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Wide baseline correspondence extraction beyond local features

IET computer vision 2011年第4期5卷 222-231页

作者： Lakemond, R. Sridharan, S. Fookes, C. Queensland Univ Technol Image & Video Res Lab Brisbane Qld 4001 Australia

Robust, affine covariant, feature extractors provide a means to extract correspondences between images captured by widely separated cameras. Advances in wide baseline correspondence extraction require looking beyond the robust feature extraction and matching approach. This study examines new techniques of extracting correspondences that take advantage of information contained in affine feature matches. Methods of improving the accuracy of a set of putative matches, eliminating incorrect matches and extracting large numbers of additional correspondences are explored. It is assumed that knowledge of the camera geometry is not available and not immediately recoverable. The new techniques are evaluated by means of an epipolar geometry estimation task. It is shown that these methods enable the computation of camera geometry in many cases where existing feature extractors cannot produce sufficient numbers of accurate correspondences.

关键词： camera geometry cameras geometry image matching Combinatorial mathematics putative matches computer vision and image processing techniques epipolar geometry estimation task affine feature matches feature extraction Optical, image and video signal processing wide baseline correspondence extraction feature extractor

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image fusion technique based on non-subsampled contourlet transform and adaptive unit-fast-linking pulse-coupled neural network

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IET image processing 2011年第2期5卷 113-121页

作者： Kong, W. W. Lei, Y. J. Lei, Y. Lu, S. Air Force Engn Univ Missile Inst Dept Comp Engn Sanyuan Xian 713800 Shaanxi Peoples R China

A new image fusion technique based on non-subsampled contourlet transform (NSCT) and adaptive unit-fast-linking pulse-coupled neural network (PCNN) is presented. By using NSCT, multi-scale and multi-direction sparse decompositions of the source images are performed. Then, the basic PCNN model is improved to be an adaptive unit-fast-linking PCNN model, which synthesises the advantages of both unit-linking PCNN and fast-linking PCNN. The novel PCNN model utilises the clarity of each pixel in images as the linking strength beta;moreover, the time matrix T of the sub-images can be obtained via the synchronous pulse burst property. Finally, the sub-images are fused by analysing the time matrix T and linking strength beta. The experimental results show that the proposed approach is better than some current methods.

关键词： Neural computing techniques Algebra matrix decomposition pulse coupled neural network non subsampled contourlet transform neural nets computer vision and image processing techniques synchronous pulse burst property image fusion transforms PCNN sparse matrices NSCT sparse matrix decompositions adaptive unit fast linking Integral transforms time matrix Optical, image and video signal processing

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Directional lifting wavelet and universal trellis-coded quantisation-based image coding algorithm and objective quality evaluation

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IET image processing 2011年第8期5卷 693-702页

作者： Hou, X. Jiang, G. Ji, R. Shi, C. Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China Xian Inst Elect Engn Xian 710100 Peoples R China

In this study, an image coding algorithm based on directional lifting wavelet transform (DLWT) and universal trellis-coded quantisation (UTCQ) is presented, and the coding performance is evaluated with three objective image quality metrics. Compared to the discrete wavelet transform, DLWT performing prediction and update along the direction of the local region can provide an efficient representation of edges in images, but shows a similar ability in representing the smooth region. To further improve the visual quality of the smooth background regions, UTCQ is adopted to quantising the wavelet coefficients. The proposed algorithm is measured with not only the dominant peak signal-to-noise ratio (PSNR), but also new metrics multi-scale structural similarity index measure (MSSIM) and visual information fidelity (VIF) which provide a better approximation to the perceived image quality than PSNR by taking the property of human visual system (HVS) into account. Experimental results show that the proposed algorithm has the best MSSIM and VIF performance among the compared algorithms (including JPEG2000) for the typical test images, and its decoded images at low bit-rate are visually more appealing in both edges and smooth background regions. For image Barbara, the proposed algorithm outperforms JPEG2000 up to 24.63% relatively in VIF and 1.93 dB in PSNR at 0.5 bpp, at most 3.62% relatively in MSSIM at 0.125 bpp. The experimental results also show that UTCQ does perform better than scalar quantisation (SQ) in MSSIM and VIF and improves the subjective visual quality, although UTCQ is not necessarily better than SQ in PSNR.

关键词： image representation edge representation multiscale structural similarity index measure image recognition directional lifting wavelet transform computer vision and image processing techniques image and video coding image coding Integral transforms peak signal-to-noise ratio smooth background region visual information fidelity image quality metrics wavelet transforms edge detection universal trellis-coded quantisation-based image coding algorithm objective quality evaluation trellis codes human visual system

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Obtaining medial responses from steerable filters

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IEE PROCEEDINGS-vision image AND SIGNAL processing 1999年第5期146卷 286-292页

作者： Bharath, AA Huberson, CJ Univ London Imperial Coll Sci Technol & Med Dept Biol & Med Syst Sir Leon Bagrit Ctr London SW7 2BX England

In building up a multiscale, medial axis representation, the generation of medial responses from a set of boundary responses at multiple scales can be a computationally intensive process. Polar separable medial weighting kernels are applied to the computation of medial responses. An approximate method for the synthesis of rotated kernels is proposed. Because of the need to not only rotate, but to displace boundary responses by an amount dependent on the image scale, the traditional steerable filter approach must be modified to yield correct medial responses. Comparisons between integral evaluation and the adaptive filtering approximations show that 100-fold increases in modestly sized images at a single scale are possible. The error in the approximation is insignificant.

关键词： integral evaluation edge strength map grey-scale images approximation theory multiscale medial axis representation image representation computer vision and image processing techniques Filtering methods in signal processing image scale rotated kernels synthesis edge detection Interpolation and function approximation (numerical analysis) boundary responses Optical, image and video signal processing polar separable medial weighting kernels medial responses adaptive filtering approximations filtering theory approximation error approximate method steerable filter steerable filters

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Pedestrians crossing intention anticipation based on dual-channel action recognition and hierarchical environmental context

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IET INTELLIGENT TRANSPORT SYSTEMS 2023年第2期17卷 255-269页

作者： Ni, Rongrong Yang, Biao Wei, Zhiwen Hu, Hongyu Yang, Changchun Jilin Univ State Key Lab Automot Simulat & Control Renmin St 5988 Changchun Peoples R China Hohai Univ Coll IoT Engn Changzhou Peoples R China Changzhou Univ Sch Microelect & Control Engn Changzhou Peoples R China

The increase in car ownership has brought convenience to people's lives, but it has also led to a sharp increase in pedestrian-vehicle conflicts. Such conflicts can be avoided by braking when pedestrians in front of the car are detected. However, frequent braking reduces driving comfort and travel efficiency. Considering that pedestrian-vehicle conflicts always occur when pedestrians cross the road, this study proposes a multi-factor fusion network (MFFN) to anticipate pedestrians' crossing intentions. MFFN contains a dual-channel action recognition sub-network to robustly identify pedestrians' actions by adaptively fusing pedestrians' skeleton and representation characteristics. Afterward, object-level and semantic-level perception of traffic scenes are realised by a hierarchical graph attention network and a lightweight semantic segmentation method, respectively. These hierarchical environmental contexts can provide critical clues for identifying pedestrians' crossing intentions. Then, a self-attention mechanism is used to integrate various factors and produce intention identification results. The proposed MFFN is evaluated on public datasets PIE and JAAD. Experimental results show that the proposed method can accurately and reliably identify pedestrian crossing intentions. The accuracy, F1 score, AUC, and precision indicators on JAAD/PIE datasets are 0.912/0.876, 0.813/0.806, 0.896/0.889, and 0.802/0.788, respectively.

关键词： traffic engineering computing image segmentation feature extraction recurrent neural nets hierarchical environmental context road safety learning (artificial intelligence) object-level Neural nets intention anticipation Optical, image and video signal processing computer vision and image processing techniques dual-channel action recognition convolutional neural nets pedestrian-vehicle conflicts Traffic engineering computing object detection multifactor fusion network deep learning (artificial intelligence) pedestrians image recognition graph theory image sequences road traffic semantic-level perception MFFN pedestrian crossing intentions road vehicles image motion analysis Combinatorial mathematics hierarchical graph attention network

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End-to-end autonomous driving decision model joined by attention mechanism and spatiotemporal features

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IET INTELLIGENT TRANSPORT SYSTEMS 2021年第9期15卷 1119-1130页

作者： Zhao, Xiangmo Qi, Mingyuan Liu, Zhanwen Fan, Songhua Li, Chao Dong, Ming Changan Univ Sch Informat Engn Xian 710064 Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Peoples R China

Autonomous driving decision is a critical component of automatic driving system, which informs and updates the unmanned vehicle of object movements. However, end-to-end autonomous driving decision is still a great challenge due to the different scales of traffic target in the wild dynamic traffic scenes. To solve these problems, this paper proposes a novel model with attention mechanism and spatiotemporal features extraction. Specifically, for the important spatial information of traffic targets with scale differences, the spatial dimensions of height H, width W and channel C are independent of each other to build sparse spatial attention map. Moreover, different sparse masks are trained for spatial network by pruning elements of feature maps at the end of each block of backbone, which improves the accuracy of the two subnetworks of spatial network by 2.3% and 3.9%, respectively. Then the extracted spatial information is introduced jointly to the time sequence network with previous speed as input to obtain the vehicle steering angle and speed. Experiments on public virtual datasets show that the prediction accuracy of the model reaches 85.8%. Compared with other state-of-the-art models, our model increased by 4.8% and 2.2%, respectively.

关键词： Optical, image and video signal processing computer vision and image processing techniques Traffic engineering computing

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Completely blind image quality assessment via contourlet energy statistics

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IET image processing 2021年第2期15卷 443-453页

作者： Li, Chaofeng Guan, Tuxin Zheng, Yuhui Jin, Bo Wu, Xiaojun Bovik, Alan Shanghai Maritime Univ Inst Logist Sci & Engn Shanghai 200135 Peoples R China Nanjing Univ Informat Sci Technol Coll Comp & Software Nanjing Peoples R China Jiangnan Univ Sch Internet Things Engn Wuxi Jiangsu Peoples R China Univ Texas Austin Lab Image & Video Engn Austin TX 78712 USA

An aim of completely blind image quality assessment (BIQA) is to develop algorithms which can grade image quality without any prior knowledge of the images. Here, a new contourlet energy statistics based completely on blind opinion-unaware BIQA (OU-BIQA) method is proposed, which can predict the perceptual severity of a range of image distortion types without requiring any prior knowledge. According to the energy distribution of the contourlet sub-bands of natural images in log-domain, the lower-scale sub-band energy can be predicted by the corresponding higher-scale sub-band energies of distorted images. A quality model is then constructed by quantifying the difference between predicted energy and realistic energy. Meanwhile, an effective method for adjusting and compensating an undesired distortion is integrated into the quality model. Experimental results show that the proposed new method outperforms state-of-the-art OU-BIQA models on relevant portions of TID2013 database, and is competitive on the LIVE IQA database. Moreover, the proposed model is very fast, suggesting a real-time solution to high-performance BIQA.

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

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Prediction and adaptive scanning in block-set wavelet image coder

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IEE PROCEEDINGS-vision image AND SIGNAL processing 2006年第2期153卷 230-236页

作者： Yin, X. -W. Fleury, M. Downton, A. C. Univ Essex Dept Elect Syst Engn Colchester CO4 3SQ Essex England

Conditional probability to predict sub-block significance improves the peak signal-to-noise ratio (PSNR) performance of a block-set wavelet image coder. Adaptive scanning of sub-blocks results in a further gain in PSNR performance. Together, the related algorithmic changes introduced by the authors result in competitive PSNR performance without the need for an arithmetic coding stage. As a consequence, timing tests reveal that the critical decoder computational complexity is reduced.

关键词： arithmetic codes Computational complexity computational complexity block-set wavelet image coder wavelet transforms adaptive scanning subblocks Integral transforms transform coding computer vision and image processing techniques PSNR arithmetic coding peak signal-to-noise ratio image coding image and video coding

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FAFEnet: A fast and accurate model for automatic license plate detection and recognition

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IET image processing 2023年第3期17卷 807-818页

作者： Zhou, Xin Cheng, Yao Jiang, Liling Ning, Bo Wang, Yanhao Dalian Maritime Univ Sch Informat Sci & Technol Dalian Peoples R China East China Normal Univ Sch Data Sci & Engn Shanghai Peoples R China

Automatic License Plate detection and Recognition (ALPR) is a key problem in intelligent transportation systems with wide applications in traffic monitoring, electronic toll collection (ETC), intelligent parking lots (IPLs), and elsewhere. Although numerous methods have been proposed in the literature, it is still challenging to strike a good balance between the accuracy and efficiency of ALPR. In this paper, a novel end-to-end CNN-based model is proposed, called Fast and Accurate Network with Feature Enhancement (FAFEnet), to jointly detect the license plates and recognize the characters with high accuracy and efficiency. Specifically, the FAFEnet model seamlessly integrates two CNN-based models, namely the detection and recognition modules, into a unified framework to reduce accumulated errors and computational overheads in two separate steps. The detection module is a lightweight model with only seven convolutional layers yet achieves over 99.8% accuracy rates for license plate detection across all datasets. The recognition module utilizes two feature enhancement blocks to compensate and enhance the shallow character features extracted by the detection module. Furthermore, the joint optimization of detection and recognition modules exploits the feature association in two modules, and thus improves the prediction accuracy while reducing the execution time. Finally, extensive experimental results on several real-world datasets demonstrate that FAFEnet outperforms all the competitors in terms of both accuracy and efficiency.

关键词： feature extraction feature enhancement blocks automatic license plate detection and recognition image recognition recognition module Neural nets computer vision and image processing techniques intelligent parking lots convolutional neural nets Traffic engineering computing object detection image recognition lightweight model FAFEnet model CNN-based models intelligent transportation systems ALPR CNN-based model detection module shallow character features

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Digital image processing approach using combined wavelet hidden Markov model for well-being analysis of insulators

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IET image processing 2011年第2期5卷 171-183页

作者： Murthy, V. S. Gupta, S. Mohanta, D. K. MVGR Coll Engn Dept Comp Sci & Engn Vizianagaram 535005 India Deemed Univ Birla Inst Technol Dept Elect & Elect Engn Ranchi 835215 Bihar India

This study employs a digital image processing (DIP) technique for video surveillance (VS) of overhead power distribution line insulators. Such VS technique is one of the promising means for augmenting the process of distribution system automation (DSA). Particularly in semi-urban areas and as well as urban areas in developing countries like India, the overhead distribution lines traverse mostly near the roads and thus VS based on surface vehicular approach equipped with moving cameras seems to be more suitable compared to VS using either remote terminal units (RTUs) or a helicopter. The proposed VS approach has unique advantage due to the fact that a non-technical person can go for patrol using a vehicle so as to capture images of power lines along with insulators for subsequent analysis. In addition, such an approach avoids the difficulties such as blurring of images and camera sight control as in case of aerial VS. Also in case of VS based on RTUs, the maintenance of RTUs becomes a challenging task, particularly because of the outdoor nature of installations. Thus, this study has uniquely employed the use of DIP for video tracking of overhead power lines using template design, feature extraction by applying wavelet transform, and subsequently hidden Markov model is being used for well-being analysis to segregate the damaged insulators from good ones. The case studies validate the efficacy of the proposed methodology for insulator monitoring to augment on-going DSA.

关键词： Overhead power lines insulators surface vehicular approach combined wavelet hidden Markov model remote terminal units overhead distribution lines template design overhead power distribution line insulators image recognition wavelet transform insulator monitoring power overhead lines image processing computer vision and image processing techniques power distribution lines distribution system automation process Integral transforms power cables camera sight control video surveillance damaged insulators wavelet transforms feature extraction power lines moving cameras Power line supports, insulators and connectors Video signal processing helicopter hidden Markov models power engineering computing video tracking image sensors Markov processes digital image processing approach Power engineering computing Power cables cameras

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