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Chinese Control Conference

作者： Jiakun Lu Yongqiang Bai Pattern Recognition and Intelligent System Beijing Institute of Technology Beijing P. R. China

In this paper, we study the problem of low altitude aerial image stitching. Though the traditional approach is well studied, batch-process image stitching is more difficult. In this work, we present a simple and effective approach to handle problems in low altitude aerial image stitching by considering GPS information of images and seam cutting algorithm. Our method develops an efficient approach to generate the flight path of UAV. We then select alternative images and calculate the overlapping region according to the distance between adjacent images. Features are efficiently detected not only in longitude overlap, but also in lateral, which largely improves stitching accuracy and decreases accumulative errors. In order to handle parallax caused by low altitude flight, we use seam finding method to avoid ghost. Experimental results show that our approach can effectively stitch a large number of images and successfully handle small parallax with low cost.

关键词： Strips Feature extraction Global Positioning system Geography Remote sensing Lapping Unmanned aerial vehicles

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Real-time Lesion Detection of Cardiac Coronary Artery Using Deep Neural Networks

Real-time Lesion Detection of Cardiac Coronary Artery Using ...

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IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC)

作者： Tianming Du Xuqing Liu Honggang Zhang Bo Xu Pattern Recognition and Intelligent System Lab Beijing University of Posts and Telecommunications Beijing China CIT Chinese Academy of Medical Sciences

In the field of cardiac arterial interventional therapy, coronary angiography imaging provides key information to physicians for treatment strategy selection, while the lesion identification process is time-consuming and error-prone even for experienced doctors. This paper proposes a method for the automatic detection of lesion in cardiac coronary angiography based on the deep learning and convolution neural network for the very first time. We used 2925 medical images for building the model. Several lesions exist on the vessel of each image. We will regard these lesion areas as objects that are different from other background areas. We designed a model based on the convolution neural network, applying some advanced building block including CReLU, Inception and other advanced technology such as batch normalization, residual connections, skip-layer connection in our network model. After training, the network model can distinguish the difference between a lesion area and a normal vessel area (background), which can detect the location of the coronary artery lesion in real time without any manual intervention. For the stenosis lesion, the recall rate of detection achieves 0.88.

关键词： Lesions Arteries Convolution Training Task analysis Biomedical imaging

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Pathology study for blood vessel of ocular fundus images by photoacoustic tomography

Pathology study for blood vessel of ocular fundus images by ...

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IEEE Symposium (IUS) Ultrasonics

作者： Jiayao Zhang Kai Deng Bin Chen Hengrong Lan Meng Zhou Fei Gao The Hybrid Imaging System Laboratory ShanghaiTech University Shanghai China The Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing China

In the entire diabetic population, the total number of patients with diabetic retinopathy is more than 50%, and the longer diabetes, the higher the incidence of retinopathy and the rate of blindness. Besides, the blood vessel of ocular fundus is the only blood vessel that can be directly observed, which has excellent application value in medical diagnostics. Photoacoustic Tomography (PAT) is an emerging technique that can obtain high-resolution 3D in-vivo images of optical absorption by sensing laser-generated ultrasound. Therefore, in this paper, we applied U-net neural network for the segmentation of blood vessel of ocular fundus images that opens up new methods for fundus medical image processing. Then we use 2D Time Reversal photoacoustic simulation based on k-WAVE MATLAB toolbox to convert the fundus segmentation of blood vessel images into photoacoustic images. Finally, we use the ResNet Network for the diagnosis of diabetes, in which the input data are the healthy and patient photoacoustic images of the fundus segmentation of blood vessel. We achieved 85% accuracy with 158 training samples. These results demonstrate the power of using deep learning for the analysis of diabetes through the fundus segmentation photoacoustic images of the blood vessel.

关键词： Economic indicators

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Deep facial expression recognition: A survey

arXiv

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arXiv 2018年

作者： Li, Shan Deng, Weihong Pattern Recognition and Intelligent System Laboratory School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China

With the transition of facial expression recognition (FER) from laboratory-controlled to challenging in-the-wild conditions and the recent success of deep learning techniques in various fields, deep neural networks have increasingly been leveraged to learn discriminative representations for automatic FER. Recent deep FER systems generally focus on two important issues: overfitting caused by a lack of sufficient training data and expression-unrelated variations, such as illumination, head pose and identity bias. In this paper, we provide a comprehensive survey on deep FER, including datasets and algorithms that provide insights into these intrinsic problems. First, we introduce the available datasets that are widely used in the literature and provide accepted data selection and evaluation principles for these datasets. We then describe the standard pipeline of a deep FER system with the related background knowledge and suggestions of applicable implementations for each stage. For the state of the art in deep FER, we review existing novel deep neural networks and related training strategies that are designed for FER based on both static images and dynamic image sequences, and discuss their advantages and limitations. Competitive performances on widely used benchmarks are also summarized in this section. We then extend our survey to additional related issues and application scenarios. Finally, we review the remaining challenges and corresponding opportunities in this field as well as future directions for the design of robust deep FER systems. Copyright © 2018, The Authors. All rights reserved.

关键词： Surveys

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Comparison of Backbones for Semantic Segmentation Network

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Journal of Physics: Conference Series 2020年第1期1544卷

作者： Rongyu Zhang Lixuan Du Qi Xiao Jiaming Liu International School Beijing University of Post and Telecommunication BEIJING 100876 CHINA International School of business&Fiance Sun Yat-Sen University GUANGZHOU 510275 CHINA Pattern Recognition and Intelligent System Lab Beijing University of Posts and Telecommunications BEIJING 100876 CHINA

As for the classification network that is constantly emerging with each passing day, different classification network as the backbone of the semantic segmentation network may show different performance. This paper selected the road extraction data set of CVPR DeepGlobe, and compared the performance differences of VGG-16 as the backbone of Unet, ResNet34, ResNet101 and Xception as the backbone of AD-LinkNet. When VGG-16 is used as the backbone of the semantic segmentation network, it performs better in the face of long and wide road extraction. As the backbone of the semantic segmentation network, ResNet has a higher ability to extract small roads. When Xception is used as the backbone of the semantic segmentation network, it not only retains the characteristics of ResNet34, but also can effectively deal with the complex situation of extracting target covered by occlusions.

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Camera Abnormal Movement and Foreign Object Invasion Detection Based on Cumulative Edge Distribution Probability Model

Camera Abnormal Movement and Foreign Object Invasion Detecti...

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2018 International Conference on Security, pattern Analysis, and Cybernetics, SPAC 2018

作者： Yu, Xiangru Cai, Fudong Dou, Yimin Li, Jinping Institute of Pattern Recognition and Intelligent System School of Information Science and Engineering University of Jinan Jinan China Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China Shandong College University Key Laboratory of Information Processing and Cognitive Computing in 13th Five-year University of Jinan Jinan China Shandong Senter Electronic Co. Ltd Zibo China

ISBN: (纸本)9781728105512

In practice, there often exist some occasions where video surveillance can only be realized by using rechargeable batteries due to the high cost of power supply. In order to extend the battery life, images can only be taken at a certain interval. Obviously, image difference is an important method to obtain various changes in this case. Among these changes, foreign invasion is one of the focuses. However, due to bolt shedding, abnormal weather and other factors, the camera may have abnormal movement which may seriously affect the detection effect of image difference method. Therefore, it is an urgent issue to find an effective way to detect the abnormal movement of camera and improve the detection accuracy of foreign invasion. Considering the characteristics of this kind of image sequences, we propose an effective algorithm to detect the camera abnormal movement and foreign object invasion based on a cumulative edge distribution probability model. Since sky region is relatively simple, we only discuss changes in sky region to detect camera abnormal movement. Our algorithm has 6 basic steps: firstly, segment the sky region;secondly, extract the edge information of the current image and the preceding adjacent image in the image sequence;thirdly, determine if the edge information of two adjacent images coincide. If consistent, then go to the next step, otherwise, it indicates that the camera has abnormal movement, then alarm;fourthly, calculate the cumulative edge probability distribution model in the sky region by using the historical image sequence;fifthly, by using adaptive Parzen window, determine if foreign object invasion exists by comparing the probability model of cumulative edge distribution with edge distribution of the current image;sixthly, update the cumulative edge distribution probability model in sky region. The algorithm achieves good results in practical applications. Through the test of thousands of images taken in the wild, the detection accuracy of

关键词： Cameras

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Directional Element HOG for Sketch recognition

Directional Element HOG for Sketch Recognition

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IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC)

作者： Yue Zhong Honggang Zhang Jun Guo Yi-Zhe Song Pattern Recognition and Intelligent System Lab Beijing University of Posts and Telecommunications Beijing China SketchX Lab Queen Mary University of London London United Kingdom

We propose a novel Directional Element Histogram of Oriented Gradient (DE-HOG) feature to human free-hand sketch recognition task that achieves superior performance to traditional HOG feature, originally designed for photographic objects. As a result of modeling the unique characteristics of free-hand sketch, i.e. consisting only a set of strokes omitting visual information such as color and brightness, being highly iconic and abstract. Specifically, we encode sketching strokes as a form of regularized directional vectors from the skeleton of a sketch, whilst still leveraging the HOG feature to meet the local deformation-invariant demands. Such a representation combines the best of two features by encoding necessary and discriminative stroke-level information, but can still robustly deal with various levels of sketching variations. Extensive experiments conducted on two large benchmark sketch recognition datasets demonstrate the performance of our proposed method.

关键词： Skeleton Feature extraction Task analysis Visualization Character recognition Histograms Image recognition

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Camera Abnormal Movement and Foreign Object Invasion Detection Based on Cumulative Edge Distribution Probability Model

Camera Abnormal Movement and Foreign Object Invasion Detecti...

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International Conference on Security, pattern Analysis, and Cybernetics (SPAC)

作者： Xiangru Yu Fudong Cai Yimin Dou Jinping Li Institute of Pattern Recognition and Intelligent System School of Information Science and Engineering University of Jinan Jinan China Shandong Senter Electronic Co. Ltd Zibo China

ISBN: (纸本)9781728105529

In practice, there often exist some occasions where video surveillance can only be realized by using rechargeable batteries due to the high cost of power supply. In order to extend the battery life, images can only be taken at a certain interval. Obviously, image difference is an important method to obtain various changes in this case. Among these changes, foreign invasion is one of the focuses. However, due to bolt shedding, abnormal weather and other factors, the camera may have abnormal movement which may seriously affect the detection effect of image difference method. Therefore, it is an urgent issue to find an effective way to detect the abnormal movement of camera and improve the detection accuracy of foreign invasion. Considering the characteristics of this kind of image sequences, we propose an effective algorithm to detect the camera abnormal movement and foreign object invasion based on a cumulative edge distribution probability model. Since sky region is relatively simple, we only discuss changes in sky region to detect camera abnormal movement. Our algorithm has 6 basic steps: firstly, segment the sky region; secondly, extract the edge information of the current image and the preceding adjacent image in the image sequence; thirdly, determine if the edge information of two adjacent images coincide. If consistent, then go to the next step, otherwise, it indicates that the camera has abnormal movement, then alarm; fourthly, calculate the cumulative edge probability distribution model in the sky region by using the historical image sequence; fifthly, by using adaptive Parzen window, determine if foreign object invasion exists by comparing the probability model of cumulative edge distribution with edge distribution of the current image; sixthly, update the cumulative edge distribution probability model in sky region. The algorithm achieves good results in practical applications. Through the test of thousands of images taken in the wild, the detection accurac

关键词： Image edge detection Image sequences Image segmentation Cameras Batteries Meteorology Adaptation models

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Semi-heterogeneous three-way joint embedding network for sketch-based image retrieval

arXiv

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arXiv 2019年

作者： Lei, Jianjun Song, Yuxin Peng, Bo Ma, Zhanyu Shao, Ling Song, Yi-Zhe School of Electrical and Information Engineering Tianjin University Tianjin300072 China Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing100876 China Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates SketchX Lab Centre for Vision Speech and Signal Processing University of Surrey Surrey GuildfordGU2 7XH United Kingdom

Sketch-based image retrieval (SBIR) is a challenging task due to the large cross-domain gap between sketches and natural images. How to align abstract sketches and natural images into a common high-level semantic space remains a key problem in SBIR. In this paper, we propose a novel semi-heterogeneous three-way joint embedding network (Semi3-Net), which integrates three branches (a sketch branch, a natural image branch, and an edgemap branch) to learn more discriminative cross-domain feature representations for the SBIR task. The key insight lies with how we cultivate the mutual and subtle relationships amongst the sketches, natural images, and edgemaps. A semi-heterogeneous feature mapping is designed to extract bottom features from each domain, where the sketch and edgemap branches are shared while the natural image branch is heterogeneous to the other branches. In addition, a joint semantic embedding is introduced to embed the features from different domains into a common high-level semantic space, where all of the three branches are shared. To further capture informative features common to both natural images and the corresponding edgemaps, a co-attention model is introduced to conduct common channel-wise feature recalibration between different domains. A hybrid-loss mechanism is designed to align the three branches, where an alignment loss and a sketch-edgemap contrastive loss are presented to encourage the network to learn invariant cross-domain representations. Experimental results on two widely used category-level datasets (Sketchy and TU-Berlin Extension) demonstrate that the proposed method outperforms state-of-the-art methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

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Statistical speech model description with VMF mixture model

arXiv

引用

arXiv 2018年

作者： Ma, Zhanyu Leijon, Arne Pattern Recognition and Intelligent System Lab Beijing University of Posts and Telecommunications Beijing100876 China School of Electrical Engineering KTH - Royal Institute of Technology StockholmSE-100 44 Sweden

Efficient quantization of the linear predictive coding (LPC) parameters plays a key role in parametric speech coding. The line spectral frequency (LSF) representation of the LPC parameters has found its applications in speech model quantization. In practical implementation of vector quantization (VQ), probability density function (PDF)-optimized VQ has been shown to be more efficient than the VQ based on training data. In this paper, we present the LSF parameters by a unit vector form, which has directional characteristics. The underlying distribution of this unit vector variable is modeled by a von Mises-Fisher mixture model (VMM). With the high rate theory, the optimal inter-component bit allocation strategy is proposed and the distortion-rate (D-R) relation is derived for the VMM based-VQ (VVQ). Experimental results show that the VVQ outperforms our recently introduced DVQ and the conventional GVQ. Copyright © 2018, The Authors. All rights reserved.

关键词： Vector quantization

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