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A BCS microwave imaging algorithm for object detection and shape reconstruction tested with experimental data

ELECTRONICS LETTERS 2021年第2期57卷 88-91页

作者： Zilberstein, Nicolas Augusto Maya, Juan Altieri, Andres Univ Buenos Aires Fac Ingn Buenos Aires DF Argentina CSC CONICET Buenos Aires DF Argentina

An approach based on the Green function and the Born approximation is used for impulsive radio ultra-wideband microwave imaging, in which a permittivity map of the illuminated scenario is estimated using the scattered fields measured at several positions. Two algorithms are applied to this model and compared: the first one solves the inversion problem using a linear operator. The second one is based on the Bayesian compressive sensing technique, where the sparseness of the contrast function is introduced as a priori knowledge in order to improve the inverse mapping. In order to compare both methods, measurements in real scenarios are taken using an ultra-wideband radar prototype. The results with real measurements illustrate that, for the considered scenarios, the Bayesian compressive sensing imaging algorithm has a better performance in terms of range and cross-range resolution allowing object detection and shape reconstruction, with a reduced computational burden, and fewer space and frequency measurements, as compared to the linear operator.

关键词： Dielectric permittivity Optical, image and video signal processing Radar equipment, systems and applications computer vision and image processing techniques

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Medical image interpretation training with a low-cost eye tracking and feedback system: A preliminary study

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HEALTHCARE TECHNOLOGY LETTERS 2021年第4期8卷 97-103页

作者： Quen, Mathieson Tan Zui Mountstephens, J. Teh, Yong Guang Teo, J. Univ Malaysia Sabah Fac Comp & Informat Kota Kinabalu 88400 Sabah Malaysia Univ Malaysia Sabah Fac Med & Hlth Sci Kota Kinabalu Sabah Malaysia

All medical students must learn to detect tumours in chest X-ray films. Eye tracking technology may be able to improve the training of this skill, with the potential benefit of saving thousands of lives each year. Eye trackers can record where students actually look in relation to known abnormalities in images, allowing the automated provision of corrective feedback during medical training. Research using dedicated eye tracking units suggests their potential as pedagogical tools but this hardware can be expensive, which prevents their use by medical students. To overcome this financial barrier, this project used eye tracking based on low-cost webcams to develop a system with the potential to allow all medical students to improve their interpretation skills. In self-study mode, students can practice and improve based on automated performance feedback in a browser-based system. Improvements in performance over time can be determined and mistakes can be played back and analysed for correction. Eight medical trainees used the system on a custom dataset of 60 chest X-ray images and were able to improve their decision times by self-study alone. The system was also rated highly for its ability to provide valuable objective information during subsequent one-on-one instruction sessions.

关键词： medical computing Patient diagnostic methods and instrumentation User interfaces feedback system biomedical education dedicated eye tracking units medical students Biology and medical computing computer vision and image processing techniques browser-based system computer aided instruction Optical, image and video signal processing corrective feedback potential benefit eye low-cost eye tracking diagnostic radiography computer-aided instruction interpretation skills medical image interpretation training eye trackers project used eye tracking tumours medical training eye tracking technology 60 chest X-ray images automated performance feedback medical image processing medical trainees human computer interaction computer based training chest X-ray films low-cost webcams

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Medical image encryption algorithm based on hyper-chaotic system and DNA coding

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COGNITIVE COMPUTATION AND SYSTEMS 2022年第4期4卷 378-390页

作者： Li, Mingzhen Pan, Shuaihao Meng, Weiming Wang Guoyong Ji, Zhihang Wang, Lin Henan Univ Sci & Technol Sch Informat Engn Luoyang Peoples R China Lushi Chinese Med Hosp Sanmenxia Peoples R China Harbin Engn Univ Sch Comp Harbin Peoples R China Luoyang Inst Sci & Technol Sch Comp & Informat Engn Luoyang 471023 Peoples R China

With the international development of the medical service informatisation, medical information sharing has become the key standard to measure the degree of medical informatisation. In this process, it is important to ensure the security of patients' medical information such as patients' records, examination reports, images and so on. In this article, an image encryption scheme based on Secure Hash Algorithm 3 (SHA-3), DNA coding and high dimensional chaos system is proposed to promote the security level of medical images in information sharing processes such as over the network. First, SHA-3 algorithm is used to calculate the hash value of the input image, and the result is taken as the initial value of the hyper-chaotic system. Second, the intensity value of the input image is converted into a serial binary digital stream. Third, the pseudo-random sequence generated by a 4-dimensional hyper-chaotic system is used to perturb the bit stream globally so as to achieve the purpose of hiding the effective information of the input image. During the operation of hyper-chaotic sequence and DNA sequence, algebraic and complementary operations are performed on DNA encoding values to enhance encryption performance. Finally, simulations have been applied on an open-source medical image database, and the results demonstrate obvious encryption effectiveness and high security level of the proposed encryption algorithm in the robustness of noise and clipping attacks.

关键词： Biology and medical computing random sequences serial binary digital stream medical service informatisation SHA-3 algorithm Cryptography high dimensional chaos system international development patients medical informatisation DNA sequence input image hyper-chaotic sequence DNA coding Secure Hash Algorithm 3 Optical, image and video signal processing computer vision and image processing techniques information sharing processes medical information sharing image encryption scheme encryption performance chaos medical images 4-dimensional hyper-chaotic system Data security high security level obvious encryption effectiveness medical image encryption algorithm intensity value hash value open-source medical image database DNA cryptography DNA encoding values

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Can you believe your eyes?

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Engineering and Technology 2020年第6期15卷 18-21页

作者： Lamb, Hilary

VIDEO FOOTAGE OF a Minneapolis police officer kneeling on the neck of George Floyd before his death has been the catalyst for an international outpouring of grief, agony, and anger. Floyd’s death and the protests that followed have been a lightning rod for disinformation, with conspiracy theories insisting that the bystander video was staged by actors and that Floyd is alive.

关键词： freedom of expression politics Video signal processing screening technology investment investment computer literacy media literacy health care AI generated synthetic media video manipulation science deep learning (artificial intelligence) Economic, social and political aspects of computing open press legislation deep neural networks responsibility of enforcement image forensics social aspects of automation Deepfakes abstract representations image manipulation Optical, image and video signal processing Legal aspects of computing computer vision and image processing techniques image representation video signal processing

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Haze removal network using unified function for image dehazing

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ELECTRONICS LETTERS 2021年第1期57卷 16-20页

作者： Oh, Hyungseok Kim, Hyena Kim, Yejin Yim, Changhoon Konkuk Univ Dept Comp Sci & Engn Seoul South Korea

The atmospheric scattering model includes two crucial parameters for dehazing: global atmospheric light and the transmission map. Most previous dehazing methods need to obtain these two parameters separately, which makes dehazing a difficult and ill-posed problem. Here, a new unified function that includes both the crucial parameters for haze removal is proposed. Then the haze removal network, which now needs to learn only one function during training, is proposed. image dehazing can be performed as a simple addition of the haze removal function with the input hazy image. Experimental results show that the proposed method gives better subjective results for indoor and outdoor synthesized images as well as natural images compared to previous state-of-the-art methods. Quantitative evaluation results show that the proposed haze removal network gives improved objective results compared with the same previous methods.

关键词： Optical, image and video signal processing computer vision and image processing techniques Neural nets

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PotNet: Pothole detection for autonomous vehicle system using convolutional neural network

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ELECTRONICS LETTERS 2021年第2期57卷 53-56页

作者： Dewangan, Deepak Kumar Sahu, Satya Prakash Natl Inst Technol Dept Informat Technol Raipur Madhya Pradesh India

Advancement in vision-based techniques has enabled the autonomous vehicle system (AVS) to understand the driving scene in depth. The capability of autonomous vehicle system to understand the scene, and detecting the specific object depends on the strong feature representation of such objects. However, pothole objects are difficult to identify due to their non-uniform structure in challenging, and dynamic road environments. Existing approaches have shown limited performance for the precise detection of potholes. The study on the detection of potholes, and intelligent driving behaviour of autonomous vehicle system is little explored in existing articles. Hence, here, an improved prototype model, which is not only truly capable of detecting the potholes but also shows its intelligent driving behaviour when any pothole is detected, is proposed. The prototype is developed using a convolutional neural network with a vision camera to explore, and validates the potential, and autonomy of its driving behaviour in the prepared road environment. The experimental analysis of the proposed model on various performance measures have obtained accuracy, sensitivity, and F-measure of 99.02%, 99.03%, and 98.33%, respectively, which are comparable with the available state-of-art techniques.

关键词： Optical, image and video signal processing Road-traffic system control Mobile robots computer vision and image processing techniques Control engineering computing Traffic engineering computing Neural nets

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Simultaneous wound border segmentation and tissue classification using a conditional generative adversarial network

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JOURNAL OF ENGINEERING-JOE 2021年第3期2021卷 125-134页

作者： Sarp, Salih Kuzlu, Murat Pipattanasomporn, Manisa Guler, Ozgur Old Dominion Univ Norfolk Batten Coll Engn & Technol Norfolk VA USA Chulalongkorn Univ Smart Grid Res Unit Bangkok Thailand EKare Inc Fairfax VA USA

Generative adversarial network (GAN) applications on medical image synthesis have the potential to assist caregivers in deciding a proper chronic wound treatment plan by understanding the border segmentation and the wound tissue classification visually. This study proposes a hybrid wound border segmentation and tissue classification method utilising conditional GAN, which can mimic real data without expert knowledge. We trained the network on chronic wound datasets with different sizes. The performance of the GAN algorithm is evaluated through the mean squared error, Dice coefficient metrics and visual inspection of generated images. This study also analyses the optimum number of training images as well as the number of epochs using GAN for wound border segmentation and tissue classification. The results show that the proposed GAN model performs efficiently for wound border segmentation and tissue classification tasks with a set of 2000 images at 200 epochs.

关键词： computer vision and image processing techniques simultaneous wound border segmentation chronic wound datasets training images generative adversarial network applications wounds medical image synthesis Biology and medical computing proper chronic wound treatment plan learning (artificial intelligence) image segmentation medical image processing Biomedical measurement and imaging GAN algorithm Optical, image and video signal processing tissue classification method utilising conditional GAN patient treatment biological tissues Patient care and treatment Patient diagnostic methods and instrumentation GAN model performs hybrid wound border segmentation tissue classification tasks Interpolation and function approximation (numerical analysis) image classification conditional generative adversarial network wound tissue classification Other topics in statistics

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Nearest feature line classifier based on collaborative representation with nearest neighbour search algorithm

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ELECTRONICS LETTERS 2021年第1期57卷 20-22页

作者： Zhuang, Zhongjie Pan, Jeng-Shyang Chu, Shu-Chuan Luo, Hao Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China Flinders Univ S Australia Coll Sci & Engn Clovelly Pk SA Australia Zhejiang Univ Sch Aeronaut & Astronaut Hangzhou Peoples R China

Nearest feature line is an effective classification algorithm. However, if a test sample cannot be linear represented by the training samples, the algorithm may not work very well. Moreover, another problem is that it will have a large computation complexity. Therefore, the authors propose a novel algorithm. To begin with, the test sample is linear represented by all the training samples, and the errors between the test sample and every training sample are calculated. The authors only keep the training samples with small errors. In this way, on the one hand, training samples are not suitable for the test sample will be ignored, on the other hand, running time can be reduced. To further reduce the computing time of the algorithm, nearest neighbour search technique is applied to the algorithm. Experiments on numerical and image database show the algorithm cannot only improve the classification accuracy, but also reduce runtime.

关键词： image recognition Optimisation techniques Computational complexity computer vision and image processing techniques Other topics in statistics Combinatorial mathematics Optimisation techniques Other topics in statistics Combinatorial mathematics Machine learning (artificial intelligence) Spatial and pictorial databases

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Fusion of semantic and appearance features for loop-closure detection in a dynamic environment

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ELECTRONICS LETTERS 2021年第2期57卷 56-58页

作者： Xu, Yan Huang, Jiani Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

Loop-closure detection is important in large-scale localisation system. However, it is still difficult in a dynamic environment. An online fast loop-closure detection algorithm based on Deeplabv3 with MobileNetV2 (DpMn2) as network backbone and local difference binary descriptor is proposed, and the algorithm is named as DpMn2-LDB. DpMn2 splits out common dynamic objects of images, and then uses visual geometry group network (VGG16) that is trained on place-centric data to extract global features for nearest neighbour image retrieval. The loop-closure matches are verified based on LDB descriptors and random sample consensus (RANSAC). Experimental results show that the proposed method can obtain a higher recall rate under 100% precision with less execution time per frame on several public datasets compared with other typical or state-of-the-art algorithms.

关键词： image recognition Interpolation and function approximation (numerical analysis) computer vision and image processing techniques Other topics in statistics Combinatorial mathematics Interpolation and function approximation (numerical analysis) Other topics in statistics Combinatorial mathematics Information retrieval techniques Other learning models (inc. Naive Bayes)

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image semantic segmentation method based on GAN network and ERFNet model

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JOURNAL OF ENGINEERING-JOE 2021年第4期2021卷 189-200页

作者： Dong, Chaoxian Sanmenxia Polytech Coll Informat Media Sanmenxia Henan Peoples R China

This article addresses the problems of traditional methods in image semantic segmentation, such as insufficient segmentation of small-scale targets and weak anti-noise ability. A method of image semantic segmentation using a generative adversarial network (GAN) combined with ERFNet model is proposed. First, the asymmetric residual module (ARM) and weak bottleneck module are used to improve the ERFNet network model. Moreover, dilated convolution is used to reduce information loss and improve the performance of small target image semantic segmentation. Then, a U-shaped network is used to improve the generator of GAN to avoid low-level information sharing. In addition, the residual module is introduced into convolution layer to realise the dynamic adjustment of generator weight. Finally, the improved ERFNet model is used as the input of the generator to output the segmented image. It is input to the discriminator together with the label for judgment, which further improves the performance of image semantic segmentation. The proposed method is demonstrated experimentally based on the PyTorch platform. The results show that mean pixel accuracy and mean intersection over the union of the proposed method on the CamVid and Cityscapes datasets are higher than those of other comparisons. In addition, the execution time is short, and the overall image semantic segmentation performance is relatively ideal.

关键词： image semantic segmentation method computer vision and image processing techniques ARM image segmentation Cityscapes datasets Optical, image and video signal processing CamVid datasets asymmetric residual module U neural nets ERFNet network model weak bottleneck module generative adversarial network image classification insufficient segmentation U-shaped network GAN network PyTorch platform Other topics in statistics

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