task. We have also shown that the self-supervised pre-training helps a model in learning better representation in general to achieve better performance compared to supervised baselines. We have mathematically derived that the contrastive loss function asymptotically treats each sample as a separate class and works by maximizing the distance between any two samples and this helps to get better performance. Finally, through exhaustive experiments, we demonstrate that self-supervised pre-training helps to surpass the performance of fully supervised models on downstream tasks. © 2021 ACM.

关键词： Learning algorithms

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End-to-End GPU-Accelerated Low-Poly Remeshing using Curvature Map and Voronoi Tessellation✱ 22

End-to-End GPU-Accelerated Low-Poly Remeshing using Curvatur...

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

作者： Preetam Chayan Chatterjee Ashutosh Soni Partha Bhowmick, Iit Kharagpur Bhowmick Department of Computer Science and Engineering IIT Kharagpur India Department of Computer Science and Engineering IIT Kharagpur India and ANSYS Software Pvt. Ltd. Hinjewadi India

ISBN: (纸本)9781450398220

We propose a novel algorithm for low-poly remeshing of 3D surfaces that runs fully in GPU. Since the input mesh is generally not well-organized, performing mesh simplification directly on the input mesh is liable to produce a low-poly mesh with a compromised face quality. Hence, instead of doing that, we voxelize the input mesh first. On the voxelized surface, we perform curvature estimation and discretization, Centroidal Voronoi Tessellation (CVT), and Delaunay triangulation to generate the output mesh. To control the vertex count, we exploit the curvature map, such that sufficiently large triangles constitute the low-curvature regions, whereas appropriately small triangles build up the high-curvature portions. We produce low-poly meshes at various levels of detail and measure their quality using Hausdorff error. We test our algorithm on different 3D scenes, and they are particularly chosen because they contain multiple objects with varied topologies. Upon testing on these datasets, our algorithm successfully achieves very low-poly approximation with a reasonable mesh quality, and at a reasonably faster pace, thus establishing its efficacy for mesh simplification.

关键词： Surface Voxelization Low-poly Approximation Remeshing GPU-based Algorithms Mesh Simplification Parallel Computing Digital Curvature Estimation Centroidal Voronoi Tessellation

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Deep Learning Assisted Retinopathy of Prematurity Screening Technique 14

Deep Learning Assisted Retinopathy of Prematurity Screening ...

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14th International Joint conference on Biomedical Engineering Systems and Technologies (BIOSTEC) / 14th Int Conf on Bio-inspired Systems and Signal processing (BIOSIGNALS) / 14th Int Conf on Biomedical Electronics and Devices (BIODEVICES)

作者： Kumar, Vijay Patel, Het Paul, Kolin Surve, Abhidnya Azad, Shorya Chawla, Rohan Indian Inst Technol Khosla Sch Informat Technol Delhi India All India Inst Med Sci Dr Rajendra Prasad Ctr Ophthalm Sci Delhi India

ISBN: (纸本)9789897584909

Retinopathy of Prematurity (ROP) is the leading cause of blindness in preterm babies worldwide. By using proper scanning and treatment, the effect of the blindness of ROP can be reduced. However, due to lack of medical facilities, a large proportion of these preterm infants remain undiagnosed after birth. As a result, these babies are more likely to have ROP induced blindness. In this paper, we propose a robust and intelligent system based on deep learning and computer vision to automatically detect the optical disk (OD) and retinal blood vessels and also classify the high severity (Zone-1) case of ROP. To test and validate the proposed system, we present empirical results using the preterm infant fundus images from a local hospital. Our results showed that the YOLO-V5 model accurately detects the OD from preterm babies fundus images. Further, the computer vision-based system accurately segmented the retinal vessels from the preterm babies fundus images. Specifically for the Zone-1 case of ROP, our system is able to achieve an accuracy of 83.3%.

关键词： Fundus image Retinopathy of Prematurity (ROP) Plus Disease computer Aided Diagnosis (CAD) image processing Machine Learning (ML) Deep Learning (DL)

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Longitudinal Analysis of Mask and No-Mask on Child Face Recognition 22

Longitudinal Analysis of Mask and No-Mask on Child Face Reco...

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

作者： Praveen Kumar Chandaliya Zahid Akhtar Neeta Nain Computer Science and Engineering The LNM Institute of Information Technology Jaipur IN and Computer Science and Engineering Malaviya National Institute of Technology Jaipur India Network and Computer Security College State University of New York Polytechnic Institute US Computer Science and Engineering Malaviya National Institute of Technology Jaipur India

ISBN: (纸本)9781450398220

Face is one of the most widely employed traits for person recognition, even in many large-scale applications. Despite technological advancements in face recognition systems, they still face obstacles caused by pose, expression, occlusion, and aging variations. Owing to the COVID-19 pandemic, contactless identity verification has become exceedingly vital. To constrain the pandemic, people have started using face mask. Recently, few studies have been conducted on the effect of face mask on adult face recognition systems. However, the impact of aging with face mask on child subject recognition has not been adequately explored. Thus, the main objective of this study is analyzing the child longitudinal impact together with face mask and other covariates on face recognition systems. Specifically, we performed a comparative investigation of three top performing publicly available face matchers and a post-COVID-19 commercial-off-the-shelf (COTS) system under child cross-age verification and identification settings using our generated synthetic mask and no-mask samples. Furthermore, we investigated the longitudinal consequence of eyeglasses with mask and no-mask. The study exploited no-mask longitudinal child face dataset (i.e., extended indian Child Longitudinal Face Dataset) that contains 26,258 face images of 7,473 subjects in the age group of [2, 18] over an average time span of 3.35 years. Due to the combined effects of face mask and face aging, the FaceNet, PFE, ArcFace, and COTS face verification system accuracies decrease by approximately, and, respectively.

关键词： Child Face Recognition

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Domain Adaptation Based Technique for image Emotion Recognition Using image Captions 5th

Domain Adaptation Based Technique for Image Emotion Recognit...

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5th International conference on computer vision and image processing, CVIP 2020

作者： Kumar, Puneet Raman, Balasubramanian Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee India

ISBN: (纸本)9789811610912

images are powerful tools for affective content analysis. image emotion recognition is useful for graphics, gaming, animation, entertainment, and cinematography. In this paper, a technique for recognizing the emotions in images containing facial, non-facial, and non-human components has been proposed. The emotion-labeled images are mapped to their corresponding textual captions. Then the captions are used to re-train a text emotion recognition model as the domain-adaptation approach. The adapted text emotion recognition model has been used to classify the captions into discrete emotion classes. As image captions have a one-to-one mapping with the images, the emotion labels predicted for the captions have been considered the emotion labels of the images. The suitability of using the image captions for emotion classification has been evaluated using caption-evaluation metrics. The proposed approach serves as an example to address the unavailability of sufficient emotion-labeled image datasets and pre-trained models. It has demonstrated an accuracy of 59.17% for image emotion recognition. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Emotion Recognition

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Magnetic resonance image reconstruction by nullspace based finite rate of innovation framework 21

Magnetic resonance image reconstruction by nullspace based f...

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

作者： Reddy, P. Sudhakar Raghavendra, B.S. Narasimhadhan, A.V. National Institute of Technology Karnataka Mangalore India

ISBN: (纸本)9781450391276

The finite rate of innovation (FRI) framework has proved that it is possible to reconstruct the analog signals which have a finite number of parameters. FRI framework is used to reconstruct the images from undersampled magnetic resonance (MR) data. The reconstruction of the MR image from the MR data is a estimation problem, which can be solved by utilizing Prony's method. However, Prony's method involves solving the polynomial roots of the annihilating filter and this fact leads to an unstable reconstruction in the high noise scenario. In this paper, we introduce a novel reconstruction approach is also based on the annihilating filter. However, it involves the use of solutions of an underdetermined linear system. The simulation results of the proposed reconstruction approach show that the peak signal to noise ratio (PSNR) and the structural similarity index measure (SSIM) are higher magnitude than that of conventional FRI methods in the high noise scenario.1 © 2021 ACM.

关键词： Magnetic resonance imaging

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HazeNet: Does the image have haze? 21

HazeNet: Does the image have haze?

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

作者： Kalaivani, P. Rooomi, S. Mohamed Mansoor Nathan, D. Sabari Government College of Engineering Bodinayakanur Theni Tamilnadu India Thiagarajar College of Engineering Tamilnadu Madurai India Couger Inc Tokyo Japan

ISBN: (纸本)9781450391276

The visibility of scene is much important in applications of Intelligent Video surveillance systems, Autonomous Vehicle Navigation. The presence of haze limits the visibility, thereby hindering the proper operation of computer vision algorithms. There are lot of algorithms to remove haze unconditionally. But the haze removal on clear images or frames will cause adverse effects. Unnecessary dehazing results in the loss of original information and also delays the following process. To address these challenges, there is a need to recognize the image as hazy and haze-free before applying dehazing on it. Hence, this paper presents three light-weight deep neural network models to automatically classify image as hazy and haze-free in the name of HazeNet, R-HazeNet and RSE-HazeNet. The HazeNet is Convolutional Neural Network having five convolutional blocks and results in classification accuracy of 97.89%. In order to improve the performance of HazeNet further, the Residual blocks have been included after each convolutional layer. The model is called R-HazeNet. The residual blocks help in producing hierarchical features. To strengthen the useful convolutional channel features alone, Squeeze and Excitation blocks have been included with Residual units in RSE-HazeNet model. Both R-HazeNet and RSE-HazeNet produced classification accuracy of 99.98% which demonstrate superior performance compared to the stat-of-the-art methods. The proposed models are light-weight models due to the percentage improvement in number of parameters as a minimum of 93% has been achieved with respect to the the baseline method. © 2021 ACM.

关键词： Convolution

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G3AN++: Exploring wide GANs with complementary feature learning for video generation 21

G3AN++: Exploring wide GANs with complementary feature learn...

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

作者： Gupta, Sonam Keshari, Arti Das, Sukhendu Indian Institute of Technology Madras Tamil Nadu Chennai India

ISBN: (纸本)9781450391276

Video generation task is a challenging problem which involves the modelling of complex real-world dynamics. Most of the existing methods have designed deep networks to tackle high-dimensional video data distributions. However, the utilization of wide networks is still under explored. Inspired by the success of wide networks in the image recognition literature, we present G3AN++, a three-stream generative adversarial network for video. The three streams are spatial, temporal and spatio-temporal processing branches. In pursuit of improving the quality of video generation, we make our network wider by splitting the spatial stream into two parallel identical branches learning complementary feature representations. We further introduce a novel adaptive masking layer to impose the complementary constraint. The masking layer encourages the parallel branches to learn distinct and richer visual features. Extensive quantitative and qualitative analysis demonstrates that our model outperforms the existing state-of-the-art methods by a significant margin on Weizmann Action, UvA-Nemo Smile and UCF101 Action datasets. Additional exploration reveals that G3AN++ is capable of disentangling the appearance and motion. We also show that the proposed method can be easily extended to solve the hard task of text-to-video generation. © 2021 ACM.

关键词： Generative adversarial networks

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Intelligent classification model for indian chickpea 5

Intelligent classification model for Indian chickpea

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5th International conference on Intelligent Computing and Control Systems, ICICCS 2021

作者： Kini, Anita S. Prema, K.V. Pai, Smitha N. Manipal Institute of Technology Dept of CSE Manipal India Manipal Institute of Technology Dept i and CT Manipal India

ISBN: (纸本)9781665412728

India is one of the major chickpea-producing countries in the world. An estimate of almost 71 % of the world's chickpea production is done here. This paper presents an approach to classify indian chickpeas using a computer vision technique. For this work image processing and eight different machine learning algorithms are used. The features considered are first of their kind. Based on these features the performance of machine learning algorithms is studied for the classification of the chickpea. Out of eight different algorithms lib Support Vector Machine and multilinear perceptron provided high accuracy of 86.07% and 84.90%. © 2021 IEEE.

关键词： Learning algorithms

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