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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Tejaswi S Tina Babu Tejaswi K Department of Computer Science Seshadripuram Degree College Mysuru India Department of Computer Science and Engineering School of Engineering Alliance University Bengaluru India Department of Computer Applications JSS Science and Technology University Mysuru India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

License plate recognition is one of the challenging tasks and it belongs to ITS, due to backgrounds, variation of illumination, occlusion the recognition is became the challenging task. These challenges enabled a comparison between the conventional image enhancement and the deep learning scheme for license plate recognition. We analyze and compare multiple techniques that we consider significant, including, the Edge detection, Morphological operations and template matching and also deep learning models like CNNs and R-CNNs. In this framework of experiment scenarios, we scale out a comprehensive evaluation of the methods given different datasets and compare their accuracy, speed as well as stability in conditions that may be hostile. In the experiments, we see that Naive Bayes and SVM outperform in low-resource conditions, but deep learning methods are significantly more accurate and more flexible in accommodating complicated cases and trends, which makes deep learning methods the method of choice for contemporary applications. This work provides an understanding of the strengths and weaknesses of both approaches to serve as a guideline in choosing the most effective detection techniques in relation to certain environmental conditions.

关键词： deep learning Support vector machines YOLO Accuracy Lighting Computer architecture real-time systems Vectors License plate recognition Meteorology

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Cotton Plant Disease Detection and Prevention Using deep learning and image processing

Cotton Plant Disease Detection and Prevention Using Deep Lea...

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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Ruthvija Kesani Jaya Prakash Sunkavalli Mahamamad Rafi Shaik Kusuma Yadala Dept. of Information Technology Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

One of the most important aspects of the agricultural economy is the production of cotton, which is threatened by diseases that lower crop quality and yield. Conventional techniques for diagnosing diseases are frequently subjective and labor-intensive. This paper presents a novel method for the automatic detection and prevention of cotton plant diseases that makes use of deep learning techniques. A convolutional neural network (CNN) model is trained using a dataset that includes various photos of both healthy and sick cotton plants. The suggested model offers a dependable and time-efficient solution by exhibiting high accuracy in differentiating between different diseases. Moreover, proactive disease detection is made possible by the integration of real-time monitoring systems, such as drones fitted with high-resolution cameras. Early detection lessens the need for broadspectrum antibiotics by enabling the prompt application of preventive measures, such as targeted therapies. Finally, we conduct a comprehensive computational analysis of eight cutting-edge object detection algorithms on the cotton plant dataset to identify diseases on the leaves and seven cutting-edge classification algorithms on the cotton plant datasets to determine if a leaf has a disease or not. computed results indicate that it has a high degree of object detection accuracy.

关键词： deep learning Plant diseases image processing Plants (biology) Transfer learning Object detection Classification algorithms

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A Novel Human Face Expression Recognition Based on image processing Assisted Convoluted deep learning Methodology

A Novel Human Face Expression Recognition Based on Image Pro...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： M. Tamilselvi Venkata Siva Prasad Ch M. Varuna Kumari R. Sai Lavanya Nandurkar Y Mohammad Rasmi Al-Mousa Dept of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Department of ECE Jeppiaar Engineering College Rajiv Gandhi salai Chennai Department of S&H QIS College of Engineering and Technology Ongole Prakasam District Andhra Pradesh India Department of MCA QIS College of Engineering and Technology Ongole Prakasam District Andhra Pradesh India Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Department of Cyber Security College of Information Technology Zarqa University zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Facial expression recognition has become a critical component in applications involving human-computer interaction, security systems, and behavioral analysis. This paper presents a novel approach to human face expression recognition using a convoluted deep learning methodology supported by image processing techniques. The system is designed to accurately classify seven fundamental emotions—Anger, Disgust, Fear, Happy, Sadness, Surprise, and Neutral—using the FER2013 dataset. The methodology includes preprocessing steps such as noise reduction, grayscale conversion, and face registration. Feature extraction is performed using Gabor filters and Local Binary Patterns (LBP), while Convolutional Neural Networks (CNNs) are employed to learn deep hierarchical features. The system was optimized through hyperparameter tuning, achieving an overall accuracy of 96.29% on the test set. Precision, recall, and F1-scores for each emotion also exceeded 95%, with “Happy” and “Surprise” emotions showing the best performance. The results indicate that the model is effective for real-time applications requiring reliable emotion detection. The paper concludes with discussions on model performance, challenges in classifying similar emotions, and recommendations for future improvements.

关键词： deep learning Emotion recognition image recognition Face recognition Computational modeling Feature extraction real-time systems Security Reliability Tuning

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Experimental Analysis of Malarial Parasites Infection by using deep learning based on Medical image processing Logic 3

Experimental Analysis of Malarial Parasites Infection by usi...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Meenakshisundaram, N. Sajiv, G. Saveetha University Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331508456

Malaria remains a significant global health challenge, particularly in resource-limited regions, necessitating accurate and rapid diagnostic tools. This study introduces deepMalariaNet, a deep learning model developed for detecting and classifying malarial parasites using the 'Malaria Parasite image - Malaria Species' dataset from Kaggle. The model employs Residual Attention Mechanisms and Parallel Convolutional Stacks (PCS) to improve diagnostic accuracy by focusing on critical image regions and capturing multi-scale features. Experimental results demonstrate that deepMalariaNet achieves 98.5% accuracy for binary classification (infected vs. non-infected) and 95.2% for multiclass classification (species identification). The model's robustness is validated through 10-fold cross-validation and ablation studies, and it outperforms state-of-the-art models such as ResNet-50 and DenseNet-121 in both accuracy and inference time. deepMalariaNet shows significant promise for real-time malaria detection in clinical settings, contributing to early and accurate diagnosis, which is crucial for effective malaria control and treatment. © 2025 IEEE.

关键词： Photointerpretation

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image processing based image to Cartoon Generation: Reducing complexity of large computation arising from deep learning

Image Processing based Image to Cartoon Generation: Reducing...

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Computational Intelligence and Sustainable Engineering Solutions (CISES), International Conference on

作者： Shruti Kumari Shrivastava Ruchi Gajjar Electronics and Communication Engineering Department Institute of Technology Nirma University Ahmedabad Gujarat India

This paper proposes an approach to convert real life images into cartoon images using image processing. The cartoon images have sharp edges, reduced colour quantity compared to the original image, and smooth colour regions. With the rapid advancement in artificial intelligence, recently deep learning methods have been developed for image to cartoon generation. Most of these methods perform extremely huge computations and require large datasets and are time consuming, unlike traditional image processing which involves direct manipulation on the input images. In this paper, we have developed an image processing based method for image to cartoon generation. Here, we perform parallel operations of enhancing the edges and quantizing the colour. The edges are extracted and dilated to highlight them in the output colour image. For colour quantization, the colours are assigned based on proposed formulation on separate colour channels. Later, these images are combined and the highlighted edges are added to generate the cartoon image. The generated images are compared with existing image processing approaches and deep learning based methods. From the experimental results, it is evident that the proposed approach generates high quality cartoon images which are visually appealing, have superior contrast and are able to preserve the contextual information at lower comnutational cost.

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Terahertz image processing - Resolution Enhancement and Object Segmentationusing W-Net deep learning

SSRN

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SSRN 2024年

作者： Mondal, Shyamal Jampani, Kashyap Raj, Akshay R. Sethi, Abhijit Chowdhury, Dibakar Roy Dept. of Applied Physics Defence Institute of Advanced Technology Maharashtra Pune411025 India Dept. of Physics Mahindra Ecole Centrale University Hyderabad500043 India

Terahertz images typically suffer from poor image quality and do not allow traditional machine learning methods to detect and identify objects. In this work, an image segmentation algorithm called W-Net is deployed for the first time, to segment concealed objects from the poor-quality Terahertz image samples. This involves developing an image enhancement and segmentation system for terahertz images. The proposed system comprises of two stages:in the first stage, the resolution of low-resolution THz images has been enhanced by training a U-Net deep learning model. In the latter stage, another U-Net model is trained to segment the enhanced images using corresponding masks to identify regions of interest. The proposed system is evaluated using a dataset of low-resolution terahertzimages with its respective high-resolution images and masks. The system performed well with an accuracy, precision, receiver operating characteristic and F1 scores of 99.1%, 0.9143, 0.9977 and 0.9146, respectively for enhancement. Moreover, values of the same evaluation metrics deduced as 99.9%, 0.9851, 0.9999 and 0.9923, respectively for segmentation. Therefore, the demonstrated scheme is expected to identify and classify objects hidden in real-time terahertz images and improve the quality of the same to enable accurate segmentation for applications like security screening, biomedical applications, quality and health monitoring in food industry, etc. © 2024, The Authors. All rights reserved.

关键词： image enhancement

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Early-Stage Brown Spot Disease Recognition in Paddy Using image processing and deep learning Techniques

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TRAITEMENT DU SIGNAL 2021年第6期38卷 1755-1766页

作者： Upadhyay, Santosh Kumar Kumar, Avadhesh Galgotias Univ Sch Comp Sci & Engn Greater Noida 203201 Uttar Pradesh India Ajay Kumar Garg Engn Coll Dept CSE Ghaziabad 201009 Uttar Pradesh India

India is an agricultural country. Paddy is the main crop here on which the livelihood of millions of people depends. Brown spot disease caused by fungus is the most predominant infection that appears as oval and round lesions on the paddy leaves. If not addressed on time, it might result in serious crop loss. Pesticide use for plant disease treatment should be limited because it raises costs and pollutes the environment. Usage of pesticide and crop loss both can be minimized if we recognize the disease in a timely manner. Our aim is to develop a simple, fast, and effective deep learning structure for early-stage brown spot disease detection by utilizing infection severity estimation using image processing techniques. The suggested approach consists of two phases. In the first phase, the brown spot infected leaf image dataset is partitioned into two sets named as early-stage brown spot and developed stage brown spot. This partition is done on the basis of calculated infection severity. Infection severity is computed as a ratio of infected pixel count to total leaf pixel count. Total leaf pixel counts are determined by segmenting the leaf region from the background image using Otsu's thresholding technique. Infected pixel counts are determined by segmenting infected regions from leaf regions using Triangle thresholding segmentation. In the second phase, a fully connected CNN architecture is built for automatic feature extraction and classification. The CNN-based classification model is trained and validated using early-stage brown spot, developed stage brown spot, and healthy leaves images of rice plants. Early-stage brown spot and developed stage brown spot images used in training and validation are the same images that are obtained in phase 1. The experimental analysis shows that the proposed fully connected CNN-based early-stage brown spot disease recognition model is an effective approach. The classification accuracy of the suggested model is found to be 99.20%. T

关键词： brown spot disease recognition rice plants CNN infection severity

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TPU Based deep learning image Enhancement for real-time Point-of-Care Ultrasound

IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING

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IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2024年 10卷 461-468页

作者： Huang, Ouwen Palmeri, Mark L. Duke Univ Dept Biomed Engn Durham NC 27708 USA

Handheld ultrasound devices are becoming more prevalent in point-of-care ultrasound workflows. However, these devices are computationally constrained which challenges the advances in deep learning methodology for real-time use on mobile Point-of-care ultrasound (POCUS) devices. In this work, we explore the feasibility of running MimickNet, a deep learning clinical post-processing model, on Tensor processing Units (TPUs), hardware designed for deep learning operations capable of running on only 2 watts of power at 1.8 V with a form factor of 10 mm x 15 mm. We show that real-time deep learning based post-processing is feasible at 20 - 120 FPS for 1472x160 to 224x224 axial sample x B-mode scan line configurations. We refer to the TPU based model as MimickNet Mobile. MimickNet Mobile achieves outputs nearly identical to the original MimickNet with a structural similarity index measurement (SSIM) of 0.98 +/- 0.001 and a mean squared error (MSE) of 0.0001 +/- 0.0 over our test set of 588 frames consisting of 168 phantom frames and 420 prospectively acquired human liver frames. We investigate the latency of other common mobile architectures such as separable convolution. Finally, we investigate the distribution of model parameter error when quantizing MimickNet float32 weights to MimickNet Mobile int8 weights. This work demonstrates that real-time POCUS deep learning image enhancement is feasible using TPUs. Future ultrasound device manufacturers can consider incorporating a TPU for the added flexibility of supporting several deep learning architectures without compromising on power management and form factor.

关键词： Clinical ultrasound deep learning image enhancement MimickNet real-time processing TPU

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Efficient Sign Language Recognition System and Dataset Creation Method Based on deep learning and image processing 13

Efficient Sign Language Recognition System and Dataset Creat...

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13th International Conference on Digital image processing (ICDIP)

作者： Cavalcante Carneiro, A. L. Silva, L. Brito Pinheiro Salvadeo, D. H. State Univ Sao Paulo Dept Stat Appl Math & Computat Av 24A1515 BR-13506700 Rio Claro SP Brazil

ISBN: (数字)9781510646018

ISBN: (纸本)9781510646018

New deep-learning architectures are created every year, achieving state-of-the-art results in image recognition and leading to the belief that, in a few years, complex tasks such as sign language translation will be considerably easier, serving as a communication tool for the hearing-impaired community. On the other hand, these algorithms still need a lot of data to be trained and the dataset creation process is expensive, time-consuming, and slow. Thereby, this work aims to investigate techniques of digital image processing and machine learning that can be used to create a sign language dataset effectively. We argue about data acquisition, such as the frames per second rate to capture or subsample the videos, the background type, preprocessing, and data augmentation, using convolutional neural networks and object detection to create an image classifier and comparing the results based on statistical tests. Different datasets were created to test the hypotheses, containing 14 words used daily and recorded by different smartphones in the RGB color system. We achieved an accuracy of 96.38% on the test set and 81.36% on the validation set containing more challenging conditions, showing that 30 FPS is the best frame rate subsample to train the classifier, geometric transformations work better than intensity transformations, and artificial background creation is not effective to model generalization. These trade-offs should be considered in future work as a cost-benefit guideline between computational cost and accuracy gain when creating a dataset and training a sign recognition model.

关键词： deep learning sign language recognition convolutional neural networks image classification object detection

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Integrating image processing Techniques with deep learning Network Models to Detect and Identify Defects on the Surface of Tomato

Integrating Image Processing Techniques with Deep Learning N...

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Dinh Do van Sao Do University Hai Duong Viet Nam

Before export, fruit should be classified to improve quality, meet customer requirements and increase product value. This article proposes a method to identify defects on the surface of tomato skin using image processing techniques combined with deep learning models. The identification method includes the following main steps: (i) data collection (image of tomato: green, ripe, diseased, scratched), (ii) image labeling, (iii) data file division, (iv) model training, (v) selection and using models. The results of using Faster R-CNN model combining Resnet-10l and testing on YOLOv5 to identify and classify tomatoes that met and failed export for high accuracy (95.3 %) and met get real time.

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