检索结果-内蒙古大学图书馆

Performance Evaluation of Median Filter Hardware Chip for image and Signal processing

Performance Evaluation of Median Filter Hardware Chip for Im...

Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Sreesh Gaur Akash Goel Pawan Kumar Pal Amit Kumar Singh Sanger Department of Computer Science KIET Group of Institutions Ghaziabad India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

The median filter is a valuable image processing tool that can be used in many applications to advance picture quality, and inferior noise, and acquire data ready for more analysis. The median filter, a non-linear image processing technique, has excessive application in many diverse fields because of its capacity to remove noise while preserving edges. The filter is significant for both simple and complex imageprocessing occupations since it is together easy to comprehend and fast to use. An active method to attain real-time performance is to devise a median filter on a FieldProgrammable Gate Array (FPGA) for image signal processing and machine vision applications. FPGAs are well-suited for photo filtering due to their capability to achieve parallel processing, which is one of their prominent advantages. The research article meets with the design and simulation of the average filter in HDL and synthesis on the FPGA for assessing the performance indices.

关键词： Performance evaluation Filtering machine vision Noise Parallel processing Logic gates Hardware Real-time systems Delays Field programmable gate arrays

来源：评论

学校读者我要写书评

暂无评论

Computer-vision-Based Industrial Algorithm for Detecting Fruit and Vegetable Dimensions and Positioning 19th

Computer-Vision-Based Industrial Algorithm for Detecting Fr...

引用

19th International Conference on Soft Computing Models in Industrial and Environmental applications, SOCO 2024

作者： Guevara, Cesar Rostan, Julen Rodriguez, Javier Gonzalez, Silvia Sedano, Javier C. López Bravo 70 Burgos09001 Spain

ISBN: (纸本)9783031750090

A computer-vision-based industrial algorithm is proposed in this study for the detection of the dimensions and the spatial positioning of fruit and vegetables on a conveyor belt for their movement to a packing machine with a robotic arm. The principal purpose of the algorithm is to identify the dimensions (length, width), and position (angle of inclination, and Cartesian coordinates) of the vegetable mesh netting without taking the product label into account. The proposed algorithm has four functions. A convolutive neuronal network model is applied for object detection, with which all objects are identified in the image while the product label is suppressed, so that recognition of the vegetable mesh netting and its dimensions is not impaired. Moreover, the Canny edge detector and the Border following algorithms are applied to perform image pre-processing and edge improvements, respectively, yielding optimal results with more clearly defined noise-free objects. The feature extraction function for dimensions yielded detection precision results of 99.60% for length, 99.00% for width, and 97.80% for inclination angle, showing optimal performance in the test phases. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Bridging the machine-Human Gap in Blurred-image Classification via Entropy Maximisation

Bridging the Machine-Human Gap in Blurred-Image Classificati...

引用

International image processing, applications and Systems Conference (IPAS)

作者： Emilio Sansano-Sansano Marina Martínez-García Javier Portilla INIT Universitat Jaume I Castellón de la Plana Spain IMAC Universitat Jaume I Castellón de la Plana Spain Instituto de Óptica CSIC Madrid Spain

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Recent studies point to an accuracy gap between humans and Artificial Neural Network (ANN) models when classifying blurred images, with humans outperforming ANNs. To bridge this gap, we introduce a spectral channel-based range-constrained entropy merit function, from which we devise a zero-phase, circular symmetric blind deblurring method. We apply it as a pre-processing step for image classification and test it using pre-trained classification models and images blurred by Gaussian kernels. We compare our method to state-of-the-art restoration methods, showing its superiority, effectively bridging the machine-human gap for most models and blur levels. Our results also rank higher than the competitors in no-reference and full-reference image quality metrics. Notwithstanding the limitation to zero-phase blur, this work shows that, for image pre-processing aimed at visual tasks, it may be advantageous to use merit functions based on vision science and information theory, rather than on the expected error to the latent image.

关键词： Measurement image quality Visualization Computer vision Artificial neural networks Entropy Kernel Optimization Information theory image classification

来源：评论

学校读者我要写书评

暂无评论

Research on machine vision online monitoring system for egg production and quality in cage environment

引用

POULTRY SCIENCE 2025年第1期104卷 104552页

作者： Wu, Zhenlong Zhang, Hengyuan Fang, Cheng South China Agr Univ Coll Engn Guangzhou 510642 Peoples R China State Key Lab Livestock & Poultry Breeding Guangzhou Peoples R China

In the domain of egg production, the application of automation technologies is essential for boosting productivity and quality. This study introduces an online monitoring system designed for egg quality assessment within caged environments, incorporating a robotic patrol system for egg localization and a fixed video stream for quality analysis. The project involved upgrading traditional henhouses with enhanced wireless connectivity and developing data transmission techniques for video streams and image data. The core of the system, an enhanced You Only Look Once Version 8-small (YOLOv8s) model, was augmented by substituting the Residual Network-18 backbone and integrating the Shuffle Attention mechanism, significantly improving egg detection precision. This refined model was implemented on Jetson AGX Orin industrial computer to facilitate real-world applications. To diverse operational needs, two distinct post-processing algorithms were developed: one for counting eggs and detecting abnormalities during robotic patrols, and another for assessing egg quality through fixed video streams, which measured crucial parameters such as egg dimensions and shape indexes. Experimental results revealed that the henhouse average network latencies of 35 ms, with signal strengths between -30 and -71 dBm, ensuring data transmission to the poultry management system. The enhanced YOLOv8s model, deployed on the Jetson AGX Orin, demonstrated well improvements: a Precision of 94.0% (+2.4 %), Recall rate of 92.8% (+4.6 %), Average Precision50:95 of 91.5 % (+3 %) and F1 score of 93.4 % (+3.9 %), with a minor decrease in detection speed to 91.7 Frame Per Second (-18.2). Field experiment in 60 chicken cages during robotic patrols achieved an egg recognition rate of 98.9 %, validating the system's effectiveness. In fixed settings, an 83-minute experiment managed to analyze egg numbers and abnormalities, attaining a 100 % recognition rate with all scoring data promptly relayed back to the mana

关键词： Egg Layered cage farming Object detection Poultry Precision livestock farming

来源：评论

学校读者我要写书评

暂无评论

Context-Aware Automated System for image Caption to ASL Translation for Improved Accessibility in Media applications

Context-Aware Automated System for Image Caption to ASL Tran...

引用

2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Aluko, Oluwafolahanmi Menon, Vineetha The University of Alabama in Huntsville Dept. of Computer Science HuntsvilleAL United States

ISBN: (纸本)9798331504847

With the emergence of Artificial Intelligence (AI) and the rapid growth of Generative AI, significant progress has been made in image captioning, delivering remarkably precise captions from static images and, more recently, videos [1]. Alt-Text has significantly progressed due to initiatives aimed at improving the context and sentiment recorded during automated captioning. This paper seeks to elaborate on a portion concerning Natural Language processing (NLP) and its significance in enhancing accessibility and context-awareness within the framework of the American Sign Language (ASL) application. This work presents a case study on significance of providing complete context regarding a caption or image to the end user, encompassing emotions and conscious attributes present within the phrase. This paper extensively elaborates on the accessibility functions presently involved in image-captioning. Is user-dependent (requiring users to supply a caption for the image) or standard machine-created alt-text sufficient for complete accessibility? Might incorporating a sign language, such as American Sign Language (ASL), be essential in providing users with more information than what the text of a caption communicates? The key question is: how can we enhance this image-caption translation to be more context-sensitive to convey the subtleties that could be crucial for user comprehension? The aim is to create a world where all voices resonate and every person feels accepted, and this paper represents an initial move towards an AI revolution that might be crucial in realizing that vision. © 2025 IEEE.

关键词： Computer aided instruction

来源：评论

学校读者我要写书评

暂无评论

Imagined Speech and Visual imagery as Intuitive Paradigms for Brain-Computer Interfaces 13

Imagined Speech and Visual Imagery as Intuitive Paradigms fo...

引用

13th International Winter Conference on Brain-Computer Interface, BCI 2025

作者： Lee, Seo-Hyun Park, Ji-Ha Kim, Deok-Seon Dept. of Brain and Cognitive Engineering Korea University Seoul Korea Republic of Dept. of Artificial Intelligence Korea University Seoul Korea Republic of

ISBN: (纸本)9798331521929

Brain-computer interfaces (BCIs) have shown promise in supporting communication for individuals with motor or speech impairments. Recent advancements such as brain-to-speech or brain-to-image technology aim to reconstruct speech from neural activity. However, robust decoding of communication-related paradigms, such as imagined speech and visual imagery, using non-invasive techniques still remains challenging. This study analyzes brain dynamics in these two paradigms by examining neural synchronization and functional connectivity through phase-locking values (PLV) in noninvasively collected EEG data. Results show that visual imagery produces higher PLV values in visual cortex, engaging spatial networks, while imagined speech demonstrates consistent synchronization, primarily engaging language-related regions. These findings suggest that imagined speech is suitable for language-driven BCI applications, while visual imagery can complement BCI systems for users with speech impairments. Furthermore, the brain connectivity results implies that personalized calibration is crucial for optimizing BCI performance. © 2025 IEEE.

关键词： vision

来源：评论

学校读者我要写书评

暂无评论

Staged transfer learning for multi-label half-face emotion recognition

引用

Journal of Engineering and Applied Science 2025年第1期72卷 1-25页

作者： ElMaksoud, Mohamed M. Abd ElGohary, Sherif H. Kandil, Ahmed H. Systems and Biomedical Engineering Department Faculty of Engineering Cairo University Giza Egypt

As fundamental drivers of human behavior, emotions can be expressed through various modalities, including facial expressions. Facial emotion recognition (FER) has emerged as a pivotal area of affective computing, enabling accurate detection of human emotions from visual cues. To enhance the efficiency and maintain accuracy, we propose a novel approach that leverages deep learning and transfer learning techniques to classify emotions based on only half of the human face. We introduce EMOFACE, a comprehensive half-facial imagery dataset annotated with 25 distinct emotion labels, providing a diverse and inclusive resource for multi-label half-facial emotion classification. By combining this dataset with the established FER2013 dataset, we employ a staged transfer learning framework that effectively addresses the challenges of multi-label half facial emotion classification. Our proposed approach, which utilizes a custom convolutional neural network (ConvNet) and five pre-trained deep learning models (VGG16, VGG19, DenseNet, MobileNet, and ResNet), achieves impressive results. We report an average binary accuracy of 0.9244 for training, 0.9152 for validation, and 0.9138 for testing, demonstrating the efficacy of our method. The potential applications of this research extend to various domains, including affective computing, healthcare, robotics, human–computer interaction, and self-driving cars. By advancing the field of half-facial multi-label emotion recognition, our work contributes to the development of more intuitive and empathetic human–machine interactions. © The Author(s) 2025.

关键词： Affective computing Computer vision Convolutional neural networks (CNNs) Deep learning Facial emotion recognition Facial expression classification Human–computer interaction image processing machine learning Transfer learning

来源：评论

学校读者我要写书评

暂无评论

VISTA: A Visual and Textual Attention Dataset for Interpreting Multimodal Models

VISTA: A Visual and Textual Attention Dataset for Interpreti...

引用

IEEE Winter applications and Computer vision Workshops (WACVW)

作者： Harshit Tolga Tasdizen School of Computing University of Utah Salt Lake City UT USA Scientific Computing and Imaging Institute University of Utah Salt Lake City UT USA

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

The recent developments in deep learning (DL) led to the integration of natural language processing (NLP) with computer vision, resulting in powerful integrated vision and Language Models. Despite their remarkable capabilities, these models are frequently regarded as black boxes within the machine learning research community. This raises a critical question: which parts of an image correspond to specific segments of text, and how can we decipher these associations? Understanding these connections is essential for enhancing model transparency, interpretability, and trustworthiness. To answer this question, we present an image-text aligned human visual attention dataset (VISTA) 1 1 The data is available at https://***/h-pal/Data-for-VISTA that maps specific associations between image regions and corresponding text segments. We then compare the internal heatmaps generated by VL models with this dataset, allowing us to analyze and better understand the model's decision-making process. This approach aims to enhance model transparency, interpretability, and trustworthiness by providing insights into how these models align visual and linguistic information. We conducted a comprehensive study on text-guided visual saliency detection in these VL models. This study aims to understand how different models prioritize and focus on specific visual elements in response to corresponding text segments, providing deeper insights into their internal mechanisms and improving our ability to interpret their outputs.

关键词： Measurement Visualization image segmentation Computer vision Analytical models Computational modeling machine vision Natural language processing Reliability Saliency detection

来源：评论

学校读者我要写书评

暂无评论

Chaos Theory Based Gravitational Search Algorithm For Medical image Segmentation 27th

Chaos Theory Based Gravitational Search Algorithm For Medica...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Rather, Sajad Ahmad Roy, Partha Pratim Das, Sujit Department of Computer Science and Engineering Indian Institute of Technology Roorkee247667 India Department of Computer Science and Engineering National Institute of Technology Telangana506004 India

ISBN: (纸本)9783031781032

Multilevel thresholding plays a crucial role in image processing, with extensive applications in object detection, machine vision, medical imaging, and traffic control systems. It entails the partitioning of an image into distinct regions based on optimal pixel values. However, as the number of threshold levels increases, so does the computational cost for segmentation. To address this challenge, a novel method is proposed namely Chaos theory based Gravitational Search Algorithm (CGSA) for multilevel thresholding. CGSA combines the standard Gravitational Search Algorithm (GSA) for exploration with chaotic maps for exploitation of the complex pixel problem space. In this study, Kapur’s entropy method is utilized to segment sample images into various partitions based on optimal pixel values. The effectiveness of CGSA in real-world scenarios is evaluated using COVID-19 chest CT scan imaging datasets from Kaggle database. The quality, symmetry, and consistency of the segmented output are assessed using metrics like Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Feature Similarity Index Measure (FSIM). Qualitative analysis includes convergence curves, segmented graphs, colormap images, and box plots. Statistical validation is conducted using the signed Wilcoxon rank sum test. Additionally, a comparison is made between CGSA’s performance and that of eight state-of-the-art heuristic algorithms. The findings demonstrate the superior performance of CGSA, evidenced by its reduced computational time and enhanced image quality metrics values. Specifically, CGSA achieved SSIM of 0.81, FSIM of 0.82, and PSNR of 24.27, surpassing the performance of other competitive algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Radar image processing Application Based on Space Cloud Computing in Basketball Game Guidance Camera

引用

machine Graphics and vision 2025年第2期34卷 3-27页

作者： Song, Jun School of Physical Education Qilu Normal University Ji’nan China

Capturing and presenting exciting moments is crucial for the audience’s experience in basketball game broadcast cameras. However, traditional radar image processing techniques are limited by various factors and cannot meet the demands of modern audiences for high quality, multi angle, and real-time performance. In response to these challenges, an innovative radar image processing system based on space cloud computing has been proposed. Compared with traditional radar image processing systems, the system proposed by the research institute had the best performance, with accuracy, recall, and F1 value reaching 97.08%, 96.88%, and 97.11%, respectively, and a transmission time of only 2.2 seconds;and the stability was greater than 90%, which was about 10% to 25% higher than other systems. In summary, the system proposed by the research institute has brought revolutionary improvements to basketball game guidance and filming through its efficient processing capabilities, accurate image recognition, fast data processing and transmission, and excellent stability. This not only greatly enriches the audience’s viewing experience, but also opens up new directions for the development of sports event broadcasting technology. With the further maturity of technology and the continuous expansion of applications, it is expected that this system will play a more important role in future sports event broadcasting, promoting the development of the entire industry towards higher quality and efficiency. © 2025 Institute of Information Technology, Warsaw University of Life Sciences - SGGW. All rights reserved.

关键词： Photointerpretation

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：