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17th International Conference on Interfaces and Human Computer Interaction 2023, IHCI 2023, the 17th International Conference on Computer Graphics, visualization, Computer vision and image processing 2023, CGvCvIP 2023 and 16th International Conference on Game and Entertainment Technologies 2023, GET 2023

作者： Sautter, Rubens Andreas Rosa, Reinaldo Roberto Alavarce, Debora Cristina Silva, Daniel Guimarães Av. Dos Astronautas 1.758 CEP 12245-027 São José dos Campos SP Brazil Hipocampus EdTech - Digital Learning R. do Serimbura 320 CEP 12243-360 São José dos Campos SP Brazil

ISBN: (纸本)9789898704498

This paper describes a new application of the technique known as Gradient Pattern Analysis (GPA), focused here on computer vision. In the GPA domain, the image is translated into a tessellation triangulation field based on the vectors positions that make up the gradient lattice of the matrix image. The GPA version considered here generates three attributes (G1, G2 and G3) that can be used as labels for a supervised machine-learning model. The case study presented here shows that GPA is a useful tool for real-time fetal biometry from 2D ultrasound images. The application in obstetrics indicates that the technique can also be useful for learning diagnostic imaging in gynecology, hepatology and oncology. The generalization of the technique to other applications in practical learning in health is discussed. © 2023 Proceedings of the International Conferences on Interfaces and Human Computer Interaction 2023, IHCI 2023;Computer Graphics, visualization, Computer vision and image processing 2023, CGvCvIP 2023;and Game and Entertainment Technologies 2023, GET 2023. All rights reserved.

关键词： Computer vision

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Detection of Plant Leaf Disease Using image processing and Automation of Pesticide Spraying 8th

Detection of Plant Leaf Disease Using Image Processing and A...

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8th International Conference on Emerging Research in Computing, Information, Communication and applications, ERCICA 2023

作者： Kulkarni, Shreeram v. Hegde, vasudha Naik, Manasa Bhavana, R. Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9789819976218

The plant diseases have a direct impact on the quality and quantity of the crop, and by diagnosing them, the market value of agricultural products increases. This exemplifies the significance of healthy plants as well as the relevance of early identification of disease on leaves. The early detection has the difficulty of manpower’s’ inadequate knowledge on usage of sophisticated technology for plant disease detection. To automate this time-consuming process, this work proposes to build a device that takes pictures as input and detects damaged leaves while classifying the plant condition. Based on the disease detected, suitable pesticide is suggested from the database and the automatic spraying in the affected part is carried out. In this article, diseases in tomato and potato plants using image processing and machine learning techniques (convolution neural network) are used to automate disease identification and automated method for suitable pesticide spraying which is implemented. The identification of the disease has been implemented with 96% accuracy. The hardware implementation has the wheels controlled by 12 v DC motor R365 connecting spraying machine nozzle. The L293D motor driver is controlled by Arduino Uno. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Agricultural automation Convolution neural network image processing Plant leaf disease detection

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A new stereo matching energy model based on image local features

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MULTIMEDIA TOOLS AND applications 2023年第23期82卷 35651-35684页

作者： Hongjin, Zhang Hui, Wei Gang, Ma Fudan Univ Sch Comp Sci Lab Algorithms Cognit Models Shanghai 200433 Peoples R China

This paper constructs an energy model based on local features used in stereo matching. The local features include the similarity between different image areas, the matching cost function pattern, the connection between neighbor pixels, and the occlusion geometric relationship. Based on these features, we define the weight of each data term and smoothing term in the energy function and then design an algorithm to solve the energy model and get disparity results. The significant improvements of this paper include as following. 1) We modify the structure of the energy function. First, we define the weight of the data term based on the reliability of its corresponding disparity result, which is obtained by cost function features and the occlusion geometric relationship. Then we define the weight of the smoothing term by analyzing the characteristic relation between neighbor super-pixels. We can also reduce the computational complexity by detecting and reducing some low-strength connections. 2) We proposed an algorithm based on pairwise Markov random field (MRF) (Taniai et al., IEEE Trans Pattern Anal machine Intell 40(11): 2725-2739, 2017) and local greedy iteratively, which can be used to solve the energy model. 3) In post-optimation, we select some areas with severe occlusion and fewer matching clues for post-interpolation fitting to optimize the results. The experiment shows that the proposed method reduced the average percentage of bad pixels (in bad 3) to 6.06 on the Middlebury dataset and 1.42 on the KITTI dataset. Finally, we compare our results with those of MC-Cnn (Zbontar and LeCun 2015), CF-Net (Shen et al., 2021), Guided-Stereo (Poggi et al., 2019), Gwc-Net (Guo et al., 2019) and Patchmatch-Net(PM-Net) (Wang et al., 2021) to verify the improved speed and accuracy of our algorithm, especially at recognizing the depth of changing edges and small objects. This paper's relevant research can contribute to practical engineering practices such as assisted vision, i

关键词： Computer vision Stereo image processing Disparity reliable Local feature Cost function Weight measurement

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Continual Learning for Blind image Quality Assessment

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND machine INTELLIGENCE 2023年第3期45卷 2864-2878页

作者： Zhang, Weixia Li, Dingquan Ma, Chao Zhai, Guangtao Yang, Xiaokang Ma, Kede Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai 200240 Peoples R China Peng Cheng Lab Shenzhen Guangdong Peoples R China City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong Peoples R China

The explosive growth of image data facilitates the fast development of image processing and computer vision methods for emerging visual applications, meanwhile introducing novel distortions to processed images. This poses a grand challenge to existing blind image quality assessment (BIQA) models, which are weak at adapting to subpopulation shift. Recent work suggests training BIQA methods on the combination of all available human-rated IQA datasets. However, this type of approach is not scalable to a large number of datasets and is cumbersome to incorporate a newly created dataset as well. In this paper, we formulate continual learning for BIQA, where a model learns continually from a stream of IQA datasets, building on what was learned from previously seen data. We first identify five desiderata in the continual setting with three criteria to quantify the prediction accuracy, plasticity, and stability, respectively. We then propose a simple yet effective continual learning method for BIQA. Specifically, based on a shared backbone network, we add a prediction head for a new dataset and enforce a regularizer to allow all prediction heads to evolve with new data while being resistant to catastrophic forgetting of old data. We compute the overall quality score by a weighted summation of predictions from all heads. Extensive experiments demonstrate the promise of the proposed continual learning method in comparison to standard training techniques for BIQA, with and without experience replay. We made the code publicly available at https://***/zwx8981/BIQA_CL.

关键词： Blind image quality assessment continual learning subpopulation shift

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PROP - where learning is made fun

PROP - where learning is made fun

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2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2024

作者： Rajeshwari, P. Banushree, B. Nair, Niveditha G. Nivya, Nivya Krupa, Shri Dayananda Sagar College of Engineering Department of Electronics and Telecommunication Engineering Bangalore India

ISBN: (纸本)9798350348460

Writing in air has become a significant research area in image processing and pattern recognition, contributing to automation and improving human-machine interfaces in various applications. Object tracking, a crucial task in Computer vision, is increasingly popular due to faster computers, high-quality video cameras, and automated video analysis demands. This study provides a comparison of virtual air canvas using OpenCv and Mediapipe and air canvas application using OpenCv and Numpy in python. Further it dwells into usage of Augmented Reality for educational purposes and how if these two if are integrated together will revolutionize teaching and learning © 2024 IEEE.

关键词： Augmented Reality Computer vision Mediapipe Numpy Object Tracking virtual Air Canvas

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A Flexible machine vision AI System for Edge-Oriented Deep Learning Accelerators

A Flexible Machine Vision AI System for Edge-Oriented Deep L...

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2023 International Technical Conference on Circuits/Systems, Computers, and Communications, ITC-CSCC 2023

作者： Song, Joon Boum Kim, Yumi Lee, Minkyu Lee, Sang-Seol Kim, Kyungho Korea Electronics Technology Institute Korea Republic of

ISBN: (纸本)9798350326413

In recent 10 years, deep learning has successfully shown its effectiveness in various computer vision fields such as autonomous vehicles, robotics, and AI surveillance. Numerous machine vision AI systems have been accordingly developed to run those deep learning algorithms. However, existing PC-based machine vision AI systems have the disadvantage of having to modify the entire system although just a small change is required. They are also considerably high cost/power consuming systems for edge device purpose which is targeted in this study. In addition, a number of FPGA-based machine vision AI systems are not suitable for multiple applications as they are dedicatedly designed. In this work, in order to overcome those disadvantages, we have developed a flexible FPGA-based machine vision AI system where various type of accelerator can be implemented. An accelerator integrated with a post processing unit(PPU) that runs an object detection was successfully demonstrated on the system satisfying the required input image resolution/format. The proposed system can also provide multiple resolution/format of input images which is proved via Xilinx integrated logic analyzer(ILA) debugging. We therefore ensure that this system can further support more accelerators which can be deployed in diverse applications. © 2023 IEEE.

关键词： Object detection

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Transparent machine vision Techniques For Medicinal Plant Species Identification 5

Transparent Machine Vision Techniques For Medicinal Plant Sp...

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5th International Conference on image processing and Capsule Networks, ICIPCN 2024

作者： Kikome, Christine Okumu, Geoffrey Wagisha, Emmanuel Jjingo, Daudi Kizito, John Marvin, Ggaliwango Makerere University Computer Science Department Kampala Uganda

ISBN: (纸本)9798350367171

Medicinal plants have long been the foundation of the medical system and a source of health and healing, but many people nowadays are unaware of these priceless natural resources or the range of possible applications they provide. Traditional medical professionals known as herbalists mostly depend on their understanding of therapeutic herbs to cure a wide range of illnesses. Unfortunately, identifying medicinal plants by hand from their leaves takes a lot of time, labour, and specialised knowledge. The purpose of this project was to create and construct a web application for the identification of plant species, namely medicinal plants that are present across Uganda. On a publicly available dataset, many machine learning and deep learning models, including Convolutional Neural Networks (CNN), vGG19 and 16, Resnet50, vision Transformer (viT), and EfficientNet B0, were trained. Many features in the dataset were extracted from each leaf such as its length, width, texture, area, and color. For the ten classes of medicinal plant species identified in the globe, the model's accuracy rates were 99%, 95.70%, 9 2. 5 8 %, 9 7. 9 0 %, 9 1. 2 0 %, and 3 5. 6 9 %, respectively. In order to improve transparency and visual explanations for its picture classifications, this model makes use of Grad-CAM and SHAP, which are explainable AI (XAI) approaches. It is anticipated that a web-based system for the automatic identification of medicinal plants will contribute significantly to the production of pharmaceutical drugs, help members of the community increase their knowledge of medicinal plants, and assist taxonomists in creating more effective methods for identifying species. © 2024 IEEE.

关键词： Medicinal chemistry

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Electoral Symbols and vote detection in Paper Ballots - A Case from Nepal's Election 17

Electoral Symbols and Vote detection in Paper Ballots - A Ca...

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17th International Conference on machine vision, ICMv 2024

作者： Shrestha, Raju Acharya, Suraj Department of Computer Science Oslo Metropolitan University Oslo Norway

ISBN: (纸本)9781510688278

This paper investigates the potential of advanced object detection technologies to automate and enhance the accuracy and efficiency of the vote counting process in democratic elections that utilize paper-based ballots with electoral symbols. The study focuses on detecting electoral symbols and votes on paper ballots by utilizing two state-of-the-art object detection models: Faster R-CNN, and YOLO. These models were fine-tuned by training them with the ballot papers created using the dataset prepared from the electoral symbols used in Nepal's general election to ensure high accuracy and reliability in recognizing and validating votes. The system's effectiveness was demonstrated through a comparison of the models, highlighting the superior performance of Faster R-CNN in terms of precision, despite its slower processing speed compared to YOLO. The results indicate that incorporating object detection technologies into electoral systems can significantly improve efficiency of vote counting process. The study underscores the potential for broader applications of these technologies in promoting transparent and fair elections, especially in countries like Nepal, where traditional paper ballots are still prevalent. This innovative approach ensures a more reliable and efficient electoral process, reducing human error and increasing trust in election outcomes. © 2025 SPIE.

关键词： Object detection Systems modeling image processing Performance modeling Transparency Data modeling Artificial intelligence Deep learning

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No Prompting Frozen Foundation Models: Interactive Medical volume Segmentation using Continual Test Time Adaptation of Compact Models 24

No Prompting Frozen Foundation Models: Interactive Medical V...

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15th Indian Conference on Computer vision Graphics and image processing

作者： Borkar, Kushal Reen, Abhilaksh Singh Jawahar, C. v. Arora, Chetan Int Inst Informat Technol Hyderabad Hyderabad India Indian Inst Technol Delhi Delhi India IIIT Hyderabad Hyderabad India

ISBN: (纸本)9798400710759

Automated segmentation of medical image volumes promises to reduce costly medical experts' time for annotation. However, using machine learning for the task is challenging due to variations in imaging modalities and scarcity of patient data. While interactive image segmentation methods and foundational models incorporating user-provided prompts to refine segmentation masks have shown promise, they overlook crucial sequential information between the slices in 3D medical image volumes and videos, resulting in discontinuities in the segmentation results. This paper proposes a new framework that dynamically updates model parameters during inference in a test time training framework using user-provided scribbles. Our framework preserves acquired knowledge from the previous slices of the current medical volume and the training dataset via student-teacher learning. We evaluate our method on diverse CT, MRI, and microscopic cell datasets. Our framework significantly reduces user annotation time by a factor of 6.72x. Compared to other interactive segmentation methods, we reduce the time by a factor of 2.64x. Our method also outperforms prompting foundation models for segmentation by achieving a dice score of 0.9 in 3-4 interactions compared to 5-8 user interactions for the foundation model, significantly reducing annotation time for the CT and MRI volumes.

关键词： Medical image Analysis image Segmentation Human-Computer Interaction Continual Learning

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Design and development of smart Internet of Things-based solid waste management system using computer vision

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ENvIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2022年第43期29卷 64871-64885页

作者： Sivakumar, Mookkaiah Senthil Gurumekala, Thangavelu Rahul, Hebbar Nipun, Haldar Hargovind, Singh Indian Inst Informat Technol Tiruchirappalli Tiruchirappalli Tamil Nadu India Anna Univ Madras Inst Technol Chennai Tamil Nadu India

Municipal solid waste (MSW) management currently requires critical attention in ensuring the best principles of socio-economic attributes such as environmental protection, economic sustainability, and mitigation of human health problems. Numerous surveys on the waste management system reveal that approximately 90% of the MSW systems are improperly disposing the wastages in open dumps and landfills. Classifying the wastages into biodegradable and non-biodegradable helps converting them into usable energy and disposing properly. The advancements of effective computational approaches like artificial intelligence and image processing provide wide range of solutions for the present problem identified in MSW management. The computational approaches can be programmed to classify wastes that help to convert them into usable energy. Existing methods of waste classification in MSW remain unresolved due to poor accuracy and higher error rate. This paper presents an experimented effective computer vision-based MSW management solution with the help of the Internet of Things (IoT), and machine learning (ML) techniques namely regression, classification, clustering, and correlation rules for the perception of solid waste images. A ground-up built convolutional neural network (CNN) and CNN by the inception of ResNet v2 models trained through transfer learning for image classification. ResNet v2 supports training large datasets in deep neural networks to achieve improved accuracy and reduced error rate in identity mapping. In addition, batch normalization and mixed hybrid pooling techniques are incorporated in CNN to improve stability and yield state of art performance. The proposed model identifies the type of waste and classifies them as biodegradable or non-biodegradable to collect in respective waste bins precisely. Furthermore, observation of performance metrics, accuracy, and loss ensures the effective functions of the proposed model compared to other existing models. The propo

关键词： machine learning Transfer learning Internet of Things Convolutional neural networks Deep learning Computer vision

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