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

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Automatic machine Learning based Real Time Multi-Tasking image Fusion 24

Automatic Machine Learning based Real Time Multi-Tasking Ima...

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16th International conference on machine Learning and Computing (ICMLC)

作者： Karim, Shahid Tong, Geng Li, Jinyang Yu, Xiaochang Hao, Jia Yu, Yiting Northwestern Polytech Univ Shenzhen Res & Dev Inst Shenzhen Peoples R China Northwestern Polytech Univ Coll Mech Engn Xian 710072 Peoples R China Northwestern Polytech Univ Key Lab Micro Nano Syst Aerosp Minist Educ Xian 710072 Peoples R China Northwestern Polytech Univ Shaanxi Prov Key Lab Micro & Nano Elect Mech Syst Xian 701172 Peoples R China

ISBN: (纸本)9798400709234

Imaging systems work diversely in the image processing domain, and each system contains specific characteristics. We are developing models to fuse images from different sensors and environments to get promising outcomes for different computer vision applications. The multiple unified models have been developed for multiple tasks such as multi-focus (MF), multi-exposure (ME), and multimodal (MM) image fusion. The careful tuning of such models is required to get optimal results, which are still not applicable to diverse applications. We propose an automatic machine learning (AML) based multi-tasking image fusion approach to overcome this problem. Initially, we evaluate source images with AML and feed them to the task-based models. Then, the source images are fused with the pre-trained and fine-tuned models. The experimental results authenticate the consequences of our proposed approach compared to generic approaches.

关键词： automatic ML multi-tasking image fusion imaging systems

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Serf: Towards better training of deep neural networks using log-Softplus ERror activation Function 23

Serf: Towards better training of deep neural networks using ...

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23rd IEEE/CVF Winter conference on applications of Computer vision (WACV)

作者： Nag, Sayan Bhattacharyya, Mayukh Mukherjee, Anuraag Kundu, Rohit Univ Toronto Toronto ON Canada SUNY Stony Brook Stony Brook NY USA IISER Mohali Ajitgarh India UCR Riverside Riverside CA USA

ISBN: (纸本)9781665493468

Activation functions play a pivotal role in determining the training dynamics and neural network performance. The widely adopted activation function ReLU despite being simple and effective has few disadvantages including the Dying ReLU problem. In order to tackle such problems, we propose a novel activation function called Serf which is self-regularized and non-monotonic in nature. Like Mish, Serf also belongs to the Swish family of functions. Based on several experiments on computer vision (image classification and object detection) and natural language processing (machine translation, sentiment classification and multi-modal entailment) tasks with different state-of-the-art architectures, it is observed that Serf vastly outperforms ReLU (baseline) and other activation functions including both Swish and Mish, with a markedly bigger margin on deeper architectures. Ablation studies further demonstrate that Serf based architectures perform better than those of Swish and Mish in varying scenarios, validating the effectiveness and compatibility of Serf with varying depth, complexity, optimizers, learning rates, batch sizes, initializers and dropout rates. Finally, we investigate the mathematical relation between Swish and Serf, thereby showing the impact of pre-conditioner function ingrained in the first derivative of Serf which provides a regularization effect making gradients smoother and optimization faster.

关键词： Training Deep learning Computer vision Neural networks Computer architecture Object detection machine translation

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vision-Based Autonomous Car Brake and Steering Assistance Using Deep Learning 1

Vision-Based Autonomous Car Brake and Steering Assistance Us...

引用

1st International conference on AIML-applications for Engineering and Technology, ICAET 2025

作者： Anees, H. Agrawal, Pooja School of Robotics Defence Institute of advanced technology Pune India

ISBN: (纸本)9798350355611

This paper presents a novel approach for enhancing vehicle safety and navigation through an integrated system for lane detection, vehicle alignment, and automatic braking using visual feedback. Our proposed system employs advanced deep learning and computer vision techniques with real-time processing to detect the exact boundaries of lane and ensures precise vehicle movement within the lane. The system continuously analyses lane markings and modifies the vehicle's position to ensure optimal lane adherence by utilizing a combination of machine learning algorithms and camera-based image processing. Additionally, the system incorporates an adaptive braking mechanism that identifies vehicles ahead using visual inputs. Furthermore, the jerks experienced during steering alignment can be greatly reduced by the suggested steering control system. The system's efficiency in various driving conditions is evidenced by its experimental simulation results, which also show improvements in collision avoidance and lane-keeping accuracy. This approach contributes to improved driving convenience and road safety by marking a substantial advancement in autonomous driving technologies. © 2025 IEEE.

关键词： Steering

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image Generation from Arabic Text: Comparative Study of Proposed Architectures 8th

Image Generation from Arabic Text: Comparative Study of Prop...

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8th International conference on Arabic Language processing

作者： Bahani, Mourad El Ouaazizi, Aziza Maalmi, Khalil Essahlaoui, Abdelouahed Sidi Mohamed Ben Abdellah Univ Natl Sch Appl Sci Artificial Intelligence Data Sci & Emerging Syst Fes Morocco Sidi Mohamed Ben Abdellah Univ Lab Engn Sci FPT Taza Morocco

ISBN: (纸本)9783031804373;9783031804380

Text-to-image generation is a cutting-edge technology that enables computers to generate images from textual descriptions. While this technology has been extensively researched and applied to English language text, applying it to Arabic language text is still in its early stages. Additionally, the Arabic language is challenging due to its right-to-left writing system and extensive vocabulary of 1.3 million words. In this paper, we explore text-to-image generation for generating images from Arabic language text descriptions. Firstly, we fine-tune a transformer-based model pre-trained on the Arabic text to transform the text information into affine transformation within the DF-GAN generator. Secondly, we present a text transformer that combines LSTM layers to address the limitation of unrecognized words. Thirdly, a mask predictor is trained into the generator using a weakly supervised method and incorporated into the affine transformation for a more effective integration of image and text features. In addition, we add the DAMSM loss function as a regularization to the loss function to achieve convergences and stability in the training phase. The experiment on two challenging datasets CUB and Oxford-flower shows that our architectures can accurately generate high-quality images faithfully representing the Arabic textual descriptions. We believe the scaling of this task could have critical applications in fields such as Arabic visual learning, e-commerce, advertising, and entertainment.

关键词： machine Learning Deep Learning Computer vision Generative Adversarial Networks Text-to-image Generation Arabic Text processing

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machine vision Inspection of Object Positioning in a Hard Disk Assembly Plant

Machine Vision Inspection of Object Positioning in a Hard Di...

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2023 International conference on Power, Energy and Innovations, ICPEI 2023

作者： Ooppakaew, Wichian Kumkiet, Boonyarit Srinonchat, Jakkree Dept. of Electronics and Telecommunication Engineering Pathum Thani Thailand

ISBN: (纸本)9798350344585

machine vision is a field of computer vision that focuses on developing and implementing automated visual inspection systems. It involves using cameras, sensors, and image processing algorithms to capture and analyze images or video data for various applications. machine vision enables object detection, recognition, measurement, and quality control, enhancing industry efficiency and accuracy, mainly focusing on object positioning inspection in this research. Object positioning inspection using machine vision has become critical in various industrial applications requiring precise object alignment and positioning. This review explores the fundamental principles, challenges, and applications of object positioning inspection, including detecting misalignment, rotation, translation, and scaling. The proposed primary objective of object positioning inspection is to ensure that objects are correctly positioned with reference points or predefined geometric parameters applied in the hard disk component assembly factory. The edge detection and decision function are operated with 1,000 images captured from the real-time camera. The experiment result indicated that the average accuracy is 95.27% for object positioning inspection. Additionally, this proposal identifies current research trends and future directions for advancing object positioning inspection using machine vision for industrial applications. © 2023 IEEE.

关键词： Inspection

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A Comparative Study of Deep Learning Algorithms for Glaucoma Classification Using Retinal images 5th

A Comparative Study of Deep Learning Algorithms for Glaucoma...

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5th International conference on Data Science, machine Learning and applications

作者： Swapna, T. Varshitha, Y. Sai Raja Sudeepthi, K. L. Manavika, B. Saishree, T. G Narayanamma Inst Technol & Sci Hyderabad Telangana India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

A group of eye conditions known as glaucoma impair the optic nerve, which is in charge of sending visual data from the eye to the brain. Glaucoma impacts 3.54% of adults aged 40 to 80 around the world. Early detection of glaucoma is crucial as it can prevent total optic nerve damage, which would cause irreversible vision loss. It is possible for specialists to diagnose glaucoma medically, but treatment options are either expensive or time-consuming and requires ongoing care from medical professionals. There have been numerous initiatives at streamlining all components of the glaucoma categorization process, however these models are challenging for users to comprehend the key predictors, resulting in them being unreliable for use by medical experts. The study uses eye fundus images to classify glaucoma patients using three distinct Deep Learning techniques: Convolutional neural network, visual Geometry Group 16 (VGG16), and Global Context Network (GC-Net). In addition, several data pre-processing techniques are used to avoid overfitting and achieve high accuracy. This research compares and analyses the performance of various architectures using the aforementioned techniques. The CNN model had the best accuracy of 83% when in contrast to the other deep learning models.

关键词： Biomedical image processing Glaucoma Blind ness machine learning visual geometry group 16 Convolutional neural network Global context network

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Comparison of Large Language And vision Models on Representative Downstream Tasks 2

Comparison of Large Language And Vision Models on Representa...

引用

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

作者： Chen, Huitong Beijing University of Posts and Telecommunications International College Beijing100000 China

ISBN: (纸本)9798350331417

The success of large model pre-training in the fields of natural language processing and computer vision has been remarkable. By pre-training on large-scale data, these models can learn richer semantic and visual representations to achieve better performance on various tasks. performance. In order to enable more researchers and practitioners to fully understand the advantages and applicable fields of different models, this paper summarizes the current large model pre-training methods, including vision Transformer (viT), Pre-trained Transformer (GPT), Text-to-Text Transfer Transformer (T5), Bidirectional Encoder Representations from Transformers (BERT) and Contrastive Language-image Pretraining (CLIP) are all included, and discusses their principles and advantages are presented. At the same time, compare their results are compared on different datasets, and compare the results of combining them with different other techniques. The final research results show that viT has advantages over other models in image classification, GPT performs best in generative tasks compared to other models, and T5 has achieved significant results in various text tasks compared to other models, while BERT performs best in various discriminative tasks compared to other models. CLIP is suitable for image and text-related tasks, including image classification, text retrieval, visual question answering, etc. Finally, applications of various models are summarized, providing direction for researchers and practitioners. © 2023 IEEE.

关键词： deep learning large language model large vision model neural network

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An Improved Reversible Data Hiding Scheme Based on Referred Frequencies for a VQ Compressed Index 2

An Improved Reversible Data Hiding Scheme Based on Referred ...

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2nd International conference on machine vision, image processing and Imaging Technology, MviPIT 2024

作者： Hwang, Min-Shiang Fatima, Kanza Wang, Yu-Lun Chan, Chi-Shiang Wu, Chia-Chun Department of Computer Science & Information Engineering Fintech and Blockchain Research Center Asia University Taichung Taiwan Department of Computer Science & Information Engineering Asia University Taichung Taiwan Department of Management Information Systems National Chung Hsing University Taichung Taiwan Department of M-Commerce and Multimedia Applications Asia University Taichung Taiwan Department of Computer Science and Information Engineering National Quemoy University Kinmen Taiwan

ISBN: (纸本)9798331543037

Nowadays, we usually compress images before uploading them to social media. However, images on social media can easily be copied, so embedding secret messages in compressed images has become increasingly popular. There are many compression methods, such as Huffman, VQ, ZIP, AMBTC, RAR, JPEG, etc. In this article, we propose an improved data hiding in VQ compression method to achieve better capacity and high quality. Experimental results show that our data-hiding approach is practical. ©2024 IEEE.

关键词： compressed image data hiding LSB matching reversible data hiding steganography vector quantization

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An Advanced Multi-Scale Feature Compression using Selective Learning Strategy for video Coding for machines

An Advanced Multi-Scale Feature Compression using Selective ...

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2023 IEEE International conference on visual Communications and image processing, VCIP 2023

作者： Yoon, Yong-Uk Han, Gyu-Woong Lee, Jooyoung Jeong, Se Yoon Kim, Jae-Gon Korea Aerospace University Department of Electronics and Information Engineering Gyeonggi-do Goyang-si Korea Republic of Electronics and Telecommunications Research Institute Daejeon Korea Republic of Korea Aerospace University School of Electronics and Information Engineering Gyeonggi-do Goyang-si Korea Republic of

ISBN: (纸本)9798350359855

machine vision-based applications have witnessed widespread adoption in diverse fields. Efficiently processing and compressing the vast amounts of video data collected by machines is crucial for these applications. To address this need, the Moving Picture Experts Group (MPEG) is developing a new' video coding standard known as video Coding for machines (VCM), specifically optimized for video consumed by machines in vision applications. This paper proposes an advanced multi-scale feature compression (advMSFC) method with a selective learning strategy (SLS) for the Feature Compression for VCM (FCVCM). By applying the SLS, the proposed method converts multi-scale features into a single-scale feature arranged based on channel-wise importance, enabling adaptive feature channel truncation based on the QP. The truncated feature is efficiently compressed using the latest video codec, Versatile video Coding (VVC). The proposed method outperforms the VCM feature anchor in instance segmentation, object detection, and object tracking tasks, achieving significant Bjontegaard delta-rate (BD-rate) gains. The adaptability of our method using a single trained model for various QPs shows promise for efficient feature compression in machine vision applications. © 2023 IEEE.

关键词： Computer vision

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Wild Animal Detection System on a Farm Using image processing

Wild Animal Detection System on a Farm Using Image Processin...

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International conference on Advances and applications of Artificial Intelligence and machine Learning, ICAAAIML 2023

作者： Barhate, Madhuri Gulhane, Nandini Gunjal, Tanvi Gudi, Swaroop Gulhane, Vedant Wagade, Prasad Gude, Ayush Vishwakarma Institute of Technology Maharashtra Pune411037 India

ISBN: (纸本)9789819795062

Human civilization is based on agriculture, and one of the biggest threats to agricultural productivity is the existence of wild animals on farmlands. Animals are taking over some farmlands, causing significant crop losses, property damage, and livestock risks. As a result, suitable solutions must be developed. A system to support farmers is necessary because the conventional method of constant guard monitoring is impractical. Modern technologies like machine learning, computer vision, and image processing are used by this wild animal detection system to effectively manage conflicts between humans and wildlife. machine learning and computer vision rely on the integration of particular algorithms to guarantee the accuracy and effectiveness of the system. With an emphasis on sustainability, the technology protects property and crops while also aiding in the conservation of wildlife. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Livestock

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