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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

引用

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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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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Infrared and ISAR image Fusion Based on Quadtree Decomposition and Bézier Interpolation

Infrared and ISAR Image Fusion Based on Quadtree Decompositi...

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IEEE international conference on signal and image processing (ICSIP)

作者： Chunlei Han Di Yang Yao Lu Kaifa Hou Wenbo An Hongmei Wang Xi'an Research Institute of Navigation Technology Xi'an China School of Astronautics Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781665446006

Inverse Synthetic Aperture Radar (ISAR) image obtained by stationary radar is the result of longitudinal and lateral two-dimensional high-resolution imaging of moving target. Infrared image is a thermal image corresponding to the temperature distribution of the scene, which formed by collecting infrared radiation emitted from the target and the surrounding. In order to make full use of the complementary information of infrared image and ISAR image, an algorithm that combines infrared image with ISAR image is researched. Firstly, this paper applies quadtree decomposition and Bézier interpolation to reconstruct the background of the ISAR image. Secondly, the salient features of the ISAR image are extracted by subtracting the reconstructed background from the original ISAR image. Finally, this algorithm fuses the salient features with the infrared image using the fusion rule proposed in this paper, through which to get the final fused image. Experimental results show that the algorithm can better preserve both the clear target and detailed information of infrared image and ISAR image in subjective evaluation. Compared with other fusion algorithms, experiments also verify the superiority of the algorithm in objective evaluation.

关键词： Interpolation Temperature distribution Fuses Shape Surveillance Radar imaging Maintenance engineering

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Color Intensity: A Study of RPPG Algorithm for Heart Rate Estimation

Color Intensity: A Study of RPPG Algorithm for Heart Rate Es...

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Computational Performance Evaluation (ComPE), international conference on

作者： Ruchika Sinhal Kavita R. Singh K. O. Gupta DMIETR Wardha Maharashtra India YCCE Nagpur Maharashtra India SVPCET Nagpur Maharashtra India

ISBN: (纸本)9781665436571

With the growing advancements and development in the field of digital image processing and computer vision, an individual’s heart pulse can be extracted from the human skin surfaces. This method is termed as remote photoplethysmography (rPPG). The method can be applied from the video recorded from the consumer-based mobile camera also. In this paper, the work presented has mainly twofold goals. Firstly to develop a fruitful yet simple rPPG algorithm that should be simple for any individual to understand and implement that will increase the understanding of the rPPG subject. Secondly, to compare the algorithm designed for the RGB color model with the state-of-art rPPG algorithms developed and presented in the literature. And finally, we present the comparative analysis of rPPG algorithms reported in the literature with our proposed rPPG algorithm which is simple and has demonstrated comparably high performance for the green channel as compared to other algorithms.

关键词： Heart rate Performance evaluation image color analysis Tracking signal processing algorithms Lighting Photoplethysmography

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