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Deep Learning-Based Depth Map Defect Removal for Industrial applications 10

Deep Learning-Based Depth Map Defect Removal for Industrial ...

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conference on Optical Metrology and Inspection for Industrial applications X

作者： Voronin, V. Gapon, N. Zhdanova, M. Tokareva, O. Khamidullin, I. Semenishchev, E. Moscow State Univ Technol STANKIN Ctr Cognit Technol & Machine Vis Moscow Russia Don State Tech Univ Rostov Na Donu Russia

ISBN: (纸本)9781510667877;9781510667884

A system for determining the distance from the robot to the scene is useful for object tracking, and 3-D reconstructions may be desired for many manufacturing and robotic tasks. While the robot is processing materials, such as welding parts, milling, drilling, etc., fragments of materials fall on the camera installed on the robot, introducing unnecessary information when building a depth map, as well as the emergence of new lost areas, which leads to incorrect determination of the size of objects. There is a problem comprising a decrease in the accuracy of planning the movement trajectory caused by wrong sections on the depth map because of erroneous distance determination to objects. We present an approach combining defect detection and depth reconstruction algorithms. The first step for image defect detection is based on a convolutional auto-encoder (U-Net). The second step is a depth map reconstruction using a spatial reconstruction based on a geometric model with contour and texture analysis. We apply contour restoration and texture synthesis for image reconstruction. A method is proposed for restoring the boundaries of objects in an image based on constructing a composite curve by cubic splines. Our technique outperforms the state-of-the-art methods quantitatively in reconstruction accuracy on the RGB-D benchmark for evaluating manufacturing vision systems.

关键词： depth map defect detection image reconstruction manufacturing deep learning U-Net

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Multi-modal Multi-scale State Space Model for Medical Visual Question Answering 33rd

Multi-modal Multi-scale State Space Model for Medical Visual...

引用

33rd International conference on Artificial Neural Networks and machine Learning (ICANN)

作者： Chen, Qishen Bian, Minjie He, Wenxuan Xu, Huahu Shanghai Univ Shanghai Peoples R China Shanghai Data Grp Shanghai Peoples R China

ISBN: (纸本)9783031723520;9783031723537

Medical Visual Question Answering (Med-VQA) is pivotal for interpreting medical queries via corresponding images. While multi-modal fusion stages in Med-VQA benefit from attention mechanisms and Transformer-based methods, the latter's computational demands limit scalability. Emerging as a robust alternative, State Space Models (SSMs), particularly the Mamba model, have shown promise in sequence modeling and deep learning network building. However, their limitation to single-modality data processing curtails their direct application in complex vision-language tasks inherent in Med-VQA. Additionally, we identify and address the underutilization of multi-scale visual information in existing Med-VQA frameworks, incorporating it into our fusion process for enhanced context comprehension. Our approach also features an innovative asymmetric fusion structure tailored to bridge the gap between open-ended and close-ended questions, optimizing question answering accuracy. Comparative analyses on benchmark datasets VQA-RAD and SLAKE underscore our method's efficiency, outperforming state-of-the-art Med-VQA models in accuracy while operating with significantly fewer parameters than Transformer-based counterparts. This study not only proposed a powerful Med-VQA model but also broadens the scope of SSMs in tackling complex multi-modal challenges.

关键词： State Space Model Multi-Modal Fusion Medical Visual Question Answering Medical image

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Spiking Neural Network in Computer vision: Techniques, Tools and Trends 4th

Spiking Neural Network in Computer Vision: Techniques, Tool...

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4th International conference on Advanced Computational and Communication Paradigms, ICACCP 2023

作者： Agarwal, Rohit Ghosal, Palash Murmu, Narayan Nandi, Debashis National Institute of Technology Durgapur713209 India Sikkim Manipal Institute of Technology Sikkim Majitar737136 India

ISBN: (纸本)9789819942831

Artificial Neural Networks (ANN) have become one of the most powerful machine learning tools that cover a wide range of applications such as surveillance, video and image recognition, medical image analysis, control systems, robotics, bioinformatics, etc. These wide applications have encouraged researchers to search for more realistic neuron models that really mimic human biological neurons. Recent neurological research explored a set of new characteristics of biological neurons which led to generating the third-generation neural networks called Spiking Neural Networks (SNN). SNNs are more faithful to drawing biological properties which makes them energy-efficient and fast with higher processing capability. Many SNN models along with various learning algorithms have been proposed in the last decade to explore fast and efficient machine learning applications. In this paper, we have reviewed recent advancements in SNN architectures, their learning algorithms, applications, advantages, limitations and future scopes. We have also presented a brief comparison among some well-cited state-of-the-art works in terms of performance, advantages and limitations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Learning algorithms

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Classification of Animal Species Using a Deep Neural Network-Based Feature Extraction Method 21

Classification of Animal Species Using a Deep Neural Network...

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21st IEEE International conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024

作者： Ibrahim, Mohammed Al-Kubaise, Khamis Alkapti, Ali Almusa, Abdullah Abdelaziz, Osama Al-Maadeed, Somaya Sadasivuni, Kishor Kumar Qatar University Computer Science and Engineering Department Doha Qatar Center for Advanced Materials Qatar University PoBox 2713 Doha Qatar

ISBN: (纸本)9798350378078

This study presents an innovative approach to animal classification and recognition utilizing machine learning and deep learning methodologies. Leveraging advanced algorithms, the proposed system achieves remarkable accuracy in identifying diverse animal species. By integrating sophisticated image processing techniques, the system enhances image quality, improving overall performance. The research demonstrated that the SVM model combined with deep neural network-based feature extraction achieved the highest accuracy of 95.65%. This paper represents a significant stride toward improving the precision and efficiency of animal classification, offering promising applications in biodiversity conservation and ecological monitoring by using advanced feature extraction approach with deep learning. © 2024 IEEE.

关键词： Animal Recognition Computer vision Deep Learning Feature Extraction KNN

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V-DETR: Pure Transformer for End-to-End Object Detection 16th

V-DETR: Pure Transformer for End-to-End Object Detection

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16th Asian conference on Intelligent Information and Database Systems (ACIIDS)

作者： Dung Nguyen Van-Dung Hoang Van-Tuong-Lan Le Hue Univ Hue Univ Sci Hue City 530000 Vietnam HCMC Univ Technol & Educ Fac Informat Technol Ho Chi Minh City 720000 Vietnam Hue Univ Dept Acad & Students Affairs Hue City 530000 Vietnam

ISBN: (纸本)9789819749843;9789819749850

In the field of computer vision, the task of object detection is one of the important tasks, with many challenges and practical applications. Its task is to classify and determine the location of objects in images or videos. Many machine learning methods, especially deep learning, have been developed to perform this task. This article introduces a model combining DETR (DEtection TRansfomer) and ViT (vision Transformer) as a method to recognize objects in images/videos that only use components of the Transformer model. The DETR model achieves good results in object detection using the Transformer architecture and without the need for complex intermediate steps. The ViT model, a Transformer-based architecture, has brought about a breakthrough in image classification. Combining both architectures opens exciting prospects in computer vision. The input image automatically extracted features using the ViT model previously trained on the imageNet21K dataset, then the features will be fed into the Transformer model to find the classification and bounding box of the objects. Experimental results on test data sets showthat this combined model has better ability in object recognition than DETR and ViT alone. This brings important prospects for the application of the Transformer model not only in the field of natural language processing but also in the field of image classification and object detection. The results of our proposed model have quite high mAP@0.5= 0.444 accuracy, slightly better than the original DETR model. The code is available at https://***/nguyendung622/vitdetr.

关键词： Computer vision Object Detection Classification Convolutional Neural Networks Deep Learning DETR ViT

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Measuring System for Elongation at Break of Cable Insulation Sheath Based on machine vision 14

Measuring System for Elongation at Break of Cable Insulation...

引用

14th International conference on Digital image processing, ICDIP 2022

作者： Su, Xu Wang, Gangwei Zhang, Zhiqiang Yang, Jiale Zhang, Zhijia School of Artificial Intelligence Shenyang University of Technology China Land and Resources Exploration Center of Hebei Geological and Mineral Resources Exploration and Development Bureau China

ISBN: (纸本)9781510657564

In the production of power cables, the performance test of the cable insulation sheath is an important part. Compared with traditional testing methods, machine vision has the advantages of stable operation, high precision, and high efficiency. Because of this situation, firstly, based on machine vision theory, the structure of the old-fashioned tensile machine was reconstructed, and the whole tensile test process of the cable insulation sheath test was imaged by a CMOS camera, and the color recognition algorithm, effective area segmentation algorithm, and workpiece were proposed. The fracture judgment detection algorithm and the corrosion difference algorithm are used to calculate the distance between the marked lines and then calculate the elongation at the break of the cable material. Through systematic experiments on the same batch of cable jackets, the deviation of the elongation at break measured by visual inspection is the largest, no more than 1%. The experimental results and practical applications show that the machine vision-based visual inspection system has higher accuracy, faster efficiency, and more stable and reliable operation than the traditional inspection system. © 2022 SPIE.

关键词： Computer vision

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AI based Plant Growth Monitoring System using Computer vision

AI based Plant Growth Monitoring System using Computer Visio...

引用

2023 IEEE Technology and Engineering Management conference - Asia Pacific, TEMSCON-ASPAC 2023

作者： Bhamare, Archana Upadhyay, Vivek Bansal, Payal Poornima University Dept of Electronics & Communication Engineering Jaipur India

ISBN: (纸本)9798350384659

This research work aims to develop an AI-based plant growth monitoring system using computer vision. By leveraging computer vision algorithms and artificial intelligence techniques, the system will enable real-time and automated assessment of plant growth and health. Traditional methods for plant growth monitoring are time-consuming and labor-intensive. The proposed system will address these limitations by analyzing images of plants using image processing algorithms and extracting relevant features such as leaf area, plant height and width. machine learning algorithms will be employed to train models capable of recognizing patterns and predicting plant growth based on the extracted features. The system will be designed to accommodate different plant types and environmental conditions. By providing accurate and timely insights into plant health and growth, this AI-based plant growth monitoring system will benefit farmers, researchers, and plant enthusiasts, enabling informed decision-making for plant care and optimizing growth conditions. This research work aims to revolutionize plant growth monitoring by offering a cost-effective, scalable, and efficient solution with wide applications in agriculture, horticulture and environmental research. © 2023 IEEE.

关键词： Computer vision

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Retinal image Analysis for Detection of Diabetic Macular Edema Using Transfer Learning 8

Retinal Image Analysis for Detection of Diabetic Macular Ede...

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8th International conference on Data Science and machine Learning applications, CDMA 2025

作者： Asim, Tayyaba Akbar, Shahzad Shahzore, Usama Rehman, Amjad Saba, Tanzila Ayesha, Noor Riphah College of Computing Riphah International University Faisalabad Pakistan Prince Sultan University AIDA Lab. CCIS Riyadh Saudi Arabia Zhengzhou University Zhengzhou China Henan450001 China

ISBN: (纸本)9798331539696

Diabetic Macular Edema (DME) is a disease of the eye's retina and it's a major factor of causing vision problems and leads towards blindness if it is undiagnosed. Early detection of DME can prevent vision loss and may reduce diabetic-related problems like cardiovascular issues. Therefore, this study presents a method for detecting DME through Optical Coherence Tomography (OCT) and fundus images using transfer learning. The proposed method is based on four stages: Pre-processing, augmentation, segmentation through DeepLabV3+, and binary classification of DME by using two (02) publicly available datasets;the first dataset is Messidor-2, which contains fundus images, and the second dataset is Retinal images of Optical Coherence Tomography (OCT). In the Messidor-2 dataset, the total number of images is 1744, and Retinal OCT images dataset consists of 84,495 images total, separated into four categories (CNV, DME, DRUSEN, NORMAL). In the proposed method, Convolutional Neural Networks (CNN) architectures ResNet50 and VGG-19 have been used for the detection of the DME. Convolutional Neural Networks (CNNs) have been extensively used in medical imaging analysis and classification. Using the well-known ResNet50 architecture for classification of each dataset, the proposed model yielded an accuracy of 98.79%, 99% of F1 Score, 98.43% of Precision, and recall of 98.89%. By using VGG-19, the proposed model gives an accuracy of 98.81%, 98.94% of Fl Score, 98.1 % of Precision and recall of 98.73%. When both models (ResNet50 and VGG-19) were compared, the VGG-19 gave the best accuracy. © 2025 IEEE.

关键词： Convolutional neural networks

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Gabor Wavelets in Face Recognition and Its applications 1

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International conference on Intelligent Emerging Methods of Artificial Intelligence and Cloud Computing, IEMAICLOUD 2021

作者： Ojha, Manoj Department of Electronics and Communication Engineering Sanskriti University Uttar Pradesh Mathura India

ISBN: (数字)9783030929053

ISBN: (纸本)9783030929046

Face recognition has become an advanced area in the field of recognition and has various applications especially in biometrics, forensic investigations, smart advertising, national database for identity cards etc. Various methods and algorithms are available for face recognition using image processing, machine vision and deep learning. In image processing, wavelets transform is one of the effective methods as they are capable of extracting both spatial and frequency information of the given signal and is a substitute of Fourier transforms in many applications of signal processing used in machine vision, speech analysis etc. Among all the wavelet transforms the most efficient is Gabor wavelet transform that is widely used in research done in the field of image processingas it has certain important mathematical properties. Gaborwavelet transform is also used as they yield distortion tolerance space for various recognitions including face recognition and pattern recognition. This paper addresses the applications of Gabor wavelets in face recognition along with face reconstruction, iris recognition, and corner detection etc. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Face recognition

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11th EAI International conference on Context-Aware Systems and applications, ICCASA 2022

11th EAI International Conference on Context-Aware Systems a...

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11th EAI International conference on Context-Aware Systems and applications, ICCASA 2022

ISBN: (纸本)9783031288159

The proceedings contain 14 papers. The special focus in this conference is on Context-Aware Systems and applications. The topics include: Prediction of Chaotic Time Series Based on LSTM, Autoencoder and Chaos Theory;an Approach to Selecting Students Taking Provincial and National Excellent Student Exams;safe Interaction Between Human and Robot Using vision Technique;application of the image processing Technique for Powerline Robot;collaborative Recommendation with Energy Distance Correlation;blockchain Model in Industrial Pangasius Farming;multiple-Criteria Rating Recommendation with Ordered Weighted Averaging Aggregation Operators;a Survey of On-Chip Hybrid Interconnect for Multicore Architectures;a Framework for Brain-Computer Interfaces Closed-Loop Communication Systems;identification of Abnormal Cucumber Leaves image Based on Recurrent Residual U-Net and Support Vector machine Techniques;lung Lesion images Classification Based on Deep Learning Model and Adaboost Techniques;balltree Similarity: A Novel Space Partition Approach for Collaborative Recommender Systems.

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