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

共 1468 条记录，以下是151-160 订阅

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Alzheimer’s severity classification using Transfer Learning and Residual Separable Convolution Network 22

Alzheimer’s severity classification using Transfer Learning...

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

作者： Bala Venkateswarlu Isunuri Dr Jagadeesh Kakarla CSE Indian Institute of Information Technology Design and Managment Kancheepuram India

ISBN: (纸本)9781450398220

Severity classification is the most pivotal task in Alzheimer’s disease diagnosis. Detection of brain structural changes from brain MR images is crucial for Alzheimer’s classification. In this paper, we have proposed a transfer learning and residual separable convolution network for the classification of Alzheimer’s. The proposed network includes three separable convolution layers with two average pooling layers. An upsampling has been performed to regain its spatial resolution for the residual connection. The main intuition of separable convolution is to optimize parameters with depth-wise convolution. Similarly, the residual connection has been used to reduce the vanishing gradient problem. Finally, a three-layer fully connected dense network has been used for the four-class Alzheimer’s classification. Kaggle dataset has been utilized for the experiments to report results. We have achieved an accuracy of 97.32% on the dataset with five-fold cross-validation. Our model has reported an improvement of 1% in jaccard similarity and outperforms the competing models in all vital metrics.

关键词： Alxheimer’s disease Severity classification Transfer Learning Residual Separable Convolution Network

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Identification of Medicinal Plants in Ardabil Using Deep learning: Identification of Medicinal Plants using Deep learning 27

Identification of Medicinal Plants in Ardabil Using Deep lea...

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27th International computer conference, computer Society of Iran, CSICC 2022

作者： Abdollahi, Jafar Islamic Azad University Ardabil Branch Department Of Computer Engineering Ardabil Iran

ISBN: (纸本)9781665480277

Ardabil is well-known for offering the ideal environment for a good, cheap medicinal herb. Various plant parts are used as essential components in producing natural medicines. According to IUCN (International Union for Conservation of Nature) records, many medicinal plants are on the verge of extinction, so employing image processing and computer vision algorithms to distinguish proof of medicinal plants is critical. As a result, the digitalization of beneficial therapeutic plants is critical for biodiversity preservation. The use of Convolutional Neural Network (CNN)-based techniques to distinguish indian leaf species is investigated in this research. Several Deep Learning frameworks have recently been used to discern, identify, and characterize various plants. This study is mostly focused on identifying medicinal plants that can be found in rural areas. The Transfer Learning technique selected a well-known pre-trained CNN architecture called mobile net v2. The medical plant dataset was built using 30 different classes of medicinal plants, totaling 3000 photos, and these models were assessed with their pre-trained weights. On a held-out test set, the trained model had an accuracy of 98.05 percent, demonstrating the practicality of this approach. © 2022 IEEE.

关键词： Deep learning computer vision image processing Transfer learning Knowledge discovery Software Data systems

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Depth estimation using Stereo Light Field Camera✱ 22

Depth estimation using Stereo Light Field Camera✱

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

作者： Suresh Nehra Jayanta Laha J. Mukhopadhyay P. K. Biswas Indian Institute of Technology Kharagpur India Indian Space Research Organisation India

ISBN: (纸本)9781450398220

Light field imaging has emerged as a new modality, enabling to capture the angular and spatial information of a scene. This additional angular information is used to estimate the depth of a 3-D scene. The continuum of virtual view-points in light field data efficiently handles occlusion and provides a robust depth estimate for smaller distances. However, a narrow baseline in a light field camera limits the depth estimation for larger distances. To have an efficient occlusion handling and increase the operating distances, we proposed a novel disparity based stereo light field depth estimation method. First, segments are obtained in central sub-aperture of left view and then estimate the disparity vector of these segments using left camera sub-aperture images. This handles occlusion efficiently. Then stereo disparity at boundaries of these segments exploiting the epipolar geometry inherent in a light field data. Finally this stereo disparity at boundaries is propagated to other pixels and normalized. We provided a synthetic stereo light field data-set having inherent characteristic of a light field. We have tested our approach on a variety of real-world scenes captured with Lytro Illum camera and also on synthetic images. The proposed method outperforms several state-of-the-art algorithms.

关键词： stereo light field stereo depth estimation. Lytro Illum Light Field Imaging

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End-to-End GPU-Accelerated Low-Poly Remeshing using Curvature Map and Voronoi Tessellationg± 13

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

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

作者： Chatterjee, Preetam Chayan Soni, Ashutosh Bhowmick, Partha Bhowmick, Iit Kharagpur Indian Institute of Technology West Bengal Kharagpur India ANSYS Software Pvt. Ltd Hinjewadi Maharashtra Pune India

ISBN: (纸本)9781450398237

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. © 2022 ACM.

关键词： graphics processing unit

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Masked Student Dataset of Expressions 22

Masked Student Dataset of Expressions

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

作者： Sridhar Sola Darshan Gera Sri Sathya Sai Institute of Higher Learning India

ISBN: (纸本)9781450398220

Facial expression recognition (FER) algorithms work well in constrained environments with little or no occlusion of the face. However, real-world face occlusion is prevalent, most notably with the need to use a face mask in the current Covid-19 scenario. While there are works on the problem of occlusion in FER, little has been done before on the particular face mask scenario. Moreover, the few works in this area largely use synthetically created masked FER datasets. Motivated by these challenges posed by the pandemic to FER, we present a novel dataset, the Masked Student Dataset of Expressions or MSD-E, consisting of 1,960 real-world non-masked and masked facial expression images collected from 142 individuals. Along with the issue of obfuscated facial features, we illustrate how other subtler issues in masked FER are represented in our dataset. We then provide baseline results using ResNet-18, finding that its performance dips in the non-masked case when trained for FER in the presence of masks. To tackle this, we test two training paradigms: contrastive learning and knowledge distillation, and find that they increase the model’s performance in the masked scenario while maintaining its non-masked performance. We further visualise our results using t-SNE plots and Grad-CAM, demonstrating that these paradigms capitalise on the limited features available in the masked scenario. Finally, we benchmark SOTA methods on MSD-E. The dataset is available at https://***/SridharSola/MSD-E.

关键词： Human-computer interaction computer vision Covid-19 Facial expression recognition

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ContextCLIP: Contextual Alignment of image-Text pairs on CLIP visual representations 13

ContextCLIP: Contextual Alignment of Image-Text pairs on CLI...

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

作者： Grover, Chanda Mastan, Indra Deep Gupta, Debayan Ashoka University Sonipat Haryana India Rajasthan Jaipur India

ISBN: (纸本)9781450398237

State-of-The-Art empirical work has shown that visual representations learned by deep neural networks are robust in nature and capable of performing classification tasks on diverse datasets. For example, CLIP demonstrated zero-shot transfer performance on multiple datasets for classification tasks in a joint embedding space of image and text pairs. However, it showed negative transfer performance on standard datasets, e.g., BirdsNAP, RESISC45, and MNIST. In this paper, we propose ContextCLIP, a contextual and contrastive learning framework for the contextual alignment of image-Text pairs by learning robust visual representations on Conceptual Captions dataset. Our framework was observed to improve the image-Text alignment by aligning text and image representations contextually in the joint embedding space. ContextCLIP showed good qualitative performance for text-To-image retrieval tasks and enhanced classification accuracy. We evaluated our model quantitatively with zero-shot transfer and fine-Tuning experiments on CIFAR-10, CIFAR-100, Birdsnap, RESISC45, and MNIST datasets for classification task. © 2022 ACM.

关键词： Embeddings

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GLNet: Low-light image enhancement via grayscale priors 13

GLNet: Low-light image enhancement via grayscale priors

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13th International conference on Signal processing Systems, ICSPS 2021

作者： Guo, Li Xie, Junwei Xue, Yuyang Li, Ru Zheng, Weixin Tong, Tong Gao, Qinquan College of Physics and Information Engineering Fuzhou University Tsukuba Japan Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology Fuzhou University Tsukuba Japan Imperial Vision Technology Fujian China Department of Computer Science University of Tsukuba Tsukuba Japan

ISBN: (数字)9781510653184

ISBN: (纸本)9781510653177

Low-light images are generally produced by shooting in a low light environment or a tricky shooting angle, which not only affect people's perception, but also leads to the bad performance of some artificial intelligence algorithms, such as object detection, super-resolution, and so on. There are two difficulties in the low-light enhancement algorithm: in the first place, applying image processing algorithms independently to each low-light image often leads to the color distortion;the second is the need to restore the texture of the extremely low-light area. To address these issues, we present two novel and general approaches: firstly, we propose a new loss function to constrain the ratio between the corresponding RGB pixel values on the low-light image and the high-light image;secondly, we propose a new framework named GLNet, which uses the dense residual connection block to obtain the deep features of the low-light images, and design a gray scale channel network branch to guide the texture restoration on the RGB channels by enhancing the grayscale image. The ablation experiments have demonstrated the effectiveness of the proposed module in this paper. Extensive quantitative and perceptual experiments show that our approach obtains state-of-the-art performance on the public dataset. © COPYRIGHT SPIE.

关键词： image enhancement

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Shack-Hartmann wavefront reconstruction speed up with embedded GPU 13

Shack-Hartmann wavefront reconstruction speed up with embedd...

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13th International conference on Information Optics and Photonics, CIOP 2022

作者： Fan, Yao Duan, Yaxuan Kou, Jingwei Zhang, Weigang Liu, Qing Da, Zhengshang Advanced Optical Instrument Laboratory Xi'an Institute of Optics and Fine Mechanics Chinese Academy of Sciences Xi’an710119 China Xi'an Key Laboratory of High Power Laser Measurement Technology and Instrument Xi’an710119 China

ISBN: (纸本)9781510660632

Shack-Hartmann sensor is widely used in adaptive optics systems, and laser beam quality measurements. The traditional method separates measures and calculations, and the wavefront reconstruction algorithm is slow to implement on the host computer. In this paper, the embedded GPU is introduced to Shack-Hartmann sensors' wavefront phase reconstruction. A parallel calculation method is proposed to speed up the wavefront phase reconstruction process. The experiment result shows the algorithm speed improves 50×with the image size of 2592×2048 pixels. © 2022 SPIE.

关键词： graphics processing unit

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Efficient-PSP-Net: A Lightweight Model for the Segmentation of Liver and Tumor in CT Scans 13

Efficient-PSP-Net: A Lightweight Model for the Segmentation ...

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

作者： Sindhura, Chitimireddy Vengama, Harsha Vardhan Reddy Gorthi, Subrahmanyam Indian Institute of Technology Tirupati Andhra Pradesh India

ISBN: (纸本)9781450398237

The segmentation of the liver and tumors present in the liver is an essential task for the diagnosis of liver cancer and surgical treatment planning. While most of the works were developed with the goal of improving segmentation accuracy, there is a high demand for the development of lightweight architectures to deploy in clinical settings with limited computational resources. Developing such resource-efficient models while maintaining the performance of the state-of-The-Art architectures is challenging due to the high class imbalance and small observable changes between tumorous and non-Tumorous regions, which would generally require extensive and complex deep learning architectures. The contributions of this paper are twofold;First, this paper proposes to encode information extracted at different scales using a "Pyramid Pooling Module"(PPM) architecture. While different variants of pyramidal architectures are widely used in the context of scene parsing in computer vision, this paper explores the use of the PPM architecture for the segmentation of liver and tumor. Second, the paper proposes using the popular lightweight "Efficient-Net"as the backbone network of PSP-Net for feature extraction, thereby reducing the overall model size while achieving relatively similar performance to state-of-The-Art architectures. The performance of the proposed model is evaluated on the MICCAI 2017 LiTS dataset. The model achieved a DSC of 95.06% and 79.08% for liver and tumor, respectively, with only 1.76M parameters. © 2022 ACM.

关键词： Tumors

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Enhanced Kernelized Conditional Spatial Fuzzy C Means Algorithm for Noisy Brain MRI Tissue Segmentation 13

Enhanced Kernelized Conditional Spatial Fuzzy C Means Algori...

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

作者： Halder, Amiya Choudhuri, Rudrajit Sarkar, Apurba St Thomas College of Engineering and Technology West Bengal Kolkata India IIEST Shibpur Howrah West Bengal India

ISBN: (纸本)9781450398237

This paper proposes an Enhanced Kernelized Conditional Spatial Fuzzy C Means (EKCSFCM) clustering algorithm targeted at segmentation of Brain Magnetic Resonance (MR) image data. The approach amalgamates kernel mapping with a spatially relevant fuzzy clustering paradigm for image segmentation. A technique for rank-based consideration of spatial information is introduced and is used along with spatial tuning parameters for controlling neighborhood effects during data point clustering. The kernelized approach and the spatial parameters handle intensity inhomogeneities and strengthen the performance of the algorithm by improving resiliency against noise and other aberrations. For analyzing the robustness of the approach, four volumes of brain MR data, each comprising 71 images are used. These images are also corrupted with noise and inhomogeneities for evaluating the efficiency of different approaches in noisy image segmentation. The experimentation results along with the qualitative and quantitative inferences verify the robustness of the algorithm across a wide variety of test cases. © 2022 ACM.

关键词： image segmentation

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