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Hyperspectral image classification using 3D 2D CNN

IET image processing 2021年第5期15卷 1083-1092页

作者： Diakite, Alou Gui, Jiangsheng Fu, Xiaping Zhejiang Sci Tech Univ Sch Informat Sci Hangzhou Peoples R China Zhejiang Sci Tech Univ Fac Mech Engn & Automat Hangzhou Peoples R China

Recent works have shown that deep-learning-derived methods based on convolutional neural network can achieve high performance in terms of accuracy when applied to computer vision task such as object detection, segmentation and classification particularly on hyperspectral image. However, the existing methods have long training times. To reduce the training time and increase the accuracy, this paper proposed a new 3D2D convolutional neural network combined for hyperspectral image classification. For this purpose, a 3D fast learning block (depthwise separable convolution block and a fast convolution block) followed by a 2D convolutional neural network was introduced to extract spectralspatial features. Four datasets were used for the experiment purpose and the results showed that the proposed method achieved excellent result on both small and large training data compared with existing methods. The proposed method increased the overall accuracies by 2% on UP and KSC datasets while significantly reducing the training time on IP and KSC datasets, respectively. The proposed method increased all accuracies for at least 6% on IP, KSC and UP datasets when compared to some state-of-the-art methods. Also, it reduced considerably the training and testing time on IP and KSC datasets when fast convolution block alone is involved.

关键词： image recognition computer vision and image processing techniques Machine learning (artificial intelligence) Neural nets

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Balancing performance factors in multisource biometric processing platforms

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IET SIGNAL processing 2009年第4期3卷 342-351页

作者： Fairhurst, M. C. Abreu, M. C. C. Univ Kent Dept Elect Canterbury CT2 7NT Kent England

It is generally recognised that no one biometric data source or processing platform is universally appropriate for optimising performance across all problem domains. Multibiometric processors, which combine identity information obtained from more than one biometric source are commonly promoted as optimal structures for maximising performance, and much research has been carried out to investigate appropriate strategies for combining the available information. However, the techniques of multiclassifier pattern recognition also offer opportunities to improve the performance of systems operating within a unimodal environment, yet such solutions have been less extensively investigated in the specific case of biometric applications. This study presents an empirical study of the relations between these two different approaches to enhancing the performance indicators delivered by biometric systems. In particular we are interested to increase our understanding of the relative merits of, on the one hand, multiclassifier/single modality systems and, on the other, full multibiometric configurations. We focus our study on three modalities, the fingerprint and hand geometry (two physiological biometrics) and the handwritten signature (a behavioural biometric).

关键词： computer vision and image processing techniques fingerprint identification handwriting recognition handwritten signature biometric data source hand geometry multiclassifier pattern recognition image recognition performance indicators handwritten character recognition multisource biometric processing platforms fingerprint geometry single modality systems

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Local binary pattern and its derivatives for face recognition

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IET computer vision 2012年第5期6卷 480-488页

作者： Suruliandi, A. Meena, K. Rose, R. Reena Manonmaniam Sundaranar Univ Dept Comp Sci & Engn Tirunelveli Tamil Nadu India Sardar Raja Coll Engn Dept Comp Sci & Engn Tirunelveli 627808 Tamil Nadu India St Xaviers Catholic Engn Coll Dept Comp Sci & Engn Kanyakumari Tamil Nadu India

Texture is the surface property that is used to identify and recognise objects. This property is widely used in many applications including texture-based face recognition systems, surveillance, identity verification and so on. The Local binary pattern (LBP) texture method is most successful for face recognition. Owing to the great success of LBP, recently many models, which are variants of LBP have been proposed for texture analysis. Some of the derivatives of LBPs are multivariate local binary pattern, centre symmetric local binary pattern, local binary pattern variance, dominant local binary pattern, advanced local binary pattern, local texture pattern (LTP) and local derivative pattern (LDP). In this scenario, it is essential to review, whether LBP or their derivatives perform better for face recognition. The real-time challenges such as illumination changes, rotations, angle variations and facial expression variations are evaluated by different LBP-based models. Experiments were conducted on the Japanese female facial expression, YALE and FRGC version2 databases. The results show that LDP and LTP perform much better than the other LBP-based models.

关键词： multivariate local binary pattern YALE databases advanced local binary pattern face recognition systems LBP-based models angle variations image recognition LDP Spatial and pictorial databases visual databases facial expression variations centre symmetric local binary pattern local derivative pattern surveillance face recognition local binary pattern texture method surface property illumination changes computer vision and image processing techniques Japanese female facial expression FRGC version2 databases image texture local binary pattern variance identity verification dominant local binary pattern

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Attention-based video object segmentation algorithm

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IET image processing 2021年第8期15卷 1668-1678页

作者： Cao, Ying Sun, Lijuan Han, Chong Guo, Jian Henan Univ Kaifeng Henan Peoples R China Nanjing Univ Posts & Telecommun Nanjing Peoples R China Nanjing Univ Posts & Telecommun Jiangsu High Technol Res Key Lab Wireless Sensor Nanjing Peoples R China

To improve the segmentation performance on videos with large object motion or deformation, a novel scheme is proposed which has two branches. In one branch, the attention mechanism is first utilized to highlight objects-related features. Then, to well consider the temporal coherence of videos, Conv3D is integrated to capture short-term temporal features, and the designed attention residual convolutional long-short-term memory is adopted to capture the long-short-term temporal information of objects under the interference of redundant video frames. Meanwhile, considering the negative effect of background motion, in another branch, the optical flow-based prediction model is introduced to predict objects regions in subsequent video frames with the annotated initial frame. At last, based on the fused results of two branches, the global thresholds and noising area clean method are employed to obtain segmented objects. The experiments on DAVIS2016 and CDnet2014 exhibit the competitive performance of the proposed scheme.

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

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Robust face recognition using posterior union model based neural networks

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IET computer vision 2009年第3期3卷 130-142页

作者： Lin, J. Ming, J. Crookes, D. Univ Elect Sci & Technol China Sch Engn & Comp Sci Chengdu 610054 Peoples R China Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast BT7 1NN Antrim North Ireland

Face recognition with unknown, partial distortion and occlusion is a practical problem, and has a wide range of applications, including security and multimedia information retrieval. The authors present a new approach to face recognition subject to unknown, partial distortion and occlusion. The new approach is based on a probabilistic decision-based neural network, enhanced by a statistical method called the posterior union model (PUM). PUM is an approach for ignoring severely mismatched local features and focusing the recognition mainly on the reliable local features. It thereby improves the robustness while assuming no prior information about the corruption. We call the new approach the posterior union decision-based neural network (PUDBNN). The new PUDBNN model has been evaluated on three face image databases (XM2VTS, AT&T and AR) using testing images subjected to various types of simulated and realistic partial distortion and occlusion. The new system has been compared to other approaches and has demonstrated improved performance.

关键词： occlusion statistical analysis neural networks partial distortion probabilistic decision multimedia information retrieval Other topics in statistics face recognition image recognition robust face recognition Neural computing techniques probability computer vision and image processing techniques face image databases posterior union model statistical method neural nets image testing

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image stitching method by multi-feature constrained alignment and colour adjustment

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IET image processing 2021年第7期15卷 1499-1507页

作者： Yuan, Xingsheng Zheng, Yongbin Zhao, Wei Su, Jiongming Wu, Jianzhai Natl Univ Def Technol Sch Intelligence Sci & Technol Deya Rd 109 Changsha 410073 Hunan Peoples R China Hunan Police Acad Dept Informat Technol Changsha Peoples R China

image alignment and colour consistency are two challenging tasks for image stitching. Traditional point correspondence methods are difficult to achieve good alignments due to their insufficiency and unreliability. The results are prone to errors and distortions. On the other hand, the problem of colour inconsistency in overlapping area between image pairs is still difficult to solve, especially when the illumination difference between images is large. To solve these problems, the authors integrate point features and line features into a warping model through a designed energy function. Line features will provide geometric constraints for image stitching, and remedy the defect of point correspondences in low-textured image stitching. A global colour consistency optimization method with colour mapping via a histogram extreme point-matching algorithm is proposed. The colour characteristic of reference images will be transferred to the others to achieve a global colour consistency. The proposed method is evaluated on a series of images, and compared with other methods. The experiments demonstrate that the proposed method provides convincing stitching results and achieves satisfied colour consistency results.

关键词： image recognition computer vision and image processing techniques

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Video super-resolution with non-local alignment network

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IET image processing 2021年第8期15卷 1655-1667页

作者： Zhou, Chao Chen, Can Ding, Fei Zhang, Dengyin Nanjing Univ Posts & Telecommun Sch Telecommun & Informat Engn Nanjing 210003 Peoples R China Nanjing Univ Posts & Telecommun Sch Internet Things Nanjing 210003 Peoples R China Nanjing Univ Posts & Telecommun Jiangsu Key Lab Broadband Wireless Commun & Inter Nanjing 210003 Peoples R China

Video super-resolution (VSR) aims at recovering high-resolution frames from their low-resolution counterparts. Over the past few years, deep neural networks have dominated the video super-resolution task because of its strong non-linear representational ability. To exploit temporal correlations, most deep neural networks have to face two challenges: (1) how to align consecutive frames containing motions, occlusions and blurring, and establish accurate temporal correspondences, (2) how to effectively fuse aligned frames and balance their contributions. In this work, a novel video super-resolution network, named NLVSR, is proposed to solve above problems in an efficient and effective manner. For alignment, a temporal-spatial non-local operation is employed to align each frame to the reference frame. Compared with existing alignment approaches, the proposed temporal-spatial non-local operation is able to integrate the global information of each frame by a weighted sum, leading to a better performance in alignment. For fusion, an attention-based progressive fusion framework was designed to integrate aligned frames gradually. To penalize the points with low-quality in aligned features, an attention mechanism was employed for a robust reconstruction. Experimental results demonstrate the superiority of the proposed network in terms of quantitative and qualitative evaluation, and surpasses other state-of-the-art methods by 0.33 dB at least.

关键词： image resolution reference frame high-resolution frames temporal correlations attention-based progressive fusion framework low-resolution counterparts image reconstruction video super-resolution network video signal processing deep neural networks computer vision and image processing techniques image fusion Optical, image and video signal processing video super-resolution task deep learning (artificial intelligence) nonlinear representational ability Video signal processing temporal-spatial nonlocal operation nonlocal alignment network NLVSR

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An efficient data collection framework in the sky: An affine transformation approach based on Internet of unmanned aerial vehicles

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IET COMMUNICATIONS 2021年第10期15卷 1315-1325页

作者： Sun, Bing Yu, Mingxing Wu, Yi Qi, Yuanhang Nie, Xin Xi, Shan Harbin Engn Univ Sch Econ & Management Harbin Peoples R China Heilongjiang Univ Sch Data Sci & Technol Harbin 150080 Peoples R China Univ Elect Sci & Technol China Zhongshan Inst Sch Comp Sci Zhongshan Peoples R China

Due to a large amount of time-series data (e.g. precipitation, temperature, humidity) in the air, reliable and fast data collection is a challenging issue. Using an unmanned aerial vehicle (UAV) as a data collector to collect time-series data is an effective method. However, due to the limited storage capacity of the UAV, the UAV must access the sensor multiple times to collect time-series data from the deployed sensors and dump the data to the data centre, which leads to significantly increased time and cost for data collection mission. To address this challenge, an efficient time-series data collection framework is proposed based on the Internet of UAVs. In this system, a min-maximum data processing strategy is adopted based on data value to store the collected time-series data. Specifically, efficient data compression storage is achieved with minimal loss of precision by extracting the dominant dataset with maximum value. Furthermore, an efficient affine transformation method is proposed to improve the efficiency of the system. Extensive case studies on some real-world datasets demonstrate that the proposed framework can achieve efficient data management and compressed storage.

关键词： Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research Optical, image and video signal processing Aerospace control Mobile robots computer vision and image processing techniques Data handling techniques Wireless sensor networks Other topics in statistics

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Remote sensing target tracking in satellite videos based on a variable-angle-adaptive Siamese network

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IET image processing 2021年第9期15卷 1987-1997页

作者： Bi, Fukun Sun, Jiayi Han, Jianhong Wang, Yanping Bian, Mingming North China Univ Technol Dept Sch Informat Sci & Technol Beijing Peoples R China Beijing Inst Spacecraft Syst Engn Qian Xuesen Lab Beijing Peoples R China

Remote sensing target tracking in satellite videos plays a key role in various fields. However, due to the complex backgrounds of satellite video sequences and many rotation changes of highly dynamic targets, typical target tracking methods for natural scenes cannot be used directly for such tasks, and their robustness and accuracy are difficult to guarantee. To address these problems, an algorithm is proposed for remote sensing target tracking in satellite videos based on a variable-angle-adaptive Siamese network (VAASN). Specifically, the method is based on the fully convolutional Siamese network (Siamese-FC). First, for the feature extraction stage, to reduce the impact of complex backgrounds, we present a new multifrequency feature representation method and introduce the octave convolution (OctConv) into the AlexNet architecture to adapt to the new feature representation. Then, for the tracking stage, to adapt to changes in target rotation, a variable-angle-adaptive module that uses a fast text detector with a single deep neural network (TextBoxes++) is introduced to extract angle information from the template frame and detection frames and performs angle consistency update operations on the detection frames. Finally, qualitative and quantitative experiments using satellite datasets show that the proposed method can improve tracking accuracy while achieving high efficiency.

关键词： Optical, image and video signal processing image recognition computer vision and image processing techniques Video signal processing

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Pedestrian re-identification based on attribute mining and reasoning

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IET image processing 2021年第11期15卷 2399-2411页

作者： Li, Chao Yang, Xiaoyu Yin, Kangning Chang, Yifan Wang, Zhiguo Yin, Guangqiang Univ Elect Sci & Technol China Ctr Publ Secur Technol 2006 Xiyuan Ave Chengdu Peoples R China

The high-level semantic information extracted from the pedestrian attribute feature is an important element for pedestrian recognition. Pedestrian attribute recognition plays an important role in both intelligent video surveillance and pedestrian re-identification promoting the convenience of searching and performance of model. This paper tries finding a practical method to improve the performance of the pedestrian re-identification by combining pedestrian attributes and identities. The multi-task learning method combines pedestrian recognition and attribute information in a direct way that considers the correlation between pedestrian attributes and identities but ignores the principle and degree of such correlation. To solve this problem, a new pedestrian recognition framework based on attribute mining and reasoning is proposed in this paper. To enhance the expression ability of attribute features, it designs spatial channel attention module (SCAM) based on attention mechanism to extract features from every attribute. SCAM can not only locate the attributes on the feature map, but also effectively mine channel features with a higher degree of association with attributes. In addition, both spatial attention model and channel attention model are integrated by multiple groups of parallel branches, which further improve the network performance. Finally, using the semantic reasoning and information transmission function of graph convolutional network, the relationship between attribute features and pedestrian features can be mined. Besides, pedestrian features with stronger expression ability can also be obtained. Experiment work is conducted in two databases, DukeMTMC-reID and Market-1501, which are commonly used in pedestrian recognition tasks. On the Market-1501 dataset, the final effect of the algorithm model CMC-1 can reach 94.74%, and mAP can reach 87.02%;on the DukeMTMC-reID dataset, CMC-1 can reach 87.03%, and mAP can reach 77.11%. The results show that our meth

关键词： image recognition computer vision and image processing techniques Video signal processing Traffic engineering computing

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