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Zhao Pei
Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi’an, China

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Journal article
Published: 31 July 2021 in Multimedia Tools and Applications
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Being able to collect rich morphological information of brain, structural magnetic resonance imaging (MRI) is popularly applied to computer-aided diagnosis of Alzheimer’s disease (AD). Conventional methods for AD diagnosis are labor-intensive and typically depend on a substantial amount of hand-crafted features. In this paper, we propose a novel framework of convolutional neural network that aims at identifying AD or normal control, and mild cognitive impairment or normal control. The centerpiece of our method are pseudo-3D block and expanded global context block which are integrated into residual block of backbone in a cascaded manner. To be specific, we transfer pseudo-3D block in the video feature representation to extract structural MRI features. Besides, we extend the 2D global context block to the 3D model which can effectively combine the features and capture the latent associations, while simulate the global context in each dimension of structural MRI results. With the preprocessed structural MRI used as the input of the overall network, our method is capable of constructing a latent representation with multiple residual blocks to promote the classification accuracy. Intrinsically, our method reduces the complexity of conventional 3D convolutional network model applied to AD diagnosis and improves the classification accuracy over the baseline. Furthermore, our network can fully take advantage of the deep 3D convolutional neural network for automatic feature extraction and representation, and thus avoids the limitation of low processing efficiency caused by the preprocessing procedure in which a specific area needs to be annotated in advance. Experimental results on Alzheimer’s Disease Neuroimaging Initiative database indicate that our proposed method reports accuracy of 89.29% on the AD/NC and 87.57% on the mild cognitive impairment/NC, whilst our approach achieves the 0.5% improvement of accuracy compared with the backbone.

ACS Style

Zhao Pei; YuanShuai Gou; Miao Ma; Min Guo; Chengcai Leng; Yuli Chen; Jun Li. Alzheimer’s disease diagnosis based on long-range dependency mechanism using convolutional neural network. Multimedia Tools and Applications 2021, 1 -16.

AMA Style

Zhao Pei, YuanShuai Gou, Miao Ma, Min Guo, Chengcai Leng, Yuli Chen, Jun Li. Alzheimer’s disease diagnosis based on long-range dependency mechanism using convolutional neural network. Multimedia Tools and Applications. 2021; ():1-16.

Chicago/Turabian Style

Zhao Pei; YuanShuai Gou; Miao Ma; Min Guo; Chengcai Leng; Yuli Chen; Jun Li. 2021. "Alzheimer’s disease diagnosis based on long-range dependency mechanism using convolutional neural network." Multimedia Tools and Applications , no. : 1-16.

Journal article
Published: 14 June 2021 in Remote Sensing
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Image registration has always been an important research topic. This paper proposes a novel method of constructing descriptors called the histogram of oriented local binary pattern descriptor (HOLBP) for fast and robust matching. There are three new components in our algorithm. First, we redefined the gradient and angle calculation template to make it more sensitive to edge information. Second, we proposed a new construction method of the HOLBP descriptor and improved the traditional local binary pattern (LBP) computation template. Third, the principle of uniform rotation-invariant LBP was applied to add 10-dimensional gradient direction information to form a 138-dimension HOLBP descriptor vector. The experimental results showed that our method is very stable in terms of accuracy and computational time for different test images.

ACS Style

Yameng Hong; Chengcai Leng; Xinyue Zhang; Zhao Pei; Irene Cheng; Anup Basu. HOLBP: Remote Sensing Image Registration Based on Histogram of Oriented Local Binary Pattern Descriptor. Remote Sensing 2021, 13, 2328 .

AMA Style

Yameng Hong, Chengcai Leng, Xinyue Zhang, Zhao Pei, Irene Cheng, Anup Basu. HOLBP: Remote Sensing Image Registration Based on Histogram of Oriented Local Binary Pattern Descriptor. Remote Sensing. 2021; 13 (12):2328.

Chicago/Turabian Style

Yameng Hong; Chengcai Leng; Xinyue Zhang; Zhao Pei; Irene Cheng; Anup Basu. 2021. "HOLBP: Remote Sensing Image Registration Based on Histogram of Oriented Local Binary Pattern Descriptor." Remote Sensing 13, no. 12: 2328.

Journal article
Published: 22 September 2020 in Pattern Recognition
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Occlusions handling poses a significant challenge to many computer vision and pattern recognition applications. Recently, Synthetic Aperture Imaging (SAI), which uses more than two cameras, is widely applied to reconstruct occluded objects in complex scenes. However, it usually fails in cases of heavy occlusions, in particular, when the occluded information is not captured by any of the camera views. Hence, it is a challenging task to generate a realistic all-in-focus synthetic aperture image which shows a completely occluded object. In this paper, semantic inpainting using a Generative Adversarial Network (GAN) is proposed to address the above-mentioned problem. The proposed method first computes a synthetic aperture image of the occluded objects using a labeling method, and an alpha matte of the partially occluded objects. Then, it uses energy minimization to reconstruct the background by focusing on the background depth of each camera. Finally, the occluded regions of the synthesized image are semantically inpainted using a GAN and the results are composited with the reconstructed background to generate a realistic all-in-focus image. The experimental results demonstrate that the proposed method can handle heavy occlusions and can produce better all-in-focus images than other state-of-the-art methods. Compared with traditional labeling methods, our method can quickly generate label for occlusion without introducing noise. To the best of our knowledge, our method is the first to address missing information caused by heavy occlusions in SAI using a GAN.

ACS Style

Zhao Pei; Min Jin; Yanning Zhang; Miao Ma; Yee-Hong Yang. All-In-Focus Synthetic Aperture Imaging Using Generative Adversarial Network-based Semantic Inpainting. Pattern Recognition 2020, 111, 107669 .

AMA Style

Zhao Pei, Min Jin, Yanning Zhang, Miao Ma, Yee-Hong Yang. All-In-Focus Synthetic Aperture Imaging Using Generative Adversarial Network-based Semantic Inpainting. Pattern Recognition. 2020; 111 ():107669.

Chicago/Turabian Style

Zhao Pei; Min Jin; Yanning Zhang; Miao Ma; Yee-Hong Yang. 2020. "All-In-Focus Synthetic Aperture Imaging Using Generative Adversarial Network-based Semantic Inpainting." Pattern Recognition 111, no. : 107669.

Journal article
Published: 02 June 2020 in Electronics
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In recent years, disparity estimation of a scene based on deep learning methods has been extensively studied and significant progress has been made. In contrast, a traditional image disparity estimation method requires considerable resources and consumes much time in processes such as stereo matching and 3D reconstruction. At present, most deep learning based disparity estimation methods focus on estimating disparity based on monocular images. Motivated by the results of traditional methods that multi-view methods are more accurate than monocular methods, especially for scenes that are textureless and have thin structures, in this paper, we present MDEAN, a new deep convolutional neural network to estimate disparity using multi-view images with an asymmetric encoder–decoder network structure. First, our method takes an arbitrary number of multi-view images as input. Next, we use these images to produce a set of plane-sweep cost volumes, which are combined to compute a high quality disparity map using an end-to-end asymmetric network. The results show that our method performs better than state-of-the-art methods, in particular, for outdoor scenes with the sky, flat surfaces and buildings.

ACS Style

Zhao Pei; Deqiang Wen; Yanning Zhang; Miao Ma; Min Guo; Xiuwei Zhang; Yee-Hong Yang. MDEAN: Multi-View Disparity Estimation with an Asymmetric Network. Electronics 2020, 9, 924 .

AMA Style

Zhao Pei, Deqiang Wen, Yanning Zhang, Miao Ma, Min Guo, Xiuwei Zhang, Yee-Hong Yang. MDEAN: Multi-View Disparity Estimation with an Asymmetric Network. Electronics. 2020; 9 (6):924.

Chicago/Turabian Style

Zhao Pei; Deqiang Wen; Yanning Zhang; Miao Ma; Min Guo; Xiuwei Zhang; Yee-Hong Yang. 2020. "MDEAN: Multi-View Disparity Estimation with an Asymmetric Network." Electronics 9, no. 6: 924.

Journal article
Published: 25 September 2019 in Electronics
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Class attendance is an important means in the management of university students. Using face recognition is one of the most effective techniques for taking daily class attendance. Recently, many face recognition algorithms via deep learning have achieved promising results with large-scale labeled samples. However, due to the difficulties of collecting samples, face recognition using convolutional neural networks (CNNs) for daily attendance taking remains a challenging problem. Data augmentation can enlarge the samples and has been applied to the small sample learning. In this paper, we address this problem using data augmentation through geometric transformation, image brightness changes, and the application of different filter operations. In addition, we determine the best data augmentation method based on orthogonal experiments. Finally, the performance of our attendance method is demonstrated in a real class. Compared with PCA and LBPH methods with data augmentation and VGG-16 network, the accuracy of our proposed method can achieve 86.3%. Additionally, after a period of collecting more data, the accuracy improves to 98.1%.

ACS Style

Zhao Pei; Hang Xu; Yanning Zhang; Min Guo; Yee-Hong Yang. Face Recognition via Deep Learning Using Data Augmentation Based on Orthogonal Experiments. Electronics 2019, 8, 1088 .

AMA Style

Zhao Pei, Hang Xu, Yanning Zhang, Min Guo, Yee-Hong Yang. Face Recognition via Deep Learning Using Data Augmentation Based on Orthogonal Experiments. Electronics. 2019; 8 (10):1088.

Chicago/Turabian Style

Zhao Pei; Hang Xu; Yanning Zhang; Min Guo; Yee-Hong Yang. 2019. "Face Recognition via Deep Learning Using Data Augmentation Based on Orthogonal Experiments." Electronics 8, no. 10: 1088.

Journal article
Published: 31 January 2019 in Sensors
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With the three-dimensional (3D) coordinates of objects captured by a sequence of images taken in different views, object reconstruction is a technique which aims to recover the shape and appearance information of objects. Although great progress in object reconstruction has been made over the past few years, object reconstruction in occlusion situations remains a challenging problem. In this paper, we propose a novel method to reconstruct occluded objects based on synthetic aperture imaging. Unlike most existing methods, which either assume that there is no occlusion in the scene or remove the occlusion from the reconstructed result, our method uses the characteristics of synthetic aperture imaging that can effectively reduce the influence of occlusion to reconstruct the scene with occlusion. The proposed method labels occlusion pixels according to variance and reconstructs the 3D point cloud based on synthetic aperture imaging. Accuracies of the point cloud are tested by calculating the spatial difference between occlusion and non-occlusion conditions. The experiment results show that the proposed method can handle the occluded situation well and demonstrates a promising performance.

ACS Style

Zhao Pei; Yawen Li; Miao Ma; Jun Li; Chengcai Leng; Xiaoqiang Zhang; Yanning Zhang. Occluded-Object 3D Reconstruction Using Camera Array Synthetic Aperture Imaging. Sensors 2019, 19, 607 .

AMA Style

Zhao Pei, Yawen Li, Miao Ma, Jun Li, Chengcai Leng, Xiaoqiang Zhang, Yanning Zhang. Occluded-Object 3D Reconstruction Using Camera Array Synthetic Aperture Imaging. Sensors. 2019; 19 (3):607.

Chicago/Turabian Style

Zhao Pei; Yawen Li; Miao Ma; Jun Li; Chengcai Leng; Xiaoqiang Zhang; Yanning Zhang. 2019. "Occluded-Object 3D Reconstruction Using Camera Array Synthetic Aperture Imaging." Sensors 19, no. 3: 607.

Conference paper
Published: 08 December 2018 in Transactions on Petri Nets and Other Models of Concurrency XV
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Learning students social affinity and modeling their social networks are beneficial for instructors to design proper pedagogical strategies. Students seating distribution contains social data and can be used for analysing their social relationships. In this paper, we propose a method to automatically construct the class social network and predict the position of a student’s seat in class. First, we determine the positions of each student in a classroom by utilizing the center projection principle and linear fitting algorithms. The intimate relationship between students is captured to model their social network based on Euclidean distance. Then, we learn the social affinities from the Social Affinity Map (SAM) which clusters the relative positions of surrounding students. Based on this, students’ seating distribution can be predicted successfully with accuracy reaching 82.1%.

ACS Style

Zhao Pei; Miaomiao Pan; Kang Liao; Miao Ma; Chengcai Leng. Predicting Student Seating Distribution Based on Social Affinity. Transactions on Petri Nets and Other Models of Concurrency XV 2018, 29 -38.

AMA Style

Zhao Pei, Miaomiao Pan, Kang Liao, Miao Ma, Chengcai Leng. Predicting Student Seating Distribution Based on Social Affinity. Transactions on Petri Nets and Other Models of Concurrency XV. 2018; ():29-38.

Chicago/Turabian Style

Zhao Pei; Miaomiao Pan; Kang Liao; Miao Ma; Chengcai Leng. 2018. "Predicting Student Seating Distribution Based on Social Affinity." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 29-38.

Journal article
Published: 01 November 2016 in Journal of Computational and Theoretical Nanoscience
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A Granular Computing (GrC) and Grey Relational Analysis (GRA) based method for image quality assessment is proposed in this paper. In this method, the original image is converted to some wavelet granules via Discrete Wavelet Packet Transform (DWPT). The conversion and combination of these granules are further discussed within the framework of quotient space theory. In addition, GRA of Grey theory is used to evaluate the image quality at different granularity levels, and an overall quality order is finally obtained by indexing the images on their grey granular quality. With the experimental results from LIVE image database, it is found that the proposed method performances best in terms of stability, suitability, and consistency with Human Visual System (HVS) and theoretical analysis.

ACS Style

Miao Ma; Zhao Pei; Li Sun. An Image Quality Assessment Method Based on Granular Computing and Grey Relational Analysis. Journal of Computational and Theoretical Nanoscience 2016, 13, 8306 -8313.

AMA Style

Miao Ma, Zhao Pei, Li Sun. An Image Quality Assessment Method Based on Granular Computing and Grey Relational Analysis. Journal of Computational and Theoretical Nanoscience. 2016; 13 (11):8306-8313.

Chicago/Turabian Style

Miao Ma; Zhao Pei; Li Sun. 2016. "An Image Quality Assessment Method Based on Granular Computing and Grey Relational Analysis." Journal of Computational and Theoretical Nanoscience 13, no. 11: 8306-8313.