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RISS 인기검색어
- 검색키워드
- 저자 : ( Ningzhong Liu ) (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
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Bivariate zero truncated Poisson INAR(1) process
Yan Liu,Dehui Wang,Haixiang Zhang,Ningzhong Shi 한국통계학회 2016 Journal of the Korean Statistical Society Vol.45 No.2
In this paper, we propose a new stationary bivariate first order integer-valued autoregressive (BINAR(1)) process with zero truncated Poisson marginal distribution. Some properties about this process are considered, such as probability generating function, autocorrelations, expectations and covariance matrix under conditional and unconditional situation. We also establish the strict stationarity and ergodicity of the process. Estimators of unknown parameters are derived by using Yule–Walker, conditional least squares and maximum likelihood methods. The performance of the proposed estimation procedures are evaluated through Monte Carlo simulations. An application to a real data example is also provided.
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Vehicle Detection in Aerial Images Based on Hyper Feature Map in Deep Convolutional Network
( Jiaquan Shen ),( Ningzhong Liu ),( Han Sun ),( Xiaoli Tao ),( Qiangyi Li ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.4
Vehicle detection based on aerial images is an interesting and challenging research topic. Most of the traditional vehicle detection methods are based on the sliding window search algorithm, but these methods are not sufficient for the extraction of object features, and accompanied with heavy computational costs. Recent studies have shown that convolutional neural network algorithm has made a significant progress in computer vision, especially Faster R-CNN. However, this algorithm mainly detects objects in natural scenes, it is not suitable for detecting small object in aerial view. In this paper, an accurate and effective vehicle detection algorithm based on Faster R-CNN is proposed. Our method fuse a hyperactive feature map network with Eltwise model and Concat model, which is more conducive to the extraction of small object features. Moreover, setting suitable anchor boxes based on the size of the object is used in our model, which also effectively improves the performance of the detection. We evaluate the detection performance of our method on the Munich dataset and our collected dataset, with improvements in accuracy and effectivity compared with other methods. Our model achieves 82.2% in recall rate and 90.2% accuracy rate on Munich dataset, which has increased by 2.5 and 1.3 percentage points respectively over the state-of-the-art methods.
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Sparse Representation based Two-dimensional Bar Code Image Super-resolution
( Yiling Shen ),( Ningzhong Liu ),( Han Sun ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.4
This paper presents a super-resolution reconstruction method based on sparse representation for two-dimensional bar code images. Considering the features of two-dimensional bar code images, Kirsch and LBP (local binary pattern) operators are used to extract the edge gradient and texture features. Feature extraction is constituted based on these two features and additional two second-order derivatives. By joint dictionary learning of the low-resolution and high-resolution image patch pairs, the sparse representation of corresponding patches is the same. In addition, the global constraint is exerted on the initial estimation of high-resolution image which makes the reconstructed result closer to the real one. The experimental results demonstrate the effectiveness of the proposed algorithm for two-dimensional bar code images by comparing with other reconstruction algorithms.
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Deeper SSD: Simultaneous Up-sampling and Down-sampling for Drone Detection
( Han Sun ),( Wen Geng ),( Jiaquan Shen ),( Ningzhong Liu ),( Dong Liang ),( Huiyu Zhou ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.12
Drone detection can be considered as a specific sort of small object detection, which has always been a challenge because of its small size and few features. For improving the detection rate of drones, we design a Deeper SSD network, which uses large-scale input image and deeper convolutional network to obtain more features that benefit small object classification. At the same time, in order to improve object classification performance, we implemented the up-sampling modules to increase the number of features for the low-level feature map. In addition, in order to improve object location performance, we adopted the down-sampling modules so that the context information can be used by the high-level feature map directly. Our proposed Deeper SSD and its variants are successfully applied to the self-designed drone datasets. Our experiments demonstrate the effectiveness of the Deeper SSD and its variants, which are useful to small drone’s detection and recognition. These proposed methods can also detect small and large objects simultaneously.
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