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Kernel quaternion principal component analysis and its application in RGB-D object recognition
Chen, Beijing,Yang, Jianhao,Jeon, Byeungwoo,Zhang, Xinpeng Elsevier 2017 Neurocomputing Vol.266 No.-
<P><B>Abstract</B></P> <P>While the existing quaternion principal component analysis (QPCA) is a linear tool developed mainly for processing linear quaternion signals, the quaternion representation (QR) used in QPCA creates redundancy when representing a color image signal of three components by a quaternion matrix having four components. In this paper, the kernel technique is used to improve the QPCA as kernel QPCA (KQPCA) for processing nonlinear quaternion signals; in addition, both RGB information and depth information are considered to improve QR for representing RGB-D images. The improved QR fully utilizes the four-dimensional quaternion domain. We first provide the basic idea of three types of our KQPCA and then propose an algorithm for RGB-D object recognition based on bidirectional two-dimensional KQPCA (BD2DKQPCA) and the improved QR. Experimental results on four public datasets demonstrate that the proposed BD2DKQPCA-based algorithm achieves the best performance among seventeen compared algorithms including other existing PCA-based algorithms, irrespective of RGB object recognition or RGB-D object recognition. Moreover, for all compared algorithms, consideration of both RGB and depth information is shown to achieve better performance in object recognition than considering only RGB information.</P>
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High-Capacity Robust Image Steganography via Adversarial Network
( Beijing Chen ),( Jiaxin Wang ),( Yingyue Chen ),( Zilong Jin ),( Hiuk Jae Shim ),( Yun-qing Shi ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.1
Steganography has been successfully employed in various applications, e.g., copyright control of materials, smart identity cards, video error correction during transmission, etc. Deep learning-based steganography models can hide information adaptively through network learning, and they draw much more attention. However, the capacity, security, and robustness of the existing deep learning-based steganography models are still not fully satisfactory. In this paper, three models for different cases, i.e., a basic model, a secure model, a secure and robust model, have been proposed for different cases. In the basic model, the functions of high-capacity secret information hiding and extraction have been realized through an encoding network and a decoding network respectively. The high-capacity steganography is implemented by hiding a secret image into a carrier image having the same resolution with the help of concat operations, InceptionBlock and convolutional layers. Moreover, the secret image is hidden into the channel B of carrier image only to resolve the problem of color distortion. In the secure model, to enhance the security of the basic model, a steganalysis network has been added into the basic model to form an adversarial network. In the secure and robust model, an attack network has been inserted into the secure model to improve its robustness further. The experimental results have demonstrated that the proposed secure model and the secure and robust model have an overall better performance than some existing high-capacity deep learning-based steganography models. The secure model performs best in invisibility and security. The secure and robust model is the most robust against some attacks.
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Li Yikai,Xue Zefeng,Shi Zhongjie,Chen Haiyan,Beijing Institute of Technology 한국자동차공학회 2023 International journal of automotive technology Vol.24 No.5
Poor atomization and ignition difficulty due to the deterioration of environmental conditions restrict the coldstart performance of diesel engine. To investigate how the ambient and injection parameters affect diesel ignition characteristics at low temperature and density, liquid spray development was measured by back-illumination method; vapor spray and ignition process were visualized using high-speed shadowgraph method in constant volume combustion chamber. The results showed that liquid-vapor penetration and two-stage ignition delay have different sensitivities to variables: the variation of ambient density greatly affects the spray development while ambient temperature is the most significant parameter affecting ignition delay. Additionally, the change of injection pressure cannot cause significant change of both liquid penetration length and low temperature ignition, but increasing injection pressure promoted the vapor penetration length downstream development. Based on the data obtained, the empirical formulation in the form of power function was fitted for the stable stage of liquid penetration length, which proposed a reference for comparing the liquid phase development characteristics of diesel spray. Similarly, revise the Arrhenius-type ignition delay prediction formula and the correction coefficient K ( and ) was optimized quantitatively instead of fixed values, provides a preliminary theoretical basis for subsequent diesel spray model.
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No-reference Image Quality Assessment With A Gradient-induced Dictionary
( Leida Li ),( Dong Wu ),( Jinjian Wu ),( Jiansheng Qian ),( Beijing Chen ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.1
Image distortions are typically characterized by degradations of structures. Dictionaries learned from natural images can capture the underlying structures in images, which are important for image quality assessment (IQA). This paper presents a general-purpose no-reference image quality metric using a GRadient-Induced Dictionary (GRID). A dictionary is first constructed based on gradients of natural images using K-means clustering. Then image features are extracted using the dictionary based on Euclidean-norm coding and max-pooling. A distortion classification model and several distortion-specific quality regression models are trained using the support vector machine (SVM) by combining image features with distortion types and subjective scores, respectively. To evaluate the quality of a test image, the distortion classification model is used to determine the probabilities that the image belongs to different kinds of distortions, while the regression models are used to predict the corresponding distortion-specific quality scores. Finally, an overall quality score is computed as the probability-weighted distortion-specific quality scores. The proposed metric can evaluate image quality accurately and efficiently using a small dictionary. The performance of the proposed method is verified on public image quality databases. Experimental results demonstrate that the proposed metric can generate quality scores highly consistent with human perception, and it outperforms the state-of-the-arts.
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