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作者： Jung, Keechul Kim, Kwang In Han, JungHyun PatternRecognition and Image Processing Lab. Michigan State Univ. United States Artificial Intelligence Lab. KAIST Korea Republic of School of Electrical and Computer Engineering Sung Kyun Kwan Univ. Korea Republic of

This paper proposes a hybrid approach of texture-based method and connected component-based one for extracting texts in real scene images. For detecting texts having a lot of variations in size, shape, etc, we use a multiple-continuously adaptive mean shift algorithm on the text probability image produced by a multi-layer perceptron. It is assumed that scene text lies on planar rectangular surfaces with homogeneous background colors. We correct perspective distortion using warping parameters calculated after segmentation of an input image. We can detect and reconstruct text images accurately and efficiently. © 2002 IEEE.

关键词： Information retrieval

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An improved method in change detection of multitemporal remote sensing image

An improved method in change detection of multitemporal remo...

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4th International Conference on intelligence Science and Big Data Engineering, IScIDE 2013

作者： Liao, Fangshun Yu, Sufen Li, Ying Zhang, Yanning Shaanxi Provincial Key Lab. of Speech and Image Information Processing Northwestern Polytechnical University Xi'an Shaanxi 710072 China Science and Technology on Electro-optic Control Laboratory Louyang 471009 China

ISBN: (纸本)9783642420566

Traditional Markov random Field (MRF) methods assume that neighboring pixels tend to have the same lab.l. However, this assumption is always inconsistent with the actual situation and affects the resultant accuracy of the algorithm. To overcome this, we propose an object-based Markov Random Field (OMRF) model and a change detection method based on OMRF model. The OMRF model assumes that pixels within same object are in the same class. First, we generate the difference image from multi-temporal remote sensing images. Second, Mean Shift is applied to extract objects from difference image. Finally, change detection map is generated by iterative algorithm. The experimental results show that the algorithm can effectively improve the detection accuracy of the algorithm on real remote sensing datasets. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Change detection

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The Depth Information Estimation of Microscope Defocus image Based-on Markov Random Field

The Depth Information Estimation of Microscope Defocus Image...

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IEEE Conference on Robotics, Automation and Mechatronics

作者： Xiangjin Zeng Xinhan Huang Min Wang Peng Li Department of Control Science and Engineering The Key Lab of Image processing and Intelligence control HUST Wuhan China

For depth information estimation of microscope defocus image, a blur parameter model of defocus image based on Markov random field has been present. It converts problem of depth estimation into optimization problem. An improved iterated conditional modes algorithm has been applied to complete optimization problem, which the select of initial point employed least squares estimate algorithm prevents that the result gets into local optimization. The experiments and simulations prove that the model and algorithm is efficiency.

关键词： Microscopy Markov random fields Charge coupled devices Charge-coupled image sensors Lenses Cameras image converters Least squares approximation Robot vision systems Orbital robotics

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Learning from Noisy lab.ls with Coarse-to-Fine Sample Credibility Modeling 17th

Learning from Noisy Labels with Coarse-to-Fine Sample Cred...

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17th European Conference on Computer Vision, ECCV 2022

作者： Zhang, Boshen Li, Yuxi Tu, Yuanpeng Peng, Jinlong Wang, Yabiao Wu, Cunlin Xiao, Yang Zhao, Cairong YouTu Lab Shenzhen China Tongji University Shanghai China Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783031250620

Training deep neural network (DNN) with noisy lab.ls is practically challenging since inaccurate lab.ls severely degrade the generalization ability of DNN. Previous efforts tend to handle part or full data in a unified denoising flow via identifying noisy data with a coarse small-loss criterion to mitigate the interference from noisy lab.ls, ignoring the fact that the difficulties of noisy samples are different, thus a rigid and unified data selection pipeline cannot tackle this problem well. In this paper, we first propose a coarse-to-fine robust learning method called CREMA, to handle noisy data in a divide-and-conquer manner. In coarse-level, clean and noisy sets are firstly separated in terms of credibility in a statistical sense. Since it is practically impossible to categorize all noisy samples correctly, we further process them in a fine-grained manner via modeling the credibility of each sample. Specifically, for the clean set, we deliberately design a memory-based modulation scheme to dynamically adjust the contribution of each sample in terms of its historical credibility sequence during training, thus alleviating the effect from noisy samples incorrectly grouped into the clean set. Meanwhile, for samples categorized into the noisy set, a selective lab.l update strategy is proposed to correct noisy lab.ls while mitigating the problem of correction error. Extensive experiments are conducted on benchmarks of different modality, including image classification (CIFAR, Clothing1M etc.) and text recognition (IMDB), with either synthetic or natural semantic noises, demonstrating the superiority and generality of CREMA. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep neural networks

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Text extraction in real scene images on planar planes

Text extraction in real scene images on planar planes

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16th International Conference on Pattern Recognition, ICPR 2002

作者： Jung, Keechul Kim, Kwang In Han, Junghyun Pattern Recognition and Image Processing Lab. Michigan State Univ. United States Artificial Intelligence Lab. KAIST Korea Republic of School of Electrical and Computer Engineering Sung Kyun Kwan Univ. Korea Republic of

ISBN: (纸本)0769516963

This paper proposes a hybrid approach of texture-based method and connected component-based one for extracting texts in real scene images. For detecting texts having a lot of variations in size, shape, etc. we use a multiplecontinuously adaptive mean shift algorithm on the text probability image produced by a multi-layer perceptron. It is assumed that scene text lies on planar rectangular surfaces with homogeneous background colors. We correct perspective distortion using warping parameters calculated after segmentation of an input image. We can delect and reconstruct text images accurately and efficiently. © 2002 IEEE.

关键词： image segmentation

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Data Fusion and Efficient Algorithm for Moving Target Tracking

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IFAC Proceedings Volumes 2008年第2期41卷 2406-2411页

作者： Guocheng Liu Yongji Wang Chinese Education Ministry Key Lab. for Image Processing and Intelligence Control Department of Control Science and Engineering Huazhong University of Science and Technology P.R. China (Tel: 0086-27-68772267

The principle of target tracking and data fusion techniques are discussed. To resolve high uncertainty that exists in sensors of mobile robots, one multi-sensor data fusion algorithm is presented. The algorithm is based on particle filter techniques, fuses the information coming from multiple sensors and merges different state space models. So it can be used to eliminate system and measurement noise and estimate value of position and headings of mobile robot. On simulation experiments, we compare different cases such as single sensors and multi-sensor data fusion, the results demonstrate the feasibility and effectiveness of this algorithm and exhibits good tracking performance.

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Selection of Cancer Biomarkers from Microarray Gene Expression Data Utilizing the Bi-Objective Optimization Method

Selection of Cancer Biomarkers from Microarray Gene Expressi...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Jiang, Hanjing Lin, Jianqing Zhu, Huachao Huang, Yabing Huazhong University of Science and Technology Key Lab. of Image Information Processing and Intelligent Control of Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Wuhan430074 China Wuhan University School of Computer Science Wuhan430064 China Renmin Hospital of Wuhan University Department of Pathology Wuhan430060 China

ISBN: (纸本)9798350337488

Cancer-associated biomarker genes play an indispensable role in the intricate tapestry of cancer development and manifestation. The expression of biomarkers in different types of tumor cells has beneficial implications for shedding light on the development of various cancers, guiding clinical diagnosis, and treatment. Microarray technology enables the expression levels of thousands of genes in samples to be sequenced simultaneously. However, sparse and high-dimensional microarray data present a formidable challenge in identifying biomarker genes. This study presents EREF-NSGA2, a novel method for cancer biomarker selection from microarray data, employing a hybrid gene selection approach. Firstly, the combination of the wrapper and embedded gene selection methods is proposed to filter the microarray data, which efficiently decreases the search space of the algorithm. After that, the improved NSGA-II algorithm is used to search the genes subset obtained from the previous step to reach the optimal subset of cancer biomarker genes. The proposed EREF-NSGA2 is compared with other reported methods on six cancer benchmark gene expression datasets. A detailed biological analysis is performed to analyze the relationship between the selected genes and the cancer data sets they belong to. To summarize, EREF-NSGA2 proves its effectiveness in selecting a feature subset comprising the fewest genes while maintaining the highest classification accuracy. © 2023 IEEE.

关键词： Bi-objective optimization Cancer biomarker Gene selection NSGA-II

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Color image filtering based on impulsive noise detection

Color image filtering based on impulsive noise detection

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MIPPR 2007: Automatic Target Recognition and image Analysis;and Multispectral image Acquisition

作者： Li, Dehua Jin, Lianghai Yao, Xingzhong Xiong, Caiquan State Key Lab. for Image Processing and Intelligent Control Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China Second Artillery Command College Wuhan 430012 China School of Computer Science Hubei University of Technology Wuhan 430068 China

ISBN: (纸本)9780819469502

This paper introduces a new class of switching vector median filter. The proposed algorithm first uses four directional masks to analyze the color difference between the central pixel and its neighborhood pixels in the RGB color space and classify each color pixel into noisy pixel or noise-free one, and then employs the standard vector median filtering operations in the detected noisy locations to restore the corrupted pixels and leave the noise-free ones unchanged. The simulation results show that the proposed method excellently suppresses impulsive noise as well as preserving the image details well, and significantly outperforms the existing vector filtering solutions in terms of both the objective measures and the perceptual visual quality.

关键词： Color image processing

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Do Not Disturb Me: Person Re-identification Under the Interference of Other Pedestrians 16th

Do Not Disturb Me: Person Re-identification Under the Interf...

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16th European Conference on Computer Vision, ECCV 2020

作者： Zhao, Shizhen Gao, Changxin Zhang, Jun Cheng, Hao Han, Chuchu Jiang, Xinyang Guo, Xiaowei Zheng, Wei-Shi Sang, Nong Sun, Xing Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Tencent Youtu Lab Shanghai China Sun Yat-sen University Guangzhou China

ISBN: (纸本)9783030585389

In the conventional person Re-ID setting, it is assumed that cropped images are the person images within the bounding box for each individual. However, in a crowded scene, off-shelf-detectors may generate bounding boxes involving multiple people, where the large proportion of background pedestrians or human occlusion exists. The representation extracted from such cropped images, which contain both the target and the interference pedestrians, might include distractive information. This will lead to wrong retrieval results. To address this problem, this paper presents a novel deep network termed Pedestrian-Interference Suppression Network (PISNet). PISNet leverages a Query-Guided Attention Block (QGAB) to enhance the feature of the target in the gallery, under the guidance of the query. Furthermore, the involving Guidance Reversed Attention Module and the Multi-Person Separation Loss promote QGAB to suppress the interference of other pedestrians. Our method is evaluated on two new pedestrian-interference datasets and the results show that the proposed method performs favorably against existing Re-ID methods. © 2020, Springer Nature Switzerland AG.

关键词： Computers

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Adversarial Refinement Network for Human Motion Prediction 15th

Adversarial Refinement Network for Human Motion Prediction

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15th Asian Conference on Computer Vision, ACCV 2020

作者： Chao, Xianjin Bin, Yanrui Chu, Wenqing Cao, Xuan Ge, Yanhao Wang, Chengjie Li, Jilin Huang, Feiyue Leung, Howard City University of Hong Kong Hong Kong China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Tencent Youtu Lab Shanghai China

ISBN: (纸本)9783030695316

Human motion prediction aims to predict future 3D skeletal sequences by giving a limited human motion as inputs. Two popular methods, recurrent neural networks and feed-forward deep networks, are able to predict rough motion trend, but motion details such as limb movement may be lost. To predict more accurate future human motion, we propose an Adversarial Refinement Network (ARNet) following a simple yet effective coarse-to-fine mechanism with novel adversarial error augmentation. Specifically, we take both the historical motion sequences and coarse prediction as input of our cascaded refinement network to predict refined human motion and strengthen the refinement network with adversarial error augmentation. During training, we deliberately introduce the error distribution by learning through the adversarial mechanism among different subjects. In testing, our cascaded refinement network alleviates the prediction error from the coarse predictor resulting in a finer prediction robustly. This adversarial error augmentation provides rich error cases as input to our refinement network, leading to better generalization performance on the testing dataset. We conduct extensive experiments on three standard benchmark datasets and show that our proposed ARNet outperforms other state-of-the-art methods, especially on challenging aperiodic actions in both short-term and long-term predictions. © 2021, Springer Nature Switzerland AG.

关键词： Errors

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