This page has only limited features, please log in for full access.

Prof. Min Guo
the school of computer science, Shaanxi Normal University

Basic Info

Basic Info is private.

Research Keywords & Expertise

0 Computer Vision
0 Signal Processing
0 Deep learning
0 Pattern recognition and artificial intelligence
0 insect acoustics

Fingerprints

Deep learning
Signal Processing

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

Min Guo received both M.S. and Ph.D. degree in the electronic technology application in the college of physics and information technology from Shaanxi Normal University, Xi’an, China in 1990 and 2003. She worked at the postdoctoral research station from Northwestern Polytechnical University in 2007, Xi’an, China. Since then, she has been a professor with the school of computer science, Shaanxi Normal University, Xi’an, China. Her research interests include pattern recognition, signal and image processing, computer vision.

Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 31 July 2021 in Multimedia Tools and Applications
Reads 0
Downloads 0

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: 15 June 2021 in Biosystems Engineering
Reads 0
Downloads 0

Quantity and quality of grain are both closely related to national development and social stability. Grain is lost during storage due to mildew and insects. Detection of damaged grain kernels not only can reduce the loss of grain, but also protect human beings from diseases caused by damaged grain. Therefore, research on the automatic detection of damaged grain is of continued urgency. In this paper, we propose a framework combining spectrogram generative adversarial network and progressive neural architecture search (SPGAN-PNAS) to detect and classify mildew-damaged wheat kernels (MDK), insect-damaged wheat kernels (IDK) and undamaged wheat kernels (UDK). First, the spectrogram generative adversarial network (SPGAN) is designed to enlarge the data set. Second, we apply progressive neural architecture search (PNAS) to generate network structure to classify three types of wheat kernels. An F1 of 96.2% is obtained using the proposed method with 5-fold cross-validation. The results are superior to the classical neural networks for detection and classification of damaged wheat kernels. Experimental results show that the structure of SPGAN-PNAS is feasible and effective.

ACS Style

Xiaojing Yang; Min Guo; Qiongshuai Lyu; Miao Ma. Detection and classification of damaged wheat kernels based on progressive neural architecture search. Biosystems Engineering 2021, 208, 176 -185.

AMA Style

Xiaojing Yang, Min Guo, Qiongshuai Lyu, Miao Ma. Detection and classification of damaged wheat kernels based on progressive neural architecture search. Biosystems Engineering. 2021; 208 ():176-185.

Chicago/Turabian Style

Xiaojing Yang; Min Guo; Qiongshuai Lyu; Miao Ma. 2021. "Detection and classification of damaged wheat kernels based on progressive neural architecture search." Biosystems Engineering 208, no. : 176-185.

Journal article
Published: 19 March 2021 in Insects
Reads 0
Downloads 0

Acoustic technology provides information difficult to obtain about stored insect behavior, physiology, abundance, and distribution. For example, acoustic detection of immature insects feeding hidden within grain is helpful for accurate monitoring because they can be more abundant than adults and be present in samples without adults. Modern engineering and acoustics have been incorporated into decision support systems for stored product insect management, but with somewhat limited use due to device costs and the skills needed to interpret the data collected. However, inexpensive modern tools may facilitate further incorporation of acoustic technology into the mainstream of pest management and precision agriculture. One such system was tested herein to describe Sitophilus oryzae (Coleoptera: Curculionidae) adult and larval movement and feeding in stored grain. Development of improved methods to identify sounds of targeted pest insects, distinguishing them from each other and from background noise, is an active area of current research. The most powerful of the new methods may be machine learning. The methods have different strengths and weaknesses depending on the types of background noise and the signal characteristic of target insect sounds. It is likely that they will facilitate automation of detection and decrease costs of managing stored product insects in the future.

ACS Style

Richard Mankin; David Hagstrum; Min Guo; Panagiotis Eliopoulos; Anastasia Njoroge. Automated Applications of Acoustics for Stored Product Insect Detection, Monitoring, and Management. Insects 2021, 12, 259 .

AMA Style

Richard Mankin, David Hagstrum, Min Guo, Panagiotis Eliopoulos, Anastasia Njoroge. Automated Applications of Acoustics for Stored Product Insect Detection, Monitoring, and Management. Insects. 2021; 12 (3):259.

Chicago/Turabian Style

Richard Mankin; David Hagstrum; Min Guo; Panagiotis Eliopoulos; Anastasia Njoroge. 2021. "Automated Applications of Acoustics for Stored Product Insect Detection, Monitoring, and Management." Insects 12, no. 3: 259.

Original article
Published: 13 August 2020 in Neural Computing and Applications
Reads 0
Downloads 0
ACS Style

Qiongshuai Lyu; Min Guo; Miao Ma. Boosting attention fusion generative adversarial network for image denoising. Neural Computing and Applications 2020, 33, 4833 -4847.

AMA Style

Qiongshuai Lyu, Min Guo, Miao Ma. Boosting attention fusion generative adversarial network for image denoising. Neural Computing and Applications. 2020; 33 (10):4833-4847.

Chicago/Turabian Style

Qiongshuai Lyu; Min Guo; Miao Ma. 2020. "Boosting attention fusion generative adversarial network for image denoising." Neural Computing and Applications 33, no. 10: 4833-4847.

Journal article
Published: 23 June 2020 in Applied Soft Computing
Reads 0
Downloads 0

Restoration of images corrupted by mixed noise (e.g., additive white Gaussian noise and impulse noise) is very difficult due to the complexity of the mixed noise distribution. Various mixed noise removal models involve the preprocessing based on outlier detection. However, the performance of these models largely depends on the accuracy of pixel location detection of outliers, and artifacts and missing image details are prone to occur when the mixture noise is strong. In this paper, a new denoising model based on generative adversarial network (DeGAN) is proposed to remove mixed noise in images. The proposed model combines generator, discriminator, and feature extractor networks. Through the mutual game between the generator and discriminator networks combined with additional training from the feature extractor network, the generator network implements a direct mapping from the noisy image domain to the noise-free image domain. In addition, we design a new joint loss function to incorporate information from image features and human visual perception into the mixed noise elimination task, which further improves the image quality and the visual effect. Abundant experiments show that the performance of our model is better than the state-of-the-art mixed noise removal methods in three different types of mixed noise scenarios, and the joint loss function does improve the denoising performance.

ACS Style

Qiongshuai Lyu; Min Guo; Zhao Pei. DeGAN: Mixed noise removal via generative adversarial networks. Applied Soft Computing 2020, 95, 106478 .

AMA Style

Qiongshuai Lyu, Min Guo, Zhao Pei. DeGAN: Mixed noise removal via generative adversarial networks. Applied Soft Computing. 2020; 95 ():106478.

Chicago/Turabian Style

Qiongshuai Lyu; Min Guo; Zhao Pei. 2020. "DeGAN: Mixed noise removal via generative adversarial networks." Applied Soft Computing 95, no. : 106478.

Journal article
Published: 02 June 2020 in Electronics
Reads 0
Downloads 0

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
Reads 0
Downloads 0

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.

Article
Published: 24 June 2019 in Multimedia Tools and Applications
Reads 0
Downloads 0

The learner’s body posture reflects the learner’s learning state. The learner’s posture recognition can effectively evaluate their learning state, which plays an important role in the teacher’s teaching process. In this paper, a new method for learner’s gesture recognition is proposed, which fuses the improved scale invariant local ternary pattern (SILTP) and the local directional pattern (LDP). Firstly, a multi-scale weighted adaptive SILTP (MWA-SILTP) algorithm is proposed. The dynamic threshold of the current neighborhood is adaptively generated according to the dispersion degree of contrast values in global and local neighborhoods, and SILTP coding is carried out to obtain adaptive SILTP. And the concept of multi-scale is introduced. By changing the sampling radius, the adaptive SILTPs of different scales are obtained. The adaptive SILTPs of different scales are merged with different weights to represent the image in multi-resolution. The MWA-SILTP algorithm is used to extract the feature of the learner’s posture image. Secondly, the LDP algorithm is used to extract the feature of the learner’s posture image. Finally, the two features are merged, and the support vector machine is used for classification and recognition. The improved SILTP can get more feature information and has stronger adaptability. The LDP algorithm has advantages in anti-interference and can extract edge information better. The fusing model of this paper fully utilizes the advantages of the two algorithms. Experimental results show that the proposed method can effectively recognize learner’s posture of sitting, raising hand and lowering head.

ACS Style

Yuqian Kuang; Min Guo; Yali Peng; Zhao Pei. Learner posture recognition via a fusing model based on improved SILTP and LDP. Multimedia Tools and Applications 2019, 78, 30443 -30456.

AMA Style

Yuqian Kuang, Min Guo, Yali Peng, Zhao Pei. Learner posture recognition via a fusing model based on improved SILTP and LDP. Multimedia Tools and Applications. 2019; 78 (21):30443-30456.

Chicago/Turabian Style

Yuqian Kuang; Min Guo; Yali Peng; Zhao Pei. 2019. "Learner posture recognition via a fusing model based on improved SILTP and LDP." Multimedia Tools and Applications 78, no. 21: 30443-30456.

Journal article
Published: 14 June 2019 in Biosystems Engineering
Reads 0
Downloads 0

Wheat kernel damage is a major source of food quality degradation, and long-term feeding on products from damaged wheat kernels will result in malnutrition or even induce diseases. Therefore, detection of damaged wheat kernels is of significant interest. An impact acoustic signal processing technique based on Gaussian modelling and an improved extreme learning machine approach was proposed for detection of insect and sprout-damaged wheat kernels. Discriminant features extracted from Gaussian-model-estimated parameters were fed to an extreme learning machine based on a C-matrix embedded optimisation approximation solution. The best results, 92.0% of undamaged, 96.0% of insect-damaged, and 95.0% of sprout-damaged wheat kernels were correctly classified by using the proposed method. Furthermore, the detection system had good processing speed. Therefore, it could be effective to detect damaged wheat kernels in real time.

ACS Style

Min Guo; Yuting Ma; Xiaojing Yang; Richard Mankin. Detection of damaged wheat kernels using an impact acoustic signal processing technique based on Gaussian modelling and an improved extreme learning machine algorithm. Biosystems Engineering 2019, 184, 37 -44.

AMA Style

Min Guo, Yuting Ma, Xiaojing Yang, Richard Mankin. Detection of damaged wheat kernels using an impact acoustic signal processing technique based on Gaussian modelling and an improved extreme learning machine algorithm. Biosystems Engineering. 2019; 184 ():37-44.

Chicago/Turabian Style

Min Guo; Yuting Ma; Xiaojing Yang; Richard Mankin. 2019. "Detection of damaged wheat kernels using an impact acoustic signal processing technique based on Gaussian modelling and an improved extreme learning machine algorithm." Biosystems Engineering 184, no. : 37-44.

Journal article
Published: 01 July 2018 in Computers and Electronics in Agriculture
Reads 0
Downloads 0

An impact acoustic signal device was tested with undamaged, insect-damaged, and mildew-damaged corn kernels, and the different signals were compared using ensemble empirical mode decomposition methods. These methods were adopted based on their known superiority in processing of non-stationary signals and in suppressing of mode mixing. Time domain, frequency domain, and Hilbert domain features were extracted from an ensemble empirical mode decomposition of the impact acoustic signals. Four features were extracted from the time domain: the average amplitude change, Wilson amplitude, average absolute value, and peak-to-peak value. Three features were extracted from the frequency domain: the mean square frequency, the root mean square of the power spectrum, and the frequency band variance. The energy of the high-frequency and low-frequency bands and the average values of the envelopes were extracted from the Hilbert domain. Subsequently, these features were used as inputs to a support vector machine which was optimized by particle swarm optimization. The use of hybrid features enabled higher classification accuracy than usage of features in each domain separately. In this study, achieving the classification accuracies were 99.2% for undamaged kernels, 99.6% for insect-damaged kernels and 99.3% for mildew-damaged kernels. These results, based on ensemble empirical mode decomposition and integration of multi-domain features, are encouraging for the potential of an automated inspection system.

ACS Style

Xuehua Sun; Min Guo; Miao Ma; Richard Mankin. Identification and classification of damaged corn kernels with impact acoustics multi-domain patterns. Computers and Electronics in Agriculture 2018, 150, 152 -161.

AMA Style

Xuehua Sun, Min Guo, Miao Ma, Richard Mankin. Identification and classification of damaged corn kernels with impact acoustics multi-domain patterns. Computers and Electronics in Agriculture. 2018; 150 ():152-161.

Chicago/Turabian Style

Xuehua Sun; Min Guo; Miao Ma; Richard Mankin. 2018. "Identification and classification of damaged corn kernels with impact acoustics multi-domain patterns." Computers and Electronics in Agriculture 150, no. : 152-161.

Journal article
Published: 27 January 2016 in Multimedia Tools and Applications
Reads 0
Downloads 0

According to the deficiencies of Local Binary Pattern (LBP), the dimension of extraction is large, and it is not conducive to describe all characteristics of image texture, this paper proposes a novel facial expression recognition algorithm “K-ELBP” which uses uniform patterns of Extended Local Binary Pattern (ELBP), and combines with the covariance matrix transform in K-L transform (KLT). In this paper, ELBP is used for the first step to extract the feature matrix of expression images, then covariance matrix transform is applied to the ELBP matrix for reducing the dimension, which aims at extracting the main feature vectors. And the best recognition performance is obtained by using SVM for classification. A series of experiments by using different divided methods are designed to evaluate the effects of characteristics which are extracted by K-ELBP algorithm. According to the results of the experiments, the proposed K-ELBP algorithm can extract facial expression features effectively, and the rates of recognition are satisfying.

ACS Style

Min Guo; Xiaohong Hou; Yuting Ma; Xiaojun Wu. Facial expression recognition using ELBP based on covariance matrix transform in KLT. Multimedia Tools and Applications 2016, 76, 2995 -3010.

AMA Style

Min Guo, Xiaohong Hou, Yuting Ma, Xiaojun Wu. Facial expression recognition using ELBP based on covariance matrix transform in KLT. Multimedia Tools and Applications. 2016; 76 (2):2995-3010.

Chicago/Turabian Style

Min Guo; Xiaohong Hou; Yuting Ma; Xiaojun Wu. 2016. "Facial expression recognition using ELBP based on covariance matrix transform in KLT." Multimedia Tools and Applications 76, no. 2: 2995-3010.

Journal article
Published: 20 December 2010 in Journal of Computer Applications
Reads 0
Downloads 0
ACS Style

Yan-Peng Zhai; Min Guo; Miao Ma; Jiao He. Color image segmentation of normalized cut and particle swarm optimization algorithm. Journal of Computer Applications 2010, 30, 3258 -3261.

AMA Style

Yan-Peng Zhai, Min Guo, Miao Ma, Jiao He. Color image segmentation of normalized cut and particle swarm optimization algorithm. Journal of Computer Applications. 2010; 30 (12):3258-3261.

Chicago/Turabian Style

Yan-Peng Zhai; Min Guo; Miao Ma; Jiao He. 2010. "Color image segmentation of normalized cut and particle swarm optimization algorithm." Journal of Computer Applications 30, no. 12: 3258-3261.

Journal article
Published: 22 October 2009 in Journal of Computer Applications
Reads 0
Downloads 0
ACS Style

Jian-Qing Wang; Min Guo; Qiu-Ping Xu. Object extraction algorithm combining boundary information and region information. Journal of Computer Applications 2009, 29, 2411 -2413.

AMA Style

Jian-Qing Wang, Min Guo, Qiu-Ping Xu. Object extraction algorithm combining boundary information and region information. Journal of Computer Applications. 2009; 29 (9):2411-2413.

Chicago/Turabian Style

Jian-Qing Wang; Min Guo; Qiu-Ping Xu. 2009. "Object extraction algorithm combining boundary information and region information." Journal of Computer Applications 29, no. 9: 2411-2413.

Journal article
Published: 07 September 2009 in Journal of Computer Applications
Reads 0
Downloads 0
ACS Style

Li-Li Tian; Min Guo; Qiu-Ping Xu. Fast concave object extraction algorithm based on graph cuts and GVF Snake. Journal of Computer Applications 2009, 28, 2633 -2635.

AMA Style

Li-Li Tian, Min Guo, Qiu-Ping Xu. Fast concave object extraction algorithm based on graph cuts and GVF Snake. Journal of Computer Applications. 2009; 28 (10):2633-2635.

Chicago/Turabian Style

Li-Li Tian; Min Guo; Qiu-Ping Xu. 2009. "Fast concave object extraction algorithm based on graph cuts and GVF Snake." Journal of Computer Applications 28, no. 10: 2633-2635.

Journal article
Published: 20 December 2008 in Journal of Computer Applications
Reads 0
Downloads 0
ACS Style

Qiu-Ping Xu; Min Guo; Ya-Rong Wang. Real-time correcting algorithm of extracted contour based on graph cuts. Journal of Computer Applications 2008, 28, 3116 -3119.

AMA Style

Qiu-Ping Xu, Min Guo, Ya-Rong Wang. Real-time correcting algorithm of extracted contour based on graph cuts. Journal of Computer Applications. 2008; 28 (12):3116-3119.

Chicago/Turabian Style

Qiu-Ping Xu; Min Guo; Ya-Rong Wang. 2008. "Real-time correcting algorithm of extracted contour based on graph cuts." Journal of Computer Applications 28, no. 12: 3116-3119.