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检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VIII"

共 3255 条记录，以下是41-50 订阅

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Design and Implementation of machine vision Experiment Platform for Virtual Production Line 9

Design and Implementation of Machine Vision Experiment Platf...

引用

9th International conference on Virtual Reality, ICVR 2023

作者： Li, Jinfang He, Mingtong Su, Jiancong Wang, Boyang Li, Zhenxian Guangdong University of Technology School of Electromechanical Engineering Guangzhou China

ISBN: (纸本)9798350345810

To meet the needs of teaching and practical applications in machine vision technology, a virtual reality-based machine vision experimental platform has been designed and developed. Unity3D was utilized as the development engine, and image processing technology was integrated to achieve the construction of virtual production line scenes, simulation of vision component parameter adjustments, and image acquisition. The platform features a graphical programming interface for visualizing image processing algorithms, which can be used to perform visual debugging of vision stations with a virtual robot system driven by software PLC. This machine vision experimental platform ensures the consistency between simulation and actual engineering processes, and enables students to explore different vision schemes on an industrial production line, thereby avoiding constraints on location, time, and equipment in related experiments. © 2023 IEEE.

关键词： Computer vision

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3D Information in Robot vision System Based on Artificial Neural Network 14th

3D Information in Robot Vision System Based on Artificial Ne...

引用

14th International conference on Frontier Computing, FC 2024

作者： Liu, Xiaoxiao Department of Mechanical and Electrical Engineering Jinan Engineering Vocational Technical College Shandong Jinan China

ISBN: (纸本)9789819627974

In the exploration of robot vision systems based on artificial neural networks, the research mainly focuses on their applications in 3D information recognition and processing. By simulating the processing of the human visual system, this technology enables robots to more effectively interpret and understand the three-dimensional spatial information of their environment, which has a revolutionary role in robot navigation, object recognition, obstacle avoidance and the execution of complex tasks. And this technology shows great potential in many fields such as industrial automation, autonomous vehicles, drone monitoring, and service robots. Not only that, it also plays a very important role in providing in-depth information, accurate positioning and efficient decision-making applications. According to the experimental data, among the 800 experimental subjects, more than 754 people were satisfied with the recognition accuracy, recognition speed, machine efficiency improvement, image recognition clarity, and overall satisfaction with the system. These findings indicate that with the continuous advancement of technology, 3D vision systems based on artificial neural networks will show more significant performance and value in future applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： machine vision

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Novel Dataset Creation of Varieties of Banana and Ripening Stages for machine Learning applications 8th

Novel Dataset Creation of Varieties of Banana and Ripening S...

引用

8th International conference on Computer vision and image processing (CVIP)

作者： Manasa, T. N. Pushpalatha, M. P. JSS Sci & Technol Univ Mysore Karnataka India

ISBN: (纸本)9783031581731;9783031581748

India holds the title of being the top banana producer globally, contributing approximately 25% of the total banana production. However, exporting it can be a challenge because of its shelf-life. To propose the best possible shelf-life extension methodology, it is important to classify based on the banana varieties and ripening stages to ensure sustainable growth and nutritional value. There are still not enough data sets with different varieties of bananas and their respective ripening stages. A review of research publications from the last five years has been conducted using electronic databases like Scopus, Google Scholar, and Research-Gate, as well as the details of publicly accessible dataset repository sites. The dataset captures images of different varieties of banana fruit as well as its respective different stages of ripening. Banana varieties considered include Robusta (MusaAA), Dwarf Cavendish (Musaacuminata), Nanjangud bananas, and Red bananas (Musa acuminata). The dataset contains over 41,900 processed images. In this paper, the authors provide researchers with an opportunity to develop and investigate machine learning and deep learning algorithms that are used to predict and extend the shelf life of banana fruits.

关键词： machine learning Banana type classification Ripening Stages Classification image processing

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Unifying image processing as Visual Prompting Question Answering 41

Unifying Image Processing as Visual Prompting Question Answe...

引用

41st International conference on machine Learning, ICML 2024

作者： Liu, Yihao Chen, Xiangyu Ma, Xianzheng Wang, Xintao Zhou, Jiantao Qiao, Yu Dong, Chao Shanghai Artificial Intelligence Laboratory China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China University of Macau China Kuaishou Technology China

image processing is a fundamental task in computer vision, which aims at enhancing image quality and extracting essential features for subsequent vision applications. Traditionally, task-specific models are developed for individual tasks and designing such models requires distinct expertise. Building upon the success of large language models (LLMs) in natural language processing (NLP), there is a similar trend in computer vision, which focuses on developing large-scale models through pretraining and in-context learning. This paradigm shift reduces the reliance on task-specific models, yielding a powerful unified model to deal with various tasks. However, these advances have predominantly concentrated on high-level vision tasks, with less attention paid to low-level vision tasks. To address this issue, we propose a universal model for general image processing that covers image restoration, image enhancement, image feature extraction tasks, etc. Our proposed framework, named PromptGIP, unifies these diverse image processing tasks within a universal framework. Inspired by NLP question answering (QA) techniques, we employ a visual prompting question answering paradigm. Specifically, we treat the input-output image pair as a structured question-answer sentence, thereby reprogramming the image processing task as a prompting QA problem. PromptGIP can undertake diverse cross-domain tasks using provided visual prompts, eliminating the need for task-specific finetuning. Capable of handling up to 15 different image processing tasks, PromptGIP represents a versatile and adaptive approach to general image processing. Codes will be available at https://***/lyh-18/PromptGIP. Copyright 2024 by the author(s)

关键词： image enhancement

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Single image Dehazing Using CNN 2nd

Single Image Dehazing Using CNN

引用

2nd International conference on Computational Intelligence in machine Learning, ICCIML 2022

作者： Bhadane, Samarth Bidwe, Ranjeet Vasant Zope, Bhushan Pune Institute of Computer Technology Maharashtra Pune India Maharashtra Lavale Pune India

ISBN: (纸本)9789819979530

Particulate matter in the atmosphere obscures the visibility of the atmosphere, causing a condition known as haze. Other natural phenomena like mist, fog, and dust also obscure the vision;this is because of scattering of light which attenuates the light intensity. All these instances are responsible for the degradation of image quality. Hazy images are problematic because these images cannot be used for computer vision and image processing applications like pattern and object recognition. Dehazing images improve the clarity and contrast of the images making them more suitable for computer vision and image processing. This paper presents a method of dehazing images using CNN. The proposed model is trained on D-HAZY (Ancuti et al. in 2016 IEEE international conference on image processing (ICIP), 2016) and SOTS (Li et al. in IEEE Trans image Process 28:492–505, 2019) datasets which contain a mix of natural and synthesized hazy images. To assess the model’s performance, we employ PSNR and SSIM metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image enhancement

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Is Grad-CAM Explainable in Medical images? 1

引用

8th International conference on Computer vision and image processing (CVIP)

作者： Suara, Subhashis Jha, Aayush Sinha, Pratik Sekh, Arif Ahmed XIM Univ Bhubaneswar India

ISBN: (数字)9783031581816

ISBN: (纸本)9783031581809;9783031581816

Explainable Deep Learning has gained significant attention in the field of artificial intelligence (AI), particularly in domains such as medical imaging, where accurate and interpretable machine learning models are crucial for effective diagnosis and treatment planning. Grad-CAM is a baseline that highlights the most critical regions of an image used in a deep learning model's decision-making process, increasing interpretability and trust in the results. It is applied in many computer vision (CV) tasks such as classification and explanation. This study explores the principles of Explainable Deep Learning and its relevance to medical imaging, discusses various explainability techniques and their limitations, and examines medical imaging applications of Grad-CAM. The findings highlight the potential of Explainable Deep Learning and Grad-CAM in improving the accuracy and interpretability of deep learning models in medical imaging. The code is available in (https://***/ beasthunter758/GradEML).

关键词： Explainable Deep Learning Gradient-weighted Class Activation Mapping (Grad-CAM) Medical image Analysis

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CNN-based On-board Intelligent processing for Remote Sensing images Interpretation 2

CNN-based On-board Intelligent Processing for Remote Sensing...

引用

2nd International conference on image processing, Computer vision and machine Learning, ICICML 2023

作者： Cui, Xiaojie Liu, Yuanyuan Chen, Xuehua Wang, Gang Beijing Institute of Remote Sensing Information Beijing China

ISBN: (纸本)9798350331417

Traditional remote sensing image processing is not able to provide timely information for near real-time applications due to the hysteresis of satellite-ground mutual communication and low processing efficiency. On-board intelligent processing is an important approach to improve the efficiency and intelligence of remote sensing satellites. This paper takes convolutional neural network (CNN) based on-board processing as the focus. Firstly, the basic workflow of CNN based on-board processing system is illustrated. Afterwards, the applications of lightweight CNN based on-board processing are thoroughly reviewed. The used CNN models are further analyzed to compare the advantages and disadvantages. Finally, current challenges are summarized and future works concerned with artificial intelligence are concluded. © 2023 IEEE.

关键词： change detection cloud detection earth observation satellites object detection on-board intelligent processing

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The Background Also Matters: Background-Aware Motion-Guided Objects Discovery

The Background Also Matters: Background-Aware Motion-Guided ...

引用

IEEE/CVF Winter conference on applications of Computer vision (WACV)

作者： Kara, Sandra Ammar, Hejer Chabot, Florian Quoc-Cuong Pham Univ Paris Saclay CEA List F-91120 Palaiseau France

ISBN: (纸本)9798350318920;9798350318937

Recent works have shown that objects discovery can largely benefit from the inherent motion information in video data. However, these methods lack a proper background processing, resulting in an over-segmentation of the non-object regions into random segments. This is a critical limitation given the unsupervised setting, where object segments and noise are not distinguishable. To address this limitation we propose BMOD, a Background-aware Motion-guided Objects Discovery method. Concretely, we leverage masks of moving objects extracted from optical flow and design a learning mechanism to extend them to the true foreground composed of both moving and static objects. The background, a complementary concept of the learned foreground class, is then isolated in the object discovery process. This enables a joint learning of the objects discovery task and the object/non-object separation. The conducted experiments on synthetic and real-world datasets show that integrating our background handling with various cutting-edge methods brings each time a considerable improvement. Specifically, we improve the objects discovery performance with a large margin, while establishing a strong baseline for object/non-object separation.

关键词： Algorithms Algorithms Algorithms and algorithms formulations image recognition and understanding machine learning architectures Video recognition and understanding

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Blur Patch Classification Approach to Single-image Depth Estimation 17

Blur Patch Classification Approach to Single-Image Depth Est...

引用

17th International conference on machine vision, ICMV 2024

作者： Kim, Huijun Lee, Deokwoo Keimyung University Dalgubeol-daero Dalseo-gu Daegu1095 Korea Republic of

ISBN: (纸本)9781510688278

Depth information is useful in many image processing and computer vision applications, but in photography, depth information is lost in the process of projecting a real-world scene onto a 2D plane. Extracting depth information from such images is a challenging task. In this paper, we propose a method to train a deep neural network to classify an image patch (16x16 in size) into 15 levels based on the level of blur. Blur is related to the distance between the focal plane and the object. The input image is shifted using a sliding window technique at 8 pixel intervals and the trained blur classifier evaluates each blur level. The obtained blur maps are subjected to a refinement process to quantitatively assess their accuracy and impact on the final result, and the final blur maps are compared with the labels of the actual input data to estimate the depth map. The proposed method demonstrates that depth information can be successfully extracted from a single image by classifying the focus levels. © 2025 SPIE.

关键词： image classification

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Swin on Axes: Extending Swin Transformers to Quadtree image Representations

Swin on Axes: Extending Swin Transformers to Quadtree Image ...

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IEEE/CVF Winter conference on applications of Computer vision (WACV)

作者： Oliu, Marc Nasrollahi, Kamal Escalera, Sergio Moeslund, Thomas B. Aalborg Univ Fredrik Bajers Vej 7KOst Aalborg Denmark Milestone Syst Banemarksvej 50 Brondby Denmark Univ Barcelona Gran Via Corts Catalanes 585 Barcelona Spain Comp Vis Ctr Campus UABEdifici O Cerdanyola Del Valles Spain

ISBN: (纸本)9798350370287;9798350370713

In recent years, Transformer models have revolutionized machine learning. While this has resulted in impressive results in the field of Natural Language processing, Computer vision quickly stumbled upon computation and memory problems due to the high resolution and dimensionality of the input data. This is particularly true for video, where the number of tokens increases cubically relative to the frame and temporal resolutions. A first approach to solve this was vision Transformers, which introduce a partitioning of the input into embedded grid cells, lowering the effective resolution. More recently, Swin Transformers introduced a hierarchical scheme that brought the concepts of pooling and locality to transformers in exchange for much lower computational and memory costs. This work proposes a reformulation of the latter that views Swin Transformers as regular Transformers applied over a quadtree representation of the input, intrinsically providing a wider range of design choices for the attentional mechanism. Compared to similar approaches such as Swin and MaxViT, our method works on the full range of scales while using a single attentional mechanism, allowing us to simultaneously take into account both dense short range and sparse long range dependencies with low computational overhead and without introducing additional sequential operations, thus making full use of GPU parallelism.

关键词： Natural language processing systems

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