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Dual-stream image recognition network based on visual field-of-view mechanism

Dual-stream image recognition network based on visual field-...

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2023 International conference on machine vision, image processing and Imaging Technology, MVIPIT 2023

作者： He, Langping Wei, Bing Hao, Kuangrong Li, Haijian Yang, Miao He, Junjie Donghua University College of Information Science and Technology Shanghai China Xi'An Jiaotong-Liverpool University School of Advanced Technology Suzhou China

ISBN: (纸本)9798350306545

It is significant that the field-of-view mechanism plays a role in the visual system of the primate, such as reducing information overload and focusing on important information, which can ensure optimal information processing capabilities and an allocation mechanism for computing resources is implemented. Notably, this mechanism operates prior to information calculation, distinguishing it from the attention mechanism, which influences the calculation process itself. We utilize the mechanism in a dual-stream network for image classification for the first time and apply an ideology of reinforcement learning to improve the accuracy of prediction. More importantly, our experiments certify the performance of our method in the Mini-imageNet and CUB-200-2011 dataset. © 2023 IEEE.

关键词： image classification

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Unsupervised Deep Learning image Stitching Model Assisted with Infrared images

Unsupervised Deep Learning Image Stitching Model Assisted wi...

引用

2023 International conference on Algorithm, Imaging processing, and machine vision, AIPMV 2023

作者： Zhu, Ming Li, Chengkun He, Xueying Xiao, Xiao School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781510672444

The rapid development of artificial intelligence facilitates the improvement of image processing algorithms. For an intelligent inspection robot, the ability to analyze the environment through image collection plays an important role. It needs to collect multiple images of the same scene from different angles of view so as to make a thorough analysis about the environment it locates and generate further decisions. Therefore, a technique called image stitching is used. Currently, the development of image stitching algorithms is getting mature – multiple algorithms have already been proposed based feature extraction techniques. However, these existing algorithms are usually unable to handle the problem of parallax existing in real world image. Therefore, in order to solve it, we proposed an unsupervised deep learning image stitching algorithm, which uses infrared images to provide auxiliary information. We utilized our own equipment to collect real world images in visible light and infrared. Finally, we implemented our own model and other popular existing image stitching algorithms and compared and contrasted their performance on our dataset. The results showed that our model has the best performance in all aspects than other algorithms on the dataset, indicating the strong advantages of deep learning methods on image stitching tasks. © 2024 SPIE.

关键词： Deep learning

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Calibration of an intuitive machine vision system based on an external high-performance image processing unit 24

Calibration of an intuitive machine vision system based on a...

引用

24th International conference on Process Control (PC)

作者： Vagas, Marek Romancik, Jaroslav Tech Univ Kosice Dept Ind Automat & Mech Kosice Slovakia

ISBN: (纸本)9798350347630

the article analyzes, parameterizes, and experimentally verifies the known "Hand-Eye" approach of calibration methods between a 2D camera system and a robotic system. Thanks to our new sensing devices (in cooperation with suitable software), we can quickly and easily set up a new application. We emphasize determining, defining, and comparing their geometric quantities and field of view by machine vision at the scene. The tested methodology uses a calibration tool as a reference. It verifies the observed approach on a practical demonstration example of the assembly to recognize the learned model object for sensing (cube). Realized experiments demonstrate the presented approach's usefulness, effectiveness, and reliability (measurement success rate 98%). The research results are based on using an external evaluation unit of the 2D camera system, its available built-in functions, and communication based on Ethernet - Non-Procedural.

关键词： Hand-Eye calibration machine vision image processing 2D camera system

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Performance of Linear Coding and Transmission in Low-Latency Computer vision Offloading 25

Performance of Linear Coding and Transmission in Low-Latency...

引用

IEEE Wireless Communications and Networking conference (IEEE WCNC)

作者： Zadnik, Jakub Trioux, Anthony Kieffer, Michel Makitalo, Markku Coudoux, Francois-Xavier Corlay, Patrick Jaaskelainen, Pekka Tampere Univ Tampere Finland Univ Lille CNRS Cent Lille UMR 8520IEMNUPHFYNCREA Lille France Univ Paris Saclay CNRS Cent Supelec Lab Signaux & Syst Paris France

ISBN: (纸本)9798350303582;9798350303599

image communication increasingly involves machine-to-machine delivery. For example, images acquired by an autonomous drone can be compressed and sent to an edge server over a wireless network for resource-intensive processing. Traditional compression techniques involving transform, quantization, and entropy coding reach high compression efficiency, but channel conditions worse than expected may lead to a sharp decrease in the decoded image quality. As an alternative, Linear Coding and Transmission (LCT) systems have been proposed to avoid this digital cliff problem: The reconstructed image quality decreases gradually as channel conditions degrade. This paper presents a comprehensive evaluation of computer vision tasks with input images processed and transmitted using LCT. It also analyses the benefits of network retraining, accounting for impairments due to LCT and noisy channel. Considering object detection and semantic segmentation over images transmitted and received by LCT systems, we show that the task accuracy degrades smoothly when the channel quality decreases, avoiding the cliff effect. Retraining with noisy images processed by LCT restores detection mAP degradation from 23.8% to 4.4% and segmentation mIoU degradation from 43.2% to 8.1% when the channel signal-to-noise ratio is 10 dB.

关键词： Linear Coding and Transmission Inference Offloading Computer vision Wireless Transmission Discrete Cosine Transform

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Research on Dark Level Correction Method for CMOS image Sensors

Research on Dark Level Correction Method for CMOS Image Sens...

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2023 International conference on Algorithm, Imaging processing, and machine vision, AIPMV 2023

作者： Wang, Yizhe Guo, Zhongjie Guo, Youmei Department of Electronic Engineering Xi'an University of Technology Xi'an710048 China

ISBN: (纸本)9781510672444

To obtain higher imaging quality, the dark level generated during the operation of CMOS image sensors (CIS) needs to be corrected. In this paper, a dark level correction circuit is designed based on a 4 T active pixel, which includes a dark current cancellation circuit and a switched capacitor amplifier circuit. First, the dark current is collected in real time by using the dark pixels in the periphery of the face array, and the dark current noise is read out and differed from the image signals output from the columns to obtain a more accurate output signal, thus eliminating the dark level caused by the dark current. Then the switched-capacitor amplifier is used to collect and amplify the signals to facilitate the subsequent ADC processing. Based on the 110 nm process for the proposed method of specific circuit design verification, the verification results show that the dark level correction circuit designed in this paper through a real-time sampling of the dark pixels of the periphery of the array can be reduced to the exposure stage of the dark current noise to more than 85% of the original. © 2024 SPIE.

关键词： Pixels

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A Comparative Study on Pruning Deep Convolutional Neural Networks Using Clustering Methods: K-Means, CLIQUE, DENCLUE, and OptiGrid 24

A Comparative Study on Pruning Deep Convolutional Neural Net...

引用

9th International conference on Multimedia and image processing (ICMIP)

作者： Alqemlas, Danah Saud Jeragh, Mohammad Esmaeel Kuwait Univ Kuwait Kuwait Kuwait Oil Co Kuwait Kuwait

ISBN: (纸本)9798400716164

In the past years, machine learning (ML) and deep learning (DL) have led to the advancement of several applications, including computer vision, natural language processing, and audio processing. These complex tasks require large models, which is a challenge to deploy in devices with limited resources. These resource-constrained devices have limited computation power and memory. Hence, the neural networks must be optimized through network acceleration and compression techniques. This paper proposes a novel method to compress and accelerate neural networks from a small set of spatial convolution kernels. Firstly, a novel pruning algorithm is proposed based on the density-based clustering method that identifies and removes redundancy in CNNs while maintaining the accuracy and throughput tradeoff. Secondly, a novel pruning algorithm based on the grid-based clustering method is proposed to identify and remove redundancy in CNNs. The performance of the three pruning algorithms (density-based, grid-based, and partitional-based clustering algorithms) is evaluated against each other. The experiments were conducted using the deep CNN compression technique on the VGG-16 and ResNet models to achieve higher accuracy on image classification than the original model at a higher compression ratio and speedup.

关键词： Neural Network Pruning Clustering Methods image processing

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A high quality low-light image enhancement method 2

A high quality low-light image enhancement method

引用

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

作者： Zhang, Xianhong Sun, Yutong Heilongjiang Institute of Technology School of Computer Science and Technology Harbin China

ISBN: (纸本)9798350331417

Factors such as insufficient lighting, backlighting, and failed focusing can lead to low brightness, poor contrast, and noise in the collected images. These issues seriously affect the further analysis and processing of images, especially for color images with more information, which often result in color distortion after enhancement. In order to improve the quality of low light color images, this paper proposes a high-quality low light color image enhancement method. According to Retinex theory, the attention map and reflection map are initialized by constructing weight functions for three color channels, and used as inputs to the network. At the same time, using a dual discriminator structure and attention mechanism, an enhanced image is generated to achieve clearer image enhancement effects. Experimental results show that the method proposed in this paper can improve the quality of low light images with higher quality and effectiveness compared to mainstream enhancement algorithms. © 2023 IEEE.

关键词： Deep learning GAN image enhancement machine learning

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Research on machine vision-based unmanned aerial vehicle landing technology 4

Research on machine vision-based unmanned aerial vehicle lan...

引用

4th International conference on Signal processing and machine Learning, CONF-SPML 2024

作者： Xie, Chao Joint Co-Op Institute of Chongqing University-University of Cincinnati Chongqing University Chongqing China

ISBN: (纸本)9781510674721

Unmanned aerial vehicles (UAVs) are widely utilized in various fields. However, during mission execution, the occurrence of mechanical failures or subsystem malfunctions, including fuel shortages, may result in the UAV landing in an unspecified area. Additionally, in emergency situations, the UAV may be forced to land in densely populated areas or treacherous terrains. Careful consideration of suitable landing points before touchdown is crucial, making research on UAV landing technology a significant contemporary topic. Current traditional landing techniques, relying on satellite navigation and inertial navigation, face challenges in adapting to complex environments and terrain interference with satellite signals. The application of machine vision-based technology to UAVs presents a promising solution, enabling autonomous landings in signal-deprived scenarios. Therefore, this paper investigates machine vision-based UAV landing technology by processing images captured by the UAV of the terrain. A series of image processing steps are applied to reconstruct the terrain in three dimensions, generating a point cloud map of the terrain. Through the analysis of this map, a range of methods is employed to determine the optimal landing points. The study successfully achieves the three-dimensional reconstruction of the terrain and identifies the optimal landing points, conducting experiments in complex terrains to successfully locate the best landing points and accomplish autonomous UAV landings. This research leverages numerous algorithms to optimize the terrain map, resulting in a more comprehensive point cloud. By combining two landing strategies, the study achieves more precise landing points. © 2024 SPIE.

关键词： Antennas

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Generating image Description Using machine Learning Algorithms 11

Generating Image Description Using Machine Learning Algorith...

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11th International conference on Emerging Trends in Engineering and Technology - Signal and Information processing, ICETET - SIP 2023

作者： Agnihotri, Khushboo Chilbule, Pujyalakshmi Prashant, Shiv Jain, Priyanshu Khobragade, Prashant G H Raisoni College of Engineering Department of Computer Science and Enginering Nagpur India

ISBN: (纸本)9798350348422

In the contemporary world, captions are one of the most essential tools. There are also integrated programmes that use deep neural network models to create and provide captions for particular photographs. This article compares deep learning models that create captions and explain images translation using machine mechanism, machine learning algorithms and computer vision. The projected work recognize and identify several things in an image and their relationships in order to produce captions. For the purpose of illustrating the suggested experiment, Flickr8k is the dataset, Python3 is the programming language, and ResNet, Xception, and Inception are utilised to create the model. This study also identifies the model that yields the best outcomes on the dataset being used. Natural language processing and machine vision both heavily rely on caption generation. To generate the caption of image Encode and decoder model of LSTM with CNN machine learning algorithm is used for correct caption of images. © 2023 IEEE.

关键词： image segmentation

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Detection of Palmprint from Contactless Smartphone-Based Video Hand Dataset 8th

Detection of Palmprint from Contactless Smartphone-Based Vid...

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8th National conference on Computer vision, Pattern Recognition, image processing and Graphics-NCVPRIPG

作者： Chodvadiya, Charulkumar Singh, Kinshuk Gaurav Vyas, Ritesh Pandit Deendayal Energy Univ Gandhinagar Gujarat India

ISBN: (纸本)9789819752119;9789819752126

A fast-expanding topic is the study of palmprint biometric identification in contactless scenario, which uses techniques from computer vision and machine learning to identify and authenticate people. In this study, we utilized a handcrafted video dataset with 60 distinct classes, each labelled as either a left or right hand, to investigate palmprint detection and matching tasks. The dataset showcases various variations in palmprint patterns, like distance from the sensor, orientation, finger positioning, and deformation, making it an ideal candidate for the development of robust and accurate palmprint recognition models. The major goal of the study is to identify palmprints in the video collection and match them with the right class or pattern. To accomplish this task, different machine learning (ML) and deep learning (DL) models were trained and evaluated. To find the best method for palmprint identification in a contactless manner, the accuracy of each model was tested. In conclusion, our study adds to the expanding body of knowledge on biometric palmprint identification and introduces a fresh handmade video dataset that can be used to compare the effectiveness of various models.

关键词： Palmprint biometric identification Computer vision Deep learning model Palmprint patterns Transfer learning

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