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Power Electronics, Computer applications (ICPECA), IEEE International Conference on

作者： Xiu He Feng Ding Guangzhou Xinhua University Guangzhou China

As a prevailing research in artificial intelligence, the application of computer vision is widely used in many fields which are closely related to people's livelihood, such as industrial automation, new retail industry, smart transportation and security monitoring. And the proposed face recognition method is a branch in the field of computer vision, it integrates neural networks, biology, image signal processing, machine learning and other fields, which promote research and cross-development among different disciplines. Hence, this paper focuses on face recognition method by using convolutional neural network(CNN), and CNN has the property of "weight sharing", which has been widely popularized in image recognition, it can greatly simplify the work of large-scale network training. The experiments demonstrate that the proposed face recognition method is successful, and the accuracy of the proposed method can be as high as 98%.

关键词： Training Computer vision image recognition image color analysis Face recognition Color Signal processing

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Generalizing Functional Error Correction for Language and vision-Language Models

Generalizing Functional Error Correction for Language and Vi...

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International Conference on machine Learning and applications (ICMLA)

作者： Wenyu Peng Simeng Zheng Michael Baluja Tao Xie Anxiao Jiang Paul H. Siegel Electrical and Computer Engineering Department University of California San Diego La Jolla CA USA Computer Science San Diego State University San Diego CA USA Department of Computer Science and Engineering Texas A&M University College Station TX USA

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

The goal of functional error correction is to preserve neural network performance when stored network weights are corrupted by noise. To achieve this goal, a selective protection (SP) scheme was proposed to optimally protect the functionally important bits in binary weight representations in a layer-dependent manner. Although it showed its effectiveness in image classification tasks on some relatively simple networks such as ResNet-18 and VGG-16, it becomes inadequate for emerging complex machine learning tasks generated from natural language processing and vision-language association domains. To solve this problem, we extend the SP scheme in three directions: task complexity, model complexity, and storage complexity. Extensions to complex natural language and vision-language tasks include text categorization and “zero-shot” textual classification of images. Extensions to more complex models with deeper block structures and attention mechanisms consist of Very Deep Convolutional Neural Network (VDCNN) and Contrastive Language-image Pre-Training (CLIP) networks. Extensions to more complex storage configurations focus on distributed storage architectures to support model parallelism. Experimental results show that the optimized SP scheme preserves network performance in all of these settings. The results also provide insights into redundancy-performance tradeoffs, generalizability of SP across datasets and tasks, and robustness of partitioned network architectures.

关键词： Adaptation models Text categorization Neural networks Noise Parallel processing Network architecture Robustness Error correction Complexity theory Protection

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image-based biomass characterization: Comparison of conventional image processing and a deep learning approach

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FUEL 2023年 341卷

作者： Lukas, Johannes Kolb, Sebastian Heinbuch, Johann Willig, Lukas Plankenbuehler, Thomas Mueller, Dominik Karl, Jurgen Friedrich Alexander Univ Erlangen Nurnberg FAU Chair Energy Proc Engn Further Str 244f D-90429 Nurnberg Germany Prosio Engn GmbH Bergstr 6 D-91207 Lauf An Der Pegnitz Germany

Due to the increased use of inconsistent low-grade biogenic solid fuels and simultaneously stricter regulatory limitations for biomass combustion, there is a rising demand for suitable methods for an online characterization of fuels to allow for optimal plant operation. Aim of this work is to evaluate two camera-based machine vision approaches using a conventional feature extraction method based classification and a deep learning approach with a convolutional neural network (CNN) to distinguish six types of biogenic solid fuels and 20 fuel mixtures thereof. A comparison of four machine-learning algorithms applied to classify the samples based on all extracted color and texture features reaches a prediction accuracy of 93.5 %. An evaluation of the feature performance of single features shows that the selected color features (color thresholds and histogram-based features) are more relevant for distinguishing the fuels than the textural features (Haralick features and features extracted from the frequency domain). Despite the small size of the image dataset, also the CNN achieves a good prediction accuracy of 73.7 % for the given classification task. An increase of the number of images by fragmentation leads to a slightly increasing prediction accuracy for the deep learning approach while the accuracy of the conventional features learning approach decreases. Both approaches are suitable to distinguish six typical biogenic solid fuels and achieve high accuracies for the classification of 26 fuels and mixtures. While the feature learning is more accurate for the mentioned classification task, the CNN does not require prior feature extraction and would benefit from a larger dataset. These are promising results for an implementation as an online fuel monitoring in various applications, with further development in the robustness and extensions by means of a broader range of fuel mixtures being required.

关键词： Biomass combustion Fuel monitoring image processing machine vision Deep learning Feature extraction

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Segmentation of Breast Lesions in Ultrasound images Based on Swin-Unet

Segmentation of Breast Lesions in Ultrasound Images Based on...

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image processing, Computer vision and machine Learning (ICICML), International Conference on

作者： Chengkang Zhang Hongmin Ren Department of Computer Science Shanghai Maritime University SMU China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Medical image segmentation plays a key role in the early detection and diagnosis of breast cancer. Based on the Swin-Unet model in this paper, combined with the Multi-Scale Dilated Fusion Attention (MDFA) module and the Simple, Parameter-Free Attention Module (SimAM) module, improvements and optimizations have been made for the breast cancer image segmentation task. The BUSI dataset is employed, which offers abundant breast cancer image samples to assist in evaluating the effect of the model in practical applications. The MDFA module enhances the model's ability to capture features of various scales through multi-scale dilated convolution techniques, overcoming the deficiencies of traditional convolutions when dealing with complex lesion areas. The SimAM module improves the recognition ability of key features by simulating the activation process of neurons and calculating the similarity between feature points and their expected states, further enhancing the accuracy of the segmentation results. Experimental results indicate that, compared with mainstream models such as FCN, UNet, SegNet, and ENC-Net, the improved Swin-Unet demonstrates a significant improvement in performance on the BUSI dataset. The introduction of the MDFA and SimAM modules endows the model with higher precision and robustness when handling breast cancer images. This research not only validates the effectiveness of the improved model but also provides a new direction for the application of deep learning in future breast cancer image segmentation tasks.

关键词： image segmentation Ultrasonic imaging Convolution Neurons Transformers Breast cancer Robustness Lesions Optimization Medical diagnostic imaging

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PID-NET: A Novel Parallel image-Dehazing Network

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ELECTRONICS 2025年第10期14卷

作者： Liu, Wei Zhou, Yi Zhang, Dehua Qin, Yi Nanyang Normal Univ Sch Intelligent Mfg & Elect Engn Nanyang 473061 Peoples R China Nanyang Normal Univ Sch Artificial Intelligence & Software Engn Nanyang 473061 Peoples R China Henan Univ Sch Artificial Intelligence Kaifeng 475004 Peoples R China

image dehazing is a critical task in image restoration, aiming to retrieve clear images from hazy scenes. This process is vital for various applications, including machine recognition, security monitoring, and aerial photography. Current dehazing algorithms often encounter challenges in multi-scale feature extraction, detail preservation, effective haze removal, and maintaining color fidelity. To address these limitations, this paper introduces a novel Parallel image-Dehazing Network (PID-Net). PID-Net uniquely combines a Convolutional Neural Network (CNN) for precise local feature extraction and a vision Transformer (ViT) to capture global contextual information, overcoming the shortcomings of methods relying solely on either local or global features. A multi-scale CNN branch effectively extracts diverse local details through varying receptive fields, thereby enhancing the restoration of fine textures and details. To optimize the ViT component, a lightweight attention mechanism with CNN compensation is integrated, maintaining performance while minimizing the parameter count. Furthermore, a Redundant Feature Filtering Module is incorporated to filter out noise and haze-related artifacts, promoting the learning of subtle details. Our extensive experiments on public datasets demonstrated PID-Net's significant superiority over state-of-the-art dehazing algorithms in both quantitative metrics and visual quality.

关键词： deep learning image dehazing parallel processing multi-scale CNN vision transformer

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Vitamin Deficiency Detection using image processing through Dermatological Symptoms

Vitamin Deficiency Detection using Image Processing through ...

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Intelligent Systems and Advanced applications (ICISAA), International Conference on

作者： Sagar Janokar Nimish Somani Purvi Solanki Samiksha Solanke Aditya Solunke Ishan Soman Department of Electronics and Telecommunications Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

This study proposes a way to detect vitamin deficiency by combining machine learning and image processing. Computer vision enables the system to recognise visual symptoms of specific vitamin deficiencies. The recommended approach is that the entire procedure can be subdivided into specific key steps, which are initiated from image acquisition, followed by the image preprocessing steps used to enhance their quality. It catches the confusing patterns that are directed toward various abnormalities through a pretrained Convolutional Neural Network (CNN) model. Finally, with such patterns at hand, the categorisation takes place, which in turn helps to identify specific *** extensive experimentation across a diverse dataset, the system demonstrates remarkable accuracy in detecting deficiency. Its non-invasive nature permits early screening. This proves its potential for widespread implementation and directions for future enhancement, such as dataset expansion and exploration of other advanced architectures apart from CNN. With its promising capabilities, this approach represents a significant stride towards enhancing healthcare diagnostics and preventive measures related to vitamin deficiencies.

关键词： Visualization Accuracy Tongue Refining Nails machine learning Medical services Skin Convolutional neural networks Testing

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Visual Algorithm Research Based on One-click image Size Fast Measurement Platform 23

Visual Algorithm Research Based on One-click Image Size Fast...

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Proceedings of the 2023 International Conference on Advances in Artificial Intelligence and applications

作者： Guiping Lu Honghua Liu Kejun Wang Jiarang Liang Beijing Institute of Technology China

ISBN: (纸本)9798400708268

This article primarily introduces a design and algorithm system of a one-click measuring platform, which can quickly and accurately measure the size and dimensions of parts. Accomplishing by combining a camera and an algorithm, the one-click detection function effectively lowers the measurement time and labor costs in industrial production, increasing production efficiency. The platform's algorithm system is based on the Halcon platform and may employ various algorithms depending on the form, size, and backdrop of the gathered photos to extract models and highlight data. This essay concludes by summarizing the various operators utilized in the algorithm design and implementation process of the platform, including threshold segmentation techniques, binary image processing, grayscale processing, and contrast-increasing techniques.

关键词： image processing machine vision Visual Algorithm

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Multi-View Rotation Double-Layer Fusion CNN-LSTM for SAR Target Recognition

Multi-View Rotation Double-Layer Fusion CNN-LSTM for SAR Tar...

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Algorithm, image processing and machine vision (AIPMV), International Conference on

作者： Zijun Wang Gong Zhang Daiyin Zhu Qijun Dai College of Electronic and Information engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798350390254

ISBN: (纸本)9798350390261

Synthetic Aperture Radar (SAR) has a wide range of applications in the military and civilian fields. Due to the angle sensitivity of targets in SAR images and the neglect of correlation information between images from different viewpoints in single-view recognition, multi-view recognition methods have received widespread attention. We propose a multi-view rotation double-layer fusion (MVRDF) CNN-LSTM network to fuse the rotation features of multi-view images. It uses three parallel CNNs to extract features from randomly extracted multi-view images, rotates the feature sequence twice to obtain three sets of features. We add a layer of rotation angle dimension LSTM after a layer of angle dimension LSTM for double-layer fusion. The experimental results based on MSTAR and a measured dataset demonstrate the effectiveness and generalization of the proposed method.

关键词： image recognition Correlation Sensitivity Target recognition Fuses Feature extraction Radar polarimetry Convolutional neural networks Synthetic aperture radar Long short term memory

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Design of vision-guided Gripping System for 6DOF Robots Combined with Dexterous Hands

Design of Vision-guided Gripping System for 6DOF Robots Comb...

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Robotics, Control and Automation Engineering (RCAE), International Conference on

作者： Chengwen Wang Guoyang Wan Hanqi Li Xuna Li Da Zheng Mingyao Teng Dept. School of Electrical Engineering Anhui University of Engineering Wuhu

ISBN: (数字)9798350355642

ISBN: (纸本)9798350355659

In the robot application system incorporating dexterous hand, a vision-based robot grasping system is proposed to address the lack of robustness of dexterous hand in grasping fixed attitude objects. First, a 6DOF robot grasping system based on machine vision is constructed using dexterous hand, depth camera and 6DOF collaborative robot, which realizes accurate grasping under vision guidance; second, to solve the problem of vision system's poor localization accuracy due to the loss of image information and features caused by image noise, occlusion and complex background in the process of image processing, a pooling layer and attention mechanism to enhance the feature extraction ability; moreover, an optimized dexterous hand grasping strategy is proposed through exhaustive grasping action design and analysis, which effectively improves the robustness of the system. The experimental results show that the accuracy of the target detection model reaches 87% through the localization measurement of the experimental objects, which is 2.1% higher than the original method, and the grasping success rate of the robotic system equipped with dexterous hand and depth camera is improved by 3.5%. These results validate the feasibility of the robotic grasping system incorporating dexterous hands in practical applications and significantly enhance the robustness of the system.

关键词： Hands Location awareness Accuracy machine vision Robot vision systems Grasping Cameras Feature extraction Robustness Robots

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A Review on image Segmentation 1st

A Review on Image Segmentation

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1st FICR International Conference on Rising Threats in Expert applications and Solutions, FICR-TEAS 2020

作者： Jaiswal, Sushma Pandey, M.K. India

ISBN: (纸本)9789811560132

Along with computer technology, the demand of digital image processing is too high and it is used massively in every sector like organization, business, medical and so on. image segmentation enables us to analyze any given image in order to extract information from the image. Numerous algorithm and techniques have been industrialized in the field of image segmentation. Segmentation has become one of the prominent tasks in machine vision. machine vision enables the machine to vision the real-world problems like human does and also acts accordingly to solve the problem, so it is utmost important to come up with the techniques that can be applied for the image segmentations. Invention of modern segmentation methods like instance, semantic and panoptic segmentation has advanced the concept of machine vision. This paper focuses on the various methods of image segmentation along with its advantages and disadvantages. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Computer vision

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