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Iterative gradient encoding network using feature co-occurrence loss for single image reflection removal 21

Iterative gradient encoding network using feature co-occurre...

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

作者： Bera, Sutanu Biswas, Prabir Kumar Indian Institute of Technology Kharagpur Kharagpur India

ISBN: (纸本)9781450391276

Removing undesired reflections from a photo taken in front of a glass is of great importance for enhancing visual computing systems' efficiency. Previous learning-based approaches have produced visually plausible results for some reflections type, however, failed to generalize against other reflection types. there is a dearth of literature for efficient methods concerning single image reflection removal, which can generalize well in large-scale reflection type. In this study, we proposed an iterative gradient encoding network for single image reflection removal. Next, to further supervise the network in learning the correlation between the transmission layer features, we proposed a feature co-occurrence loss. Extensive experiments on the public benchmark dataset of SIR2 demonstrated that our method can remove reflection favorably against the existing state of the art method on all imaging settings, including diverse backgrounds. Moreover, as the reflection strength increases, our method can still remove reflection even where other state of the art methods failed. © 2021 ACM.

关键词： Iterative methods

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computer Aided Diagnosis of Autism Spectrum Disorder Based on thermal Imaging 1

引用

6th International conference on computer vision and image processing, CVIP 2021

作者： Ganesh, Kavya Umapathy, Snekhalatha Krishnan, Palani thanaraj Department of Biomedical Engineering College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Electronics and Instrumentation Engineering St. Joseph College of Engineering Anna University Chennai India

ISBN: (数字)9783031113468

ISBN: (纸本)9783031113451

Autism Spectrum Disorder is a fast-growing area of study in the field of neurodevelopmental sciences. It is widely recognized that individuals with ASD have emotional processing impairments which ultimately leads to the inability to recognize facial expressions. the aim of the study was as follows: (1) To compare the facial skin temperature for different emotions namely happiness, sadness and anger using thermal imaging by doing a comparison between autistic and non-autistic children;(2) To develop a CAD tool which comprises of segmentation using K-means algorithm, GLCM feature extraction and classification using SVM. A number of 30 autistic and 30 non-autistic subjects were considered for the study. the thermography approach was used to acquire the temperature of the many facial regions such as eyes, cheek, forehead, and nose while the subjects were asked to react to the projected formats. the mean temperature difference between the autistic and non-autistic individuals in the nose region for the emotion happy, anger and sad is 2.77%, 12.7%, and 13% respectively. the accuracy obtained by classifying the thermal images of the autistic and non-autistic children using SVM classifier was found to be 88% respectively compared to random forest (73%) and Naïve bayes classifier (66%). the Dense-net 121provided better accuracy of 89.47% compared to the other machine learning classifiers. thermography is a diagnostic approach used to acquire temperature differences with high resolution. the computer-aided diagnostic tool can be a reliable and an enduring method in the diagnosis of the autistic individuals. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： K-means clustering

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Superlinear Speedup on GPGPU Using Laplacian Algorithm with Convolution Filtering as A Case Study 6

Superlinear Speedup on GPGPU Using Laplacian Algorithm with ...

引用

6th International conference on computer and Information Sciences, ICCOINS 2021

作者： Vadiveloo, Mogana Almazrooie, Mishal Abdullah, Rosni Kampar31900 Malaysia Independent Researcher Jeddah Saudi Arabia Penang11800 Malaysia

ISBN: (纸本)9781728171517

In this paper, the main idea is to investigate the hypothesis that superlinear speedup occurs when the concurrent threads on General Purposes Graphic processing Units (GPGPU) carry heavy workloads. In order to evaluate this hypothesis, Laplacian image edge detection algorithm with convolution filtering is chosen as a case study. In this work, local memories of GPGPU are utilized in order to achieve the superlinear speedup. the convolution filtering kernels of the Laplacian edge detection algorithm are invoked in these local memories. By this, the low latency of the GPGPGU local memory are deployed efficiently and this subsequently leads to a higher speedup. the results obtained presented that the superlinear speedup is achieved when the size of the convolution kernel is large. In this study, when the convolution kernel size is 7×7, superlinear speedup is observed for image dataset of sizes between 1KB-2500KB. © 2021 IEEE.

关键词： graphics processing unit

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A Scalable 3D Array Architecture for Accelerating Convolutional Neural Networks 6th

A Scalable 3D Array Architecture for Accelerating Convolutio...

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6th International conference on Cognitive Systems and Signal processing, ICCSIP 2021

作者： Ji, Yafei Wang, Xiang Zhou, Yangfan Cheng, Chen Li, Jiang Wang, Haoyuan Wang, Xuguang Liu, Xin Chinese Academy of Sciences 398 Ruoshui Road Suzhou Industrial Park Jiangsu Suzhou China Gusu Laboratory of Materials 388 Ruoshui Road Suzhou Industrial Park Jiangsu Suzhou China

ISBN: (纸本)9789811692468

Convolutional neural network (CNN) is widely used in computer vision and image recognition, and the structure of the CNN becomes more and more complex. the complexity of CNN brings challenges of performance and storage capacity for hardware implementation. To address these challenges, in this paper, we propose a novel 3D array architecture for accelerating CNN. this proposed architecture has several benefits: Firstly, the strategy of multilevel caches is employed to improve data reusage, and thus reducing the access frequency to external memory;Secondly, performance and throughout are balanced among 3D array nodes by using novel workload and weight partitioning schemes. thirdly, computing and transmission are performed simultaneously, resulting in higher parallelism and lower hardware storage requirement;Finally, the efficient data mapping strategy is proposed for better scalability of the entire system. the experimental results show that our proposed 3D array architecture can effectively improve the overall computing performance of the system. © 2022, Springer Nature Singapore Pte Ltd.

关键词： image recognition

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image Generation Method Based on Variable Scale Pyramid Attention with Text Data Enhancement 23

Image Generation Method Based on Variable Scale Pyramid Atte...

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Proceedings of the 2023 6th International conference on Artificial Intelligence and Pattern Recognition

作者： Pingan Qiao Ziying Lei Xiaofan Zhang Department of Computer Science and Technology Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

In recent years, research on methods for generating images from text based on generative adversarial networks has made some progress. the study of text-generated images is at the intersection of natural language processing and computer vision, a task that requires computers to be able to generate images that are consistent with the semantics of the text on the basis of understanding the semantics of the input text. Aiming at the problems of text-image matching and poor performance on image details in traditional text-generated image methods, a VT-AttnGAN (Variable Scale Pyramid Attention with Text Data Augmentation for Generative Adversarial Networks) text-based image generation method. A Vector Data Enhancement Network (TDEN) is designed to reduce the text dimension to enhance the model's ability to learn the probability distribution of text features, and optimize the alignment of image and text. In addition, to enhance the detailed features of the image, a Variable Scale Pyramid Attention Module (VSPAM) is proposed, which extracts the features at different scales of the initial image, and efficiently utilizes the useful information between the feature channels through the attention approach. We train and test on two datasets, CUB-200-2011 and Oxford-102, and the experimental results show that compared with AttnGAN, the model proposed in this paper solves the problems of image blurring and distortion, and has the image generation effect of text image alignment enhancement and picture integrity improvement.

关键词： Generating Adversarial Networks Pyramid of attention Text To image Vector Data Enhancement

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GPU-based centroidal voronoi tessellation using local search on thinnest digital surface 21

GPU-based centroidal voronoi tessellation using local search...

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

作者： Soni, Ashutosh Bhunre, Piyush Kanti Bhowmick, Partha Indian Institute of Technology Kharagpur India Techno India University Kolkata India

ISBN: (纸本)9781450391276

Voronoi tessellation is a classical geometric problem with different variants and nature of constraints. Centroidal Voronoi tessellation (CVT) is one such leading variant and used in many applications. Although there exists a multitude of CVT techniques for 3D objects considered as discrete volumes, e.g., as sets of voxels, there is no significant work in the literature on CVT computation for voxelized (a.k.a. 'digital') surfaces. In this paper we focus on this problem and propose a novel GPU-based algorithm for CVT computation on a digital surface. Its novelty rests on several fundamental ideas. Firstly, the digital surface is thinnest by construction;that is, while being voxelized from a triangulated surface, its every triangle is 2-minimal in topological sense. As a result, each voxel of the digital surface has the smallest possible neighborhood, which eventually limits the search space and aids in efficient computation. Secondly, as the optimization constraint, a novel formulation of Voronoi energy is used, which is easy to compute and hence leads to quick convergence of the algorithm. the GPU-based algorithm with related procedures and their complexity analysis have been discussed in detail, and related experimental results have also been furnished to adjudge the merit and usefulness of the proposed algorithm. © 2021 ACM.

关键词： graphics processing unit

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A Multi-stage Feature Enhancement Network with Second-Attention for image Captioning 23

A Multi-stage Feature Enhancement Network with Second-Attent...

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Proceedings of the 2023 6th International conference on Artificial Intelligence and Pattern Recognition

作者： Xiaobao Yang Yang Yang Junsheng Wu Sugang Ma Zhiqiang Hou Bohui Song School of Computer Science Xi'an University of Posts and Telecommunications China School of Software Northwestern Polytechnical University China

ISBN: (纸本)9798400707674

In the past few years, self-attention in Transformer has been widely used in natural language processing (NLP) and computer vision (CV) due to its excellent ability to capture global information, especially in image captioning, where the use of self-attention can significantly improve the representation of visual information. However, the obtained image feature from Transformer-based models suffers from two main problems, one is information redundancy due to the global aggregation of self-attention, and the other is the lack of semantic information caused by single-scale feature extraction. therefore, we first propose second-attention in this paper, by redistributing image attention weights, second-attention can effectively remove irrelevant information and enhance the attention to essential objects and relations. Meanwhile, to enrich semantic information and further enhance the role of second-attention, we design a Multi-stage Feature Enhancement (MFE) network to improve the ability to represent visual information. After conducting extensive experiments on the MS COCO dataset, we achieve significant improvements in all popular benchmarks, particularly BLEU4 increased by 1.6%, CIDEr increased by 3.4%.

关键词： Self-Attention Transformer feature enhancement image captioning

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Self-Attention vision Transformer with Transfer Learning for Efficient Crops and Weeds Classification

Self-Attention Vision Transformer with Transfer Learning for...

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International conference on Information Systems and computer Networks (ISCON)

作者： Shubham Sharma Manu Vardhan Department of Computer Science and Engineering National Institute of Technology Raipur India

Classifying and mapping weeds are crucial for ensuring that plantations remain free of unwanted plants, particularly in cases where plant spacing is uneven, making weed detection more challenging than it is for crops. To address this issue, Deep Neural Networks (DNNs) are often used in agriculture to identify the unique features of weeds and classify them based on low-resolution imaging, which helps to control weed populations. Convolutional Neural Networks (CNN) in Deep Learning (DL) models have advanced, achieving remarkable performances in plant-weed classification. Despite Convolutional Neural Networks (CNNs) success in computer vision, they still need help with massive labeled datasets, intra-class classification, and high processing costs when used in classification tasks. Meanwhile, transformer-based models have shown significant modeling capabilities globally but are yet to be thoroughly investigated in agriculture. In this study, we explore Self-Attention vision Transformers (ViT) using a Transfer Learning (TL) approach to classify weeds in benchmark datasets. the proposed model, ViT-S16, was fine-tuned on the imageNet-1k dataset, pretrained on the imageNet-21k, and evaluated on the public benchmark agriculture dataset DeepWeed, which contains plants grown with various weed species under different conditions. Our experimental results show that ViT-S16 outperforms state-of-the-art CNN models ResNeXt-50 and EfficientNet-B7, achieving an utmost accuracy of 98.54%. these results show how helpful ViT can be in different image classification tasks. In the future, researchers can look into the strengths and weaknesses of various ViT models.

关键词： Deep learning Computational modeling Transfer learning Sociology Benchmark testing Transformers Agriculture

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Exponentially Biased Discriminant Analysis Based Classification of Covid 19 Chest images Using Generalized Regression Neural Network 6

Exponentially Biased Discriminant Analysis Based Classificat...

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6th International conference on image Information processing, ICIIP 2021

作者： Greeshma, K. Hari, C.V. Union Christian College Aluva Department of Computer Science Kochi India Rajagiri School of Engineering Technology Department of Applied Electronics Instrumentation Engineering Kochi India

ISBN: (纸本)9781665433617

Classification is always an interesting problem in the field of computer vision. In a two class problem, there will be an uncertainty in the classification of adjacent images of two classes. To avoid this uncertainty, an exponentially biased discriminant analysis is proposed for the classification. Initially, the entire database is projected to an exponentially biased space. In this space the data is more separated than the original space. Discriminant analysis is then used to classify the objects in this new space. After the training, the test data are approximated to this space using Generalized Regression Neural Network. the proposed algorithm is evaluated using the database of Covid 19 chest images. A better accuracy is observed for the proposed method by comparing with the normal discriminant analysis. But, this accuracy may not be a very good value. Better scientific approaches on the selection of the exponential biasing may give better classification accuracy. © 2021 IEEE.

关键词： Discriminant analysis

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Deep Learning Framework for Early Detection of Diseases in Coconut Tree from Leaf images

Deep Learning Framework for Early Detection of Diseases in C...

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Advances in Science and Technology (ICAST), International conference on

作者： Dhanashree Toradmalle Nidhi Bhanushali Meet Dama Manasi Dhote Department of Computer Engineering KJSIT Mumbai Maharashtra India

Coconut tree is one of the most beneficial and popular crops in the world due to its numerous uses. In countries like India, Indonesia, Philippines, Sri Lanka, etc., people primarily depend on coconut farming for their livelihood. Pest infestation or disease can largely affect the production of crops and lead to losses. Finding the disease at its earliest stage of development is necessary for disease control in plants. image processing technologies, computer vision and deep learning models are now-a-days used to detect disease in crops. Many researchers have developed effective methods using deep learning models for disease detection, Convolutional Neural Networks (CNNs) and various architectures of CNNs are used for Disease Detection in plants. this study aims to review various research methodologies and apply them to a new dataset that includes images of leaves classified primarily into 5 diseases: yellowing, drying, flaccidity, caterpillars, and leaflets. CNN, DenseNet121, InceptionResNetV2, and ANN were major algorithms used in ***2 CNN, and DenseNet121 all performed remarkably well and provided the desired accuracy of around 99% and higher.

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