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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Zhicheng Wang Jian Liu Jinwen Tian Zeping Xie State Education Commission Key Laboratory for Image Processing and Intelligent Control Institute for Pattern Recognition and Artificial Intelligence Huazhong Univ. of Sci. & Tech. Wuhan 430074 China Key Lab. for Image Process. & Intelligent Control State Educ. Comm. School of Life Science & Technology Huazhong University of Science and Technology Wuhan China

This paper introduces the new qualitative and quantitative methods, which can diagnose breast tumors. Qualitative methods include blood vessel display inside and outside of pathological changes part of breast, display of equivalent pixel curves at the part of pathological changes and display of breast tumor image edge. Accordingly, three feature extraction operators are proposed, i.e. the combination operators of anisotropic gradient and smoothing operator, an improved Sobel operator and an edge sharpening operator. Furthermore, quantitative diagnostic approaches are discussed based on blood and oxygen contents according to abundant clinical data and pathological mechanism of breast tumors. The results of clinic show that the methods of combining qualitative and quantitative diagnose are effective for breast tumor images, especially for early and potential breast cancer

关键词： Breast tumors Displays Pathology Blood vessels Biomedical imaging Pixel Feature extraction Anisotropic magnetoresistance Smoothing methods Breast cancer

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Point-and-Shoot All-in-Focus Photo Synthesis from Smartphone Camera Pair

arXiv

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arXiv 2023年

作者： Luo, Xianrui Peng, Juewen Zhao, Weiyue Xian, Ke Lu, Hao Cao, Zhiguo The Key Laboratory of Image Processing and Intelligent Control Ministry of Education The School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The S-Lab for Advanced Intelligence Nanyang Technological University Singapore

All-in-Focus (AIF) photography is expected to be a commercial selling point for modern smartphones. Standard AIF synthesis requires manual, time-consuming operations such as focal stack compositing, which is unfriendly to ordinary people. To achieve point-and-shoot AIF photography with a smartphone, we expect that an AIF photo can be generated from one shot of the scene, instead of from multiple photos captured by the same camera. Benefiting from the multi-camera module in modern smartphones, we introduce a new task of AIF synthesis from main (wide) and ultra-wide cameras. The goal is to recover sharp details from defocused regions in the main-camera photo with the help of the ultra-wide-camera one. The camera setting poses new challenges such as parallax-induced occlusions and inconsistent color between cameras. To overcome the challenges, we introduce a predict-and-refine network to mitigate occlusions and propose dynamic frequency-domain alignment for color correction. To enable effective training and evaluation, we also build an AIF dataset with 2686 unique scenes. Each scene includes two photos captured by the main camera, one photo captured by the ultra-wide camera, and a synthesized AIF photo. Results show that our solution, termed EasyAIF, can produce high-quality AIF photos and outperforms strong baselines quantitatively and qualitatively. For the first time, we demonstrate point-and-shoot AIF photo synthesis successfully from main and ultra-wide cameras. Copyright © 2023, The Authors. All rights reserved.

关键词： Cameras

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Remote Sensing image Scene Classification Based on SURF Feature and Deep Learning

Remote Sensing Image Scene Classification Based on SURF Feat...

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Asia-Pacific Signal and Information processing Association Annual Summit and Conference (APSIPA)

作者： Jinxiang Liang Jianwu Dang Yangping Wang Jingyu Yang Zhenhai Zhang Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphic & Image Processing Gansu Provincial Key Lab of System Dynamics and Reliability of Rail Transport Equipment Lanzhou Bocai Technology Co. Ltd.

ISBN: (数字)9781728132488

ISBN: (纸本)9781728132495

Remote sensing image scene classification is one of the key points in remote sensing image interpretation. The traditional remote sensing image scene classification feature performance is not strong, and the deep learning extraction semantic feature process is complex. This paper proposes a fusion feature remote sensing image scene classification method which is based on artificial features and deep learning semantic features. Firstly, the SURF feature of the remote sensing image is extracted and encoded by the VLAD algorithm. The semantic feature of a remote sensing image is extracted by transfer learning. Then the feature reduction is performed by PCA algorithm and feature fusion is performed. Finally, the scene classifier is trained by using the random forest algorithm. The experimental results show that the classification accuracy and Kappa coefficient of this method are higher and the method is effective.

关键词： Feature extraction Remote sensing Machine learning Semantics image analysis Classification algorithms Forestry

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Dual-Source CBCT for Larger Longitudinal Coverage: System Design and image Reconstruction 46

Dual-Source CBCT for Larger Longitudinal Coverage: System De...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Zhang, Xusheng Lyu, Tianling Shi, Yijie Zhong, Xinyun Wu, Zhan Xi, Yan Wang, Shijie Chen, Yang Zhu, Wentao Southeast University School of Software Nanjing China Zhejiang Lab Hangzhou China First-Imaging Tech. Shanghai China Southeast University Laboratory of Image Science and Technology The School of Computer Science and Engineering Nanjing China Southeast University Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Nanjing China

ISBN: (纸本)9798350371499

The application of CBCT systems in intraoperative environments has become increasingly common, but concurrent CBCT systems are unsuitable for situations that require a large longitudinal imaging FoV, such as orthopedics. To increase longitudinal coverage, we developed a dual-source CBCT (DS-CBCT) system in which two ray sources are symmetrically placed along the central plane. After further analyzing its geometric characteristics, we propose an analytical reconstruction algorithm termed DT-FDK specialized for DS-CBCT, which combines cone-beam rebinning and two rays with smaller cone angles among the four conjugate rays to further suppress cone beam artifacts. The system design and reconstruction algorithm are tested on both simulated and real-scanned data. Results show that the DS-CBCT can expand the effective imaging volume by 37.1% compared to single-source CBCT systems. The reconstructed images by DT-FDK also show improved image quality compared to traditional reconstruction algorithms. © 2024 IEEE.

关键词： Cones

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Deep neural network and switching Kalman filter based continuous affect recognition

Deep neural network and switching Kalman filter based contin...

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IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

作者： Ercheng Pei Xiaohan Xia Le Yang Dongmei Jiang Hichem Sahli School of Computer Science Shaanxi Key Lab on Speech and Image Information Processing Xi'an China Dept. ETRO Vrije Universiteit Brussel (VUB) Brussels NPU-VUB Joint AVSP Lab Interuniversity Microelectronics Centre Heverlee Belgium

ISBN: (纸本)9781509015535

In this paper, we propose the deep neural network - switching Kalman filter (DNN-SKF) based frameworks for both single modal and multi-modal continuous affective dimension estimation. The DNN-SKF framework firstly models the complex nonlinear relationship between the input (audio, visual, or lexical) features and the affective dimensions via the non-recurrent DNN, then models the temporal dynamics embedded in the emotions via the segmental linear SKF. Affective dimension estimation experiments are carried out on the Audio Visual Emotion Challenge (AVEC2012) database. Single modal estimation results are compared to those from the Support Vector Regression (SVR) models and Bidirectional Long Short-Term Memory Recurrent Neural Network (BLSTM-RNN) models, results show that for all modalities, and for all affective dimensions except for arousal from the audio features, the DNN-SKFs outperform SVR and BLSTM-RNN models in estimating the affective dimensions. Multi-modal estimation results are compared with the state of the art results on the competition of AVEC2012. Results show that both on the development set and test set, the proposed DNN-SKF models obtain the best performance in estimating the affective dimensions. On the test set, with the audio visual features, the average Pearson correlation coefficient (COR) is improved to 0.326 from 0.226 of the linear regression method [1], while with the audio visual and lexical features, the COR is improved to 0.355 from 0.344 of the particle filter fusion method (SVR-PF) [2].

关键词： Estimation Visualization Switches Kalman filters Hidden Markov models Training Feature extraction

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Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

Understanding the Robustness of 3D Object Detection with Bir...

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Institute for Ai Tsinghua University BNRist Center Thbi Lab Dept. of Comp. Sci. and Tech. China School of Computer Science and Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province China Saic Motor Ai Lab Zhongguancun Laboratory China

ISBN: (纸本)9798350301298

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. © 2023 IEEE.

关键词： Autonomous driving

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Indoor Localization with Incomplete Observation Using Set-membership Filter

Indoor Localization with Incomplete Observation Using Set-me...

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International Symposium on Micro-NanoMechatronics and Human Science

作者： Yuan Wang Jian Huang Key Lab of Image Processing and Intelligent Control School of Automation Huazhong University of Science and Technology

ISBN: (纸本)9781467382182

Indoor localization is a popular topic because of the poor performance of GPS in the indoor environment. This paper has provided a new method to achieve the goal of indoor localization. Combining the usage of posture sensors with the Radio Frequency Identification (RFID) technology, we solved the problem of plentiful tags in the traditional RFID indoor localization. The relative localization subsystem is based on posture sensors and the absolute localization subsystem is based on RFID. A set-membership theory based data fusion algorithm is proposed to fuse the data of relative localization and absolute localization. Experimental results show that the proposed data fusion algorithm could significantly reduce the accumulated error of our indoor localization system.

关键词： Ellipsoids Indoor environments RFID tags Filtering theory Filtering algorithms

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Gait Planning and Motion Control Based on Vrep Simulation for Quadruped Robot

Gait Planning and Motion Control Based on Vrep Simulation fo...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Linqi Zhou Zhihua Chen Jun Liu Zhi Liu Yumeng Chen Liting Zhang key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition and MOE Key Lab of Nondestructive Testing Technology Nanchang Hangkong University Nanchang China State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing China

Gait planning of quadruped robots plays an important role in achieving less walking, including dynamic and static gait. In this article, a static and dynamic gait control method based on center of gravity stability margin is proposed. Firstly, the robot model and kinematics modeling are introduced. Secondly, the robot’s foot static and dynamic gait were planned and the foot trajectory was designed. Finally, two types of gait of the robot were simulated using Vrep simulation software, and the differences in stability and speed between the coordinated gait with speed and stability in the static and dynamic gait of a 12 degree of freedom robot were analyzed, verifying the effectiveness of the gait control method proposed in this paper.

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Hybrid Deep Neural Network--Hidden Markov Model (DNN-HMM) Based Speech Emotion Recognition

Hybrid Deep Neural Network--Hidden Markov Model (DNN-HMM) Ba...

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International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII

作者： Longfei Li Yong Zhao Dongmei Jiang Yanning Zhang Fengna Wang Isabel Gonzalez Enescu Valentin Hichem Sahli VUB-NPU Joint AVSP Research Lab Northwestern Polytechnical University Xi'an China Shaanxi Provincial Key Lab on Speech and Image Information Processing VUB-NPU Joint AVSP Research Lab Vrije Universiteit Brussel Brussels Belgium Electronics & Informatics Department

Deep Neural Network Hidden Markov Models, or DNN-HMMs, are recently very promising acoustic models achieving good speech recognition results over Gaussian mixture model based HMMs (GMM-HMMs). In this paper, for emotion recognition from speech, we investigate DNN-HMMs with restricted Boltzmann Machine (RBM) based unsupervised pre-training, and DNN-HMMs with discriminative pre-training. Emotion recognition experiments are carried out on these two models on the eNTERFACE'05 database and Berlin database, respectively, and results are compared with those from the GMM-HMMs, the shallow-NN-HMMs with two layers, as well as the Multi-layer Perceptrons HMMs (MLP-HMMs). Experimental results show that when the numbers of the hidden layers as well hidden units are properly set, the DNN could extend the labeling ability of GMM-HMM. Among all the models, the DNN-HMMs with discriminative pre-training obtain the best results. For example, for the eNTERFACE'05 database, the recognition accuracy improves 12.22% from the DNN-HMMs with unsupervised pre-training, 11.67% from the GMM-HMMs, 10.56% from the MLP-HMMs, and even 17.22% from the shallow-NN-HMMs, respectively.

关键词： Hidden Markov models Emotion recognition Training Speech recognition Speech Databases Neural networks

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An improved algorithm of pattern matching for information security audit system

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Journal of Computational Information Systems 2008年第5期4卷 2221-2228页

作者： Ding, Lei Yu, Fei Xu, Cheng Yue, Guangxue School of Physics Science and Information Engineering Jishou University Jishou 416000 China Provincial Key Lab. of Image Processing and Imagecom Communication Nanjing University of Post and Telecommunications Nanjing 210003 China College of Computer and Communication Hunan University Changsha 410082 China

At present, network information audit system is almost based on text information filtering. Text categorization is the basic technology of text information filtering. Pattern matching algorithms are very important for the rule based information audit system. It directly influences the accuracy and real-time performance of the system. The KMP and BM algorithms are introduced in this paper. Research is carried out to improve the BM algorithm and a better BM algorithm is proposed According to the situation that the system characters group is bigger and that characters of the pat tern are less, the thesis puts forward an improved string-matching algorithm. The results of our experiments prove that it promotes the precision of text categorization. © 2008 Binary Information Press.

关键词： Pattern matching

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