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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

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Baker’s Map Based Chaotic image Encryption in Military Surveillance systems 1

引用

International conference on Internet of Everything and Quantum Information processing, IEQIP 2023

作者： Rajendiran, Nithya Paranthaman Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9783031619298

ISBN: (纸本)9783031619281

One of the primary problems in military surveillance systems is the safety of the visual data gathered by the sensor nodes installed in the different areas of the Visual sensor network and the remotely operated devices. There are several cryptographic algorithms available to secure the visual information. Several chaotic schemes worked well because they were more resistant to threats and had greater entropy levels. But, it takes a lot of time to finish the encryption step. Traditional approaches had the drawback that increasing encryption strength slowed calculation speed. This paper presents a scheme called "baker’s map chaotic method based image encryption" to accelerate the encryption process in a network of visual sensors without weakening the encryption method. The findings indicate that the system’s functionality is improved when the computation time is reduced to sixty seconds and the PSNR is increased to 15 dB. The performance of the chaotic method is compared by using key space analysis, key sensitivity analysis, correlation coefficient analysis, noise analysis, Entropy analysis, and Speed analysis. The IBM SPSS statistical tool was also used to do statistical analysis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Sensitivity analysis

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Study on Hyperspectral Remote Sensing images of GF-5 De-Blurring Based on Sparse Representation 2023

Study on Hyperspectral Remote Sensing Images of GF-5 De-Blur...

引用

5th International Symposium on Signal processing systems, SSPS 2023

作者： Wang, Hong School of Electronic Information Sichuan Modern Vocational College Chengdu China

ISBN: (纸本)9798400700040

Deblurring high resolution remote sensing image is a very important problem in remote sensing research. In this paper, we propose a new deblurring algorithm for high-resolution remote sensing images (HSI) based on sparse representation. The purpose of this study is to apply compressed sensing measurement and reconstruction technology to realize the processing of remote sensing image, and discuss the Under what circumstances can CS achieve better results in remote sensing image processing. The algorithm uses fast gradient projection algorithm to achieve deblurring and retain the important ground information of the original image. Experiments on remote sensing images obtained by GF-5 show that the algorithm can filter the blurring of remote sensing images well and improve the peak-to-noise ratio (PSNR) of images. The algorithm has better performance than other sparse representation algorithms. This paper explores the application of dictionary learning theory and sparse decomposition in remote sensing image processing. By further extending the algorithm proposed in this paper and adding new constraints, remote sensing image restoration, target recognition, deblurring, fusion and so on can be carried out. © 2023 ACM.

关键词： image reconstruction

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CBAM-DCE: A Non-Reference image Correction Algorithm for Uneven Illumination 5

CBAM-DCE: A Non-Reference Image Correction Algorithm for Une...

引用

5th International conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Fan, Mengyu Lu, Jin Kong, xianguang Sun, Wei Sun, Wei Sun, Yijun School of Computer Science and Technology Xi'an University of Posts and Telecommunications 618 Chang'an West St Shaanxi Xi'an710121 China School of Communication and Information Engineering Xi'an University of Posts and Telecommunications 618 Chang'an West St Shaanxi Xi'an710121 China Research Institute of Industrial Internet Xi'an University of Posts and Telecommunications 618 Chang'an West St Shaanxi Xi'an710121 China

ISBN: (纸本)9781450396899

Affected by the change in daytime illumination sequence and by the shooting angle in the complex field environment, the kiwifruit images possess the unfriendly features of uneven illumination, such as local darkness and local brightness. The ill-posed image with uneven illumination will seriously constraint the subsequent image analysis processing. Current deep learning methods have achieved satisfactory results, and a large number of paired images (one is the input image, one is the ground truth image) is required to train the better network performance. However, it is difficult to capture ground truth images of the kiwifruit in the field. Based on this, the paper proposed Convolutional Block Attention Module Deep Curve Estimation (CBAM-DCE) to accomplish a non-reference illumination unevenness correction for field kiwifruit images. A deep learning network model is used to estimate the image-specific curve for image enhancement, and a non-reference loss function is applied to evaluate the image enhancement effect. Compared with seven related enhancement algorithms, the presented algorithm shakes off uneven illumination or normal-light image pairs for training. Five different public datasets and our Kiwifruit dataset were used in the experiments. Experiments demonstrate that our proposed CBAM-DCE is superior to other state-of-the-art algorithms for enhancing natural images under different lighting conditions. © 2022 ACM.

关键词： image enhancement

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Detection of Parkinson's cognitive impairment patients based on motor and non-motor symptoms 3

Detection of Parkinson's cognitive impairment patients based...

引用

3rd International conference on Advanced algorithms and Signal image processing, AASIP 2023

作者： Shi, Zhan Chen, xi Qi, Hongbin Li, Zeyang Jiang, Hu Beijing Institute of Technology Mechanical and Electrical College Beijing100010 China

ISBN: (纸本)9781510668522

Gait measurement is an objective analysis method that can detect abnormal gait, facilitate early disease identification, and support doctors in formulating rehabilitation treatment plans. In recent years, cognitive scales and biomarkers have been widely used to detect Parkinson's cognitive impairment (PD-MCI). However, a lack of objective and reliable detection methods for PD-MCI exists. This study aims to identify patients with Parkinson's cognitive impairment utilizing two approaches: gait and executive function. Non-contact measurement was employed to obtain the gait signal, while the clock drawing test was utilized to obtain the executive function score of the patients. Ten PD-MCI patients and 10 ordinary PD were tested, and the kinematics data of the subjects during walking were recorded. Using patients' gait parameters and executive function scores as data characteristics, machine learning methods were utilized to classify and identify patients. The most suitable machine learning method for PD-MCI patient detection was discovered utilizing this approach. The results indicate that the XGBoost algorithm can identify and classify PD-MCI patients with more than 90% accuracy, providing effective support for doctors in identifying Parkinson's cognitive impairment early and customizing the most suitable rehabilitation treatment plan. These findings have significant research significance. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Machine learning

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Advancements in image Deblurring and Performance Metrics Using Deep Learning Technique 12th

Advancements in Image Deblurring and Performance Metrics Usi...

引用

12th International conference on Recent Trends in Computing, ICRTC 2024

作者： Dhavalagimath, Shankramma S. Rajesh, T.M. Singh, Rakesh Kumar Department of Mathematics Dayananda Sagar University Bangalore562112 India Department of Computer Science and Engineering Dayananda Sagar University Bangalore562112 India

ISBN: (纸本)9789819788354

In computer vision and image processing, image deblurring is a crucial phase that attempts to restore the sharpness of the image and clarity of images that have been damaged due to motion blur, defocus, or other factors. The multiple image deblurring methods are thoroughly investigated in this work, along with the performance evaluation metrics that were employed to evaluate each method's efficiency. We started by examining the most recent deblurring techniques, which include both deep learning-based and traditional algorithms. Every technique's advantages, disadvantages, and suitability are examined in different scenarios. Furthermore, we investigate the finer details of performance metrics that are frequently used to rate the quality of deblurred images. These metrics include subjective assessments from user research &objective measurements like PSNR and SSIM. This study aims to elucidate the benefits and drawbacks of current deblurring methods in diverse contexts through extensive experimentation. Moreover, we examine the applicability and consistency of performance evaluation metrics for evaluating the accuracy and perceived quality of deblurred images. By supporting researchers and professionals in identifying suitable techniques and assessment standards for their particular applications, the knowledge gained from this study improves the field of image deblurring. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Comparison, classification, and analysis of retinal blood vessels using image processing and data mining algorithms: A review article 4

Comparison, classification, and analysis of retinal blood ve...

引用

4th International conference on Electrical, Control and Instrumentation Engineering, ICECIE 2022

作者： Dabiri, AbdolRasool Khorram Askari, Milad Ieee Ias Vienna Austria Ieee Ias KualaLumpur Malaysia

ISBN: (纸本)9781665480765

The retinal arteries separation and classification algorithms are one of the fundamental components of eye diseases diagnostic systems using image processing. In other words, it can be said that morphological changes in the retinal blood vessels and accurately analyzing them can contribute to diagnosing diseases including diabetes mellitus (DM), hypertension, or glaucoma. In recent years, numerous studies have been carried out on blood vessels classification and their processing algorithms. In the present study, the aim was to identify and apply the most recent and most practical algorithms in the ophthalmology industry and examine their performance. In addition, it was attempted to discuss important parameters in the diagnosis and operation of the classification algorithm. © 2022 IEEE.

关键词： Gabor filter image processing Neural network Retinal blood vessels Vessel classification

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Segmentation of skin layers in ultrasound images using a crowdsourcing and deep learning-based system 20

Segmentation of skin layers in ultrasound images using a cro...

引用

20th ACS/IEEE International conference on Computer systems and Applications (AICCSA)

作者： Moran, Maira Vaz Oliveira, Larissa Aparecida Faria, Marcelo Bastos, Luciana Giraldi, Gilson Canella, Clarissa Conci, Aura Univ Fed Fluminense Niteroi Brazil Univ Estado Rio de Janeiro Rio De Janeiro Brazil Lab Nacl Comp Cient Petropolis Brazil

ISBN: (纸本)9798350319439

Ultrasonography (US) has demonstrated many advantages in the detection, characterization, and monitoring of different diseases. Through high frequency probes, it is possible to visualize and characterize the anatomical layers, such as tissues, which can constitute a very helpful supporting tool in several procedures, e.g., surgeries. However, the visual identification of tissues in this type of image is still a challenge for some professionals. In this work, we evaluate deep learning (DL) segmentation algorithms for the tissue segmentation task in US. Moreover, we briefly assess whether their performance can be improved by including a crowdsourcing step. In order to perform the segmentation task, different segmentation models are trained, as posteriorly, the crowdsourcing step is included. The proposed approach is composed of the following steps: 1- automatic segmentation using a deep learning algorithm;2 - crowd evaluation and correction of the results. In order to perform step 1, different segmentation models are trained. The second step includes a visual interface where users can: a) validate the quality of the automatic segmentation;or b) correct the segmentation whether the DL result is inconsistent. All users are scored, denoting the quality of their annotations, considering the manual annotations provided by an expert for a small group of images. In order to evaluate the method and compare it to a DL algorithms alone, a total of 100 US images were used. Our experiments show that the inclusion of crowdsourcing significantly improved the performance of the tissue segmentation task compared to using the DL models alone. The performance of our method demonstrated the feasibility of applying this type of solution for the considered problem of segmenting tissues in US facial images. Moreover, the results suggest that this tool can be employed as an auxiliary tool in oral procedures.

关键词： image processing Ultrasound Segmentation Collective Intelligence Crowdsourcing

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Design of embedded identification system for motor vehicle engine fault based on deep learning 3

Design of embedded identification system for motor vehicle e...

引用

3rd International conference on Advanced algorithms and Signal image processing, AASIP 2023

作者： Chen, xi Cheng, Yanxing Yang, Jinsong Zhang, Jinquan Liao, Yizhen Zhejiang Institute of Metrology Hangzhou Zhejiang310018 China Jinhua Anchang Vehicle Comprehensive Testing Co. Ltd Jinhua321000 China Traffic Police Brigade of Qingtian County Public Security Bureau Qingtian county Zhejiang323000 China Dongyang Yuanhang Vehicle Inspection Co. Ltd Dongyang Zhejiang322100 China Hefei Jidian Technology Co. Ltd Anhui Hefei230001 China

ISBN: (纸本)9781510668522

Aiming at the low efficiency of fault identification in the process of engine fault identification, an embedded identification system of motor vehicle engine fault based on deep learning is designed. A hardware system consisting of sensor, core processor and communicator is established. The sensor module contains qualitative sensor and quantitative sensor. The qualitative sensor adopts LEI sensor, the quantitative sensor adopts TDK sensor, and the core processor is used as the central processor. The insulation fault information of three bands of high, medium and low frequency is decomposed, the feature vector is determined, and the fault information extraction program is established to complete the design of embedded identification system for motor vehicle engine fault. The experimental results show that the designed system has the best denoising effect, retains the overall trend of the original signal to a greater extent, and presents the detailed information in the signal well, which improves the fault identification accuracy and has stronger fault identification performance. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Engines

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A Face Morph Detection Method Based on Convolutional Neural Networks and Occlusion Test 7

A Face Morph Detection Method Based on Convolutional Neural ...

引用

7th International conference on image, Vision and Computing, ICIVC 2022

作者： Ma, Tian Bamweyana, Arnold Guo, Ming Benon, Kavuma Xi'an University of Science and Technology Department of Computer Science and Technology Xi'an China Kangchiao International School Department of Computer Science Xi'an China

ISBN: (数字)9781665467346

ISBN: (纸本)9781665467346

Face morphing is by far the greatest threat to effective automatic boarder control systems. However, there is a lack of consideration and examination of a morphed face image frame as a blend of two images;hence, one image obstructs the other. To address this, we proposed a combination of an occlusion detection method, FSG-FD and a VGG19 neural network architecture to detect these face morphs. First, we designed our algorithms for each of our proposed systems;one for FSG-FD and the other for the VGG19 architecture. Then, we blended the two methods into a joined model by pre-training the neural network and increasing its convolutional layers by an additional three layers. The experiments showed that our algorithm achieved higher accuracy than other methods at detecting face morphs. © 2022 IEEE.

关键词： Convolution

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A Review of Brain Information processing for Robot Control 7

A Review of Brain Information Processing for Robot Control

引用

7th International conference on image, Vision and Computing, ICIVC 2022

作者： Cheng, Hao Wang, Mei Ma, Chen Yu, Chaofei Xi'an University of Science and Technology College of Computer Science and Technology Xi'an China

ISBN: (数字)9781665467346

ISBN: (纸本)9781665467346

In recent years, brain-controlled robot systems have made great progress. Electroencephalography (EEG) has become the most popular signal acquisition method because of its advantages of being non-invasive, easy to use, and low cost. Brain-controlled robots can help disabled people return to normal life. This paper reviews the methods used in EEG -based robot control systems and introduces some common brain-controlled robots, such as robotic arms, exoskeletons, and wheelchairs. Finally, the development prospect of the brain-controlled robot is summarized. © 2022 IEEE.

关键词： Brain computer interface

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