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A sky region segmentation method for outdoor visual-inertial SLAM

A sky region segmentation method for outdoor visual-inertial...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Wenyu Tao Zhiqiang Dai Xiangwei Zhu Fang Li School of Electronics and Communication Engineering Sun Yat-sen University Guangzhou China

Visual Simultaneous Localization and Mapping (VSLAM) can build a map of an unknown environment and perform self-localization simultaneously by computer vision, which has been playing an important role in UAV, UGV and mobile robot etc. At present, slow calculation speed and low accuracy are still the main problems in outdoor scene. In this paper, we segment the image of the visual part of the sky region. At first, threshold segmentation is carried out for gradient images, then boundary line extraction is carried out for segmented images using improved energy function, and then refined region stripping is performed using polynomial fitting. This algorithm avoids feature extraction of large sky region and further optimizes the accuracy and computing speed of visual inertial odometer in outdoor scene. Experiments show that the new system has improvements in translation and rotation errors.

关键词： image segmentation Visualization Simultaneous localization and mapping Fitting signal processing algorithms Time of arrival estimation Feature extraction

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Multi-color Space Medical Endoscope image Enhancement Method

Multi-color Space Medical Endoscope Image Enhancement Method

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IEEE international conference on signal and image processing (ICSIP)

作者： Hongrui Liu Chao Xu Bo Feng Kai Li School of Electronics and Information Engineering Anhui University Anhui Engineering Laboratory of Agro-Ecological Big Data HeFei China

ISBN: (纸本)9781665446006

Aiming at the problems of low contrast between the vascular part and the tissue part of the medical endoscope, and the vessels and lesions are not obvious enough, this paper proposes an improved method of medical endoscopic image enhancement based on the characteristics of medical images. First, we convert the image from RGB space to HSV space, split the V channel into a low frequency part and a high frequency part, use gamma correction for the low frequency part to adjust the brightness, denoise the high frequency part, and then do saturation correction on the S channel. Then, the image is converted from the HSV space to the YCrCb space, and the Y channel is weighted CLAHE to enhance the contrast. Next, we convert the image from YCrCb space to RGB color space. Finally, the Sobel operator edge detection is performed on the image, and the detected edge is enhanced with a mask to highlight the details. We use multiple sets of pictures to evaluate images from a subjective and objective perspective. The experimental results show that the algorithm proposed in this paper not only enhances the blood vessel, but also retains the authenticity of the image, and the effect is satisfactory.

关键词： Endoscopes image color analysis image edge detection Brightness Blood vessels Prediction algorithms Frequency conversion

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11th international Workshop of advanced Manufacturing and Automation, IWAMA 2021

11th International Workshop of Advanced Manufacturing and Au...

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11th international Workshop of advanced Manufacturing and Automation, IWAMA 2021

ISBN: (纸本)9789811905711

The proceedings contain 98 papers. The special focus in this conference is on advanced Manufacturing and Automation. The topics include: signal Denoising Algorithm of Massage Chair Movement Based on iForest-EEMD;Prediction of Remaining Life of Massage Chair Movement Based on ARIMA-BP Model;real-Time Data-Driven Digital Twin Workshop Web Interactive Application;digital Twin Construction Method for Docking Mechanism Test-Bed;visualized Interaction Method of Mechanical Arm Based on Augmented Reality;research on image Acquisition Preprocessing and Data Augmentation Algorithm Based on Strip Surface Defect Detection Technology;experimental Investigation of the Effect of Ultrasonic Wave on the Saturated Hydrocarbons in Castilla Crude Oil;random Assembly Task Evaluation Based on Human-Robot Collaboration;a Review on Application of Eddy Current Separation for the Recycling of Scraped Vehicles;kanban System in Industry 4.0 Era: A Systematic Literature Review;influence of Magnetic Fluid Hydraulic Medium on Cushioning Performance of Hydraulic Cylinder;DEM-CFD Simulation for Recovering Metal Particles with Aero-Electrostatic Separator;a Bibliometric Analysis of the Logistical Challenges and Methods for Vaccine Distribution Under the Pandemic;numerical Stress Analysis and Fatigue Life Prediction of the Classical External Geneva Mechanism;tensile and Flexural Properties of Acacia Tortilis/Glass Fiber Reinforced Hybrid Composites;multi Robot Welding Path Planning Based on Improved Ant Colony Algorithm;data Acquisition and processing of Space Docking Physics Experiment Platform;Research on SDN-Based New In-vehicle Network Packet Scheduling Technology;design and Transmission Performance Analysis of a Novel Reducer with Abnormal Cycloidal Gear;noncontact Clearance Measurement Research Based on Machine Vision;Optimal Design of Truss Based on LA-GSA.

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An Attention Based Wavelet Convolutional Model for Visual Saliency Detection

An Attention Based Wavelet Convolutional Model for Visual Sa...

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IEEE international conference on Acoustics, Speech and signal processing

作者： Reshmi S. Bhooshan Suresh K College of Engineering Trivandrum Thiruvananthapuram India Govt. Engineering College Thiruvananthapuram India

ISBN: (纸本)9781728176055;9781728176062

The emergence of deep neural architectures greatly enhanced the accuracy of salient region detection algorithms that plays a vital role in computer vision applications. However, the accurate extraction of regions with fine boundaries still remains as a challenge. In this work, an attention based Wavelet Convolutional Neural Network (WCNN) is implemented that efficiently extracts the spatial, spectral and semantic features of the image in multiple resolution and it turns out to be suitable for locating the visually salient regions. Further enhancement of the fine boundaries of the predicted map is made possible by the inclusion of a combinational loss function of balanced cross entropy loss, SSIM loss and edge loss. The effectiveness of the method is evaluated using three benchmark datasets and the results shows better performance achieving a minimum Mean Absolute Error (MAE) of 0.032 and maximum F-measure of 0.938.

关键词： Visualization Convolution image edge detection Semantics Benchmark testing Feature extraction Entropy

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Gesture Recognition Based on YCbCr Color Space and Neural Network

Gesture Recognition Based on YCbCr Color Space and Neural Ne...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： HU Junping XIANG Siping Mechanical and Electrical Engineering Central South University Chang Sha Hunan China

ISBN: (纸本)9781665447300

Aiming at the problem of gesture recognition in complex background, a convolutional neural network gesture recognition algorithm based on improved YCbCr color space is proposed. Firstly, 8000 images of four gestures in the complex background are collected as the data set of this study. Then, the data set is preprocessed based on YCbCr color space, and the adaptive threshold method is used to improve it. Finally, a shallow convolutional neural network is built and trained with the preprocessed data set. The experimental results show that the gesture recognition accuracy of this method can reach 98.2% on the collected data set, which is higher than 85.7% and 90.6% using AlexNet and VGG-16.

关键词： Adaptive systems image color analysis Convolution Neural networks signal processing algorithms Gesture recognition Convolutional neural networks

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1st international conference on Systems and Information Sciences, ICCIS 2020

1st International Conference on Systems and Information Scie...

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1st international conference on Systems and Information Sciences, ICCIS 2020

ISBN: (纸本)9783030591939

The proceedings contain 37 papers. The special focus in this conference is on Systems and Information Sciences. The topics include: Development of an Algorithm Capable of Classifying the Starting, Gear Change and Engine Brake Variables of a Vehicle by Analyzing OBD II signals;Characterization of Braking and Clutching Events of a Vehicle Through OBD II signals;PESTEL Analysis as a Baseline to Support Decision-Making in the Local Textile Industry;occupational Health and Safety for Decision-Making in the Framework of Corporate Social Responsibility: Models, Guidelines, and Indicators;model of Classification of University Services Using the Polarity of Opinions Taken from Social Networks;clustering Analysis of Electricity Consumption of Municipalities in the Province of Pichincha-Ecuador Using the K-Means Algorithm;Behavioral signal processing with Machine Learning Based on FPGA;complementary Admission Processes Implemented by Ecuadorian Public Universities Promote Equal Opportunities in Access: An Analysis Through Knowledge Discovery in Databases;LOLY 1.0: A Proposed Human-Robot-Game Platform Architecture for the Engagement of Children with Autism in the Learning Process;aphids Detection on Lemons Leaf image Using Convolutional Neural Networks;alternative Psychological Treatment for Patients with a Diagnosis of Entomophobia with Virtual Reality Environments;designing a Lighting System for Smart Homes;green Energy for the Reception and processing of Satellite and Microwave signals;Network Design Defined by Software on a Hyper-converged Infrastructure. Case Study: Northern Technical University FICA Data Center;Study of the Fronthaul WDM Applied to the Cloud RAN of 5G-CRAN;Evaluation of the Broadcast Storm Problem Based on Hybrid Retransmissions algorithms in FANET Networks.

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Multiresolution Convolutional Neural Network for Underwater Acoustic Target Recognition*

Multiresolution Convolutional Neural Network for Underwater ...

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IEEE international conference on signal and image processing (ICSIP)

作者： Zhang Yi Li Pingzheng Xiong Shuidong Yao Qiong Ma Yanxin Liu Mengqi College of Advanced Interdisciplinary Studies Hunan Key Laboratory for Marine Detection Technology National University of Defense Technology Changsha China College of Meteorology and Oceanology Hunan Key Laboratory for Marine Detection Technology National University of Defense Technology Changsha China College of Advanced Interdisciplinary Studies National University of Defense Technology Changsha China

ISBN: (纸本)9781665446006

Convolution neural network(CNN) has become a popular method for underwater acoustic target recognition because of its advantages. Due to the complexity of underwater acoustic environment, a single-scale CNN shows low robustness for complex broadband target effectively. Therefore, a multi-scale residual convolution neural network for complex broadband underwater acoustic targets is proposed in this paper. In this method, two-dimensional feature map is firstly formed based on auditory feature Mel Cepstrum Coefficient and acoustic information. And then multi-resolution analysis is realized through multi-scale convolution kernel, which improve the robustness of the model to complex broadband targets. The results of verification experiments show that the proposed network achieves recognition accuracy and performs high tolerance to noise interference.

关键词： image recognition Target recognition Convolution Interference Feature extraction Robustness Broadband communication

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Develop on real-time image processing system based on FPGA and RAM combined with PC or Cloud system

Develop on real-time image processing system based on FPGA a...

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Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Ma Juncheng Gao Juan Zhang Hong Wang Zheng Xie Shuang Chengdu College of University of Electronic Science and Technology of China Chengdu Sichuan

ISBN: (数字)9781728190044

ISBN: (纸本)9781728190051

To address the current Instant image Process, we designed an embedded hardware system based on FPGA(Field-Programmable Gate Array) and ARM(advanced RISC Machine) in this project, in order to realize the instant acquisition, processing, recognizing and displaying of images. The system acquires and initially processes the image via the cameras controlled by FPGA, after that the data is buffered into the SDRAM (synchronous dynamic random-access memory). And then the ARM sends the SDRAM cached data to the PC (cloud) side through the WIFI module for advanced processing. The processed data then is transmitted back to the SDRAM through WIFI and displayed by the LCD. By the above means, the system realizes the high-speed acquisition and parallel processing of images.

关键词： Filtering Software algorithms Parallel processing Hardware SDRAM Field programmable gate arrays Wireless fidelity

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image Feature Detection and Clustering for UAV Multiple Obstacles Avoidance 1

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16th international conference on Intelligent Information Hiding and Multimedia signal processing, IIH-MSP 2020 in conjunction with the 13th international conference on Frontiers of Information Technology, Applications and Tools, FITAT 2020

作者： Zhao, Baohua Sung, Tien-Wen Zhang, Xin Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Computer Science and Mathematics Fujian University of Technology Fuzhou China

ISBN: (数字)9789813364202

ISBN: (纸本)9789813364196

In the past decade, a lot of researches have been done on the topic of real-time obstacle avoidance of Unmanned Aerial Vehicle (UAV). For example, the use of Light Detection and Ranging (LIDAR), ultrasonic sensors, and now more popular visual sensors can effectively avoid obstacles. Although these methods are simple and effective, there are few of UAV real-time obstacle avoidance for complex environments, and most of them are used to avoid a single obstacle. When facing multiple obstacles, they cannot choose the optimal strategy, which inevitably consumes more power and time. In view of the complex environment that UAV may face multiple obstacles, in order to avoid multiple obstacles with minimum efforts in the process of real obstacle avoidance, this paper proposes a new method to detect multiple obstacles. Firstly, it extracts obstacle feature points, and then uses unsupervised clustering algorithm in machine learning to divide the feature points of different obstacles into different categories, and finally detects the obstacles represented by convex hull of same category feature points. The method is simple and effective. This paper compares the time of several popular feature point detection and unsupervised clustering algorithms through experiments and estimates the approximate relationship between the number of feature points detected and the distance when the UAV approaches the obstacle, which is a priori study for the future practical obstacle avoidance link. It summarizes which algorithm can save more time and is more suitable for real-time obstacle avoidance of multiple obstacles. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Clustering algorithms

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Regulated 2D Grayscale image for Finding Power Quality Abnormalities in Actual Data 2

Regulated 2D Grayscale Image for Finding Power Quality Abnor...

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2022 2nd international conference on Artificial Intelligence, Automation and algorithms, AI2A 2022

作者： Chen, Yeong-Chin Syamsudin, M. Berutu, S.S. Department of Computer Science and Information Engineering Asia University Taichung413 Taiwan Department of Electrical Engineering Politeknik Negeri Pontianak Kalimantan Barat Indonesia

It is possible to preserve power quality by classifying and identifying abnormalities. Prior studies focused on enhancing the PQD classification performance in one-dimensional (1D) CNNs. Recently, various image conversion methods have been established to facilitate CNN for PQD classification. PQD is a 1D signal that needs to be converted to a 2D image through data pre-processing since 2D images may include more PQD information than 1D signals. However, the PQD data used for the power quality classifier is synthetic PQD produced using mathematical models with parameter modifications in accordance with IEEE Std. 1159, which places limitations on prior research. This study uses data from the Amrita Honeywell Hackathon 2021 to examine how the response-based 2D deep CNN power quality classifier responds to actual field power quality disruptions. The results of the study show that a 2D deep CNN with regulated 2D grayscale pictures based on a process-regulated 2D image matrix can classify real data power quality disturbances with accuracy, precision, recall, and F1-score of 98.80%, 98.99%, and 98.60%, respectively. Additionally, 2D images can potentially contain more PQD data than 1D signals, enhancing identification performance on actual data. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Data handling

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