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Automatic Multiple Choice Question Evaluation Using Tesseract OCR and YOLOv8 2

Automatic Multiple Choice Question Evaluation Using Tesserac...

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2nd IEEE conference on Artificial Intelligence (CAI)

作者： Mahmud, Saikat Biswas, Kawshik Alain, Api Rudro, Rifat Al Mamun Anannya, Nusrat Jahan Mouri, Israt Jahan Nur, Kamruddin Amer Int Univ Bangladesh AIUB Dept Comp Sci Dhaka Bangladesh

ISBN: (纸本)9798350354102;9798350354096

This paper presents a novel approach for automating the grading of multiple-choice question (MCQ) answer sheets using computer vision and pattern recognition techniques. The system examines student's marked answer sheet images by comparing with the question sheet image and answer keys. The computer vision and pattern recognition helps extracting pertinent data such as question number detection, MCQ option detection and the answer markings. The proposed approach reliably produces the output report that displays the students' correct answers with an accuracy of 0.98 F1 score and 0.99 mAP from any form or unstructured question script. This approach can provide a dependable and effective grading system, reducing manual work and offering prompt feedback to students without any constraints on the answer sheets.

关键词： Optical character recognition (OCR) image processing object detection YOLOv8

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Jitter error detection and compensation method based on H-infinity filter fusion for remote sensing satellite image 2

Jitter error detection and compensation method based on H-in...

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2nd IEEE International conference on Signal, Information and data Processing, ICSIDP 2024

作者： Tian, Yingqi Yu, Haojie Zhu, Ying Wuhan Institute of Technology School of Electrical and Information Engineering Wuhan China

ISBN: (纸本)9798331515669

Satellite jitter is a common phenomenon in high-resolution satellite applications, leading to image blurring, geometric distortion, difficulties in image stitching, and reduced spatial resolution. Therefore, accurately detecting satellite jitter and compensating for it in images is crucial. In this paper, we propose a novel framework for multispectral satellite jitter detection and compensation, which integrates the results of two different detection methods to enhance detection accuracy and effectively compensate for the images. First, dense matching is performed between the bands of the multispectral imagery to obtain the relative registration error between two bands;Then, dense control point matching is conducted between the single-band image and the corresponding Ortho-images. Next, a method based on the H-infinity filtering model is proposed to fuse the results of the two detection methods and compensate for the original image. Comparative experimental analysis shows that the compensation results after fusion exhibit significant improvements compared to those obtained using a single detection method. © 2024 IEEE.

关键词： Remote sensing

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An image-to-image Target reconstruction Network for Capacitively Coupled Electrical Resistance Tomography Based on Transfer Learning

An Image-to-image Target Reconstruction Network for Capaciti...

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Computer, Vision and Intelligent Technology (ICCVIT), International conference on

作者： Chunfen Luo Yandan Jiang Vivo Mobile Communication Co. Ltd Hangzhou China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798331540043

ISBN: (纸本)9798331540050

This work develops an image-to-image target reconstruction network for capacitively coupled electrical resistance tomography. The novel target reconstruction network is constructed by two Unets to extract and fuse the multi-frequency features of the coarse images reconstructed by traditional image reconstruction algorithms, and incorporated with transfer learning to improve the generalization ability of the network. Experimental results show that the developed target reconstruction network is effective. The Unet-based image-to-image framework improves the target reconstruction quality with quantitatively higher image score. The introduced transfer learning strategy fills the gap between simulation data and experimental data, and further improves the performance of the network.

关键词： Resistance Computer vision Fluids Fuses Transfer learning reconstruction algorithms Feature extraction image reconstruction image fusion

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2D cluster finding with the help of enhanced deep residual networks 2

2D cluster finding with the help of enhanced deep residual n...

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2nd International conference on Big data, Computational Intelligence, and Applications, BDCIA 2024

作者： Xia, Taixi Zhang, Xin Xu, Zheming Department of Biomedical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Mathematics Jinan University Guangdong Guangzhou511443 China Department of Mathematics and Physics University of Science and Technology Beijing Beijing100000 China

ISBN: (纸本)9781510689053

In recent years, advancements in image processing technology have made cluster detection a vital area of research across various scientific fields, from particle physics to astronomy. Accurately identifying clusters in images is essential for enhancing experimental outcomes and applications. In this paper, a novel algorithm for two-dimensional cluster finding using Enhanced Deep Residual Network (EDSR) is proposed. The algorithm solves the challenge of accurately identifying points in an image by processing the pixel matrix to efficiently locate clusters. The algorithm uses a three-step process: identifying local maxima, clustering around these points, and calculating a weighted average to determine the cluster center. While traditional methods have difficulty in solving the problem of equality of adjacent maxima, our method solves this problem by redefining local maximum detection to minimize bias. By considering a 5 × 5 matrix around each local maximum, clustering was achieved, capturing over 98.73% of the peak values and ensuring comprehensive clustering formation without overlapping issues. Before clustering analysis, the EDSR algorithm is used to improve image resolution and reduce errors in identifying cluster centers. The model evaluation results indicate that the accuracy of cluster center determination has been improved and the error margin has been reduced, highlighting the effectiveness of the EDSR enhancement method. © 2025 SPIE.

关键词： Noise abatement

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Soil Classification Using Deep Learning Techniques 2

Soil Classification Using Deep Learning Techniques

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2nd International conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Rajini, A. Parthasarathy, Priyanka Shyam, G. Harshit Kumar, B. Shawn, Safwan Department of Mathematics and Statistics Bhavan's Vivekananda College of Science Secunderabad India BSC Hons. Data Science Bhavan's Vivekananda College of Science Secunderabad India

ISBN: (纸本)9798350366570

The objective of this project is to classify the four types of soil images using deep learning models that is the Convolutional Neural Networks (CNN) and the Artificial Neural Networks (ANN). Each model classifies four types of soils, namely alluvial soil, black soil, red soil, and clay soil using images. The input imagesconsisted of over 1600 images of the above-mentioned soil types, 70% were used for training the model and the remaining 30% were usedas the testing set, prior to model training the transforms module of the pytorch library was used to pre-process the dataset and improve the model generalization by using data wrangling methods to avoid model overfitting. The Pytorch library was used again to create the Neural Network Classes in both the models, and the dask library was used to obtain the distribution of images according to the different soil types. The ANN-based model was then trained and tested until it showed an improvement in accuracy of around 88% to 91%. Similarly, the CNN-based Model was trained until it showed an increase in accuracy of 92% to 96%. The f1 scorecalculated for CNN was 0.9253 and for ANN was 0.9318. Furthermore, the ROC Curves and Confusion Matrix were used from the sklearn metrics library to calculate the accuracy of the predictions made by the models. from the analysis it is interpreted that the Convolutional Neural Network-based Model is the most efficient classifier for image datasets. © 2024 IEEE.

关键词： Neural network models

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Electronic image Processing for Writer-independent Offline Signature Recognition based upon Fourier Descriptors 2

Electronic Image Processing for Writer-independent Offline S...

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2nd International conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Irtaza, Ghulam Raza, Hammad University of Education Lahore Pakistan NCBAE Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan

ISBN: (纸本)9798350335644

The Signature Recognition system collects and compares a set of distinctive features that are derived from the writer's typical patterns of behaviour throughout the signature creation process. One signature verification process's goal is to confirm or reject a sample, while another's seeks to identify the sample's author. In this work, we offer efficient algorithms for signature recognition based on Fourier Descriptors. The Fourier Transform is a highly effective technique for object recognition. Fourier transforms of shape signatures produce FDs. The following is a boundary tracing of the signature image. Through the application of morphological processes, we are able to encompass the full signature within a closed curve that is a good fit for it. The generated curve is unique for each signature and so may be efficiently utilised to calculate FDs in the context of form recognition. The FAR is a metric used to measure the success and exactness of a system through calculating the number of times it incorrectly verifies inaccurate patterns. In this study, a local data of 60 test sign patterns was utilized, and it was discovered that 10% of those patterns were mistakenly validated, resulting in a False Acceptance Rate (FAR) of 0.175. © 2023 IEEE.

关键词： Fourier transforms

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VGG16-Based MaizeLeafNet Model for an Efficient Multiclass Classification of Maize Leaf Diseases 2

VGG16-Based MaizeLeafNet Model for an Efficient Multiclass C...

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2nd International conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Saini, Archana Guleria, Kalpna Sharma, Shagun Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350327533

Maize holds substantial importance as a cereal crop in India, serving both as a staple food for human consumption and as fodder for livestock. The presence of diseases that affect maize leaves poses a significant threat, capable of causing substantial reductions in crop yield and quality. The timely detection and effective management of these diseases are imperative for ensuring the preservation of agricultural productivity. India is a country with a sizable and expanding population, and a major portion of its food comes from maize. A steady and safe supply of food for the populace is ensured by keeping maize crops free from diseases. Crops that are resistant to disease are crucial for providing for the dietary needs of an expanding population. Hence, in the proposed work, a deep learning-based VGG16 model called MaizeLeafNet has been proposed, for the early prediction of the maize leaf diseases. It is well known for having powerful feature-learning capabilities. The collection of a large dataset featuring a range of images of diseased maize leaves ensures a representation of the challenges faced in real-world farming scenarios. Measures like accuracy and loss are used to carefully evaluate the model's performance in multiclass classification. Through the use of visualization techniques, the model's interpretability is enhanced, improving understanding of disease patterns and providing new perspectives on the features that have been learned. Developing crop disease classification frameworks that use state-of-the-art neural network designs, such as VGG16 is a significant step towards creating resilient and sustainable farming methods. This article compares the loss and accuracy at different epochs in the training and validation stages. At the epoch value of 10, the accuracy of 90.34% and the loss of 0.4226 have been identified during validation, whereas the accuracy of 98.79% and the loss of 0.0380 resulted during training. These outcomes show that as the epoch incre

关键词： classification Convolutional Neural Network Corn deep learning diseases image processing leaf Maize Visual Geometry Group16

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A Novel Multi-Task Face Super-Resolution Framework Embedding Degraded Augmented GAN Networks 2

A Novel Multi-Task Face Super-Resolution Framework Embedding...

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2022 2nd International conference on Artificial Intelligence and Industrial Technology Applications, AIITA 2022

作者： Zhao, Shuai Sun, Jifeng Ou, Hongshi Lin, Yibin School of Electronic and Information Engineering South China University of Technology Guangdong Province Guangzhou City China

The training method that uses predefined degradation methods to provide training data for deep convolutional neural networks suffers from overfitting and image distortion when performing face super-resolution reconstruction. In this paper, we propose a multi-task facial super-resolution reconstruction framework embedded in a degraded augmented GAN network. Compared with the commonly used method of generating low-quality face images through pixel interpolation, this paper inversely embeds a generative adversarial network composed of residual coding blocks in the network. Through the feature learning ability of the network, the feature fitting of the face images collected in the natural scene is performed to generate training data that can more fully represent the real noise in the natural scene, thereby improving the noise reduction and image reconstruction capabilities of the network. In order to take full advantage of the task-specific features, the semantic features of the dataset are extracted through a pre-trained facial organ semantic detection framework and the data is used to add face semantic information in the super-resolution reconstruction network. The experimental results show that the model proposed in this study can achieve super-resolution reconstruction of ultra-low-resolution face images simply and efficiently, and achieve super-resolution face images with lower visual quality than other advanced methods with lower model complexity. It solves the problem of single style and distortion of face reconstruction images caused by the training method of the data set obtained by the usual predefined degradation. © Published under licence by IOP Publishing Ltd.

关键词： image reconstruction

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Analyzing the Role of Machine Learning and Satellite image Processing in Predicting Deforestation 2

Analyzing the Role of Machine Learning and Satellite Image P...

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2nd DMIHER International conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Rawarkar, Chetan W. Agrawal, Sudhir Sage University Indore India

ISBN: (纸本)9798331528713

Loss of species, climate change, and ecosystem damage are all caused by cutting down trees, which is a major world environmental problem. Predicting when trees will be cut down is important for starting protection efforts at the right time. As a result, machine learning and picture processing from satellites have become useful and accurate ways to predict deforestation on a large scale. This research looks into how machine learning techniques and high-resolution satellite images can be used together to very accurately predict when forests will be cut down. Land cover changes over time can be seen in satellite pictures, which give us a lot of information about space. Advanced methods, like remote sensing and picture segmentation, can be used to process these photos and pull out important traits that show deforestation. Then, machine learning methods like Random Forests, Support Vector Machines, and Convolutional Neural Networks are used on these traits to model and predict trends of destruction. The study compares different machine learning models based on how accurate they are, how quickly they can be run, and how well they can work in different parts of the world. Integrating satellite picture processing with machine learning makes it possible to find out what causes trees to be cut down, like expanding farms, cutting, and urbanization. This makes it easier to come up with specific conservation plans. Using time-series satellite data lets us see how deforestation changes over time, which helps us understand how forest cover loss changes over time. Traditional methods are often limited by not having enough data from the ground and not being able to work over big areas. This method has a lot of benefits over those. The results show that using machine learning and satellite picture processing together can make very accurate predictions. This could be a useful tool for lawmakers and environmentalists. By making it easier to predict deforestation, this method encourages

关键词： Convolutional neural networks

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Super-resolution reconstruction of remote sensing images based on SRGAN 2

Super-resolution reconstruction of remote sensing images bas...

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2nd International conference on Optics and Communication Technology, ICOCT 2022

作者： Yu, Mengbei Wang, Hongjuan Liu, Chang Lin, Deping School of Information Engineering Beijing Institute of Graphic Communication Beijing China School of New Media Beijing Institute of Graphic Communication Beijing China

ISBN: (纸本)9781510660533

In the field of remote sensing images, due to the limitations of hardware equipment, image transmission, natural environment and other reasons, the resolution of the obtained remote sensing images cannot reach the desired resolution. The emergence of image super-resolution reconstruction technology can improve the resolution of remote sensing images without increasing the high cost. image super-resolution reconstruction refers to the fact that low-resolution images can obtain high-resolution images through certain algorithmic techniques. With the rapid development of deep learning ideas, researchers have applied it to the field of image super-resolution reconstruction and achieved good results. image super-resolution reconstruction also shifts from traditional reconstruction methods to deep learning-based methods. The emergence of the idea of Generative Adversarial Networks has further advanced the field of image super-resolution reconstruction. By using the idea of Generative Adversarial Network (GAN), researchers can obtain more realistic high-resolution images. This paper mainly uses the SRGAN model, the image dataset DIV2K for super-resolution reconstruction, and uses a dense residual structure in the generator network to obtain more image information, so that the effect of image reconstruction is more realistic. Through the experimental verification on the SIRI-WHU remote sensing test data set, the two evaluation indicators of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) are compared, and the effect is improved. Better generation results can also be observed through subjective human vision. © 2022 SPIE.

关键词： image reconstruction

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