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A robust matching algorithm for active appearance models 1

引用

Workshops Bildverarbeitung fur die Medizin: Algorithmen - Systeme - Anwendungen, BVM 2005 - Workshop on image processing for Medicine: Algorithms - Systems - Applications, BVM 2005

作者： Beichel, Reinhard Bischof, Horst Langs, Georg Sonka, Milan Institute for Computer Graphics and Vision Graz University of Technology Inffeldgasse 16/2 A-8010 Graz Austria Pattern Recognition and Image Processing Group Vienna University of Technology Favoritenstr. 9 A-1040 Vienna Austria Department of Electrical and Computer Engineering University of Iowa Iowa City IA 52242 United States

ISBN: (数字)9783540264316

ISBN: (纸本)3540250522

This paper proposes a method for robustly matching active appearance models (AAMs) on images with gross disturbances (outliers). The method consists of two steps. First, an initial residual is calculated by comparing model and image appearance, and modes of the residual are analyzed. Second, all possible mode combinations are tested by evaluating an objective function. The objective function allows the selection of an outlier-free mode combination. Experiments demonstrate the ability of the robust matching method to successfully cope with outliers - compared to standard AAM matching, no degeneration of the model during matching occurs.

关键词： Statistics

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Accuracy vs Speed Trade-offs in Multi-Frame LiDAR-based Linear Correction for Visual Odometry

Accuracy vs Speed Trade-offs in Multi-Frame LiDAR-based Line...

引用

IEEE International Conference on Intelligent computer Communication and processing (ICCP)

作者： Corvin-Petruţ Cobârzan Cătălin-Cosmin Golban Sergiu Nedevschi Computer Science Department Technical University of Cluj-Napoca Romania Image Processing and Pattern Recognition Group Technical University of Cluj-Napoca Romania

Our previously proposed linear approach for reducing the global drift of a video-based frame-to-frame trajectory estimation method corrects it at selected points in time based on the alignment of one past and the current 3D LiDAR measurements (see [7]). In this paper we improve on that method essentially by adding multi-past frame LiDAR point cloud alignment constraints and by performing the correction more often. While this significantly improves the accuracy of the estimation, it has the downside effect of decreasing the overall runtime performance. We thus study the trade-off between accuracy and speed and propose a couple of higher accuracy but real time correction versions. Their evaluation on the KITTI dataset results in an overall performance falling into the so called SLAM-nonSLAM gap.

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Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Guo, Kuo Li, Yifan Chen, Hao Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Image Processing and Pattern Recognition Shanghai200240 China Carnegie Mellon University School of Computer Science Computational Biology Department PittsburghPA15213 United States

ISBN: (纸本)9798350337488

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function. © 2023 IEEE.

关键词： alternative splicing gene ontology heterogeneous graph attention network isoform function prediction protein language model

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Automatic image analysis of plant root structures

Automatic image analysis of plant root structures

引用

International Conference on pattern recognition

作者： Q. Huang A.K. Jain G.C. Stockman A.J.M. Smucker Department of Computer Science Pattern Recognition and Image Processing Laboratory Department of Crop and Soil Sciences Michigan State University East Lansing MI USA

The minirhizotron technique has provided agricultural scientists the opportunity of observing rhizosphere activities without destroying root structures. Nonetheless, the laborious analysis of the data still prohibits its wide applications. Advanced image understanding techniques are needed to derive satisfactory descriptions of plant root networks in an efficient and robust way. The paper presents a plant root image analysis system designed as a blackboard architecture with a hierarchy of data abstractions. Important properties of plant roots are used throughout the processing and multiple sources of information are combined to resolve uncertainties in image interpretation. Experimental results from some stages of the research are given which support the overall processing scheme.< >

关键词： image analysis Soil image processing Plants (biology) image edge detection Data structures pattern recognition Laboratories computer science Crops

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A Flexible Fully-Multiplicative Orthogonal-Group Based ICA Algorithm

A Flexible Fully-Multiplicative Orthogonal-Group Based ICA A...

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IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： Yalan Ye Jing Wan Zhi-Lin Zhang Chen Jia Wu Lei School of Computer Science and Engineering University of Electronic Science and Technology Chengdu China Institute of Image Processing and Pattern Recognition Shanghai JiaoTong University Shanghai China

In this paper, we propose a flexible fully-multiplicative orthogonal-group based ICA (FlexibleOgICA) algorithm, which can instantaneously separate the mixture of sub-Gaussian and super-Gaussian source signals. It adopts a self-adaptive nonlinear function, which adjusts its parameter to achieve better performance based on the estimation of the kurtosis of super-Gaussian source signals. We also have successfully applied the algorithm to obtain the fetal electrocardiogram (FECG) signal, showing its fast convergence speed and high separation performance

关键词： Independent component analysis Signal processing algorithms Convergence Computational intelligence Bioinformatics Computational biology Signal processing Multidimensional signal processing Biomedical signal processing Switches

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Text scanner with text detection technology on image sequences

Text scanner with text detection technology on image sequenc...

引用

16th International Conference on pattern recognition, ICPR 2002

作者： Jung, Keechul Kim, Kwang In Kurata, Takeshi Kourogi, Masakastu Han, Junghyun Pattern Recognition and Image Processing Lab. Michigan State Univerisity United States Artificial Intelligence Lab. KAIST Korea Republic of National Institute of Advanced Industrial Science and Technology Japan School of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

ISBN: (纸本)0769516963

We propose a text scanner, which detects wide text strings in a sequence of scene images. For scene text detection, we use a multiple-CAMShift algorithm on a text probability image produced by a multi-layer perceptron. To provide enhanced resolution of the extracted text images, we perform the text detection process after generating a mosaic image in a fast and robust image registration method. © 2002 IEEE.

关键词： image enhancement

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Text Scanner with text detection technology on image sequences

Text Scanner with text detection technology on image sequenc...

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作者： Jung, Keechul Kim, Kwang In Kurata, Takeshi Kourogi, Masakastu Han, JungHyun Pattern Recognition and Image Processing Lab. Michigan State Univerisity United States Artificial Intelligence Lab. KAIST Korea Republic of National Institute of Advanced Industrial Science and Technology Japan School of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

关键词： Text processing

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Ant Colony Optimization Algorithm for Feature Selection and Classification of Multispectral Remote Sensing image

Ant Colony Optimization Algorithm for Feature Selection and ...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Lintao Wen Qian Yin Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China

In classification of a multispectral remote sensing image, it is usually difficult to obtain higher classification accuracy if we only consider the image's spectral feature or texture feature alone. In this paper, we present a new approach by applying the Ant Colony Optimization (ACO) algorithm to find a multi-feature vector composed of spectral and texture features in order to get a better result in the classification. The experimental results show that ACO algorithm is helpful in subset searching of the features used to classify the multispectral remote sense image. Using the combination of the spectral and texture features obtained by ACO in classification always produces a better accuracy.

关键词： Ant colony optimization Remote sensing Remote monitoring Multispectral imaging image analysis Data mining Feature extraction Entropy image processing pattern recognition

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Extraction of complex wavelet features for iris recognition

Extraction of complex wavelet features for iris recognition

引用

International Conference on pattern recognition

作者： Xiaofu He Pengfei Shi Department of Computer Science and Technology East China Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Shandong Jiaotong University Shanghai China

ISBN: (纸本)9781424421749

This paper presents a new feature extraction method for iris recognition. Since two dimensional complex wavelet transform (2D-CWT) does not only keep wavelet transformpsilas properties of multiresolution decomposition analysis and perfect reconstruction, but also adds its new merits: approximate shift invariance, good directional selectivity for 2-D image, and limited redundancy, which are useful for iris feature extraction. So, a set of high frequency 2D-CWT coefficients are selected as features for iris recognition. The phase information of the coefficients is used for feature encoding and Hamming distance is adopted for classification. Experimental results show that the proposed algorithm can get good recognition rate.

关键词： Iris recognition Feature extraction Wavelet analysis Wavelet transforms image resolution image analysis image reconstruction Two dimensional displays Frequency Encoding

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Noise Tolerance of Linear vs Non-Linear LiDAR Based Ego-Motion Drift Correction Methods

Noise Tolerance of Linear vs Non-Linear LiDAR Based Ego-Moti...

引用

IEEE International Conference on Intelligent computer Communication and processing (ICCP)

作者： Corvin-Petruț Cobârzan Cătălin-Cosmin Golban Sergiu Nedevschi Computer Science Department Technical University of Cluj-Napoca Romania Image Processing and Pattern Recognition Group Technical University of Cluj-Napoca Romania

ISBN: (纸本)9781665464383

We have previously proposed a linear approach for reducing the global drift of a video-based frame-to-frame trajectory estimation method by correcting it at selected points in time based on the alignment of past and current 3D LiDAR measurements (see [7]). In this paper we assess the tolerance to noise of a series of methods derived from the one previously proposed, this time using both linear and non-linear optimization methods to calculate the correction transform. We generate synthetic datasets with various noise pollution levels and assess the performance of each method under investigation in recovering artificially induced odometry estimation errors.

关键词： Visualization Laser radar Three-dimensional displays Pollution Optimization methods Transforms Time measurement

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