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ChromTR:chromosome detection in raw metaphase cell images via deformable transformers

Frontiers of Medicine 2024年第6期18卷 1100-1114页

作者： Chao Xia Jiyue Wang Xin You Yaling Fan Bing Chen Saijuan Chen Jie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong UniversityShanghai200240China Shanghai Institute of Hematology State Key Laboratory of Medical GenomicsNational Research Center for Translational Medicine at ShanghaiRuijin Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai200025China

Chromosome karyotyping is a critical way to diagnose various hematological malignancies and genetic diseases,of which chromosome detection in raw metaphase cell images is the most critical and challenging *** this work,focusing on the joint optimization of chromosome localization and classification,we propose ChromTR to accurately detect and classify 24 classes of chromosomes in raw metaphase cell *** incorporates semantic feature learning and class distribution learning into a unified DETR-based detection ***,we first propose a Semantic Feature Learning Network(SFLN)for semantic feature extraction and chromosome foreground region segmentation with object-wise ***,we construct a Semantic-Aware Transformer(SAT)with two parallel encoders and a Semantic-Aware decoder to integrate global visual and semantic *** provide a prediction with a precise chromosome number and category distribution,a Category Distribution Reasoning Module(CDRM)is built for foreground-background objects and chromosome class distribution *** evaluate ChromTR on 1404 newly collected R-band metaphase images and the public G-band dataset *** proposed ChromTR outperforms all previous chromosome detection methods with an average precision of 92.56%in R-band chromosome detection,surpassing the baseline method by 3.02%.In a clinical test,ChromTR is also confident in tackling normal and numerically abnormal *** extended to the chromosome enumeration task,ChromTR also demonstrates state-of-the-art performances on R-band and G-band two metaphase image *** these superior performances to other methods,our proposed method has been applied to assist clinical karyotype diagnosis.

关键词： chromosome detection deformable transformer metaphase cell image distribution reasoning

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Small target detection using an optimization-based filter 40

Small target detection using an optimization-based filter

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40th IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2015

作者： Xie, Kai Fu, Keren Zhou, Tao Yang, Jie Wu, Qiang He, Xiangjian Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China School of Computing and Communications University of Technology Sydney Australia

ISBN: (纸本)9781467369978

Small target detection is a critical problem in the Infrared Search And Track (IRST) system. Although it has been studied for years, there are some challenges remained, e.g. cloud edges and horizontal lines are likely to cause false alarms. This paper proposes a novel method using an optimization-based filter to detect infrared small target in heavy clutter. First, we design a certain pixel area as active area. Second, a weighted quadratic cost function is performed in the active area. Finally, a filter based on statistics of active area is derived from the cost function. Our method could preserve heterogeneous area, meanwhile, remove target region. Experimental results show our method achieves satisfied performance in heavy clutter. © 2015 IEEE.

关键词： active area area statistics heavy clutter optimization-based filter small target detection

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Radio frequency interference mitigation using pseudoinverse learning autoencoders

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Research in Astronomy and Astrophysics 2020年第8期20卷 121-128页

作者： Hong-Feng Wang Mao Yuan Qian Yin Ping Guo Wei-Wei Zhu Di Li Si-Bo Feng Image Processing and Pattern Recognition Laboratory School of Artificial IntelligenceBeijing Normal UniversityBeijing 100875China CAS Key Laboratory of FAST National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China School of Information Management Dezhou UniversityDezhou 253023China Image Processing and Pattern Recognition Laboratory School of System ScienceBeijing Normal UniversityBeijing 100875China Institute for Astronomical Science Dezhou UniversityDezhou 253023China University of Chinese Academy of Sciences Beijing 100049China Hanvon Technology Co. LtdBeijing 100193China NAOC-UKZN Computational Astrophysics Centre University of KwaZulu-NatalDurban 4000South Africa

Radio frequency interference(RFI)is an important challenge in radio *** comes from various sources and increasingly impacts astronomical observation as telescopes become more *** this study,we propose a fast and effective method for removing RFI in pulsar *** use pseudo-inverse learning to train a single hidden layer auto-encoder(AE).We demonstrate that the AE can quickly learn the RFI signatures and then remove them from fast-sampled spectra,leaving real pulsar *** method has the advantage over traditional threshold-based filter method in that it does not completely remove contaminated channels,which could also contain useful astronomical information.

关键词： pulsars:general methods:numerical methods:data analysis

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Object tracking within the framework of concept drift

Object tracking within the framework of concept drift

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11th Asian Conference on Computer Vision, ACCV 2012

作者： Chen, Li Zhou, Yue Yang, Jie Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai 200240 China

ISBN: (纸本)9783642374302

It is well known that the backgrounds or the targets always change in real scenes, which weakens the effectiveness of classical tracking algorithms because of frequent model mismatches. In this paper, an object tracking algorithm within the framework of concept drift is proposed to solve this problem. We detect the driftpoints using a simple message-passing algorithm based on Bayesian Approach. The analyzed probability distribution lays the foundation for the self-adaption of our new model. Our tracking algorithm within the framework of concept drift improves the tracking robustness and accuracy which is illustrated by the two experiments on two real-world changing scenes. © 2013 Springer-Verlag.

关键词： Probability distributions

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Efficient word-level sequential coding scheme of bit plane coding for EBCOT in JPEG2000

Efficient word-level sequential coding scheme of bit plane c...

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9th International Symposium on Consumer Electronics 2005, ISCE 2005

作者： Xiong, Cheng-Yi Tian, Jin-Wen Liu, Jian Institute of Pattern Recognition and Artificial Intelligence South-Center University for Nationalities Huazhong University of Science and Technology Wuhan China Key Laboratory of Education Ministry for Image Processing and Intelligent Control Institute of Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China HUST

The embedded block coding with optimized truncation (EBCOT) is the state-of-the-art coding technique for image compression, which is the heart of the latest still image compression standard JPEG2000. EBCOT can be partitioned into three steps: bit plane coding (BPC), context adaptive binary arithmetic coding (CABAC) and postcompression rate-distortion (PCRD) optimization. The bit plane coding in JPEG2000 is a fractional bit-plane coding method, in which the transformed coefficients of a code block are coded bit plane by bit plane with 3 passes in each bit-plane, and context-decision (CX-D) pair of the coded symbol is generated for CABAC. In this paper, a new word-level sequential (WLS) coding scheme for BPC is presented, which makes all the passes coding be completed in one scan, with the context formation in all bit-planes being performed in parallel and all the samples being coded in sequential. The status variables used in context formation are produced on-the-fly. This new algorithm can efficiently implement both the regular mode and vertical causal one recommended by JPEG2000. The proposed algorithm reduces efficiently the size of memory and the amount of memory accessing, and increases significantly the speed of coding. It is well suited for both software and hardware implementation.1 © IEEE.

关键词： Image coding

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Image fusion algorithm using nonsubsampled contourlet transform

Image fusion algorithm using nonsubsampled contourlet transf...

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MIPPR 2007: Multispectral Image processing

作者： Yang, Xiao Zhiguo, Cao Kai, Wang Zhengxiang, Xu Institute of Pattern Recognition and Artificial Intelligence State Education Commission Key Laboratory for Image Processing and Intelligent Control Huazhong Uni. of Sci. and Tech. Wuhan 430074 China

ISBN: (纸本)9780819469519

In this paper, a pixel-level image fusion algorithm based on Nonsubsampled Contourlet Transform (NSCT) has been proposed. Compared with Contourlet Transform, NSCT is redundant, shift-invariant and more suitable for image fusion. Each image from different sensors could be decomposed into a low frequency image and a series of high frequency images of different directions by multi-sacle NSCT. For low and high frequency images, they are fused based on local-contrast enhancement and definition respectively. Finally, fused image is reconstructed from low and high frequency fused images. Experiment demonstrates that NSCT could preserve edge significantly and the fusion rule based on region segmentation performances well in local-contrast enhancement.

关键词： Image fusion

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Impulse noise removal in two-dimensional electrophoresis images based on dome recognition 8

Impulse noise removal in two-dimensional electrophoresis ima...

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8th International Conference on BioMedical Engineering and Informatics, BMEI 2015

作者： Ou, Qiaofeng Zhang, Huisheng Li, Lixin Xiong, Bangshu School of Electronic Information Northwestern Polytechnical University Xi'an710072 Hong Kong Key Laboratory of Image Processing and Pattern Recognition of Jiangxi Province Nanchang Hangkong University Nanchang330063 China

ISBN: (纸本)9781509000227

Two-dimensional gel electrophoresis (2DE) images are often corrupted by impulse noise in broad sense (including various artifacts, such as fingerprints, hairs, gel cracks, strips, water stains, dust and so on). In this paper, we propose a novel adaptive directional filter (ADF) to remove impulse noise completely and accurately. Firstly, we detect the impulse noise based on the observation that the directional derivative distributions around the protein spot centers and noise pixels are very different. Then, according to the bell-shaped three-dimensional appearance of protein spots, the greatest and smallest gradient directions were used to restore the noised pixels. Lastly, the pixels adjacent to the modified pixels are iteratively detected and restored until no impulse noise is detected. Experimental results on the synthetic and real 2DE images show that the proposed method performs much better than the previous approaches. © 2015 IEEE.

关键词： Pixels

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Analysis Model of Terrorist Attacks Based on Big Data 32

Analysis Model of Terrorist Attacks Based on Big Data

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第32届中国控制与决策会议

作者： LIN Zhenkai DOU Yimin LI Jinping School of Information Science and Engineering University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing(University of Jinan) Shandong College and University Key Laboratory of Information Processing and Cognitive Computing in 13th Five-year Institute of Pattern Recognition and Intelligent Systems University of Jinan

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

With the continuous deepening of international anti-terrorism movement,the anti-terrorism has entered a new stage,and it is facing new *** of the new challenges is to extract useful and valuable information from massive data *** anti-terrorism system model based on local shallow information is imperfect,which is not conducive to obtaining accurate prediction *** shortcomings of existing research are the lack of comprehensive analysis and deeper mining of *** order to improve the efficiency and accuracy of the present anti-terrorism system,we propose an effective method for risk assessment and prediction based on machine learning by using Global Terrorism Database(GTD).There are four basic steps:first,we reduce the data dimension through correlation calculation and Singular Value Decomposition(SVD),then,the function is established to rank the harmfulness of terrorist attacks;second,the cascaded network with attention mechanism is used to predict suspects;third,k-means is used to cluster the regions of terrorist attacks,and then we establish a generalized linear regression model to predict the situation of terrorist *** verify the feasibility of the model by comparing with the real *** experimental results show that the proposed method can analyze and predict the information related to terrorist attacks comprehensively and accurately.

关键词： anti-terrorism warning data modeling SVD cascade network with attention mechanism K-means

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D2GL: Dual-level dual-scale graph learning for sketch-based 3D shape retrieval

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pattern recognition 2024年 156卷

作者： Li, Wenjing Bai, Jing Zheng, Hu The School of Computer Science and Engineering North Minzu University Yinchuan750021 China The Key Laboratory of Images Processing and Pattern Recognition Laboratory Commission: IPPRLab North Minzu University Yinchuan750021 China

Sketch-based 3D shape retrieval (SBSR) is an active research area in the computer vision community, but it is still very challenging. One main reason is that existing deep learning-based methods usually treat sketches as 2D images, neglecting the sparsity and diversity. In this paper, we propose a novel Dual-level Dual-scale Graph Learning (D2GL) method to effectively enhance structural information and produce robust representations for sparse and diverse hand-drawn sketches. Specifically, in addition to the traditional branches for SBSR, we introduce a Dual-level Dual-scale Graph Self-attention (DLDS-GSA) as an auxiliary branch. DLDS-GSA further consists of two levels of encoders, i.e., a local structural encoder and a dual-scale global structural encoder, to capture both local discriminative and multi-scale global structures while minimizing the impact of various sketch drawing details. Comprehensive experiments on SHREC’13 and SHREC’14 datasets demonstrate the superiority of D2GL for SBSR, with extended experiments on PART-SHREC’14 confirming its generalization for unseen classes in SBSR. © 2024 Elsevier Ltd

关键词： Deep learning

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Classification Fusion in Wireless Sensor Networks

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自动化学报 2006年第6期32卷 947-955页

作者： LIU Chun-Ting HUO Hong FANG Tao LI De-Ren SHEN Xiao Institute of Image Processing ＆ Pattern Recognition Shanghai Jiaotong University Shanghai 200240 State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan 430079

In wireless sensor networks, target classification differs from that in centralized sensing systems because of the distributed detection, wireless communication and limited resources. We study the classification problem of moving vehicles in wireless sensor networks using acoustic signals emitted from vehicles. Three algorithms including wavelet decomposition, weighted k-nearest-neighbor and Dempster-Shafer theory are combined in this paper. Finally, we use real world experimental data to validate the classification methods. The result shows that wavelet based feature extraction method can extract stable features from acoustic signals. By fusion with Dempster's rule, the classification performance is improved.

关键词： Wireless sensor networks classification fusion wavelet decomposition weighted k-nearest-neighbor Dempster-Shafer theory

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