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Journal articles on the topic 'Projection extraction'

Author: Grafiati

Published: 28 June 2021

Last updated: 18 February 2022

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1

ZHAO, JIAN, and JIAN AN. "AN ITERATIVE CONVEX HULL APPROACH FOR IMAGE SEGMENTATION AND CONTOUR EXTRACTION." International Journal of Pattern Recognition and Artificial Intelligence 26, no. 07 (November 2012): 1255013. http://dx.doi.org/10.1142/s0218001412550130.

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The contours and segments of objects in digital images have many important applications. Contour extractions of gray images can be converted into contour extractions of binary images. This paper presents a novel contour-extraction algorithm for binary images and provides a deduction theory for this algorithm. First, we discuss the method used to construct convex hulls of regions of objects. The contour of an object evolves from a convex polygon until the exact boundary is obtained. Second, the projection methods from lines to objects are studied, in which, a polygon iteration method is presented using linear projection. The result of the iteration is the contour of the object region. Lastly, addressing the problem that direct projections probably cannot find correct projection points, an effective discrete ray-projection method is presented. Comparisons with other contour deformation algorithms show that the algorithm in the present paper is very robust with respect to the shapes of the object regions. Numerical tests show that time consumption is primarily concentrated on convex hull computation, and the implementation efficiency of the program can satisfy the requirement of interactive operations.

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Giannella, Chris R., Ransom K. Winder, and Joseph P. Jubinski. "Annotation projection for temporal information extraction." Natural Language Engineering 25, no. 3 (May 2019): 385–403. http://dx.doi.org/10.1017/s1351324919000044.

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AbstractApproaches to building temporal information extraction systems typically rely on large, manually annotated corpora. Thus, porting these systems to new languages requires acquiring large corpora of manually annotated documents in the new languages. Acquiring such corpora is difficult owing to the complexity of temporal information extraction annotation. One strategy for addressing this difficulty is to reduce or eliminate the need for manually annotated corpora through annotation projection. This technique utilizes a temporal information extraction system for a source language (typically English) to automatically annotate the source language side of a parallel corpus. It then uses automatically generated word alignments to project the annotations, thereby creating noisily annotated target language training data. We developed an annotation projection technique for producing target language temporal information extraction systems. We carried out an English (source) to French (target) case study wherein we compared a French temporal information extraction system built using annotation projection with one built using a manually annotated French corpus. While annotation projection has been applied to building other kinds of Natural Language Processing tools (e.g., Named Entity Recognizers), to our knowledge, this is the first paper examining annotation projection as applied to temporal information extraction where no manual corrections of the target language annotations were made. We found that, even using manually annotated data to build a temporal information extraction system, F-scores were relatively low (<0.35), which suggests that the problem is challenging even with manually annotated data. Our annotation projection approach performed well (relative to the system built from manually annotated data) on some aspects of temporal information extraction (e.g., event–document creation time temporal relation prediction), but it performed poorly on the other kinds of temporal relation prediction (e.g., event–event and event–time).

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Yi, Huangjian, Xu Zhang, Jinye Peng, Fengjun Zhao, Xiaodong Wang, Yuqing Hou, Duofang Chen, and Xiaowei He. "Reconstruction for Limited-Projection Fluorescence Molecular Tomography Based on a Double-Mesh Strategy." BioMed Research International 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/5682851.

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Limited-projection fluorescence molecular tomography (FMT) has short data acquisition time that allows fast resolving of the three-dimensional visualization of fluorophore within small animal in vivo. However, limited-projection FMT reconstruction suffers from severe ill-posedness because only limited projections are used for reconstruction. To alleviate the ill-posedness, a feasible region extraction strategy based on a double mesh is presented for limited-projection FMT. First, an initial result is rapidly recovered using a coarse discretization mesh. Then, the reconstructed fluorophore area in the initial result is selected as a feasible region to guide the reconstruction using a fine discretization mesh. Simulation experiments on a digital mouse and small animal experiment in vivo are performed to validate the proposed strategy. It demonstrates that the presented strategy provides a good distribution of fluorophore with limited projections of fluorescence measurements. Hence, it is suitable for reconstruction of limited-projection FMT.

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Li, Bo, Jing Du, and Xiao-Ping Zhang. "Feature extraction using maximum nonparametric margin projection." Neurocomputing 188 (May 2016): 225–32. http://dx.doi.org/10.1016/j.neucom.2014.11.105.

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Hainaut, Donatien, and Michel Denuit. "WAVELET-BASED FEATURE EXTRACTION FOR MORTALITY PROJECTION." ASTIN Bulletin 50, no. 3 (June 25, 2020): 675–707. http://dx.doi.org/10.1017/asb.2020.18.

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AbstractWavelet theory is known to be a powerful tool for compressing and processing time series or images. It consists in projecting a signal on an orthonormal basis of functions that are chosen in order to provide a sparse representation of the data. The first part of this article focuses on smoothing mortality curves by wavelets shrinkage. A chi-square test and a penalized likelihood approach are applied to determine the optimal degree of smoothing. The second part of this article is devoted to mortality forecasting. Wavelet coefficients exhibit clear trends for the Belgian population from 1965 to 2015, they are easy to forecast resulting in predicted future mortality rates. The wavelet-based approach is then compared with some popular actuarial models of Lee–Carter type estimated fitted to Belgian, UK, and US populations. The wavelet model outperforms all of them.

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6

Xu, Jie, and Shengli Xie. "Recursive locality preserving projection for feature extraction." Soft Computing 20, no. 10 (June 21, 2015): 4099–109. http://dx.doi.org/10.1007/s00500-015-1745-y.

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7

Yuan, Sen, Xia Mao, and Lijiang Chen. "Sparsity Regularization Discriminant Projection for Feature Extraction." Neural Processing Letters 49, no. 2 (April 24, 2018): 539–53. http://dx.doi.org/10.1007/s11063-018-9842-4.

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8

Li, Guo Ping, and Zhao Yan. "The Method of Character Recognition Based on Projection Transformation Combined with LS-SVM." Advanced Materials Research 468-471 (February 2012): 3050–55. http://dx.doi.org/10.4028/www.scientific.net/amr.468-471.3050.

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A method of character feature extraction based on circular projection transformation is proposed. First, it transforms the Cartesian coordinates into polar coordinates using the centroid of character image as the pole. It makes circular projection calculation and generates character feature vector by transforming 2-D character image into 1-D projection curves. Based on circular projection transformation feature extraction, the least squares support vector machine (LS-SVM) is introduced into the small character set embossed concave-convex character recognition.It adopted LS-SVM training software in the experiment for the simulation of embossed concave-convex characters’ number set. It studied on the effect of different core function and multi-classifier method, and compared with the results of neural networks, pattern matching and other identification classification methods.The experiment results show that the character feature has the scale and rotation invariance by the method of circular projection transformation extraction. Which combined with LS-SVM method has high recognition rate and more practicability in recognition small character set .

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9

Liu, Xiao-Zhang, and Guan Yang. "Block-Wise Two-Dimensional Maximum Margin Criterion for Face Recognition." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/875090.

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Maximum margin criterion (MMC) is a well-known method for feature extraction and dimensionality reduction. However, MMC is based on vector data and fails to exploit local characteristics of image data. In this paper, we propose a two-dimensional generalized framework based on a block-wise approach for MMC, to deal with matrix representation data, that is, images. The proposed method, namely, block-wise two-dimensional maximum margin criterion (B2D-MMC), aims to find local subspace projections using unilateral matrix multiplication in each block set, such that in the subspace a block is close to those belonging to the same class but far from those belonging to different classes. B2D-MMC avoids iterations and alternations as in current bilateral projection based two-dimensional feature extraction techniques by seeking a closed form solution of one-side projection matrix for each block set. Theoretical analysis and experiments on benchmark face databases illustrate that the proposed method is effective and efficient.

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YANG, JIAN, JING-YU YANG, ALEJANDRO F. FRANGI, and DAVID ZHANG. "UNCORRELATED PROJECTION DISCRIMINANT ANALYSIS AND ITS APPLICATION TO FACE IMAGE FEATURE EXTRACTION." International Journal of Pattern Recognition and Artificial Intelligence 17, no. 08 (December 2003): 1325–47. http://dx.doi.org/10.1142/s0218001403002903.

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In this paper, a novel image projection analysis method (UIPDA) is first developed for image feature extraction. In contrast to Liu's projection discriminant method, UIPDA has the desirable property that the projected feature vectors are mutually uncorrelated. Also, a new LDA technique called EULDA is presented for further feature extraction. The proposed methods are tested on the ORL and the NUST603 face databases. The experimental results demonstrate that: (i) UIPDA is superior to Liu's projection discriminant method and more efficient than Eigenfaces and Fisherfaces; (ii) EULDA outperforms the existing PCA plus LDA strategy; (iii) UIPDA plus EULDA is a very effective two-stage strategy for image feature extraction.

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Li, Bo, Chao Wang, and De-Shuang Huang. "Supervised feature extraction based on orthogonal discriminant projection." Neurocomputing 73, no. 1-3 (December 2009): 191–96. http://dx.doi.org/10.1016/j.neucom.2008.09.030.

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Wu, Songsong, Mingming Sun, and Jingyu Yang. "Stochastic neighbor projection on manifold for feature extraction." Neurocomputing 74, no. 17 (October 2011): 2780–89. http://dx.doi.org/10.1016/j.neucom.2011.03.036.

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YILDIRIM, Savaş. "Improving word embeddings projection for Turkish hypernym extraction." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 27, no. 6 (November 26, 2019): 4418–28. http://dx.doi.org/10.3906/elk-1903-65.

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Fang, Xiaozhao, Na Han, Jigang Wu, Yong Xu, Jian Yang, Wai Keung Wong, and Xuelong Li. "Approximate Low-Rank Projection Learning for Feature Extraction." IEEE Transactions on Neural Networks and Learning Systems 29, no. 11 (November 2018): 5228–41. http://dx.doi.org/10.1109/tnnls.2018.2796133.

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15

Liang, Xing Zhu, Yu E. Lin, and Jing Zhao Li. "Two-Dimensional Orthogonal Unsupervised Discriminant Projection." Advanced Materials Research 542-543 (June 2012): 1343–46. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.1343.

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Unsupervised Discriminant Projection (UDP) is one of the most promising feature extraction methods. However, UDP suffers from the small sample size problem and the optimal basis vectors obtained by the UDP are nonorthogonal. In this paper, we present a new method called Two-dimensional Orthogonal Unsupervised Discriminant Projection (2DOUDP), which is not necessary to convert the image matrix into high-dimensional image vector and does not suffer the small sample size problem. To further improve the recognition performance, the orthogonal projection matrix obtained based on Gram–Schmidt orthogonalization is given. Experimental results on ORL database indicate that the proposed 2DOUDP method achieves better recognition rate than the UDP method and some other orthogonal feature extraction algorithms.

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Zhou, Jian, Hong Gang Bu, and Jun Wang. "Feature Extraction Using Auto-Regression Spectral Analysis for Fabric Defect Detection." Advanced Materials Research 175-176 (January 2011): 366–70. http://dx.doi.org/10.4028/www.scientific.net/amr.175-176.366.

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A new feature extraction method for fabric defect detection is proposed, which is based on one-dimensional projection series of fabric images. By using horizontal projection and vertical projection of the image, the characteristics of periodicity and orientation of fabric texture can be fully utilized. In terms of detection defects, it helps acquire information at most, and the computational complexity can also be greatly decreased with one-dimensional projection series. The proposed new method, named Auto-Regressive spectral analysis (AR), is a kind of modern spectral analysis method which is very suitable for analyzing short data with a high spectral resolution. The Burg algorithm is applied to estimate the AR spectrum. Finally, t-test is applied to verify the effectiveness of AR spectral features. This approach has been applied to various cases of defect detections with satisfactory results.

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Hu, Haoshuang, and Da-Zheng Feng. "Minimum Eigenvector Collaborative Representation Discriminant Projection for Feature Extraction." Sensors 20, no. 17 (August 24, 2020): 4778. http://dx.doi.org/10.3390/s20174778.

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High-dimensional signals, such as image signals and audio signals, usually have a sparse or low-dimensional manifold structure, which can be projected into a low-dimensional subspace to improve the efficiency and effectiveness of data processing. In this paper, we propose a linear dimensionality reduction method—minimum eigenvector collaborative representation discriminant projection—to address high-dimensional feature extraction problems. On the one hand, unlike the existing collaborative representation method, we use the eigenvector corresponding to the smallest non-zero eigenvalue of the sample covariance matrix to reduce the error of collaborative representation. On the other hand, we maintain the collaborative representation relationship of samples in the projection subspace to enhance the discriminability of the extracted features. Also, the between-class scatter of the reconstructed samples is used to improve the robustness of the projection space. The experimental results on the COIL-20 image object database, ORL, and FERET face databases, as well as Isolet database demonstrate the effectiveness of the proposed method, especially in low dimensions and small training sample size.

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18

Zhang, Bao Feng, Ying Kui Jiao, Zhi Jun Ma, Yong Chen Li, and Jun Chao Zhu. "A Method of Features Extraction Based on Fisheye Image." Applied Mechanics and Materials 668-669 (October 2014): 1029–32. http://dx.doi.org/10.4028/www.scientific.net/amm.668-669.1029.

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In this paper, feature extraction algorithm based on spherical perspective projection model for the matching fisheye image is proposed. The fisheye image is mapped to the image plane through spherical mapping. Then the diffusion equation is formed by convolution of the image projection and spherical Gaussian function. The feature points of image are extracted based on the SIFT at the scale of spherical correlation function. Compared with SURF(Speeded Up Robust Features), more feature points in a shorter time are obtained.

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Chen, Xue Song, Cheng Wang, Xue Jun Xu, Hong Bo Zhu, and Shao Hua Jiang. "Skeleton-Based Feature Extraction Method for Two-Dimensional Potential Energy." Advanced Materials Research 139-141 (October 2010): 2051–54. http://dx.doi.org/10.4028/www.scientific.net/amr.139-141.2051.

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A good feature extraction method can improve the performance of pattern recognition system or classification system. Using potential energy theory into binary image feature extraction and feature store is a new method for image processing. The skeleton can be better display the whole features of the object. In target recognition system, using potential energy of skeleton-point projection into the plane coordinate system. The method can be better to show a skeleton in the structural feature. In addition, it can better avoid the matrix storage redundancy. In all energy projection method, potential energy projection is better shown its superiority in the structure information, the time of consumption and the storage space. The skeleton potential energy can be used in target recognition and target classification field and so on.

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Cardinaletti, Anna. "Cliticization as Extraction: The Big DP Hypothesis Revisited." Revista da Associação Portuguesa de Linguística, no. 5 (November 21, 2019): 1–16. http://dx.doi.org/10.26334/2183-9077/rapln5ano2019a1.

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In this paper, it is argued that cliticization is extraction out of a big DP. Big DPs contain both clitic pronouns and associate DP arguments (Torrego, 1995; Uriagereka, 1995, 2005, among others). The DP can be pronounced, which gives rise to a clitic doubling configuration, or can be null, in which case a simple clitic configuration is obtained. This hypothesis allows us to explain not only the limited movement possibilities of clitic pronouns (cf. Belletti 1999), but also their restricted inventory: Not all complements can be cliticized. If clitic pronouns are reduced maximal projections (Cardinaletti and Starke, 1999), the big DP hypothesis needs to be revisited. Clitic pronouns cannot be assumed to be the heads of big DPs, as was suggested in the abovementioned proposals. The proposal is that clitic pronouns are merged as specifiers of the highest projection of big DPs, namely K(ase)P, which also captures the fact that they are the only elements which bear morphological case in languages like Italian.

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21

Kim, Seokhwan, Minwoo Jeong, Jonghoon Lee, and Gary Geunbae Lee. "Cross-Lingual Annotation Projection for Weakly-Supervised Relation Extraction." ACM Transactions on Asian Language Information Processing 13, no. 1 (February 2014): 1–26. http://dx.doi.org/10.1145/2529994.

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22

Huang, Pu, Caikou Chen, Zhenmin Tang, and Zhangjing Yang. "Discriminant similarity and variance preserving projection for feature extraction." Neurocomputing 139 (September 2014): 180–88. http://dx.doi.org/10.1016/j.neucom.2014.02.047.

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23

Wang, Zhan, Qiuqi Ruan, and Gaoyun An. "Projection-optimal local Fisher discriminant analysis for feature extraction." Neural Computing and Applications 26, no. 3 (November 4, 2014): 589–601. http://dx.doi.org/10.1007/s00521-014-1768-9.

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Huang, Pu, Tao Li, Guangwei Gao, Yazhou Yao, and Geng Yang. "Collaborative representation based local discriminant projection for feature extraction." Digital Signal Processing 76 (May 2018): 84–93. http://dx.doi.org/10.1016/j.dsp.2018.02.009.

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Zhang, Qi Rong, and Zhong Shi He. "Two-Dimensional Locality Discriminant Preserving Projections for Face Recognition." Advanced Materials Research 121-122 (June 2010): 391–98. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.391.

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In this paper, we propose a new face recognition approach for image feature extraction named two-dimensional locality discriminant preserving projections (2DLDPP). Two-dimensional locality preserving projections (2DLPP) can direct on 2D image matrixes. So, it can make better recognition rate than locality preserving projection. We investigate its more. The 2DLDPP is to use modified maximizing margin criterion (MMMC) in 2DLPP and set the parameter optimized to maximize the between-class distance while minimize the within-class distance. Extensive experiments are performed on ORL face database and FERET face database. The 2DLDPP method achieves better face recognition performance than PCA, 2DPCA, LPP and 2DLPP.

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Intrator, Nathan, and Joshua I. Gold. "Three-Dimensional Object Recognition Using an Unsupervised BCM Network: The Usefulness of Distinguishing Features." Neural Computation 5, no. 1 (January 1993): 61–74. http://dx.doi.org/10.1162/neco.1993.5.1.61.

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We propose an object recognition scheme based on a method for feature extraction from gray level images that corresponds to recent statistical theory, called projection pursuit, and is derived from a biologically motivated feature extracting neuron. To evaluate the performance of this method we use a set of very detailed psychophysical three-dimensional object recognition experiments (Bülthoff and Edelman 1992).

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Khoa, Truong Quang Dang, Ho Huu Minh Tam, and Vo Van Toi. "Extracting Fetal Electrocardiogram from Being Pregnancy Based on Nonlinear Projection." Mathematical Problems in Engineering 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/439084.

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Fetal heart rate extraction from the abdominal ECG is of great importance due to the information that carries in assessing appropriately the fetus well-being during pregnancy. In this paper, we describe a method to suppress the maternal signal and noise contamination to discover the fetal signal in a single-lead fetal ECG recordings. We use a locally linear phase space projection technique which has been used for noise reduction in deterministically chaotic signals. Henceforth, this method is capable of extracting fetal signal even when noise and fetal component are of comparable amplitude. The result is much better if the noise is much smaller (Pwave andTwave can be discovered).

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Jang, Seok-Woo. "Extraction of mosaic regions through projection and filtering of features from image big data." Computer Science and Information Systems 18, no. 2 (2021): 553–74. http://dx.doi.org/10.2298/csis200227010j.

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When uploading multimedia data such as photos or videos on social network services, websites, and so on, certain parts of the human body or personal information are often exposed. Therefore, it is frequent that the face of a person is blurred out to protect the portrait right of a particular person, and that repulsive objects are covered with mosaic blocks to prevent others from feeling disgusted. In this paper, an algorithm that detects mosaic regions blurring out certain blocks based on the edge projection is proposed. The proposed algorithm initially detects the edge and uses the horizontal and vertical line edge projections to detect the mosaic candidate blocks. Subsequently, geometrical features such as size, aspect ratio and compactness are used to filter the candidate mosaic blocks, and the actual mosaic blocks are finally detected. The experiment results showed that the proposed algorithm detected mosaic blocks more accurately than other methods.

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DiFrancesco, Paul-Mark, David Bonneau, and D. Jean Hutchinson. "The Implications of M3C2 Projection Diameter on 3D Semi-Automated Rockfall Extraction from Sequential Terrestrial Laser Scanning Point Clouds." Remote Sensing 12, no. 11 (June 10, 2020): 1885. http://dx.doi.org/10.3390/rs12111885.

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Rockfall inventories are essential to quantify a rockfall activity and characterize the hazard. Terrestrial laser scanning and advancements in processing algorithms have resulted in three-dimensional (3D) semi-automatic extraction of rockfall events, permitting detailed observations of evolving rock masses. Currently, multiscale model-to-model cloud comparison (M3C2) is the most widely used distance computation method used in the geosciences to evaluate 3D changing features, considering the time-sequential spatial information contained in point clouds. M3C2 operates by computing distances using points that are captured within a projected search cylinder, which is locally oriented. In this work, we evaluated the effect of M3C2 projection diameter on the extraction of 3D rockfalls and the resulting implications on rockfall volume and shape. Six rockfall inventories were developed for a highly active rock slope, each utilizing a different projection diameter which ranged from two to ten times the point spacing. The results indicate that the greatest amount of change is extracted using an M3C2 projection diameter equal to, or slightly larger than, the point spacing, depending on the variation in point spacing. When the M3C2 projection diameter becomes larger than the changing area on the rock slope, the change cannot be identified and extracted. Inventory summaries and illustrations depict the influence of spatial averaging on the semi-automated rockfall extraction, and suggestions are made for selecting the optimal projection diameter. Recommendations are made to improve the methods used to semi-automatically extract rockfall from sequential point clouds.

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Newman, Julius, Richard L. Dolsky, and Abram Nguyen. "Facial Profileplasty by Liposuction Extraction." Otolaryngology–Head and Neck Surgery 93, no. 6 (December 1985): 718–31. http://dx.doi.org/10.1177/019459988509300605.

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Facial profileplasty can include the removal of abnormal fat deposits In the face and neck as an Individual begins to age and, In many cases, the correction of chin projection to achieve a better neck-chin contour line. In this report we retrospectively analyze the complications and results from 142 patients undergoing facial liposuction alone or In combination with chin augmentation or rhytldectomy during a follow-up period of 14 months, the liposuction technique offers very low morbidity and Is superior to conventional exclslonal methods.

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Wang, Ge, Wenxiang Cong, Haiou Shen, and Yu Zou. "Varying Collimation for Dark-Field Extraction." International Journal of Biomedical Imaging 2009 (2009): 1–7. http://dx.doi.org/10.1155/2009/847537.

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Although x-ray imaging is widely used in biomedical applications, biological soft tissues have small density changes, leading to low contrast resolution for attenuation-based x-ray imaging. Over the past years, x-ray small-angle scattering was studied as a new contrast mechanism to enhance subtle structural variation within the soft tissue. In this paper, we present a detection method to extract this type of x-ray scattering data, which are also referred to as dark-field signals. The key idea is to acquire an x-ray projection multiple times with varying collimation before an x-ray detector array. The projection data acquired with a collimator of a sufficiently high collimation aspect ratio contain mainly the primary beam with little scattering, while the data acquired with an appropriately reduced collimation aspect ratio include both the primary beam and small-angle scattering signals. Then, analysis of these corresponding datasets will produce desirable dark-field signals; for example, via digitally subtraction. In the numerical experiments, the feasibility of our dark-field detection technology is demonstrated in Monte Carlo simulation. The results show that the acquired dark field signals can clearly reveal the structural information of tissues in terms of Rayleigh scattering characteristics.

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YIN, Hai-bing, Zhao LIU, Ya-dong LIU, and De-wen HU. "Unsupervised spike extraction and classification based on locality preserving projection." Journal of Computer Applications 30, no. 9 (November 30, 2010): 2559–62. http://dx.doi.org/10.3724/sp.j.1087.2010.02559.

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Deshpande, Anand, Prashant P. Patavardhan, and D. H. Rao. "Iterated Back Projection Based Super-Resolution for Iris Feature Extraction." Procedia Computer Science 48 (2015): 269–75. http://dx.doi.org/10.1016/j.procs.2015.04.181.

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CHEN, JIUN-LIN, and HSI-JIAN LEE. "AN EFFICIENT ALGORITHM FOR FORM STRUCTURE EXTRACTION USING STRIP PROJECTION." Pattern Recognition 31, no. 9 (September 1998): 1353–68. http://dx.doi.org/10.1016/s0031-3203(97)00156-8.

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Ma, Chunfei, June-Young Jung, Seung-Wook Kim, and Sung-Jea Ko. "Random projection-based partial feature extraction for robust face recognition." Neurocomputing 149 (February 2015): 1232–44. http://dx.doi.org/10.1016/j.neucom.2014.09.004.

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Jianchang Mao and A. K. Jain. "Artificial neural networks for feature extraction and multivariate data projection." IEEE Transactions on Neural Networks 6, no. 2 (March 1995): 296–317. http://dx.doi.org/10.1109/72.363467.

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Wei, Yi-Kang, and Cong Jin. "Locality sensitive discriminant projection for feature extraction and face recognition." Journal of Electronic Imaging 28, no. 04 (August 24, 2019): 1. http://dx.doi.org/10.1117/1.jei.28.4.043028.

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Zhang, Nan, and Jian Yang. "Low-rank representation based discriminative projection for robust feature extraction." Neurocomputing 111 (July 2013): 13–20. http://dx.doi.org/10.1016/j.neucom.2012.12.012.

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Kim, W. S., and Z. H. Cho. "Cardiac cycle extraction from projection data using static signal suppression." Magnetic Resonance in Medicine 24, no. 1 (March 1992): 182–88. http://dx.doi.org/10.1002/mrm.1910240121.

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Zhang, Wei, Peipei Kang, Xiaozhao Fang, Luyao Teng, and Na Han. "Joint sparse representation and locality preserving projection for feature extraction." International Journal of Machine Learning and Cybernetics 10, no. 7 (June 20, 2018): 1731–45. http://dx.doi.org/10.1007/s13042-018-0849-y.

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Kim, Han-Bin, and Rae-Hong Park. "Extraction of periodicity vectors from structural textures using projection information." Pattern Recognition Letters 11, no. 9 (September 1990): 625–30. http://dx.doi.org/10.1016/0167-8655(90)90015-t.

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Angermann, Christoph, and Markus Haltmeier. "Deep structure learning using feature extraction in trained projection space." Computers & Electrical Engineering 92 (June 2021): 107097. http://dx.doi.org/10.1016/j.compeleceng.2021.107097.

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Chen, Yudong, Zhihui Lai, Jiajun Wen, and Can Gao. "Nuclear norm based two-dimensional sparse principal component analysis." International Journal of Wavelets, Multiresolution and Information Processing 16, no. 02 (March 2018): 1840002. http://dx.doi.org/10.1142/s0219691318400027.

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Two-Dimensional Principal Component Analysis (2D-PCA) is one of the most simple and effective feature extraction methods in the field of pattern recognition. However, the traditional 2D-PCA lacks robustness and the function of sparse feature extraction. In this paper, we propose a new feature extraction approach based on the traditional 2D-PCA, which is called Nuclear Norm Based Two-Dimensional Sparse Principal Component Analysis (N-2D-SPCA). To improve the robustness of 2D-PCA, we utilize nuclear norm to measure the reconstruction error of loss function. At the same time, we obtain sparse feature extraction by adding [Formula: see text]-norm and [Formula: see text]-norm regularization terms to the model. By designing an alternatively iterative algorithm, we can solve the optimization problem and learn a projection matrix for use with feature extraction. Besides, we present a bilateral projections model (BN-2D-SPCA) to further compress the dimensions of the feature matrix. We verify the effectiveness of our method on four benchmark face databases including AR, ORL, FERET and Yale databases. Experimental results show that the proposed method is more robust than some state-of-the-art methods and the traditional 2D-PCA.

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Dorobanțiu, Alexandru, Valentin Ogrean, and Remus Brad. "Coronary Centerline Extraction from CCTA Using 3D-UNet." Future Internet 13, no. 4 (April 19, 2021): 101. http://dx.doi.org/10.3390/fi13040101.

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The mesh-type coronary model, obtained from three-dimensional reconstruction using the sequence of images produced by computed tomography (CT), can be used to obtain useful diagnostic information, such as extracting the projection of the lumen (planar development along an artery). In this paper, we have focused on automated coronary centerline extraction from cardiac computed tomography angiography (CCTA) proposing a 3D version of U-Net architecture, trained with a novel loss function and with augmented patches. We have obtained promising results for accuracy (between 90–95%) and overlap (between 90–94%) with various network training configurations on the data from the Rotterdam Coronary Artery Centerline Extraction benchmark. We have also demonstrated the ability of the proposed network to learn despite the huge class imbalance and sparse annotation present in the training data.

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Xue, Dan, and Weiqi Yuan. "Dynamic Partition Gaussian Crack Detection Algorithm Based on Projection Curve Distribution." Sensors 20, no. 14 (July 17, 2020): 3973. http://dx.doi.org/10.3390/s20143973.

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When detecting the cracks in the tunnel lining image, due to uneven illumination, there are generally differences in brightness and contrast between the cracked pixels and the surrounding background pixels as well as differences in the widths of the cracked pixels, which bring difficulty in detecting and extracting cracks. Therefore, this paper proposes a dynamic partitioned Gaussian crack detection algorithm based on the projection curve distribution. First, according to the distribution of the image projection curve, the background pixels are dynamically partitioned. Second, a new dynamic partitioned Gaussian (DPG) model was established, and the set rules of partition boundary conditions, partition number, and partition corresponding threshold were defined. Then, the threshold and multi-scale Gaussian factors corresponding to different crack widths were substituted into the Gaussian model to detect cracks. Finally, crack morphology and the breakpoint connection algorithm were combined to complete the crack extraction. The algorithm was tested on the lining gallery captured on the site of the Tang-Ling-Shan Tunnel in Liaoning Province, China. The optimal parameters in the algorithm were estimated through the Recall, Precision, and Time curves. From two aspects of qualitative and quantitative analysis, the experimental results demonstrate that this algorithm could effectively eliminate the effect of uneven illumination on crack detection. After detection, Recall could reach more than 96%, and after extraction, Precision was increased by more than 70%.

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MORIN, EMMANUEL, and AMIR HAZEM. "Exploiting unbalanced specialized comparable corpora for bilingual lexicon extraction." Natural Language Engineering 22, no. 4 (June 15, 2016): 575–601. http://dx.doi.org/10.1017/s1351324916000140.

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AbstractThe main work in bilingual lexicon extraction from comparable corpora is based on the implicit hypothesis that corpora are balanced in terms of size. However, the historical context-based projection method is relatively insensitive to the size of each part of the comparable corpus. Within this context, we have carried out a study on the influence of unbalanced specialized comparable corpora and on the quality of bilingual terminology extraction by doing different experiments. Moreover, we have introduced a strategy into the context-based projection method to re-estimate word co-occurrence observations. This is done by using smoothing or prediction techniques that boost the observations of word co-occurrences which are mainly useful for the smallest part of an unbalanced comparable corpus. Our results show that the use of unbalanced specialized comparable corpora results in a significant improvement in the quality of extracted lexicons.

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Friedmann, Drew, Albert Pun, Eliza L. Adams, Jan H. Lui, Justus M. Kebschull, Sophie M. Grutzner, Caitlin Castagnola, Marc Tessier-Lavigne, and Liqun Luo. "Mapping mesoscale axonal projections in the mouse brain using a 3D convolutional network." Proceedings of the National Academy of Sciences 117, no. 20 (May 1, 2020): 11068–75. http://dx.doi.org/10.1073/pnas.1918465117.

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The projection targets of a neuronal population are a key feature of its anatomical characteristics. Historically, tissue sectioning, confocal microscopy, and manual scoring of specific regions of interest have been used to generate coarse summaries of mesoscale projectomes. We present here TrailMap, a three-dimensional (3D) convolutional network for extracting axonal projections from intact cleared mouse brains imaged by light-sheet microscopy. TrailMap allows region-based quantification of total axon content in large and complex 3D structures after registration to a standard reference atlas. The identification of axonal structures as thin as one voxel benefits from data augmentation but also requires a loss function that tolerates errors in annotation. A network trained with volumes of serotonergic axons in all major brain regions can be generalized to map and quantify axons from thalamocortical, deep cerebellar, and cortical projection neurons, validating transfer learning as a tool to adapt the model to novel categories of axonal morphology. Speed of training, ease of use, and accuracy improve over existing tools without a need for specialized computing hardware. Given the recent emphasis on genetically and functionally defining cell types in neural circuit analysis, TrailMap will facilitate automated extraction and quantification of axons from these specific cell types at the scale of the entire mouse brain, an essential component of deciphering their connectivity.

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Liu, Xi Wen, and Chao Ying Liu. "Paper Currency Image Features Extraction and Recognition Based on Gabor Filter." Applied Mechanics and Materials 635-637 (September 2014): 1030–34. http://dx.doi.org/10.4028/www.scientific.net/amm.635-637.1030.

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The paper currency image recognition method based on Gabor filter set is discussed in this paper. According to the paper currency image features, the suitable parameters of Gabor filter set are selected for the extraction of paper currency characteristics, the multi-scale and multi-directional texture characteristics of paper currency image are gotten; then the texture images are meshed, and the row and column projection sum of grid pixels' average grey are calculated, finally, the template match method based on grid projection characteristics is used for paper currency recognition. Experiments show that, this method has strong anti-interference ability, it can raise the recognition rate of old or dirty paper currency greatly, and it costs little time.

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Gong, Wei Yu, and Fang Xia Lu. "Research on Locality Preserving Discriminant Projection Algorithm Based on Gabor for Face Expression Recognition." Applied Mechanics and Materials 721 (December 2014): 766–70. http://dx.doi.org/10.4028/www.scientific.net/amm.721.766.

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For the problem of features extraction and dimensionality reduction of expression recognition, the paper proposes Gabor Locality Preserving Discriminant Projection (GLPDP) algorithm, which is based on Gabor Wavelet. Firstly, we use Gabor wavelet transform to have an expression feature extraction. Secondly, we improved the locality preserving projection (LPP) algorithm, introducing scatter difference in the LPP objective function to increase divergence constraints among the sample classes and extracts more discriminated features while having the dimensionality reduction. Finally, we use the nearest neighbor classifier to have a classification for expression category. The effectiveness of the proposed methods is validated through the experimental results on JAFFE and Cohn-Kanade Facial expression databases.

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YANG, JIANWEI, LIANG ZHANG, and ZHENGDA LU. "THE MELLIN CENTRAL PROJECTION TRANSFORM." ANZIAM Journal 58, no. 3-4 (March 7, 2017): 256–64. http://dx.doi.org/10.1017/s1446181116000341.

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The central projection transform can be employed to extract invariant features by combining contour-based and region-based methods. However, the central projection transform only considers the accumulation of the pixels along the radial direction. Consequently, information along the radial direction is inevitably lost. In this paper, we propose the Mellin central projection transform to extract affine invariant features. The radial factor introduced by the Mellin transform, makes up for the loss of information along the radial direction by the central projection transform. The Mellin central projection transform can convert any object into a closed curve as a central projection transform, so the central projection transform is only a special case of the Mellin central projection transform. We prove that closed curves extracted from the original image and the affine transformed image by the Mellin central projection transform satisfy the same affine transform relationship. A method is provided for the extraction of affine invariants by employing the area of closed curves derived by the Mellin central projection transform. Experiments have been conducted on some printed Chinese characters and the results establish the invariance and robustness of the extracted features.

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