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A novel face recognition method based on fusion of LBP and HOG

IET image processing 2021年第14期15卷 3559-3572页

作者： Chen, Ting Gao, Tao Li, Shuying Zhang, Xi Cao, Jinpei Yao, Dachun Li, Yh Changan Univ Sch Informat Engn Middle Sect Naner Huan Rd Xian 710064 Shaanxi Peoples R China Xian Univ Posts & Telecommun Sch Automat Changan West St Xian 710121 Shaanxi Peoples R China

As one of the hot topics in the field of computer vision research, face recognition technology has received significant attention due to its potentiality for a wide range of applications in government as well as commercial purposes. In practical applications, although several existing face recognition methods have achieved good performances in specific scenes, they easily suffer from a sharp decline in recognition rate if affected by different conditions of light, expression, posture and occlusion. Among many factors, influences of complex illuminations on face recognition are particularly significant. To further improve the performance of the existing local binary pattern (LBP) operator, neighbourhood weighted average LBP (NWALBP) is first proposed for fully considering the strong correlations between pixel pairs in the neighbourhood, which extends the traditional LBP uni-layer neighbourhood template window to the bi-layer neighbourhood template window and calculates the weighted average of bi-layer neighbourhood pixels in each direction. Then, inspired by center symmetric LBP (CS-LBP), centre symmetric NWALBP (CS-NWALBP) is further proposed, which can effectively reduce computation complexity by only comparing the weighted average values of the neighbourhood pixels that are symmetric about the centre pixel. Finally, by combining the merit of histogram of oriented gradient (HOG), a feature fusion algorithm named CS-NWALBP+HOG is suggested. Several experiments have eventually demonstrated that our proposed algorithms have more robust performance under complex illumination conditions if compared with many other latest algorithms.

关键词： image recognition Computational complexity computer vision and image processing techniques

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Window-aware guided image filtering via local entropy

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IET image processing 2021年第7期15卷 1459-1470页

作者： Liu, Chong Yang, Cui Wang, Jun Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing Peoples R China Anqing Normal Univ Coll Math & Comp Sci Anqing Anhui Peoples R China

Guided image filtering is one of the widely used techniques in computer vision. However, it commonly leads to over-smoothed edges and a distorted appearance when tackling intricate texture patterns and complex noise. In this paper, a window-aware image filtering framework based on the bilateral filter guided by the local entropy is presented. The key idea of the authors' proposed approach is to design a novel guidance input and a non-box filtering window. Specifically, using the Gaussian spatial kernel and the local entropy, a GEF that can maintain image feature details and yield a robust guidance input for BF is constructed. Meanwhile, based on an intensity-similar strategy, the local non-box filtering window is designed for the further preservation of edge structures. The authors' approach not only inherits the advantages of bilateral filter i.e. simplicity, parallelisation and easiness of programming, but also is more powerful than bilateral filter and its variants. In addition, the guided entropy filter and the non-box window can also be transplanted to other local filters and can effectively improve the filtering effects. The qualitative and quantitative experimental results demonstrate that the authors' approach has good performance in image denoising, texture (or background) smoothing, edge extraction and other applications in image processing.

关键词： Other topics in statistics image recognition Other topics in statistics computer vision and image processing techniques

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Object scale selection of hierarchical image segmentation with deep seeds

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IET image processing 2021年第1期15卷 191-205页

作者： Al-Huda, Zaid Peng, Bo Yang, Yan Algburi, Riyadh Nazar Ali Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu Sichuan Peoples R China Southwest Jiaotong Univ Natl Engn Lab Integrated Transportat Big Data App Chengdu Sichuan Peoples R China Southwest Jiaotong Univ Sch Mech Engn Chengdu Sichuan Peoples R China

Hierarchical image segmentation is a prevalent technique in the literature for improving segmentation quality, where the segmentation result needs to be searched at different scales of the hierarchy to identify objects represented from various scales. In this paper, a novel framework for improving the quality of object segmentation is presented. To this end, the authors first select the optimal segments among several hierarchical scales of the input image using simple mid-level features and dynamic programming. Simultaneously, deep seeds are localised on the input image for the foreground and background classes using a deep classification network and a saliency network, respectively. Then, a graphical model is constructed as a set of nodes that jointly propagate information from deep seeds to unmarked regions to obtain the final object segmentation. Comprehensive experiments are performed on different datasets for popular hierarchical image segmentation algorithms. The experimental results show that the proposed framework can significantly improve the quality of object segmentation at low computational costs and without training any segmentation network.

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

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A dual-attention V-network for pulmonary lobe segmentation in CT scans

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IET image processing 2021年第8期15卷 1644-1654页

作者： Zheng, Shaohua Nie, Weiyu Pan, Lin Zheng, Bin Shen, Zhiqiang Huang, Liqin Pei, Chenhao She, Yuhang Chen, Liuqing Fuzhou Univ Coll Phys & Informat Engn 2 Xueyuan Rd Fuzhou 350108 Fujian Peoples R China Fujian Med Univ Thorac Dept Union Hosp Fuzhou Peoples R China Fuzhou Univ Coll Mech Engn & Automat Fuzhou Peoples R China

The reliable and automatic segmentation of pulmonary lobes in computed tomography scans is an important pre-condition for the diagnosis, assessment, and treatment of lung diseases. However, due to the incomplete lobar structures and morphological changes caused by diseases, the lobe segmentation still encounters great challenges. Recently, convolution neural network has exerted a tremendous impact on medical image analysis. Nevertheless, the basic convolution operations mainly obtain local features that are insufficient for accurate lobe segmentation. The idea that the global features are equally crucial especially when lesions appear is considered. Here, a dual-attention V-network named DAV-Net for pulmonary lobe segmentation is proposed. First, a novel dual-attention module to capture global contextual information and model the semantic dependencies in spatial and channel dimensions is introduced. Second, a progressive output scheme is used to avoid the vanishing gradient phenomenon and obtain relatively effective features in hidden layers. Finally, an improved combo loss is devised to address input and output lobe imbalance problem during training and inference. In the evaluation using the LUNA16 dataset and our in-house dataset, the proposed DAV-Net obtains Dice similarity coefficients of 0.947 and 0.934, respectively;these values are superior to those obtained by existing methods.

关键词： Optical, image and video signal processing X-ray techniques: radiography and computed tomography (biomedical imaging/measurement) computer vision and image processing techniques X-rays and particle beams (medical uses) Patient diagnostic methods and instrumentation Biology and medical computing Neural nets

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Deep residual deconvolutional networks for defocus blur detection

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IET image processing 2021年第3期15卷 724-734页

作者： Zeng, Kai Wang, Yaonan Mao, Jianxu Zhou, Xianen Hunan Univ Sch Elect & Informat Engn Changsha Peoples R China Hunan Univ Natl Engn Lab Robot Visual Percept & Control Tech Changsha Peoples R China

Accurate defocus blur detection has instigated wide research interest for the last few years. However, it is still a meaningful yet challenging machine vision task, and most methods rely on prior knowledge. Convolutional neural networks have proved the huge success for different tasks within the computer vision, and machine learning flew. A simple yet effective method of defocus blur detection was proposed in this paper, which by applying the deep residual convolutional encoder-decoder network. The aims of DRDN is to automatically generate pixel-level predictions for defocus blur images, and reconstruct output detection results of the same size as the input, which by performing several deconvolution operations at multiple scales through the transposed convolution, and skip connection. Afterwards, we used the slide window detection strategy and traversed the input image with a certain stride. Experiments on challenging benchmarks of defocus blur detection show that our algorithm achieved state-of-the-art performance, and powerfully balanced the detection accuracy, and detection time.

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

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A remote-sensing image enhancement algorithm based on patch-wise dark channel prior and histogram equalisation with colour correction

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IET image processing 2021年第1期15卷 47-56页

作者： Dharejo, Fayaz Ali Zhou, Yuanchun Deeba, Farah Jatoi, Munsif Ali Du, Yi Wang, Xuezhi Univ Chinese Acad Sci Chinese Acad Sci Comp Network Informat Ctr Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Barrett Hodgson Univ Dept Biomed Engn Karachi Sindh Pakistan

The object identification within an image captured during rough weather conditions (such as haze, fog) poses difficulty due to the reduction of an image. The rough weather conditions lead not only to the variation of the image's visual effect but also to the disadvantage of post-processing of an image. Furthermore, it causes inconvenience of all types of instruments that rely on optical imaging, such as satellite remote-sensing systems, aerial photo systems, outdoor monitoring systems, and object identification systems, respectively. Hence, the improvement and restorement of the visual effects and enhanced post-processing are needed. This research introduces a new image enhancement approach for image dehazing based on dark channel prior and piecewise linear transformation;also, the histogram equalisation technique, i.e. contrast limited adaptive histogram equalisation is applied. A dark channel prior is well known for its simplicity and productivity. In this work, the dark channel prior to a new angle is analysed in the first step, where average patch sizes are estimated for the computation of haze densities. Furthermore, the sky is approximated up to 5-10% of the hazy images, which has a good effect in removing the haze from the image. Using the dark channel, the proposed algorithm significantly boosted the effects of the dark images as well as reduced the influence of haze and noise. Eventually, for colour correction, the piecewise linear transformation technique is applied, which enhances the colour close to the original image. Experimental results demonstrate that the proposed method significantly improves the visibility of the algorithm on dark remote-sensing images as well as on hazy natural images.

关键词： Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research Optical, image and video signal processing Geophysical techniques and equipment computer vision and image processing techniques Geophysics computing

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A robust sperm cell tracking algorithm using uneven lighting image fixing and improved branch and bound algorithm

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IET image processing 2021年第9期15卷 2068-2079页

作者： Alhaj Alabdulla, Ahmad Hasiloglu, Abdulsamet Hicazi Aksu, Emrah Ataturk Univ Fac Engn Dept Comp Engn Erzurum Turkey Ataturk Univ Fac Vet Med Dept Reprod & Artificial Inseminat Erzurum Turkey

An accurate and robust sperm cells tracking algorithm that is able to detect and track sperm cells in videos with high accuracy and efficiency is presented. It is fast enough to process approximately 30 frames per second. It can find the correct path and measure motility parameters for each sperm. It can also adapt with different types of images coming from different cameras and bad recording conditions. Specifically, a new way is offered to optimize uneven lighting images to improve sperm cells detection which gives us the ability to get more accurate tracking results. The shape of each detected object is used to specify collided sperms and utilized dynamic gates which become bigger and smaller according to the sperm cell's speed. For assigning tracks to the detected sperm cells positions an improved version of branch and bound algorithm which is faster than the normal one is offered. This sperm cells tracking algorithm outperforms many of the previous algorithms as it has lower error rate in both sperm detection and tracking. It is compared with six other algorithms, and it gives lower tracking error rates. This method will allow doctors and researchers to obtain sperm motility data instantly and accurately.

关键词： Biological transport cellular and subcellular transmembrane physics Optical, image and video signal processing computer vision and image processing techniques Other topics in statistics Other topics in statistics Optical and laser radiation (medical uses) Patient diagnostic methods and instrumentation Biology and medical computing

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TSDCN: Traffic safety state deep clustering network for real-time traffic crash-prediction

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IET INTELLIGENT TRANSPORT SYSTEMS 2021年第1期15卷 132-146页

作者： Li, Haitao Bai, Qiaowen Zhao, Yonghua Qu, Zhaowei Xin, Wang Jilin Univ Coll Transportat Changchun Jilin Peoples R China Jilin Univ Publ Comp Educ & Res Ctr Changchun Jilin Peoples R China

Traffic safety state clustering has always been the focus of traffic safety research and the foundation of real-time crash potential prediction. How to mine effective latent crash risk information and improve clustering effect are the goals and difficulties of traffic safety state clustering task. The conventional methods adopt independent feature extraction and clustering processing, which leads to mismatch problems and decrease clustering effect. To deal with the problems, a novel traffic safety state deep clustering network (TSDCN) is proposed. TSDCN integrates the feature extraction and clustering into an end-to-end deep hybrid network. A custom autoencoder is constructed to extract expressive risk feature and iteratively optimize clustering effects and feature extraction using a deep clustering layer. The three-stage multitask strategy is designed to joint-adjust shared network parameters and ensure convergence at different stages. The comparative experiments show the TSDCN achieves more outstanding cluster performance than those existing models. Moreover, the traffic safety state cluster results are statistically analysed and the crash risk level is quantified for each safety state. The risk-quantized results are consistent with the real road crash situation and this confirms the safety state clustering effectiveness of TSDCN.

关键词： Optimisation techniques computer vision and image processing techniques Data handling techniques Traffic engineering computing Other topics in statistics Other topics in statistics

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Facial expression recognition using a combination of enhanced local binary pattern and pyramid histogram of oriented gradients features extraction

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IET image processing 2021年第2期15卷 468-478页

作者： Sharifnejad, Maede Shahbahrami, Asadollah Akoushideh, Alireza Hassanpour, Reza Zare Univ Guilan Dept Comp Engn Rasht Iran Tech & Vocat Univ TVU Fac Shahid Chamran Guilan Branch Dept Elect Engn Rasht Iran Erasmus Univ Dept Technol & Operat Management Rotterdam Netherlands

Automatic facial expression recognition, which has many applications such as drivers, patients, and criminals' emotions recognition, is a challenging task. This is due to the variety of individuals and facial expression variability in different conditions, for instance, gender, race, colour and changing illumination. In addition, there are many regions in a face image such as forehead, mouth, eyes, eyebrows, nose, cheeks and chin, and extracting features of all these regions are expensive in terms of computational time. Each of the six basic emotions of anger, disgust, fear, happiness, sadness and surprise affect some regions more than the other regions. The goal of this study is to evaluate the performance of enhanced local binary pattern, pyramid histogram of oriented gradients feature-extraction algorithms and their combination in terms of recognition accuracy, feature vector length and computational time on one, two and three combined regions of a face image. Our experimental results show that the combination of both feature-extraction algorithms yields an average recognition accuracy of 95.33% using three regions, that is, the mouth, nose and eyes on Cohn-Kanade dataset. Besides, the mouth region is the most important part in terms of accuracy in comparison to eyes, nose and combination of both eyes and nose regions.

关键词： image recognition computer vision and image processing techniques

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Medical image fusion and noise suppression with fractional-order total variation and multi-scale decomposition

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IET image processing 2021年第8期15卷 1688-1701页

作者： Zhang, Xuefeng Yan, Hui Northeastern Univ Coll Sci Shenyang 110819 Peoples R China

Fusion and noise suppression of medical images are becoming increasingly difficult to be ignored in image processing, and this technique provides abundant information for the clinical diagnosis and treatment. This paper proposes a medical image fusion and noise suppression model in pixel level. This model decomposes the original image into a noiseless base layer, a large-scale noiseless detail layer and a small-scale detail layer which contains details and noise information. The fractional-order derivative and saliency detection are used to construct the weight functions to fuse the base layers. The proposed total variation model combines the fractional-order derivative to fuse the small-scale detail layers. The mathematical properties and time complexity of the total variation model are also analysed. And choose-max method is used to fuse the large-scale detail medical layers simply. Our approach is based on fractional-order derivative, which enables keep more information and decrease blocky effects more effectively compared with the integer-order derivative. To verify the validity, the proposed method is compared with some fusion methods in the subjective and objective aspects. Experiments show that the proposed model fuses the source information fully and decreases noise cleanly.

关键词： Optical, image and video signal processing Filtering methods in signal processing computer vision and image processing techniques Patient diagnostic methods and instrumentation Biology and medical computing

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