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1. Monocular Mobile Cube SLAM Based on Fusion of IMU Initialization | |||
CUI Xiaoyang,WANG Jing | |||
Computer Science and Technology 28 December 2021 | |||
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Abstract:With the continuous development of human-computer interaction, the challenging lip reading task has become a research hotspot. As a part of the whole lip reading model, feature extraction module determines the upper limit of the overall ability of the lip reading model from the source. The current lip reading feature extraction model is composed of 2D and 3D convolution layers, which can not meet the strong correlation of local features. In order to solve this problem, this paper first analyzes the shortcomings of existing models based on anatomical principles and basic models; then proposes global features to enhance local features through the interaction of global features and local features; finally, based on the above design ideas, designs a lip reading feature extraction model with local feature enhancement. Through the experimental verification on the data set, the lip reading feature extraction model constructed in this paper has stronger representation ability. | |||
TO cite this article:CUI Xiaoyang,WANG Jing. Monocular Mobile Cube SLAM Based on Fusion of IMU Initialization[OL].[28 December 2021] http://en.paper.edu.cn/en_releasepaper/content/4755986 |
2. A novel key frame selection method for aerial image stitching by integrating navigation information and trusted key points | |||
Zheng Yongji,Wang Guoyou | |||
Computer Science and Technology 01 April 2021 | |||
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Abstract:Fast and high-precision video based image stitching plays an important role in many machine vision applications, such as UAV mapping and reconnaissance. Due to the large number of frames and high redundancy of video sequence, image stitching is very time-consuming. Therefore, from the perspective of reducing the number of redundant frames, this paper proposes a novel video sequence key frame selection method based on rough camera external parameters and key point fusion, which selects the appropriate key frames by optimizing the overlap rate and the number of reliable key points between two adjacent key frames. The algorithm not only greatly reduces the number of key frames, but also ensures the reliable video mosaic. The experimental results on Bu S\' videos show that our method can reduce the number of key frames by 92%. In addition, compared with the key frame selection method based only on navigation information, this method also overcomes the problem of missing stitched images caused by insufficient key points in overlapping regions. | |||
TO cite this article:Zheng Yongji,Wang Guoyou. A novel key frame selection method for aerial image stitching by integrating navigation information and trusted key points[OL].[ 1 April 2021] http://en.paper.edu.cn/en_releasepaper/content/4754321 |
3. Head Pose Estimation Based on Dlib and Savitzky-Golay Smoothing Algorithm | |||
LU Xiaoning,LIU Wen | |||
Computer Science and Technology 18 January 2021 | |||
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Abstract:In this paper, We apply the Savitsky-Golay filter algorithm to head pose estimation. Firstly, the Dlib algorithm is to detect key points of the corresponding face in the video. Then, the function solvepnp built in opencv is to estimate the pose. Finally, through MATLAB simulation, we can find that the Savitsky-Golay filter algorithm can filter the noisiness in the observed data to obtain a smoother and more accurate change trajectory of head pose. | |||
TO cite this article:LU Xiaoning,LIU Wen. Head Pose Estimation Based on Dlib and Savitzky-Golay Smoothing Algorithm[OL].[18 January 2021] http://en.paper.edu.cn/en_releasepaper/content/4753447 |
4. A generality enhanced forensic towards GAN facial images | |||
Xiong Xiao-Fang,Yang Gao-Bo | |||
Computer Science and Technology 13 May 2020 | |||
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Abstract:\justifying Recent advances in GAN technique have made it much easier than ever to generate believable face images, which brings some potential security issues to the public. Currently a variety of architectures have been proposed to detect these generated fake images, but few works address the problem of generalization ability of forensics models, which means most of them need prior knowledge of the structure of GAN model when detecting a specific GAN fake face images, and unable to detect unseen fake images generated by other GAN models. In this work, we tackle the generality enhanced problem by adding a fixed weight convolutional layer before CNN structure, which contain three designed high-pass filters. Firstly, we convert RGB images to YCbCr color space to get more obvious edge information. Then, EfficientNet is adopted as our basic CNN structure to extract inhenrent features and classify them. Finally, a fixed weight layer is added to the first convolutional layer, which could greatly suppress the semantic image content and magnify microscopic characteristics of images, thus help to enhance the generalization ability of the model. A series of experiments demonstrate that our approach achieve superior performance compare with the state-of-the-art work in terms of the generality of forensics model. | |||
TO cite this article:Xiong Xiao-Fang,Yang Gao-Bo. A generality enhanced forensic towards GAN facial images[OL].[13 May 2020] http://en.paper.edu.cn/en_releasepaper/content/4752048 |
5. Super-resolution Reconstruction of Mosaic Face Images Based on GAN | |||
Xu Yonghui,Yang Gaobo | |||
Computer Science and Technology 11 May 2020 | |||
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Abstract:With the rapid development of artificial intelligence technologies, image super-resolution which is one of the hottest topics in computer vision community, has achieved attractive progresses. To restore mosaic face images, we present an effective model, namely DemosaicGAN. It combines existing SRGAN and RDN and optimizes the perceptual loss functions. As far as we concerned, our experimental results show that the proposed DemosaicGAN achieves the best results in super-resolution reconstruction of mosaic face images so far. | |||
TO cite this article:Xu Yonghui,Yang Gaobo. Super-resolution Reconstruction of Mosaic Face Images Based on GAN[OL].[11 May 2020] http://en.paper.edu.cn/en_releasepaper/content/4752050 |
6. Image completion algorithm based on label differentiation | |||
Ye Zixiao, Tan Guanghua | |||
Computer Science and Technology 29 April 2020 | |||
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Abstract:Image completion is an important research direction in computer vision and has broad application prospects. Deep learning based image completion methods are generally based on three technologies, namely the autoencoder-based method, the generation of adversarial networks based method and the recurrent network based method. However, the output results of most methods are very single, for each masked Image input can only generate one completion result. Because the probability space corresponding to the possible results of each defective image is very large, in order to obtain the diversity of the completion results, this paper proposes an image completion method based on label differentiation, called LD-PICNet (Label Differentiation PICNet) This method can not only generate a complete image with clear and good semantic information, but also actively edit the tags on the generated results to maximize the diversity of the output. Specifically, this paper introduces an auxiliary classifier similar to ACGAN, which uses a single label of the image ground truth to reconstruct the image, and uses the label to actively increase the difference of the latent vectors during image completion to achieve variability of output. In addition, this paper also introduces a depth-weighted loss function through information entropy. The deeper the position of the image masked area, the lower the weight is given to further enhance the ability of the model to diversify the output. In order to evaluate the ability of LD-PICNet, this paper conducted experiments on 4 different data sets, and tested the model's ability to complete different types of targets, namely face (CelebA), architecture (Paris), landscape (Place2) ,and ordinary pictures (ImageNet). The results show that this method has the ability to generate diversified results, and it has higher clarity than the more advanced methods. | |||
TO cite this article:Ye Zixiao, Tan Guanghua. Image completion algorithm based on label differentiation[OL].[29 April 2020] http://en.paper.edu.cn/en_releasepaper/content/4751876 |
7. A Low Bit-Rate Image Compression Method for Tunnel Inspection | |||
ZHU Zhi-Qiang,LI Qing-Yong,WANG Jian-Zhu,JIA Lei | |||
Computer Science and Technology 18 March 2020 | |||
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Abstract:Tunnels play a critical role in civil transportation infrastructures, and keeping them in optimal operation conditions is of great significance. With the development of computer vision technology, manual inspection is gradually being replaced by automatic or interactive vision inspection systems. During the inspection, these systems usually need to process a massive number of images, which inevitably causes storage problem. Therefore, a variety of image compression approaches have been proposed. However, most of the existing methods overlook the property of high consistency and homogeneity of tunnel images and are not fully applicable to tunnel inspection scenarios. In this paper, we propose a content-aware sparse coding based method for tunnel image compression. Firstly, we train a dictionary for predefined image patches. Secondly, an adaptive sparse coding algorithm is designed by considering the diversity of image content. Specifically, coefficients with more and less non-zero elements are adaptively allocated for complex and plain image textures, respectively. Thirdly, a novel non-uniform quantization method is presented, which has been proved to improve effectively the coding performance. Experimental results show that our method enjoys better visual results and outperforms JPEG and JPEG2000 in terms of Peak-Signal-to-Noise Ratio (PSNR) and structural similarity (SSIM) index in lower bitrates. | |||
TO cite this article:ZHU Zhi-Qiang,LI Qing-Yong,WANG Jian-Zhu, et al. A Low Bit-Rate Image Compression Method for Tunnel Inspection[OL].[18 March 2020] http://en.paper.edu.cn/en_releasepaper/content/4751259 |
8. Class-balanced and Local Median Loss Jointly Supervised for Wild Facial Expression Recognition | |||
Shi Cong-Cong,TIAN,TIAN Mei | |||
Computer Science and Technology 07 February 2020 | |||
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Abstract:Over the past few years, Convolutional Neural Networks (CNNs) have shown effective performance on facial expression recognition. However, it is still a challenge problem for facial expression in the wild. The facial expression recognition dataset in the wild usually has the problem that imbalanced distribution of facial expression data and large intra-class differences caused by factors such as pose, lighting and gender. In order to solve this problem, this paper presents a novel loss function -- CALM Loss (Class-balanced and Local Median Loss). CALM Loss contains two parts. The first part is the class-balanced softmax loss function, which is uesd to solve the problem of data imbalance. The data set is divided into two classes, with two expressions with less data as one class and the other five as one class. During the network training process, the weight of the class with less data is adaptively increased. The second part is the local median loss function, which uses the median of serveral neatest neighbors in the same class as the center of class, weakens the influence of difficult samples on the selection of class center. Finally, the CALM Loss training network was adopted in this paper. The average recognition accuracy on the RAF dataset reaches 77.34$\%$, which proves the effectiveness of the proposed method. | |||
TO cite this article:Shi Cong-Cong,TIAN,TIAN Mei. Class-balanced and Local Median Loss Jointly Supervised for Wild Facial Expression Recognition[OL].[ 7 February 2020] http://en.paper.edu.cn/en_releasepaper/content/4750634 |
9. An Improved Visual-Inertial Odometry Based on Self-Adaptive Attention-Anticipation Feature Selector | |||
Ruan Wenlong,Wang Jing | |||
Computer Science and Technology 22 November 2019 | |||
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Abstract:Visual inertia odometers have achieved great success with the development of robot vision. However, it remains a challenging problem to achieve robust and efficient pose estimation on low-power platforms such as smartphones. This paper proposes a new visual inertial odometer scheme for low-power platforms, named visual inertial odometer based on adaptive attention-anticipation mechanism, which adds visual information to the VINS-based visual inertial odometer. The attention distribution module and the motion information forward anticipation module are controlled by the adaptive adjustment module to reduce the system operation load and improve the system tracking accuracy. We contribute in the following three aspects: 1) A attention mechanism for visual inertia history is proposed, which provides visual attention distribution for system radical motion in complex space environment, and extracts vision with high weight on system influence. Feature tracking; 2) A visual feature screening mechanism based on motion prediction is proposed to filter the visual features that will escape the camera perspective in advance; 3) use the adaptive adjustment module for front-end control and efficiently allocate restricted computing resources. Our approach achieves advanced estimation performance on the Euroc MAV datasets. | |||
TO cite this article:Ruan Wenlong,Wang Jing. An Improved Visual-Inertial Odometry Based on Self-Adaptive Attention-Anticipation Feature Selector[OL].[22 November 2019] http://en.paper.edu.cn/en_releasepaper/content/4750006 |
10. Separable Robust Reversible Watermarking in Encrypted 2D Vector Graphics | |||
Peng Fei,Qi Ying,Lin Zi-xing,Long Min | |||
Computer Science and Technology 26 April 2019 | |||
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Abstract:With the popular use of cloud computing, much attention has been paid to reversible watermarking in encryption domain. However, most existing algorithms are designed for redundant carriers, and they are difficult to resist common attacks. Furthermore, watermark can only be extracted in the plaintext domain or the ciphertext domain. In this paper, a separable robust reversible watermarking in encrypted 2D vector graphics is proposed. Firstly, a content owner uses an encryption key to scramble the polar angle of the vertices to encrypt the graphics in the polar coordinate system. After that, a watermark embedder maps the encoded watermark bits to different vertices under the control of an embedded key and a hash function, and then the polar angle of the vertex is slightly adjusted to embed a watermark. Since the decryption operation does not affect the embedded watermark, the watermark can be extracted both in the plaintext and ciphertext domain. Experimental results and analysis show that the proposed algorithm can achieve good invisibility, and it can effectively resist common operations (such as rotation, translation, scaling (RST)and entity reordering) and malicious attacks (such as the addition and deletion of vertices or entities). | |||
TO cite this article:Peng Fei,Qi Ying,Lin Zi-xing, et al. Separable Robust Reversible Watermarking in Encrypted 2D Vector Graphics[OL].[26 April 2019] http://en.paper.edu.cn/en_releasepaper/content/4748714 |
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