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A novel exponential loss function for pathological lymph node image classification 11

A novel exponential loss function for pathological lymph nod...

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11th international symposium on multispectral image processing and pattern recognition (MIPPR) - Parallel processing of images and Optimization Techniques;and Medical Imaging

作者： Xu, Guoping Cao, Hanqiang Udupa, Jayaram K. Yue, Chunyi Dong, Youli Li, Cao Torigian, Drew A. Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Hubei Peoples R China Univ Penn Dept Radiol Med Image Proc Grp 602 Goddard Bldg3710 Hamilton Walk Philadelphia PA 19104 USA

ISBN: (纸本)9781510636408

Recent progress in deep learning, especially deep convolutional neural networks (DCNNs), has led to significant improvement in natural image classification. However, research is still ongoing in the domain of medical image analysis in part due to the shortage of annotated data sets for training DCNNs, the imbalanced number of positive and negative samples, and the difference between medical images and natural images. In this paper, two strategies are proposed to train a DCNN for pathological lymph node image classification. Firstly, the transfer learning strategy is used to deal with the shortage of training samples. Second, a novel exponential loss function is presented for the imbalance in training samples. Four state-of-the-art DCNNs (GoogleNet, ResNet101, Xception, and MobileNetv2) are tested. The experiments demonstrate that the two strategies are effective to improve the performance of pathological lymph node image classification in terms of accuracy and sensitivity with a mean of 0.13% and 1.50%, respectively, for the four DCNNs. In particular, the proposed exponential loss function improved the sensitivity by 3.9% and 4.0% for Xception and ResNet101, respectively.

关键词： lymphadenopathy pathological lymph nodes image classification PET/CT loss function

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Increment adaptive correlation filter for visual tracking 11

Increment adaptive correlation filter for visual tracking

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11th international symposium on multispectral image processing and pattern recognition: pattern recognition and Computer Vision, MIPPR 2019

作者： Chen, Gang Fang, Zhiwen Yue, Zhou Liu, Bo Xiao, Yang Li, Yanan Department of Information Hunan University of Humanities Science and Technology Loudi Hunan China Department of Energy and Electromechanical Engineering Hunan University of Humanities Science and Technology Loudi Hunan China School of Biomedical Engineering Southern Medical University Guangzhou Guangdong China National Key Lab of Sci. and Tech. on Multispectral Information Processing School of Automation Huazhong Univ. of Sci. and Tech. Wuhan Hubei China School of Computer Science and Engineering Wuhan Institute of Technology Wuhan Hubei China

ISBN: (纸本)9781510636378

Currently, the correlation filter is widely used in visual tracking because of its effectiveness and efficiency. To adapt the representation to changing target appearances, a linear interpolation is used to update tracking models according to a manually designed learning rate. However, The limitation of manually tricks make methods only apply to some special scenes because the threshold parameters are sensitive to different response maps in complex scenes. In this paper, to overcome this problem, an adaptive increment correlation filter based tracker is proposed. Different from traditional linear interpolation depending on a manual learning rate, the increment is learned by linear regression based on the history tracking model and the current training samples. Experimentally, we show that our algorithm can outperform state-of-the-art key point-based trackers. © SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Adaptive filtering

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Accurate Bounding box for Ship Detection On Remote Sensing images With Complex Background 11

Accurate Bounding box for Ship Detection On Remote Sensing I...

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SPIE 11th international symposium on multispectral image processing and pattern recognition (MIPPR) - Remote Sensing image processing, Geographic Information Systems, and Other Applications

作者： Ji, Kaixiang Chen, Zhong Jiang, Zhongze Yang, Jian Huazhong Univ Sci & Technol Sch Automat Luoyu Rode 1037 Wuhan 430074 Peoples R China Natl Key Lab Sci & Technol Multispectral Informat Luoyu Rd 1037 Wuhan 430074 Peoples R China Minist Educ Image Proc & Intelligent Control Key Lab Luoyu Rd 1037 Wuhan 430074 Peoples R China Chinese Acad Sci Inst Remote Sensing & Digital Earth Datun Rd 20 Chaoyang Dist Peoples R China

ISBN: (纸本)9781510636422

The detection of ship targets in remote sensing satellite images is an important means to obtain all ships on the sea surface by satellite image. It can realize the monitoring of sea surface resources, so it has important civil and military significance. Because of the complex background, ship detection in harbour is one of the difficulties. In recent years, many target detection methods based on deep learning have been proposed, and they have achieved good results in natural scene images. YOLOv3 is an advanced end-to-end method because of its high detection accuracy and fast detection speed. But even advanced methods have their shortcomings in this task. Ships in port usually dock side by side, which leads to missed detection of many targets when NMS (Non-Maximum Suppression) operation is performed on the predicted bounding boxes. In this paper, we replace the original NMS with Soft-NMS on the basis of YOLOv3. This operation makes the detector miss fewer targets. At the same time, we added IoU loss when calculating the loss of the prediction box and ground truth box. IoU loss takes the prediction box and the IoU value of its corresponding ground truth box as the evaluation criterion, which makes the target box generated by the detector more fitted to the target. In order to validate the effectiveness of the proposed algorithm, we use harbour remote sensing data collected from Google image and GaoFen-2 (GF-2) satellite, the experimental results show good performance of the proposed method in the detection of ship targets in harbour.

关键词： ship detection remote sensing deep learning

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Multi-scale oriented object detection in aerial images based on convolutional neural networks with global attention 11

Multi-scale oriented object detection in aerial images based...

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SPIE 11th international symposium on multispectral image processing and pattern recognition (MIPPR) - Remote Sensing image processing, Geographic Information Systems, and Other Applications

作者： Fei, Jingjing Wang, Zhicheng Yu, Zhaohui Gu, Xi Wei, Gang Tongji Univ Res Ctr CAD Shanghai Peoples R China

ISBN: (纸本)9781510636422

Object detection is a fundamental yet challenging problem in natural scenes and aerial scenes. Although region based deep convolutional neural networks (CNNs) have brought impressive improvements for object detection in natural scenes, detecting oriented objects in aerial images still remains challenging, due to the complexity of the aerial image backgrounds and the large degree of freedom in scale, orientation, and density. To tackle these problems, we propose a novel network, composed of backbone structure with global attention module, multi-scale object proposal network and final oriented object detector, which can efficiently detect small objects, arbitrary direction objects, and dense objects in aerial images. We utilize pyramid pooling blocks as a global attention module on the top of the backbone structure to generate discriminative feature representations, which provide diverse context information and complementary receptive field for the detector. The global attention module can help the model reduce false alarms and incorrect classifications in the complex aerial image backgrounds. The multi-scale object proposal network aims to generate object-like regions at different scales through several intermediate layers. After that, these regions are sent to the detector for refined classification and regression, which can alleviate the problem of variant scales in aerial images. The oriented object detector is designed to generate predictions for inclined box. The quantitative comparison results on the challenging DOTA dataset show that our proposed method is more accurate than baseline algorithms and is effective for objection detection in aerial images. The results demonstrate that the proposed method significantly improves the performance.

关键词： Object detection aerial image convolution neural networks attention mechanism

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A novel spatial-frequency filtering scheme for X-ray angiograms with simultaneous denoising and enhancement 11

A novel spatial-frequency filtering scheme for X-ray angiogr...

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11th international symposium on multispectral image processing and pattern recognition (MIPPR) - Parallel processing of images and Optimization Techniques;and Medical Imaging

作者： Huang, Zhenghua Chen, Liang Fang, Hao Zhang, Tianxu Wuhan Inst Technol Wuhan Peoples R China Wuhan EasyDiag BioMed CoLTD Wuhan Peoples R China Wuhan Donghu Univ Wuhan Peoples R China Huazhong Univ Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9781510636408

X-ray angiograms, which suffer from low-contrast and noise, need to be improved by both the image enhancement and denoising techniques. However, the goals of these two tasks usually conflict, which makes it difficult to efficiently combine the enhancement and denoising in one scheme. To solve this problem, we propose a novel spatial-frequency filtering (SFF) scheme to simultaneously enhance and denoise low-quality X-ray cardiovascular angiogram images. The proposed scheme includes three key components: Firstly, a relative total variation method is employed as a guide filter to separate an input image into two parts, including the base layer with strong structures and the detail layer with weak structures and noise. Then the base layer is enhanced by a proposed improved histogram equalization (IHE) method while the detail layer is extracted by a short-time Fourier transform and is further enhanced by using a proposed adaptive correction parameter. Finally, the improved image is the combination of results obtained by the two components. Both quantitative and qualitative results of experiments on real-world low-quality X-ray angiogram images demonstrate that the proposed method outperforms the state-of-the-arts in terms of contrast enhancement, structure preservation, and noise reduction.

关键词： X-ray angiograms spatial-frequency filtering image enhancement image denoising

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A Method for Centroid Extraction Based on Faster-RCNN 11

A Method for Centroid Extraction Based on Faster-RCNN

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11th international symposium on multispectral image processing and pattern recognition (MIPPR) - Automatic Target recognition and Navigation

作者： Zhang, Xiaodan Qiu, Zhifeng Jiao, Luofang Yang, Yu Sun, Bin Xu, Limei Univ Elect Sci & Technol China Sch Aeronaut & Astronaut Chengdu 611731 Peoples R China Aircraft Swarm Intelligent Sensing & Cooperat Con Chengdu 611731 Peoples R China

ISBN: (纸本)9781510636361

In the MBZIRC 2020 competition, an Unmanned Aerial Vehicle (UAV) is required to intercept a moving balloon and put it into a specific location. The core of the task is to accurately identify the balloon's centroid, which is also the purpose of this article. The process is composed of two sections: first identify the balloon candidate region based on Faster-RCNN, an end to end object detection algorithm, following a new method based on the color of balloon to extract the centroid finally. In terms of Faster-RCNN, images of balloon sample library are used to generate a number of target candidate regions by region proposal network(RPN), next the neural network is trained to generate a model, which can finally output the boundary box of the balloon, which we called candidate region. Next, in the candidate region, the process includes three parts: feature extraction, target segmentation and centroid marking. Improve the saturation to enhance the image, thus reducing the impact of reflection of sunlight. Then replace the color of the balloon to pure black, with the use of adaptive filtering to segment the balloon region preliminarily. Finally, to minimize the affections of noise, the largest connected region in the image is chosen to calculate its centroid position. Experimented with different backgrounds of images such as sky, grass, flowers and buildings, our method has gotten wonderful results, thus verifying the high accuracy of our method.

关键词： image processing Faster-RCNN Centroid extraction

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Medical image segmentation based on tree-structured MRF in wavelet domain 11

Medical image segmentation based on tree-structured MRF in w...

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11th international symposium on multispectral image processing and pattern recognition (MIPPR) - Parallel processing of images and Optimization Techniques;and Medical Imaging

作者： Xia, Ping Shi, Dong-xia Lei, Bang-jun Ren, Qiang Three Gorges Univ Hubei Key Lab Intelligent Vis Based Monitoring Hy Yi Chang 443002 Peoples R China Three Gorges Univ Coll Comp & Informat Technol Yi Chang 443002 Peoples R China

ISBN: (纸本)9781510636408

Medical image have the characteristics of the complex overlapping of organ and tissue, and accompanied by noise, local volume effect, artifact. So the traditional segmentation method is not ideal. To solve this problem, a medical image segmentation algorithm based on tree-structured MRF in wavelet domain (WTS-MRF) was proposed. For expressing medical image information. WTS-MRF model defines the same tree structure at every scale of wavelet decomposition. At the same time, wavelet transform has good directional selectivity, non-redundancy and multi-scale characteristics. Multiscale and multi direction expression by wavelet decomposition improved the ability of TS-MRF to describe the non-stationary characteristics of images. Then, it can more accurately describe the statistical characteristics of images, and effectively extract the feature information of medical image. In the WTS-MRF model, there are two structures in the layer TS-MRF structure and the interlayer four fork tree structure of wavelet coefficient. The TS-MRF model is built in the layer, and the node potential function is modeled by Potts model. The Gaussian model is used to build the model for the observed characteristics with the same label. The interlayer wavelet coefficients have the property of first-order Markov. The maximum posterior probability is obtained by recursive operation, and the classification hierarchy tree label is implemented to realize medical image segmentation. the experiment results indicate that the algorithm not only can effectively extract the details but also can relatively completely extract target area of medical image, and has higher segmentation accuracy and robustness.

关键词： wavelet transform Medical image segmentation tree-structured Markov random field (TS-MRF) tree-structured Markov random field in Wavelet domain (WTS-MRF)

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Restoration of haze-free images using Generative Adversarial Network 11

Restoration of haze-free images using Generative Adversarial...

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SPIE 11th international symposium on multispectral image processing and pattern recognition (MIPPR) - Remote Sensing image processing, Geographic Information Systems, and Other Applications

作者： Yi, Weichao Liu, Ming Dong, Liquan Zhao, Yuejin Liu, Xiaohua Hui, Mei Beijing Inst Technol Sch Opt & Photon Beijing Key Lab Precis Photoelect Measuring Instr Beijing 100081 Peoples R China

ISBN: (纸本)9781510636422

Haze is the result of the interaction between specific climate and human activities. When observing objects in hazy conditions, optical system will produce degradation problems such as color attenuation, image detail loss and contrast reduction. image haze removal is a challenging and ill-conditioned problem because of the ambiguities of unknown radiance and medium transmission. In order to get clean images, traditional machine vision methods usually use various constraints/prior conditions to obtain a reasonable haze removal solutions, the key to achieve haze removal is to estimate the medium transmission of the input hazy image in earlier studies. In this paper, however, we concentrated on recovering a clear image from a hazy input directly by using Generative Adversarial Network (GAN) without estimating the transmission matrix and atmospheric scattering model parameters, we present an end-to-end model that consists of an encoder and a decoder, the encoder is extracting the features of the hazy images, and represents these features in high dimensional space, while the decoder is employed to recover the corresponding images from high-level coding features. And based perceptual losses optimization could get high quality of textural information of haze recovery and reproduce more natural haze-removal images. Experimental results on hazy image datasets input shows better subjective visual quality than traditional methods. Furthermore, we test the haze removal images on a specialized object detection network-YOLO, the detection result shows that our method can improve the object detection performance on haze removal images, indicated that we can get clean haze-free images from hazy input through our GAN model.

关键词： Haze-removal image restoration GAN object detection

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A Fast Color Cast Detection and Correction Method for Large-field Video-image in Nature Scene 10

A Fast Color Cast Detection and Correction Method for Large-...

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10th international symposium on multispectral image processing and pattern recognition (MIPPR) - multispectral image Acquisition, processing, and Analysis (MIPPR)

作者： Hong, Ke Shi, Yu Hua, Xia Wuhan Inst Technol Lab Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China

ISBN: (数字)9781510617186

ISBN: (纸本)9781510617186;9781510617179

Color cast is a major problem of target detection and recognition base on image feature analysis. A color cast detection and correction algorithm for video-image in nature scene is proposed in this paper. To overcome the inaccurate detection in existed color cast detection method based on RGB space,the analysis method based on the color distribution characteristics of the equivalent circle in Lab space is proposed. In order to achieve better color correction results, the gray balance and perfect reflection method is combined to reproduce the actual color of the objects, especially a modified method for images with severe color deviation. Experimental results show that the proposed methods can detect the degree of color cast correctly, achieve better correction results, and meet the real-time requirements.

关键词： color cast detection color correction image enhancement real-time

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Infrared vehicle recognition using unsupervised feature learning based on K-Feature 10

Infrared vehicle recognition using unsupervised feature lear...

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10th international symposium on multispectral image processing and pattern recognition (MIPPR) - Automatic Target recognition and Navigation

作者： Lin, Jin Tan, Yihua Xia, Haijiao Tian, Jinwen Huazhong Univ Sci & Technol Natl Key Lab Sci & Technol Multispectral Informat Sch Automat Wuhan 430074 Hubei Peoples R China

ISBN: (数字)9781510617209

ISBN: (纸本)9781510617209;9781510617193

Subject to the complex battlefield environment, it is difficult to establish a complete knowledge base in practical application of vehicle recognition algorithms. The infrared vehicle recognition is always difficult and challenging, which plays an important role in remote sensing. In this paper we propose a new unsupervised feature learning method based on K-feature to recognize vehicle in infrared images. First, we use the target detection algorithm which is based on the saliency to detect the initial image. Then, the unsupervised feature learning based on K-feature, which is generated by K-means clustering algorithm that extracted features by learning a visual dictionary from a large number of samples without label, is calculated to suppress the false alarm and improve the accuracy. Finally, the vehicle target recognition image is finished by some post-processing. Large numbers of experiments demonstrate that the proposed method has satisfy recognition effectiveness and robustness for vehicle recognition in infrared images under complex backgrounds, and it also improve the reliability of it.

关键词： vehicle recognition K-Feature infrared image unsupervised feature learning

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