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نتیجه جستجو - Object recognition

تعداد مقالات یافته شده: 28
ردیف عنوان نوع
1 AI-based computer vision using deep learning in 6G wireless networks
بینایی کامپیوتر مبتنی بر هوش مصنوعی با استفاده از یادگیری عمیق در شبکه های بی سیم 6G-2022
Modern businesses benefit significantly from advances in computer vision technology, one of the important sectors of artificially intelligent and computer science research. Advanced computer vision issues like image processing, object recognition, and biometric authentication can benefit from using deep learning methods. As smart devices and facilities advance rapidly, current net- works such as 4 G and the forthcoming 5 G networks may not adapt to the rapidly increasing demand. Classification of images, object classification, and facial recognition software are some of the most difficult computer vision problems that can be solved using deep learning methods. As a new paradigm for 6Core network design and analysis, artificial intelligence (AI) has recently been used. Therefore, in this paper, the 6 G wireless network is used along with Deep Learning to solve the above challenges by introducing a new methodology named Optimizing Computer Vision with AI-enabled technology (OCV-AI). This research uses deep learning – efficiency al- gorithms (DL-EA) for computer vision to address the issues mentioned and improve the system’s outcome. Therefore, deep learning 6 G proposed frameworks (Dl-6 G) are suggested in this paper to recognize pattern recognition and intelligent management systems and provide driven meth- odology planned to be provisioned automatically. For Advanced analytics wise, 6 G networks can summarize the significant areas for future research and potential solutions, including image enhancement, machine vision, and access control.
keywords: SHG | ارتباطات بی سیم | هوش مصنوعی | فراگیری ماشین | یادگیری عمیق | ارتباطات سیار | 6G | Wireless communication | AI | Machine learning | Deep learning | Mobile communication
مقاله انگلیسی
2 PortiK: A computer vision based solution for real-time automatic solid waste characterization – Application to an aluminium stream
PortiK: یک راه حل مبتنی بر بینایی کامپیوتری برای شناسایی خودکار زباله جامد در زمان واقعی - کاربرد در جریان آلومینیوم-2022
In Material Recovery Facilities (MRFs), recyclable municipal solid waste is turned into a precious commodity. However, effective recycling relies on effective waste sorting, which is still a challenge to sustainable develop- ment of our society. To help the operations improve and optimise their process, this paper describes PortiK, a solution for automatic waste analysis. Based on image analysis and object recognition, it allows for continuous, real-time, non-intrusive measurements of mass composition of waste streams. The end-to-end solution is detailed with all the steps necessary for the system to operate, from hardware specifications and data collection to su- pervisory information obtained by deep learning and statistical analysis. The overall system was tested and validated in an operational environment in a material recovery facility. PortiK monitored an aluminium can stream to estimate its purity. Aluminium cans were detected with 91.2% precision and 90.3% recall, respectively, resulting in an underestimation of the number of cans by less than 1%. Regarding contaminants (i.e. other types of waste), precision and recall were 80.2% and 78.4%, respectively, giving an 2.2% underestimation. Based on five sample analyses where pieces of waste were counted and weighed per batch, the detection results were used to estimate purity and its confidence level. The estimation error was calculated to be within ±7% after 5 minutes of monitoring and ±5% after 8 hours. These results have demon- strated the feasibility and the relevance of the proposed solution for online quality control of aluminium can stream.
keywords: امکانات بازیابی مواد | شناسایی مواد زائد جامد | یادگیری عمیق | شبکه عصبی عمیق | بینایی کامپیوتر | Material recovery facilities | MRF | Solid waste characterization | Deep-learning | Deep neural network | Computer vision
مقاله انگلیسی
3 Unsupervised foveal vision neural architecture with top-down attention
معماری عصبی چشم انداز بدون نظارت با توجه از بالا به پایین-2021
Deep learning architectures are an extremely powerful tool for recognizing and classifying images. However, they require supervised learning and normally work on vectors of the size of image pixels and produce the best results when trained on millions of object images. To help mitigate these issues, we propose an end-to-end architecture that fuses bottom-up saliency and top-down attention with an object recognition module to focus on relevant data and learn important features that can later be fine- tuned for a specific task, employing only unsupervised learning. In addition, by utilizing a virtual fovea that focuses on relevant portions of the data, the training speed can be greatly improved. We test the performance of the proposed Gamma saliency technique on the Toronto and CAT 2000 databases, and the foveated vision in the large Street View House Numbers (SVHN) database. The results with foveated vision show that Gamma saliency performs at the same level as the best alternative algorithms while being computationally faster. The results in SVHN show that our unsupervised cognitive architecture is comparable to fully supervised methods and that saliency also improves CNN performance if desired. Finally, we develop and test a top-down attention mechanism based on the Gamma saliency applied to the top layer of CNNs to facilitate scene understanding in multi-object cluttered images. We show that the extra information from top-down saliency is capable of speeding up the extraction of digits in the cluttered multidigit MNIST data set, corroborating the important role of top down attention.© 2021 Elsevier Ltd. All rights reserved.
Keywords: Unsupervised Learning | Foveal vision | Top-down saliency | Deep learning
مقاله انگلیسی
4 Computer vision technologies for safety science and management in construction: A critical review and future research directions
فناوری های بینایی رایانه ای برای علم ایمنی و مدیریت در ساخت و ساز: مروری انتقادی و جهت تحقیقات آینده-2021
Recent years have seen growing interests in developing and applying computer vision technologies to solve safety problems in the construction industry. Despite the technological advancements, there is no research that exams the theoretical links between computer vision technology and safety science and management. Thus, the ob- jectives of this paper are to: (1) investigate the current status of applying computer vision technology to con- struction safety, (2) examine the links between computer vision applications and key research themes of construction safety, (3) discuss the theoretical challenges of applying computer vision to construction safety, and(4) recommend future research directions. A five-step review approach was adopted to search and analyze peer- reviewed academic journal articles. A three-level computer vision development framework was proposed to categorized computer vision applications in the construction industry. The links between computer vision and three main safety research traditions: safety management system, behavior-based safety program, and safety culture, were discussed. The results suggest that the majority of past efforts were focused on object recognition, object tracking, and action recognition, with limited research focused on recognizing unsafe behavior. There are even fewer studies aimed at developing vision-based safety assessment and prediction systems. Based on the review findings, four future research directions are suggested: (1) develop and test a behavioral-cues-based safety climate measure, (2) develop safety behavior datasets, (3) develop a formal hazard identification and assessment model, and (4) develop criteria to evaluate the real impacts of vision-based technologies on safety performance.
Keywords: Computer vision | Construction health and safety | Safety science | Safety culture | Safety Climate, Hazard | Safety management system | Digital technologies | Automation
مقاله انگلیسی
5 Taxonomy, state-of-the-art, challenges and applications of visual understanding: A review
طبقه بندی ، آخرین فن آوری ، چالش ها و کاربردهای درک بصری: مروری-2021
Since the dawn of Humanity, to communicate both abstract and concrete ideas, visualization through visual imagery has been an effective way. With the advancement of scientific technologies, vision has been imparted to machines like humans do. Computer vision give ability to machines, to receive and analyze visual data on its own, and then make decisions about it, hence computer vision is more than machine learning applied. So, visualization of computer models to learn without being explicitly programmed using machine learning algorithms is called Visual learning. This work aims to review the state-of-the-art in computer vision by highlighting the contributions, challenges and applications. We first provide an overview of important visual learning approaches and their recent developments, and then describes their applications in diverse vision tasks, such as image classification, object detection, object recognition, visual saliency detection, semantic and instance segmentation, human pose estimation and image retrieval. Hardware constraints are also highlighted for better understanding of model selection. Finally, some important challenges, trends and outlooks are also discussed for better design and training of learning modules, along with several directions that may be further explored in the future.© 2021 Elsevier Inc. All rights reserved.
Keywords: Computer vision | Visual learning | Developments | Applications | Trends | Challenges
مقاله انگلیسی
6 Optimization of Underwater Marker Detection Based on YOLOv3
بهینه سازی تشخیص نشانگر زیر آب بر اساس YOLOv3-2021
The research on the detection and recognition technology of marker is of great significance for some underwater operations, such as marine resource exploration, underwater robot operation and so on. The existing image processing methods can effectively detect and recognize the markers in the air. Nevertheless, in the underwater environment, the complex imaging environment of the ocean leads to serious degradation of underwater images obtained by the optical vision system. Due to the lack of effective information for object recognition, the severely degraded underwater images increases the difficulty of detection and recognition of underwater objects. With the development of high-tech underwater imaging equipment, the quality of underwater images has been improved to a certain extent, but there are still some phenomena such as color fading, low contrast and blurred details. Solutions to overcome these problems are important for the exploration of the ocean. In this paper, we introduce a deep learning model to optimize the performance of detection, and make a unique marker dataset for the application scene of our experiment. We first use the deep learning network to pre-train the marker images in the air. Next, we use the underwater marker images for fine-tuning. Finally, after the target marker is detected, the traditional image processing method is used to recognize the marker. Experimental results show that the optimization method we proposed achieves better performance on the dataset.© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)Peer-review under responsibility of the scientific committee of the International Conference on Identification, Information and Knowledge in the internet of Things, 2020.
Keywords: Computer vision | Underwater marker detection | Deep learning
مقاله انگلیسی
7 A review on 3D image reconstruction on specific and generic objects
A review on 3D image reconstruction on specific and generic objects-2021
With the emergence of various techniques involved in deep learning the researchers of computer vision tends to focus on the strategies such as object recognition and segmentation of image. This has inclined to accomplish the deep learning techniques in 3D reconstruction of both specific and generic objects. As the space for reconstruction of 3D images either in single or multi view has envisioned the researchers to concentrate on the available technologies used for reconstruction. With the available built in methods and technologies in deep learning, the performance of the proposed methods were reviewed and analyzed using several parameters. As the remaking of 2D images is still in the beginning stage, it is important to study the 3D shape representations, various network architecture, methodologies and approaches behind 3D reconstruction. In this work a review of deep learning methods for single or multiple RGB images of specific and generic object 3D reconstruction was done. Several methods and their importance were also discussed along with the challenges encountered and with further research directions. This paper critically analyzes the various 3D Shaped Representations, 3D Data Network Architectures, Depth Estimation methods, Multi View Representations and the Data Representation Techniques.© 2021 Elsevier Ltd. All rights reserved.
Keywords: Reconstruction | Computer vision | Segmentation | Object recognition | 3D deep learning | Shape representations
مقاله انگلیسی
8 A self-organizing developmental cognitive architecture with interactive reinforcement learning
یک معماری شناختی توسعه ای خود سازمان دهی شده با یادگیری تقویتی تعاملی-2020
Developmental cognitive systems can endow robots with the abilities to incrementally learn knowledge and autonomously adapt to complex environments. Conventional cognitive methods often acquire knowl- edge through passive perception, such as observing and listening. However, this learning way may gener- ate incorrect representations inevitably and cannot correct them online without any feedback. To tackle this problem, we propose a biologically-inspired hierarchical cognitive system called Self-Organizing De- velopmental Cognitive Architecture with Interactive Reinforcement Learning (SODCA-IRL). The architec- ture introduces interactive reinforcement learning into hierarchical self-organizing incremental neural networks to simultaneously learn object concepts and fine-tune the learned knowledge by interacting with humans. In order to realize the integration, we equip individual neural networks with a memory model, which is designed as an exponential function controlled by two forgetting factors to simulate the consolidation and forgetting processes of humans. Besides, an interactive reinforcement strategy is designed to provide appropriate rewards and execute mistake correction. The feedback acts on the for- getting factors to reinforce or weaken the memory of neurons. Therefore, correct knowledge is preserved while incorrect representations are forgotten. Experimental results show that the proposed method can make effective use of the feedback from humans to improve the learning effectiveness significantly and reduce the model redundancy.
Keywords: Cognitive development | Online learning | Self-organizing neural network | Object recognition | Interactive reinforcement learning
مقاله انگلیسی
9 Goal driven network pruning for object recognition
هرس شبکه هدف محور برای شناسایی هدف -2020
Pruning studies up to date focused on uncovering a smaller network by removing redundant units, and fine-tuning to compensate accuracy drop as a result. In this study, unlike the others, we propose an approach to uncover a smaller network that is competent only in a specific task, similar to top-down attention mechanism in human visual system. This approach doesn’t require fine-tuning and is proposed as a fast and effective alternative of training from scratch when the network focuses on a specific task in the dataset. Pruning starts from the output and proceeds towards the input by computing neuron importance scores in each layer and propagating them to the preceding layer. In the meantime, neurons determined as worthless are pruned. We applied our approach on three benchmark datasets: MNIST, CIFAR-10 and ImageNet. The results demonstrate that the proposed pruning method typically reduces computational units and storage without harming accuracy significantly.
Keywords: Deep learning | Computer vision | Network pruning | Network compressing | Top-down attention | Perceptual visioning
مقاله انگلیسی
10 A network view on brain regions involved in experts’ object and pattern recognition: Implications for the neural mechanisms of skilled visual perception
نمای شبکه در مورد مناطق مغز درگیر در تشخیص موضوع و الگوی متخصصان: پیامدهای مکانیسم های عصبی درک بصری ماهر-2019
Skilled visual object and pattern recognition form the basis of many everyday behaviours. The game of chess has often been used as a model case for studying how long-term experience aides in perceiving objects and their spatio-functional interrelations. Earlier research revealed two brain regions, posterior middle temporal gyrus (pMTG) and collateral sulcus (CoS), to be linked to chess experts’ superior object and pattern recognition, respectively. Here we elucidated the brain networks these two expertise-related regions are embedded in, employing resting-state functional connectivity analysis and meta-analytic connectivity modelling with the BrainMap database. pMTG was preferentially connected with dorsal visual stream areas and a parieto-prefrontal network for action planning, while CoS was preferentially connected with posterior medial cortex and hippocampus, linked to scene perception, perspective-taking and navigation. Functional profiling using BrainMap meta-data revealed that pMTG was linked to semantic processing as well as inhibition and attention, while CoS was linked to face and shape perception as well as passive viewing. Our findings suggest that pMTG subserves skilled object recognition by mediating the link between object identity and object affordances, while CoS subserves skilled pattern recognition by linking the position of individual objects with typical spatio-functional layouts of their environment stored in memory.
Keywords: Skilled perception | Chess expertise | Functional connectivity | Resting-state fMRI | MACM | Functional decoding
مقاله انگلیسی
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