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

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Performance Evaluation of Recent Object Detection Models for Traffic Safety applications on Edge 23

Performance Evaluation of Recent Object Detection Models for...

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5th International conference on image processing and machine vision, IPMV 2023

作者： Bulut, Anilcan Ozdemir, Fatmanur Bostanci, Yavuz Selim Soyturk, Mujdat VeNIT Lab Marmara University Turkey

ISBN: (纸本)9781450397926

Real-time objection detection is becoming more important and critical in all application areas, including Smart Transport and Smart City. From safety/security to resource efficiency, real-time image processing approaches are used more than ever. On the other hand, low-latency requirements and available resources present challenges. Edge computing integrated with cloud computing minimizes communication delays but requires efficient use of resources due to its limited resources. For example, although deep learning-based object detection methods give very accurate and reliable results, they require high computational power. This overhead reveals a need to implement deep learning models with less complex architectures for edge deployment. In this paper, the performance of evolving deep learning models with their lightweight versions such as YOLOv5-Nano, YOLOX-Nano, YOLOX-Tiny, YOLOv6-Nano, YOLOv6-Tiny, and YOLOv7-Tiny are evaluated on a commercially available edge device. The results show that YOLOv5-Nano and YOLOv6-Nano with their TensorRT versions can provide real-time applicability in approximately 35 milliseconds of inference time. It is also observed that YOLOv6-Tiny gives the highest average precision while YOLOv5-Nano gives the lowest energy consumption when compared to other models. © 2023 ACM.

关键词： Object detection

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Generalizing Functional Error Correction for Language and vision-Language Models

Generalizing Functional Error Correction for Language and Vi...

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International conference on machine Learning and applications (ICMLA)

作者： Wenyu Peng Simeng Zheng Michael Baluja Tao Xie Anxiao Jiang Paul H. Siegel Electrical and Computer Engineering Department University of California San Diego La Jolla CA USA Computer Science San Diego State University San Diego CA USA Department of Computer Science and Engineering Texas A&M University College Station TX USA

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

The goal of functional error correction is to preserve neural network performance when stored network weights are corrupted by noise. To achieve this goal, a selective protection (SP) scheme was proposed to optimally protect the functionally important bits in binary weight representations in a layer-dependent manner. Although it showed its effectiveness in image classification tasks on some relatively simple networks such as ResNet-18 and VGG-16, it becomes inadequate for emerging complex machine learning tasks generated from natural language processing and vision-language association domains. To solve this problem, we extend the SP scheme in three directions: task complexity, model complexity, and storage complexity. Extensions to complex natural language and vision-language tasks include text categorization and “zero-shot” textual classification of images. Extensions to more complex models with deeper block structures and attention mechanisms consist of Very Deep Convolutional Neural Network (VDCNN) and Contrastive Language-image Pre-Training (CLIP) networks. Extensions to more complex storage configurations focus on distributed storage architectures to support model parallelism. Experimental results show that the optimized SP scheme preserves network performance in all of these settings. The results also provide insights into redundancy-performance tradeoffs, generalizability of SP across datasets and tasks, and robustness of partitioned network architectures.

关键词： Adaptation models Text categorization Neural networks Noise Parallel processing Network architecture Robustness Error correction Complexity theory Protection

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Analysis of Medical images using image Registration Feature-based Segmentation Techniques 2

Analysis of Medical Images using Image Registration Feature-...

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2nd International conference on Technological Advancements in Computational Sciences, ICTACS 2022

作者： Sindhu Madhuri, G. Shashikala, H.K. Faculty of Engineering & Technology Department of Computer Science & Engineering Bangalore India

ISBN: (数字)9781665476577

ISBN: (纸本)9781665476577

image Segmentation is one of the very important optimistic and emerging fields in all image processing applications. It has a wide range of applications like machine vision, fingerprint recognition, digital forensics, medical imaging, and face recognition and so on. Based on specific application, various image segmentation techniques like thresholding, region growing, watershed, clustering algorithms, fuzzy algorithms etc., are used to segment or partition the input images, labels each pixel in the images, locate the points, edges, boundaries and objects to identify various problems in the medical images. Also the identification of important parameters, detection of fractures and diseases, to decrease the death rate of patients suffering from various health problems is challenging research work in medical images. In this paper, author carryout the analysis for the automatic detection of bone fracture in early stage by taking two input x-ray medical images that are captured at different timings. This process is carried out and registered in 4 stages: In first stage-acquire input images and perform pre-processing by using geometrical transformation and register the input images, in second stage- the registered image is segmented using adaptive k-means clustering method, in third stage- automatic detection of the important features in x-ray image is extracted using image registration feature-based method. Automatic feature extraction is carried out for the observation of bone fracture in initial phase to increase the complexity of geometrical alignments of input images. Finally in the fourth stage, the performance of the results is analyzed with respect to accuracy and error rate. © 2022 IEEE.

关键词： Medical imaging

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ROT-Harris: A Dynamic Approach to Asynchronous Interest Point Detection 17

ROT-Harris: A Dynamic Approach to Asynchronous Interest Poin...

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17th International conference on machine vision applications (MVA)

作者： Harrigan, S. Coleman, S. Ker, D. Yogarajah, P. Fang, Z. Wu, C. Ulster Univ Sch Comp Engn & Intelligent Syst Londonderry BT48 0QR North Ireland Northeastern Univ Fac Robot Sci & Engn Shenyang 110169 Liaoning Peoples R China

ISBN: (纸本)9784901122207

Event-based vision sensors are a paradigm shift in the way that visual information is obtained and processed. These devices are capable of low-latency transmission of data which represents the scene dynamics. Additionally, low-power benefits make the sensors popular in finite-power scenarios such as high-speed robotics or machine vision applications where latency in visual information is desired to be minimal. The core datatype of such vision sensors is the 'event' which is an asynchronous per-pixel signal indicating a change in light intensity at an instance in time corresponding to the spatial location of that sensor on the array. A popular approach to event-based processing is to map events onto a 2D plane over time which is comparable with traditional imaging techniques. However, this paper presents a disruptive approach to event data processing that uses a tree-based filter framework that directly processes raw event data to extract events corresponding to interest point features, which is then combined with a Harris interest point approach to isolate features. We hypothesise that since the tree structure contains the same spatial information as a 2D surface mapping, Harris may be applied directly to the content of the tree, bypassing the need for transformation to the 2D plane. Results illustrate that the proposed approach performs better than other state-of-the-art approaches with limited compromise on the run-time performance.

关键词： Performance evaluation visualization Interest point detection machine vision Detectors vision sensors Feature extraction

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Robust Face Detection and Recognition using image processing and OpenCV 6

Robust Face Detection and Recognition using Image Processing...

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6th International conference on Computing Methodologies and Communication, ICCMC 2022

作者： Bhat, Aruna Jha, Rahul Kumar Kedia, Vansh Delhi Technological University Department of Computer Engineering Delhi India

ISBN: (纸本)9781665410281

An enormous progress in the fields of Artificial Intelligence, machine Learning, and Computer vision is witnessed over the last decade, owing to a doubling in processing capacity per year, as well as a massive rise in data internationally. Facial identity, biometrics, and face identification have gone from a rarefied issue to a prominent topic for academics in computer science these days. Due to the basic nature of the topic, breakthroughs in computer image processing and understanding studies will give insights into how the human brain operates, according to the common belief. Face detection and recognition are often employed in national defense, CCTV surveillance, criminal investigation, personal protection, law enforcement, biometrics, and a variety of other applications. For this project, OpenCV, Haar cascade, NumPy, and Matplotlib Python packages are utilized. Haar-Cascades will be utilized for face detection, and OpenCV provides three built-in face recognizers: Face recognition algorithms such as Eigenfaces, Fisher faces, and Local binary pattern histograms are used. The approach employed in this research is literature review, which entails reading and examining numerous books or literature relevant to mathematical ideas that underpin the development of algorithms to detect a human's face, which are subsequently followed by implementation in code, particularly Python code. © 2022 IEEE.

关键词： Face recognition

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5th International conference on Recent Innovations in Computing , ICRIC 2022

5th International Conference on Recent Innovations in Comput...

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5th International conference on Recent Innovations in Computing , ICRIC 2022

ISBN: (纸本)9789819906000

The proceedings contain 66 papers. The special focus in this conference is on Recent Innovations in Computing. The topics include: The Importance of Selected LMS Logs Pre-processing Tasks on the Performance Metrics of Classification Models;analysis of Deep Pre-trained Models for Computer vision applications: Dog Breed Classification;an Efficient Recognition and Classification System for Paddy Leaf Disease Using Naïve Bayes with Optimization Algorithm;development of Decision-Making Prediction Model for Loan Eligibility Using Supervised machine Learning;prediction of Osteoporosis Using Artificial Intelligence Techniques: A Review;analyzing Stock Market with machine Learning Techniques;machine Learning Techniques for image Manipulation Detection: A Review and Analysis;improving Real-Time Intelligent Transportation Systems in Predicting Road Accident;Consumer Buying Behavior Analysis During COviD-19 Phase Using Statistical Data Mining and Multi-verse Stochastic Algorithm;clinical Named Entity Recognition Using U-Net Classification Model;addressing Role of Ambient Intelligence and Pervasive Computing in Today’s World;Improving the AODV Routing Protocol Against Network Layer Attacks Using AntNet Algorithm in VANETs;a Comparative Study of IoT-Based Automated Hydroponic Smart Farms: An Urban Farming Perspective;Smart Healthcare System Based on AIoT Emerging Technologies: A Brief Review;Intelligent Communication for Internet of Things (IoRT);energy-Efficient Priority-Based Routing Model for Smart Health Care;An Internet of Things-Based Mining Worker Safety Helmet Using ESP32 and Blynk App;Time Series-Based IDS for Detecting Botnet Attacks in IoT and Embedded Devices;swarm Intelligence-Based Energy-Efficient Framework in IoT;Performance Analysis of MADM Techniques in Cognitive Radio for Proximity Services;neural machine Translation from English to Marathi Using Various Techniques.

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ADMC: An Attention-based Dense Multi-scale Convolutional architecture for Medical image Segmentation

ADMC: An Attention-based Dense Multi-scale Convolutional arc...

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image processing, Computer vision and machine Learning (ICICML), International conference on

作者： Zhao Hao Harbin University of Science and Technology Harbin China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Medical image segmentation is crucial for many healthcare applications, and deep learning networks have shown great potential in performing semantic segmentation tasks effectively. However, existing methods often suffer from the loss of important local features. To solve this problem, We introduce an innovative approach: a multi-scale and multi-connection feature adaptive fusion method guided by an attention mechanism. The proposed method emphasizes adaptive fusion of multi-scale and multi-connection features, effectively capturing meaningful features at different scales, while also utilizing attention mechanism to enhance feature representation. Specifically, Our approach leverages dense multi-scale skip connections to bridge the semantic gap between the feature maps of the encoder and decoder, where the feature map in each decoder module is connected with each feature map in encoder module. Besides, our network incorporates a deep attention block (DCS) in the encoder to capture meaningful local features. In the decoder, we introduce an efficient multi-scale convolution block (MSDC) to refine feature maps by performing deep convolution across multiple scales. Experimental results in the well known The ISIC dataset results demonstrate that our approach significantly outperforms the baseline method in the task of medical image segmentation.

关键词： image segmentation Attention mechanisms Convolution Semantic segmentation Semantics Refining Computer architecture Feature extraction Decoding Medical diagnostic imaging

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A New video Summarization Approach Using Object Density Based image Similarity for Smart City applications

A New Video Summarization Approach Using Object Density Base...

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International Scientific-Professional conference on Information Technology (IT)

作者： Turan Goktug Altundogan Mehmet Karakose Fatih Mert Computer Engineering Department Manisa Celal Bayar University Manisa Türkiye Computer Engineering Department Firat University Elazig Türkiye Huawei Telecommunication Foreign Trade Co. Ltd. Istanbul Türkiye

ISBN: (数字)9798331517649

ISBN: (纸本)9798331517656

Summarization approaches are currently proposed solutions that focus on meaningfully reducing different types of data such as text, audio, and video. Many techniques such as machine learning, signal processing, image processing, computer vision, and deep learning can be used to develop summarization approaches. In this study, we performed object detection on videos that can be used in smart city applications using a pretrained YOLOv8 model. As a result of the object detection, we created a feature vector for each image frame by using the location information covered by the classes used in the object detection process. Then, we used several different approaches to determine the reference feature vector for the video. Finally, we calculated the cosine similarities of the feature vector for each frame to this reference feature vector using different methods. With the method we developed, we presented a similarity-focused summary created by selecting the video frames expressed with maximum similarity. We also developed an evaluation approach to evaluate the summaries we presented, comparing the overall heat maps of the video with the heat maps of the summary videos. Experimental results demonstrate the efficiency of our summarization approaches.

关键词： Deep learning Computer vision Smart cities Computational modeling Object detection Signal processing Feature extraction Vectors Heat maps Information technology

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Feature extraction and multi-period classification of sea mushroom based on image information

Feature extraction and multi-period classification of sea mu...

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2022 International conference on Artificial Intelligence, Information processing and Cloud Computing, AIIPCC 2022

作者： Zhong, Yang Du, Wanhe Yang, Shuzhen Yu, Tao Zheng, Xueping School of Mechatronic Engineering and Automation Shanghai University Shanghai China College of Engineering Shanghai Polytechnic University Shanghai China Shanghai Rongmei Agricultural Technology Co LTD Shanghai China

ISBN: (纸本)9781665462877

The phenotype of edible fungus basically relies on visual observation and empirical judgment at present, and there is still a lack of reports on the phenotypic techniques and applications for cultivation of seafood mushroom. Accurate characterization of different growth stages in the reproductive culture step of sea mushroom and rapid judgment of its growth stage is the foundation for its industrial cultivation to intelligent cultivation. In this work, a method of multi-period growth feature extraction and period classification of sea mushroom based on machine vision technology is proposed. According to the characteristics of the image itself, the method extracts 28 features of color, shape, and texture from the acquired data set. Subsequently, the KNN (K-nearest Neighbor) is utilized to classify the images represented by the selected features. The results indicated that the multi-period classification accuracy of the growth period of the mushroom image based on the comprehensive representation of the three types of features was 85%, which could better distinguish the growth period through the image, and made guidelines for the regulation of the cultivation environment. © 2022 IEEE.

关键词： Classification (of information)

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1st International conference on Artificial Intelligence and its applications, ICAIA 2023

1st International Conference on Artificial Intelligence and ...

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1st International conference on Artificial Intelligence and its applications, ICAIA 2023

ISBN: (纸本)9783031843938

The proceedings contain 37 papers. The special focus in this conference is on Artificial Intelligence and its applications. The topics include: The Hybrid Cardiac Risk Assessment and Prediction Model Using Convolutional Neural Networks;deep Learning applications for Malaria Detection and Diagnosis: A Review;Environmental Considerations in the Ethics of AI Adoption in Healthcare: Striving for Sustainable and Responsible Practices;harnessing the Power of Cognitive Computing: Assessing Point-of-Care Decision Support Tools in Oncology Practice;Critical Analysis in Use of AI in Health Care Management;classification and Prediction of Spinal Tuberculosis Disease Using Optimization of Convolution Neural Network Using Spatial and Temporal Constraints;artificial Intelligence for Remote Healthcare in Underserved Areas: Enhancing Access and Quality of Healthcare Delivery;machine Learning Based Skin Cancer Detection and Recognitions Techniques in IoT Environment;validation of a Chronic Kidney Disease Prediction System Using machine Learning Techniques;real-Time Feedback Detection Using Emotion Detection and Facial Recognition;revolutionizing vision Tasks: Unlocking Potential Through Patch-Based Approaches;automated Knee Implant Identification from 2D Templates Using image processing and Artificial Intelligence – An Experimental Approach;crop Analysis and Classification Based on Phenotype Using Ensemble Learning;Classification and Identification with Health Benefit Assessment and Nutrient Profile of Brewed Tea Utilizing Computer vision with ML and DL and Sensory Approaches;enhancing Industrial Automation Flexibility Through Neural Network-Empowered machine vision applications.

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