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2022 IEEE Aerospace Conference, AERO 2022

作者： Towfic, Zaid Ogbe, Dennis Sauvageau, Joe Sheldon, Douglas Jongeling, Andre Chien, Steve Mirza, Faiz Dunkel, Emily Swope, Jason Ogut, Mehmet Cretu, Vlad Pagnotta, Chris California Institute of Technology Jet Propulsion Laboratory 4800 Oak Grove Dr. PasadenaCA91109 United States Qualcomm Technologies Inc. 5775 Morehouse Drive San DiegoCA92121 United States

ISBN: (数字)9781665437608

ISBN: (纸本)9781665437608

As some space missions become more challenging due to new environments, greater distances, or more limited size, weight, and power (SWaP) constraints, spacecraft avionics must adapt to allow the spacecraft to be more autonomous and agile-eliminating the Spacecraft-Earth-Spacecraft feedback loop whenever possible. Prime examples of such missions include Aerobots (such as Ingenuity with extremely low SWaP constraints and demanding signal/image processing during flight) and landers in possibly hostile environments (such as a Europa lander mission, with limited communication capacity, high latency, and constrained power budget). To address these challenges, JPL worked with Qualcomm to demonstrate the use of their Snapdragon 801 system-on-chip (SoC) onboard the Ingenuity Helicopter on Mars. The Qualcomm Snapdragon SoC contains various subsystems, including an ARM cluster, a Graphics processing unit, a Digital Signal processing subsystem, a Neural processing Engine, image Signal processing subsystem, among others. Since the success of Ingenuity, JPL is continuing to work with Qualcomm to address other applications of the Snapdragon SoC technology. This includes the deployment of two 855 Snapdragon development boards onboard the International Space Station (ISS) for successful in-situ benchmarking of applications in space (beyond those tested on Ingenuity). In this paper, we will examine the performance of various applications that have been identified to benefit from greater onboard computational capability. These applications include (among others): machine vision algorithms that are expected to be critical in autonomous entry-descent-and-landing scenarios and real-time Aerobot flight navigation;Hyperspectral compression algorithms;Synthetic Aperture Radar processing along with various instrument processing algorithms. We discuss how the infusion of Qualcomm's Snapdragon SoC is capable of enabling missions that may not have been able to achieve their goals with tradition

关键词： System-on-chip

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Future of Fashion: AI-Powered Virtual Dressing for E-Commerce applications

Future of Fashion: AI-Powered Virtual Dressing for E-Commerc...

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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Nitasha, Er. Kumari, Sneha Kumar, Aditya Bhardwaj, Rizul Maddheshiya, Vinita Khan, Altamas Chandigarh University Department of Computer Science and Engineering Punjab Mohali India

ISBN: (纸本)9798350376470

This paper introduces an innovative AI-powered Virtual Dressing technology designed to revolutionize the way consumers shop for clothing online. Utilizing advanced computer vision techniques and machine learning models, our system offers a seamless virtual dressing experience. The process begins with high-resolution image capture, followed by dynamic resizing for optimal processing. A sophisticated cloth segmentation method is employed to accurately identify and separate the clothing item from the background. Utilizing the models for cloth segmentation and semantic segmentation, combined with posing coordinates, our system ensures precise body posture and garment alignment. The removal of background elements is achieved through semantic segmentation masks, enhancing the realism of the virtual experience. The final virtual dressing visualization supports both scenarios of retaining or omitting the background, based on user preference. To assess consumer interest in such technology, we conducted a survey which revealed that a vast majority of online shoppers would consider buying clothing if they could see a virtual picture of themselves in the attire beforehand. This finding underscores the potential of our AI -powered Virtual Dressing technology to not only enhance the online shopping experience by providing a realistic visualization of clothing fit but also significantly reduce return rates due to size or fit dissatisfaction. Our experiments demonstrate the system's efficacy in creating realistic and customizable virtual dressing experiences, showcasing its potential to become an integral part of future e-commerce platforms. © 2024 IEEE.

关键词： Semantic Segmentation

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Comparing Bag of features Techniques for image Retrieval 6

Comparing Bag of features Techniques for Image Retrieval

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6th International Conference on Intelligent Computing and Control Systems, ICICCS 2022

作者： Ali, Roohi Maheshwari, Manish Makhanlal Chaturvedi University of Journalism and Communication Department of Computer Science and Applications Bhopal India

ISBN: (纸本)9781665410359

Bag of Feature (BOF) is a customizable technique that provides a free hand on the type of feature selection for creating visual-word vocabulary that fits better for the various image retrieval applications and this is the main advantage of using this technique to create a customizable feature mix of local and global features. One of the two prominent techniques for creating Bag of features in image processing and computer vision is through grid formation and custom extractor. Therefore, selection of type of feature used for Bag creation is very basic and critical one as whole process of Retrieval depends upon it. Feature Color is the well known one to be used most. In this paper Lab color space is used for creating one of the feature Bag as Lab Colorbag. While two other KAZEbag and SURFbag are created using different descriptor methods namely SURF and Kaze. For unfolding different hidden patters out of collected visual words some machine learning intelligence is adopted like support Vector machine is used here for clustering and creating cluster centers. MATLAB R2021a is used for all the implementation. Along with the Dataset of Natural images with ten categories is used for evaluation. All the three created Bags are compared on the different parameters such as number of features extracted, strongest feature metric, word frequency and average precision. © 2022 IEEE.

关键词： image retrieval

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机器视觉在农作物种子检测中的研究进展

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计算机工程与应用 2023年第22期59卷 69-83页

作者：王昊祝玉华李智慧甄彤粮食信息处理与控制教育部重点实验室(河南工业大学) 郑州450001 河南工业大学信息科学与工程学院郑州450001

农作物种子是农业生产的基础。种子检测作为一种重要的手段,在种子生产、贸易和利用的各个环节都扮演着不可或缺的角色。然而传统的农作物种子识别方法效率低,需要人力以及专业检测设备的支持。相比之下,机器视觉技术能够通过模拟人的... 详细信息

农作物种子是农业生产的基础。种子检测作为一种重要的手段,在种子生产、贸易和利用的各个环节都扮演着不可或缺的角色。然而传统的农作物种子识别方法效率低,需要人力以及专业检测设备的支持。相比之下,机器视觉技术能够通过模拟人的视觉功能来实现对目标的无损检测,效率高、准确度高,有助于实现农作物种子的品种识别、分级、分类的自动化、智能化。首先简单叙述了机器视觉技术中图像采集、预处理的方法,并以玉米种子为例给出了目前主流的处理流程,然后具体叙述了机器视觉技术中传统机器学习和深度学习两种检测方式在农作物种子检测中的应用,最后针对玉米不完善粒的研究,在分为以上两种检测方式进行具体叙述的同时,指出了目前存在的问题以及玉米不完善粒检测未来的研究方向。

关键词：种子检测机器视觉不完善粒图像处理

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A review of real-time human action recognition involving vision sensing

A review of real-time human action recognition involving vis...

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Conference on Real-Time image processing and Deep Learning

作者： Majumder, S. Kehtarnavaz, N. Univ Texas Dallas Embedded Machine Learning Lab Richardson TX 75083 USA

ISBN: (数字)9781510643109

ISBN: (纸本)9781510643109

Human action recognition has been utilized in many applications such as human-computer interaction, video surveillance, assistive living, and gaming. Deployment of human action recognition demands the processing to be carried out in real-time or in a computationally efficient manner. The real-time requirement is addressed by only a subset of the developed methods in the literature. This paper provides a review of computationally efficient human action recognition methods in which a vision sensor is used. The reviewed papers are categorized in terms of conventional and deep learning approaches as well as in terms of single vision and multi-vision modality sensing.

关键词： Human action recognition Review of vision-based human action recognition Real-time human action recognition

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Lemon-YOLO: An efficient object detection method for lemons in the natural environment

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IET image processing 2021年第9期15卷 1998-2009页

作者： Li, Guojin Huang, Xiaojie Ai, Jiaoyan Yi, Zeren Xie, Wei Guangxi Univ Sch Elect Engn Nanning 530004 Peoples R China South China Univ Technol Sch Automat Sci & Engn Guangzhou Peoples R China

Efficient Intelligent detection is a key technology in automatic harvesting robots. However, citrus detection is still a challenging task because of varying illumination, random occlusion and colour similarity between fruits and leaves in natural conditions. In this paper, a detection method called Lemon-YOLO (L-YOLO) is proposed to improve the accuracy and real-time performance of lemon detection in the natural environment. The SE_ResGNet34 network is designed to replace DarkNet53 network in YOLOv3 algorithm as a new backbone of feature extraction. It can enhance the propagation of features, and needs less parameter, which helps to achieve higher accuracy and speed. Moreover, the SE_ResNet module is added to the detection block, to improve the quality of representations produced from the network by strengthening the convolutional features of channels. The experimental results show that the proposed L-YOLO has an average accuracy(AP) of 96.28% and a detection speed of 106 frames per second (FPS) on the lemon test set, which is 5.68% and 28 FPS higher than the YOLOv3, respectively. The results indicate that the L-YOLO method has superior detection performance. It can recognize and locate lemons in the natural environment more efficiently, providing technical support for the machine's picking lemon and other fruits.

关键词： food products YOLOv3 algorithm SE-ResGNet34 network SE-ResNet module DarkNet53 network image colour analysis object detection method intelligent detection Computer vision and image processing techniques colour similarity production engineering computing Robotics Optical, image and video signal processing fruits Lemon-YOLO mobile robots Agriculture object detection feature extraction natural environment agricultural robots Industrial applications of IT Control engineering computing citrus detection automatic harvesting robots lemon detection L-YOLO method random occlusion Control applications in agriculture Production engineering computing control engineering computing Mobile robots robot vision

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Comparison of image Feature Detection Algorithms 9

Comparison of Image Feature Detection Algorithms

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9th International Conference on Dependable Systems and Their applications, DSA 2022

作者： Xu, Fan Liu, Xia Cui, Yanli Yan, Mingdie Lai, Zhongyuan Intelligent Manufacturing of JianghanUniversity Hubei Wuhan China Artificial Intelligence of JianghanUniversity Hubei Wuhan China

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

The key of image processing is to extract feature points and feature vectors by appropriate methods. In order to analyze the application effect of common feature extraction methods in different scenarios, this paper adopts SIFT, BRISK, ORB, KAZE and AKAZE methods to rotate, adjust brightness, blur and zoom images based on two data sets. The operating efficiency, the ratio of feature points before and after operation, the best matching rate and accuracy rate of feature points pair of the five methods are analyzed, and the suggestions for selecting reasonable feature detection methods for different application scenarios are given. © 2022 IEEE.

关键词： Feature extraction

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Feature-set characterization for target detection based on artificial color contrast and principal component analysis with robotic tealeaf harvesting applications

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INTERNATIONAL JOURNAL OF INTELLIGENT ROBOTICS AND applications 2021年第4期5卷 494-509页

作者： Lu, Jin Huang, Yang Lee, Kok-Meng Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China Guilin Univ Elect Technol Sch Mech & Elect Engn Guilin 541004 Guangxi Peoples R China Georgia Inst Technol George W Woodruff Sch Mech Engn Atlanta GA 30332 USA

For the benefits of productivity, efficiency and sustainability, the adoption of machine vision with robotics in applications has become essential in agriculture industry;for example, outdoor tender-tealeaf harvesting, where tasks are highly repetitive and laborious. As machine vision must cope with changing daylight conditions, among the challenges is an efficient technique to accurately detect/locate randomly distributed targets in natural background that has closely similar color as the targets of interests. The success of developing a harvesting machine depends on robust, accurate, and efficient target-detection, which is the first step of the automation. Built upon the concept of an artificial color contrast (ACC) model developed for color-feature classification using principal component analysis (PCA), this paper presents an improved ACC/PCA method to overcome commonly encountered target-detection problems for outdoor agriculture applications where targets in a closely similar background must be identified/located in real time for subsequent robotic handling. Specifically, several methods have been developed to determine an optimal feature-set boundary for effective target detection, which require only a limited set of training data. The effectiveness of the methods is evaluated using experimentally obtained samples in terms of three practical measures (% detection error, % numerical noise and computation time) by comparing results with commonly used methods.

关键词： machine vision Color image processing Principal component analysis Artificial color contrast

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DiagGCN: An End-to-end Framework for Online Handwritten Diagram Recognition

DiagGCN: An End-to-end Framework for Online Handwritten Diag...

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International Conference on vision, image and Signal processing (ICVISP)

作者： Hao-Zhe Wang Yan-Ming Zhang Fei Yin Lin-Lin Huang Beijing Jiaotong University MAIS Institute of Automation of Chinese Academy of Sciences Beijing China MAIS Institute of Automation of Chinese Academy of Sciences Beijing China Beijing Jiaotong University Beijing China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

Diagrams are among the most common elements in documents, widely used in human-machine interfaces, education, etc. Online handwritten diagram recognition has attracted con-siderable attention due to its potential applications. However, it remains challenging because of complex 2D structures and diverse writing styles. To handle these difficulties, we formulate symbol segmentation and recognition as node clustering and node classification on graphs, solving them using a novel graph neural network (GNN) model with enhanced structure modeling ability. In detail, we introduce a stroke graph construction method to extract the local structure of diagrams and propose DiagGCN to capture their high-level features. Additionally, we design a mean-shift clustering layer to improve node clustering performance. We introduce a unified learning framework to jointly train the model and perform the diagram recognition in an end-to-end manner. Extensive experiments on three popular datasets demonstrate that our method consistently outperforms previous methods, achieving state-of-the-art performance.

关键词： Handwriting recognition image recognition Human-machine systems Education Symbols Writing Signal processing Feature extraction Graph neural networks

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Chicken Quality Evaluation Using Deep Learning 2nd

Chicken Quality Evaluation Using Deep Learning

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2nd International Conference on Emerging Trends in Expert applications and Security, ICE-TEAS 2023

作者： Madan, Rishi Choudhury, Tanupriya Sarkar, Tanmay Bansal, Nikunj Toe, Teoh Teik Uttarakhand Dehradun248007 India Adjunct Professor CSE Dept. Graphic Era Hill University Uttarakhand Dehradun248002 India The AI University CutbankMT59427 United States Department of Food Processing Malda Polytechnic West Bengal Malda732102 India Singapore Singapore

ISBN: (纸本)9789819919451

In recent years, the demand for food materials is reaching sky, thus making the artificial intelligence-based evaluation technique the research hot-spot now because the quality determination of these materials can be really difficult without proper understanding and experience. As a result, we have developed a classification model for artificial neural networks based on computer vision. On a primary dataset, we are utilizing a convolutional-based architecture to focus on shape and texture, which are the most crucial factors to experts. An example of a DNN (Deep Neural Network) is a convolutional neural network, which has been shown to be particularly effective in extracting features from input images. This project demonstrates that deep learning improved the accuracy of classification tasks over traditional image analysis methods. And thus, it can be inferred that it is a potential future tool for grading the food quality. We also intend to do an analysis of different machine learning algorithms available and compare it with our model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image analysis

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