检索结果-内蒙古大学图书馆

Advances in Computer vision and Deep Learning and Its Applications

ELECTRONICS 2025年第8期14卷 1551-1551页

作者： Wang, Aili Wu, Haibin Iwahori, Yuji Harbin Univ Sci & Technol Heilongjiang Prov Key Lab Laser Spect Technol & Ap Harbin 150080 Peoples R China Chubu Univ Dept Comp Sci 1200 Matsumoto cho Kasugai 4878501 Japan

(1) Computer vision: The field of computer vision is making significant strides in dynamic reasoning capability through test-time scaling (TTS) [1] technology. TTS optimizes the robustness and interpretability of models in complex tasks by flexibly allocating computational resources. Multimodal base models, such as CLIP (contrastive language-image pre-training) [2] and Florence, facilitate the deep fusion of vision and language through cross-modal alignment techniques. These advancements have significantly improved the accuracy of visual question answering (VQA) and cross-modal retrieval. Generative AI technologies, such as Stable Diffusion, have also broken through the limitations of 2D image generation, enabling the transition to semantics-driven 3D scene models, like neural radiance fields (NeRF) [3]. This shift supports the generation of spatial models with physically interactive attributes from a single sheet of input, providing a new paradigm for virtual reality and industrial design. In addition, the introduction of the spatial intelligence [4] concept allows computer vision systems to simulate physical interactions in 3D space, driving the development of embodied intelligence and robot *** articles included in this Special Issue cover advancements in ten research directions: computer vision, feature extraction and image selection, pattern recognition for image processing techniques, image processing in intelligent transportation, neural networks, machine learning and deep learning, biomedical image processing and recognition, image processing for intelligent surveillance, deep learning for image processing, robotics and unmanned systems, and AI-based image processing, understanding, recognition, compression, and reconstruction. I have categorized the 33 articles included in this Special Issue based on these research directions, with the classification system not only demonstrating the vertical extension of the technological depth but also embodyin

关键词：

来源：评论

学校读者我要写书评

暂无评论

image Stitching techniques Applied to Plane or 3-D Models: A Review

引用

IEEE SENSORS JOURNAL 2023年第8期23卷 8060-8079页

作者： Fu, Mengyin Liang, Hao Zhu, Chunhui Dong, Zhipeng Sun, Rundong Yue, Yufeng Yang, Yi Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Nanjing Univ Sci & Technol Sch Automat Nanjing 210014 Peoples R China Beijing Inst Technol Integrated Nav & Intelligent Nav Lab Beijing 100081 Peoples R China Beijing Inst Technol Natl Key Lab Autonomous Intelligent Unmanned Syst Beijing 100081 Peoples R China

image stitching is a technique in which multiple overlapping images of the scene are stitched together to generate an image with a wide view and high resolution. image stitching methods can be broadly classified into feature-based and deep learning methods. Feature-based methods use manually designed features to establish transformation relationships between multiple images. This technology has played an important role in medical, industrial, military, and other fields. With the rise of deep learning in computer vision, it has also become the mainstream method in the field of image stitching. This article provides a systematic literature review of image stitching techniques applied on the plane and 3-D models for both feature-based and deep learning methods. We divide the stitching methods into two categories, namely, mosaic stitching methods for generating stitched plane images and panoramic stitching methods for generating stitched panoramic images. Based on the camera type, it is further divided into pinhole camera plane stitching methods, pinhole camera panoramic stitching methods, fisheye camera panoramic stitching methods, and light field camera plane stitching methods. An extensive search was conducted in International conference on image processing (ICIP), IEEE Transactions on image processing (TIP), International conference on Computer vision (ICCV), European conference on Computer vision (ECCV), IEEE conference on Computer vision and Pattern Recognition (CVPR), British Machine vision conference (BMVC), International conference on Pattern Recognition (ICPR), International Journal of Computer vision (IJCV), IEEE/ASME International conference on Advanced Intelligent Mechatronics (AIM), IEEE Transactions on Intelligent Transportation systems (ITS), IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), and ACM SIGGRAPH Computer Graphics (SIGGRAFH) to summarize related image stitching techniques;89 articles are selected for systematic literatur

关键词： Cameras image stitching Pipelines Sensors Light fields Bibliographies Systematics Geometric correction image models image stitching reconstruction registration

来源：评论

学校读者我要写书评

暂无评论

Enhancing manufacturing process accuracy: A multidisciplinary approach integrating computer vision, machine learning, and control systems

引用

JOURNAL OF MANUFACTURING PROCESSES 2025年 142卷 453-467页

作者： Ramesh, Kaki Deshmukh, Sandip Ray, Tathagata Parimi, Chandu Birla Inst Sci & Technol Pilani Dept Mech Engn Hyderabad Campus Hyderabad 500078 Telangana India Birla Inst Sci & Technol Pilani Dept Comp Sci & Informat Syst Hyderabad Campus Hyderabad 500078 Telangana India Birla Inst Sci & Technol Pilani Dept Civil Engn Hyderabad Campus Hyderabad 500078 Telangana India

Manufacturing industries face significant challenges in producing high-quality, faultless products within limited timeframes. Conventional human-based inspection methods are still prone to errors and cannot guarantee precise component placement, potentially leading to product failures, user hazards, and substantial financial and reputational losses. This research presents a workflow to automate an inspection system that integrates computer vision, machine learning, image processing, and control systems to address these challenges. The proposed system employs a microcontroller and stepper motors to control a highly calibrated camera, enabling precise and efficient product inspection. At its core, the system utilizes the YOLOv5 model for object detection, specifically identifying hole marks and holes on products pre-assembly. This deep learning model was chosen for its real-time detection capabilities and high accuracy, achieving a mean Average Precision (mAP) of 0.95, which surpasses many current industry standards. Following object detection, advanced image processing techniques are applied to determine the precise position of detected features. Our approach achieves a notable error rate of 0.2 %, offering improvements over traditional inspection methods. Our system offers the potential to reduce inspection processing time and improve fault identification accuracy in real-time applications. Our research contributes to the field of industrial automation by introducing a seamless integration of state-of-the-art computer vision techniques with practical control systems. The system's modular design allows for easy adaptation to various manufacturing environments, benefiting industries with complex assembly processes, such as electronics, automotive manufacturing, etc. While the current implementation focuses on hole detection, future work will explore expanding the system's capabilities to identify a broader range of defects and adapt to different product types. This re

关键词： Deep learning image processing Assembly Manufacturing industries Quality assurance

来源：评论

学校读者我要写书评

暂无评论

vision transformer-based model for early detection of dysgraphia among school students

引用

MICROSYSTEM TECHNOLOGIES-MICRO-AND NANOsystems-INFORMATION STORAGE AND processing systems 2025年第3期31卷 775-785页

作者： Sharma, Prateek Agarwal, Basant Yadav, Gyan Singh Jain, Sonal Indian Inst Informat Technol Kota Rajasthan India Cent Univ Rajasthan Kishangarh Rajasthan India Sardar Patel Univ PG Dept Comp Sci & Tech Anand India

Learning disorders, an umbrella term for a range of learning difficulties, impair a person's capacity to learn new skills. Dysgraphia is one of the prevalent learning disorders among children all over the globe. It is defined as a child's functional restriction in establishing accurate letter or word construction, inadequate speed, and readability of written text. Lack of availability of experts who can diagnose and high diagnostic cost, makes it important to discover a diagnostic approach for dysgraphia that is accurate, accessible and simple to use. Analyzing handwriting is the most common technique to detect dysgraphia which can be automated through image processing techniques. The use of deep learning algorithms has become increasingly widespread in image processing over the course of the last few decades and analysis. However, the effective classification of these handwritten images presents a number of challenges like low accuracy, inadequate availability of labelled data for training purposes. Considering the notable efficacy demonstrated by the vision Transformer (ViT) in image classification, we proposed a ViT-based classification model in this paper. This model splits handwriting images into patches and then process those through a transformer. Just like word embedding, these input image patches are passed in a sequence to the transformer. To compare performance of proposed model, we also applied transfer learning techniques VGG16, VGG19, ResNet50 and InceptionV3. After comparing the results, it was found that vision Transformers are best suitable for the classification. vision transformer has outperformed with macro average F1 score value of 0.92 for the classification. Out of all pretrained models VGG16 performed best with the macro average F1 score value of 0.90. The findings of this study indicate that ViT-based model has the potential to assist experts in the early detection of dysgraphia.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Flexible and Transportable Illumination Control System for Machine vision Applications 10th

Flexible and Transportable Illumination Control System for M...

引用

International conference on new Technologies, Development and Application - Advanced Production Processes and Intelligent systems

作者： Gusic, Muhamed Sokic, Emir Univ Sarajevo Dept Automat Control & Elect Fac Elect Engn Sarajevo 71000 Bosnia & Herceg

ISBN: (纸本)9783031662706;9783031662713

During the development of machine vision setups for industrial applications, namely automatic visual inspection systems, the type of required lighting and correct illumination position and intensity are often not known in advance. In order to solve a specific machine vision task, an engineer needs to propose and implement a suitable image processing algorithm and design the accompanying lighting system. Testing different illumination concepts, especially "in the field", using off-the-shelf lighting systems is time consuming. The paper presents a custom-made, cost effective, transportable and flexible system which may be used for testing different illumination techniques and simultaneously control multiple lighting modules. In addition to the hardware part, the developed software used for control the system is presented in the paper. The proposed device usefulness is demonstrated by solving a suitable multi-light digital image processing task.

关键词： machine vision visual inspection illumination control system embedded systems digital image processing

来源：评论

学校读者我要写书评

暂无评论

Evaluating Accuracy and Efficiency of Fruit image Generation Using Generative AI Diffusion Models for Agricultural Robotics 39

Evaluating Accuracy and Efficiency of Fruit Image Generation...

引用

39th International conference on image and vision Computing new Zealand

作者： Zhao, Kun Minh Nguyen Yan, Weiqi Auckland Univ Technol Dept Comp Sci & Software Engn Auckland New Zealand

ISBN: (纸本)9798331518783;9798331518776

The automation of fruit picking using robotic systems is rapidly advancing within precision agriculture, relying heavily on the accurate detection and segmentation of fruit objects through computer vision. This study explores the use of generative artificial intelligence (AI) to create extensive fruit image datasets, concentrating on cherries, bananas, oranges, apples, and pineapples. Five generative models-Stable Diffusion v1.4, Stable Diffusion v1.5, Stable Diffusion v2, Stable Diffusion XL Refiner 1.0, and SDXL Turbo-were employed to generate 1000 images per fruit type, each set against a white background. Thresholding techniques were used to automatically extract fruit boundaries, which were then utilized to train segmentation models. The performance of these models was evaluated based on their accuracy and processing speed. SDXL Turbo consistently delivered the highest accuracy across all fruit types, though it required more processing time per image. Stable Diffusion XL Refiner 1.0 also exhibited strong accuracy but balanced performance differently. In contrast, Stable Diffusion v2 showed significant shortcomings, particularly in producing accurate images for cherries and oranges. This comparative analysis highlights the potential of advanced generative models in enhancing synthetic dataset creation for fruit object detection and segmentation in agricultural robotics. Future research will focus on refining these models to improve accuracy, broaden their applicability to a wider variety of fruits and environments, and optimize the trade-off between image quality and generation

关键词： Precision agriculture fruit detection object segmentation generative AI synthetic dataset Stable Diffusion agricultural robotics image processing computer vision automation

来源：评论

学校读者我要写书评

暂无评论

Key Technologies for Machine vision for Picking Robots:Review and Benchmarking

引用

Machine Intelligence Research 2025年第1期22卷 2-16页

作者： Xu Xiao Yiming Jiang Yaonan Wang College of Electrical and Information Engineering Hunan UniversityChangsha 410082China National Engineering Research Center for Robot Vision Perception and Control Technology Hunan UniversityChangsha 410082China

The increase in precision agriculture has promoted the development of picking robot technology,and the visual recognition system at its core is crucial for improving the level of agricultural *** paper reviews the progress of visual recognition tech-nology for picking robots,including image capture technology,target detection algorithms,spatial positioning strategies and scene *** article begins with a description of the basic structure and function of the vision system of the picking robot and em-phasizes the importance of achieving high-efficiency and high-accuracy recognition in the natural agricultural ***-sequently,various image processing techniques and vision algorithms,including color image analysis,three-dimensional depth percep-tion,and automatic object recognition technology that integrates machine learning and deep learning algorithms,were *** the same time,the paper also highlights the challenges of existing technologies in dynamic lighting,occlusion problems,fruit maturity di-versity,and real-time processing *** paper further discusses multisensor information fusion technology and discusses methods for combining visual recognition with a robot control system to improve the accuracy and working rate of *** the same time,this paper also introduces innovative research,such as the application of convolutional neural networks(CNNs)for accurate fruit detection and the development of event-based vision systems to improve the response speed of the *** the end of this paper,the future development of visual recognition technology for picking robots is predicted,and new research trends are proposed,including the refinement of algorithms,hardware innovation,and the adaptability of technology to different agricultural *** purpose of this paper is to provide a comprehensive analysis of visual recognition technology for researchers and practitioners in the field of agricul-tural rob

关键词： Picking robot visual system perception technology image processing machine learning deep learning.

来源：评论

学校读者我要写书评

暂无评论

A hybrid transformer-sequencer approach for age and gender classification from in-wild facial images

引用

NEURAL COMPUTING & APPLICATIONS 2024年第3期36卷 1149-1165页

作者： Singh, Aakash Singh, Vivek Kumar Banaras Hindu Univ Dept Comp Sci Varanasi 221005 India

The advancements in computer vision and image processing techniques have led to emergence of new application in the domain of visual surveillance, targeted advertisement, content-based searching, human-computer interaction, etc. Out of the various techniques in computer vision, face analysis, in particular, has gained much attention. Several previous studies have tried to explore different applications of facial feature processing for a variety of tasks, including age and gender classification. However, despite several previous studies having explored the problem, the age and gender classification of in-wild human faces is still far from achieving the desired levels of accuracy required for real-world applications. This paper, therefore, attempts to bridge this gap by proposing a hybrid model that combines self-attention and BiLSTM approaches for age and gender classification problems. The proposed model's performance is compared with several state-of-the-art models proposed so far. An improvement of approximately 10% and 6% over the state-of-the-art implementations for age and gender classification, respectively, is noted for the proposed model. The proposed model is thus found to achieve superior performance and is found to provide a more generalized learning. The model can, therefore, be applied as a core classification component in various image processing and computer vision problems.

关键词： Age classification Gender classification Hybrid classification approach Sequencer vision transformer

来源：评论

学校读者我要写书评

暂无评论

UAV Detection Using Template Matching and Centroid Tracking

引用

IEEE ACCESS 2024年 12卷 129362-129375页

作者： Hanzla, Muhammad Yusuf, Muhammad Ovais Sadiq, Touseef Al Mudawi, Naif Rahman, Hameedur Alazeb, Abdulwahab Alarfaj, Aisha Ahmed Algarni, Asaad Air Univ Fac Comp & AI Islamabad 44000 Pakistan Univ Agder Ctr Artificial Intelligence Res Dept Informat & Commun Technol N-4879 Grimstad Norway Najran Univ Coll Comp Sci & Informat Syst Dept Comp Sci Najran 55461 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia Northern Border Univ Fac Comp & Informat Technol Dept Comp Sci Rafha 91911 Saudi Arabia

In computer vision and image processing, vehicle detection and tracking in complicated aerial images have become important subjects. The need for automated systems that can precisely detect and track vehicles in aerial image data is growing due to the abundance of data coming from numerous sources, including drones and satellites. This study introduces a new method for lane extraction that relies on centroid tracking and template matching, followed by co-registration and geo-referencing. Our approach offers robust vehicle detection and tracking over a range of sizes and positions in complicated backgrounds, while also efficiently segmenting the region of interest. Our suggested method, which makes use of machine learning and feature extraction techniques, shows excellent precision and effectiveness when it comes to detecting and tracking vehicles in complex aerial images. This finding has important implications for traffic management and urban planning, going beyond computer vision and image processing. Our technology has the potential to transform traffic management procedures by making it simpler to detect traffic bottlenecks and monitor traffic flow. Furthermore, our method can help identify damaged vehicles in disaster response scenarios, which will help prioritize rescue and recovery activities. All things considered, our suggested approach is a significant addition to the domains of computer vision and image processing, with a broad range of uses in traffic control, urban planning, and disaster management.

关键词： Accuracy Vehicle detection Monitoring Real-time systems Feature extraction Roads image segmentation Smart transportation Computer vision image processing Autonomous aerial vehicles Georeferencing multiple object detection segmentation smart traffic monitoring vehicle detection

来源：评论

学校读者我要写书评

暂无评论

Fine-tuned depth-augmented U-Net for enhanced semantic segmentation in indoor autonomous vision systems

引用

JOURNAL OF REAL-TIME image processing 2025年第1期22卷 1-12页

作者： Tran, Hoang N. Le, Thu A. N. Nguyen, Nghi V. Nguyen, Nguyen T. Nguyen, Anh D. FPT Univ Can Tho 94000 Vietnam Vietnam Natl Univ Ho Chi Minh City Univ Technol HCMUT Fac Mech Engn Dept Mechatron Engn Ho Chi Minh City Vietnam

Recent technological advancements have significantly improved indoor autonomous vision systems (IAVSs), underscoring the critical need to enhance their capability to interpret real-world environments in a manner similar to human perception. In response to this challenge, this paper introduces DEADFL-UNet, a groundbreaking framework that enhances the existing EADFL-UNet architecture. EADFL-UNet utilized the EfficientNetB3 model, supplemented by a new Super Attention Block and CBW-FL Loss Function, to tackle the significant data imbalance found in the NYUv2 dataset. Our enhancement focuses on using the MobileNetV2 model in conjunction with several fine-tuning techniques to maximize Depth characteristics in tandem with RGB ones inside the prior architecture. By applying the proposed techniques, we achieved an improvement of approximately 6% in mIoU (Mean Intersection over Union) compared to the original EADFL-UNet model, which was previously published. Furthermore, the difference between the fine-tuned and non-fine-tuned versions is 1.91% in mIoU, demonstrating the significant effectiveness of the fine-tuning technique. To confirm the real-time FPS (Frame Per Second) performance of each model, this technique has undergone extensive testing and assessment using standard metrics, not only on pre-existing datasets but also in a ROS2 (Robot Operating System) simulation environment. These proven techniques have potential for various applications in autonomous systems, such as robotic vision, GPS (Global Positioning System) position tracking, autonomous vehicles, and security, improving accuracy and efficiency.

关键词： Deep learning Autonomous systems (AS) Segmentation EADFL-UNet EfficientNet MobileNet Fine-tuning Scene detection

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：