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Noise robust intuitionistic fuzzy c-means clustering algorithm incorporating local information

IET image processing 2021年第3期15卷 805-817页

作者： Yang, Zhenzhen Xu, Pengfei Yang, Yongpeng Kang, Bin Nanjing Univ Posts & Telecommun Sch Sci Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Natl Engn Res Ctr Commun & Networking Nanjing Peoples R China Nanjing Vocat Coll Informat Technol Sch Network & Commun Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Inst Internet Things Nanjing Peoples R China

The human brain magnetic resonance image (MRI) is always contaminated by noise and has uncertainty on the boundary between different tissues. These characteristics bring challenges to the human brain image segmentation. To handle these limitations, many variants of standard fuzzy c-means (FCM) algorithm have been proposed. Some methods attempt to incorporate the local spatial information in the standard FCM algorithm. However, they can't solve the problem of data uncertainty very well. And some other methods can handle the problem of data uncertainty, but they are sensitive to noise since it doesn't incorporate any local spatial information. In this paper, we propose a noise robust intuitionistic fuzzy c-means (NR-IFCM) algorithm, which can handle noise and uncertainty problems simultaneously. In order to process the human brain MRI with noise better, we introduce a noise robust intuitionistic fuzzy set (NR-IFS) which is noise robust in this NR-IFCM algorithm. Meanwhile, in order to handle the data uncertainty, we also introduce a new intuitionistic fuzzy factor to this NR-IFCM algorithm which combine the local gray-level and the spatial information together. A large number of experimental results on human brain MRI validate the effectiveness and the superiority of our proposed NR-IFCM algorithm.

关键词： Optical, image and video signal processing Biomedical magnetic resonance imaging and spectroscopy computer vision and image processing techniques Combinatorial mathematics Combinatorial mathematics Medical magnetic resonance imaging and spectroscopy Patient diagnostic methods and instrumentation Biology and medical computing

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PROCESSOR SYSTEM FOR REALTIME VIDEO SIGNAL-processing

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IEE PROCEEDINGS-E computerS AND DIGITAL techniques 1988年第3期135卷 181-187页

作者： LIEBSCH, W ZIER, D Heinrich-Hertz Institut Berlin West Germany

A 10 ns programmable signal processor system for realtime applications in video signal and image processing has been developed and realised. Up to six instructions can be processed in one operation cycle of 10 ns. The high data throughput is attained by pipeline-structure and parallel circuit design. Several program realisations using this processor system have demonstrated that realtime processing of broadband signals with such different algorithms as edgeextractions, two-dimensional DPCM and movingpattern generation are possible in a programmable processor.

关键词： image processing pipeline-structure video signals moving-pattern generation Optical information, image and video signal processing programmable signal processor two-dimensional DPCM 10 ns processor systems real-time systems parallel circuit design program realisations computerised picture processing computer vision and image processing techniques realtime video signal processing

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A novel decoder based on Bayesian rules for task-driven object segmentation

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IET image processing 2023年第3期17卷 832-848页

作者： Cai, Yuxiang Yu, Yuanlong Jiang, Weijie Chen, Rong Zheng, Weitao Wu, Xi Su, Renjie Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China State Grid Fujian Infotmat & Telecommun Co Technol Dept Fuzhou Peoples R China Fuzhou Univ Coll Comp & Data Sci Fuzhou 350108 Peoples R China

As a challenging problem in computer vision, salient object segmentation has attracted increasing attention in recent years. Though a lot of works based on encoder-decoder have been made, these methods can only recognize and segment one class of objects, but cannot segment the other classes of objects in the same image. To address this issue, this paper proposes a novel decoder based on Bayesian rules to perform task-driven object segmentation, in which a control signal is added to the decoder to determine which class of objects need to be segmented. What's more, a Bayesian rule is established in the decoder, in which the control signal is set as the prior, and the latent features learned in encoder is transferred to the corresponding layer of decoder as observation, thus the posterior probability of each object with respect to the specific-class can be calculated, and the objects belonging to this class can be segmented. This proposed method is evaluated for task-driven salient object segmentation on several benchmark datasets, including MS COCO, DUT-OMRON, ECSSD etc. Experimental results show that the approach tends to segment accurate, detailed, and complete objects, and improves the performance compared with the previous state-of-the-art.

关键词： image coding image segmentation feature extraction encoder-decoder Machine learning (artificial intelligence) object detection image and video coding computer vision control signal Other topics in statistics task-driven salient object segmentation complete objects Bayesian rule image recognition learning (artificial intelligence) Bayes methods computer vision and image processing techniques

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An unsupervised approach for traffic motion patterns extraction

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IET image processing 2021年第2期15卷 428-442页

作者： Moradi, Amin Shahbahrami, Asadollah Akoushideh, Alireza Univ Guilan Dept Comp Engn Fac Engn Rasht Iran Tech & Vocat Univ Guilan Branch Shahid Chamran Coll Rasht Iran

Automatic analysis, understanding typical activities, and identifying vehicle behaviour in crowded traffic scenes are fundamental and challenging tasks for traffic video surveillance. Some recent researches have been using machine learning approaches to extract meaningful patterns occurring in a traffic scene, for example, intersection. In this regard, we convert visual patterns and features to visual words using dense and sparse optical flow and learning traffic motion patterns with group sparse topical coding (GSTC) algorithm. In the first step of the proposed algorithm, the input traffic video is divided into non-overlapping clips. After that, motion vectors are extracted using dual TV-L1 as a dense optical flow and Lucas-Kanade as a sparse optical flow and converted to flow words. For learning traffic motion patterns, the GSTC algorithm, that is, a non-probabilistic topic model (TM) has been applied. These patterns represent priors on observable motion, which can be utilised to describe a scene and answer behaviour questions such as what are the motion patterns in a traffic scene and what is going on. The experimental results which have been obtained using a real dataset, QUML, show that the combination of the GSTC + dual TV-L1 extracts more traffic motion patterns in comparison with the GSTC + Lucas-Kanade and previous studies.

关键词： Optical, image and video signal processing computer vision and image processing techniques Video signal processing Traffic engineering computing Other topics in statistics

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Deep learning serves traffic safety analysis: A forward-looking review

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IET INTELLIGENT TRANSPORT SYSTEMS 2023年第1期17卷 22-71页

作者： Razi, Abolfazl Chen, Xiwen Li, Huayu Wang, Hao Russo, Brendan Chen, Yan Yu, Hongbin Clemson Univ Sch Comp Clemson SC 29634 USA Univ Arizona Dept Elect & Comp Engn Tucson AZ 85721 USA No Arizona Univ Dept Civil Engn Flagstaff AZ 86011 USA Arizona State Univ Polytech Sch Mesa AZ USA Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ USA

This paper explores deep learning (DL) methods that are used or have the potential to be used for traffic video analysis, emphasising driving safety for both autonomous vehicles and human-operated vehicles. A typical processing pipeline is presented, which can be used to understand and interpret traffic videos by extracting operational safety metrics and providing general hints and guidelines to improve traffic safety. This processing framework includes several steps, including video enhancement, video stabilisation, semantic and incident segmentation, object detection and classification, trajectory extraction, speed estimation, event analysis, modelling, and anomaly detection. The main goal is to guide traffic analysts to develop their own custom-built processing frameworks by selecting the best choices for each step and offering new designs for the lacking modules by providing a comparative analysis of the most successful conventional and DL-based algorithms proposed for each step. Existing open-source tools and public datasets that can help train DL models are also reviewed. To be more specific, exemplary traffic problems are reviewed and required steps are mentioned for each problem. Besides, connections to the closely related research areas of drivers' cognition evaluation, crowd-sourcing-based monitoring systems, edge computing in roadside infrastructures, automated driving systems-equipped vehicles are investigated, and the missing gaps are highlighted. Finally, commercial implementations of traffic monitoring systems, their future outlook, and open problems and remaining challenges for widespread use of such systems are reviewed.

关键词： traffic engineering computing speed estimation traffic analysts operational safety metrics video signal processing remaining challenges learning (artificial intelligence) road safety trajectory extraction providing general hints open problems Neural nets video enhancement video stabilisation traffic videos anomaly detection Optical, image and video signal processing computer vision and image processing techniques traffic safety analysis exemplary traffic problems autonomous vehicles Traffic engineering computing object detection deep learning (artificial intelligence) required steps open-source tools crowd-sourcing-based monitoring systems custom-built processing frameworks driving safety deep learning methods traffic monitoring systems event analysis intelligent transportation systems Video signal processing road traffic successful conventional DL-based algorithms traffic video analysis edge computing road vehicles typical processing pipeline semantic incident segmentation processing framework comparative analysis automated driving systems-equipped vehicles

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A novel weighted total variation model for image denoising

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IET image processing 2021年第12期15卷 2749-2760页

作者： Li, Meng-Meng Li, Bing-Zhao Beijing Inst Technol Sch Math & Stat Beijing Peoples R China Beijing Inst Technol Beijing Key Lab MCAACI Beijing Peoples R China

image denoising is a very important problem in image processing field. In order to improve denoising effects and meanwhile keep image structures, a novel weighted total variation (WTV) model is proposed in this paper. The WTV model consists of data fidelity and l1 norm based regularisation terms. In the WTV model, a weight function w in exponential form is incorporated into the regularisation term, which only depends on the given image itself without extra parameters. The nonlinearly monotone formulation of w helps to increase gaps between lower and higher frequencies of images, which is effective to highlight edges and keep textures. For solving the proposed model, the alternating direction method of multipliers is explored and the according convergence is analysed. Compared experiments of TV, HOTV, ATV and TVp models are conducted and the results show the effectiveness and efficiency of the proposed model.

关键词： image structures regularisation term weight function w image denoising TVp models computer vision and image processing techniques HOTV model WTV model data fidelity ℓ1 norm image texture Optical, image and video signal processing novel weighted total variation model alternating direction method of multipliers image processing field ATV model TV model

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Paralleled attention modules and adaptive focal loss for Siamese visual tracking

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IET image processing 2021年第6期15卷 1345-1358页

作者： Zhao, Yuyao Jiang, Min Kong, Jun Li, Sha Jiangnan Univ Jiangsu Prov Engn Lab Pattern Recognit & Computat Wuxi 214122 Jiangsu Peoples R China Wuxi Taihu Univ Jiangsu Key Construct Lab IoT Applicat Technol Wuxi Jiangsu Peoples R China

Recently, Siamese-based trackers have drawn amounts of attention in visual tracking field because of their excellent performance on different tracking benchmarks. However, most Siamese-based trackers encounter difficulties under circumstances such as similar objects interference and background clutters. Besides, there exists an extreme foreground-background data imbalance that weakens the performance during training but few loss functions pay attention to it. The authors intend to address the issues mentioned above by introducing a module named paralleled spatial and channel attention (PSCA) and adaptive focal loss (AFL). Firstly, paralleled spatial and channel attention is proposed to enhance the extracted features and eliminate the noise information from both spatial and channel aspects. Secondly, adaptive focal loss is proposed as the loss function to make the model focus on hard samples that contribute more to training process. Finally, paralleled spatial and channel attention and modified ResNet are combined for extracting more powerful features. Experimental results show that the authors' method achieves outstanding performance in multiple benchmarks while keeping a beyond-real-time frame rate.

关键词： Optical, image and video signal processing computer vision and image processing techniques Other topics in statistics Other topics in statistics

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Part-level attention networks for cross-domain person re-identification

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IET image processing 2021年第14期15卷 3599-3607页

作者： Zhao, Qun Du, Nisuo Ouyang, Zhi Kang, Ning Liu, Ziyan Wang, Xu He, Qing Xu, Yiling Ge, Shichun Song, Jingkuan Guizhou Univ Coll Big Data & Informat Engn Guiyang 550025 Guizhou Peoples R China Guizhou Prov Key Lab Publ Big Data Guiyang Guizhou Peoples R China Univ Elect Sci & Technol China Chengdu 611731 Peoples R China Guizhou Inst Quantum Informat & Big Data Appl Tec Guiyang Guizhou Peoples R China

Person re-identification (Re-ID) is in significant demand for intelligent security and single or multiple-target tracking. However, there are issues in the person Re-ID tasks, such as sharp decline in cross-data sets detection accuracy, poor generalization and cross-domain ability of the model. This work mainly studies the generalization and adaptation of cross-domain person Re-ID models. Different from most existing methods for cross-domain Re-ID tasks, the authors use diversified spatial semantic feature in pixel-level learning in the target domain to improve the generality and adaptability of the model. In the case that no information of the target domain is used during the model training, the trained model is directly tested on the data set of the target domain. It has proven effective to add the attention cascade module into the backbone network combining with the part-level branch. The authors conducted extensive experiments based on the three data sets of Market-1501, DukeMTMC-ReID and MSMT17, resulting in both single-domain and cross-domain tests with an average improvement of Rank1 and mAP values of about 10% compared with Baseline through the authors' proposed method named Part-Level Attention Network.

关键词： Optical, image and video signal processing computer vision and image processing techniques Data security Machine learning (artificial intelligence)

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Improving histogram-based reversible data hiding by interleaving predictions

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IET image processing 2010年第4期4卷 223-234页

作者： Yang, C-H. Tsai, M-H. Natl Pingtung Univ Educ Dept Comp Sci Pingtung 900 Taiwan Natl Sun Yat Sen Univ Dept Comp Sci & Engn Kaohsiung 80424 Taiwan

Data hiding is an important way of realising copyright protection for multimedia. In this study, a new predictive method is proposed to enhance the histogram-based reversible data hiding approach on grey images. In those developed histogram-based reversible data hiding approaches, their drawbacks are the number of predictive values less to the number of pixels in an image. In these interleaving prediction methods, the predictive values are as many as the pixel values. All predictive error values are transformed into histogram to create higher peak values and to improve the embedding capacity. Moreover, for each pixel, its difference value between the original image and the stego-image remains within +/- 1. This guarantees that the peak signal-to-noise ratio (PSNR) of the stego-image is above 48 dB. Experimental results show that the histogram-based reversible data hiding approach can raise a larger capacity and still remain a good image quality, compared to other histogram-based approaches.

关键词： grey images computer vision and image processing techniques data encapsulation image and video coding multimedia image coding Data security PSNR histogram-based reversible data hiding copyright protection image colour analysis interleaving prediction method stego-image peak signal-to-noise ratio predictive error value

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Transfer learning-based radar imaging with deep convolutional neural networks for distributed frequency modulated continuous waveform multiple-input multiple-output radars

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IET RADAR SONAR AND NAVIGATION 2021年第10期15卷 1209-1220页

作者： Seo, Jiho Yang, Yunji Hong, Yong-gi Park, Jaehyun Pukyong Natl Univ Dept Elect Engn Div Smart Robot Convergence & Applicat Engn Pusan 48515 South Korea

Deep-learning-based radar imaging is developed with distributed frequency modulated continuous waveform multiple-input multiple-output (FMCW MIMO) radars in which a deep-learning approach based on the convolutional neural network (CNN) is proposed to achieve radar images robust to adverse circumstances. Differently from the existing deep-learning methods applied to radar object recognition, the deramped radar signal is exploited as the input of the proposed deep CNN (DCNN) without any processing related to the spectrogram transform and the subspace decomposition. To effectively train the proposed DCNN, the received signal is reformulated in terms of the reflection gain values in the (azimuth, range) patches in the image region of interest such that the output vector of the DCNN is composed of the reflection gain values in the associated patches. Furthermore, to overcome the limitations on the amount of training data and training time, the transfer learning approach is effectively applied to the distributed FMCW MIMO radar imaging. The proposed radar imaging is assessed with synthetic simulation data. Specifically, by transferring the pretrained DCNN model for a given reference radar to other distributed radars, the distributed radars can save about 52.4 % in training time compared with a DCNN having the same architecture but without transfer learning.

关键词： radar signal processing computer vision and image processing techniques distributed radars given reference radar transfer learning-based radar learning (artificial intelligence) deramped radar signal radar imaging distributed FMCW MIMO radar imaging deep-learning-based radar imaging Signal processing and detection distributed frequency radar images Optical, image and video signal processing training time radar object recognition reflection gain values existing deep-learning methods Radar equipment, systems and applications image region transfer learning approach output vector object recognition convolutional neural network neural nets frequency modulation DCNN deep CNN convolution MIMO communication continuous waveform multiple-input multiple-output radars CW radar deep convolutional neural networks FM radar radar antennas MIMO radar Other topics in statistics

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