一種基于Hough變換和神經(jīng)網(wǎng)絡(luò)的分層類星體識(shí)別方法
A STRATIFIED APPROACH FOR QUASAR RECOGNITION BASED ON HOUGH TRANSFORM AND NEURAL NETWORK
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摘要: 類星體是宇宙中最明亮、密集的天體。它產(chǎn)生于宇宙誕生早期,具有重要的研究價(jià)值。觀測的類星體光譜由于紅移現(xiàn)象,光譜向長波方向偏移,因此識(shí)別類星體觀測光譜中的發(fā)射線和確定類星體的紅移是類星體識(shí)別的主要目標(biāo)。類星體光譜固有的高噪聲和觀測光譜特性,給類星體識(shí)別帶來很大困難。一般來說基于規(guī)則的直接匹配方法在類星體識(shí)別中效果不佳。本文介紹一種神經(jīng)網(wǎng)絡(luò)和Hough變換(HT)結(jié)合的類星體自動(dòng)識(shí)別方法。該方法具有簡單、快速、高效、魯棒性強(qiáng)和通用性強(qiáng)等特點(diǎn)。
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關(guān)鍵詞:
- 類星體; Hough變換（HT）; 神經(jīng)網(wǎng)絡(luò)
Abstract: Quasar Objects (QSOs) are detectable at very large distance,with broad,red-shifted emission lines,strong ultraviolet and strong time variability of the optical light.QSOs play an important role in the research of the universe.The main purposes of quasar recog-nition are to identify the emission peaks in an observable quasar spectrum and to determine the observable quasars redshift value.Due to the inherent extremely noisy characteristics of quasar spectrums and the limitation of observable conditions,automatic quasar recognition is a hard problem to tackle,and the commonly used direct matching approaches based on rules are ineffective.This paper introduces a stratified approach based on Hough transform and neural network which is shown to be simple,efficient,robust and easy to generalize. -
Robert A M,Steven N S.Encyclopedia of Astronomy and Astrophysics.San Diego:Academic Press,1989,571-574.[2]中國科學(xué)院.LAMOST項(xiàng)目計(jì)劃建議書.1995年9月.[3]吳永東，馬頌德．多尺度形態(tài)濾波彈性匹配技術(shù)在類星體譜線識(shí)別中的應(yīng)用．中國圖形圖象學(xué)報(bào)，1997，2(1)：1-6．[4]焦李成．神經(jīng)網(wǎng)絡(luò)計(jì)算．西安：西安電于科技大學(xué)出版社，1993，第二章．[5]Janakiraman J,Hona Var V.Adaptive learning rate selection for backpropagation network.Pro-ceeding of SPIE｀93 Orlando,Florida:1993,1-17.[6]Illingworth J,Kittler J.A survey of the Hough transform[J].Computer Vision,Graphics,and Image Processing.1988,44(1):87-116[7]LeaVers V F.Survey which Hough transform[J].CVGIP:Image Understanding.1993,58(2):250-264[8]Palmer P L,Petrou M,Kittler J.Hough transform algorithm with a 2D hypothesis testing kernel[J].CVGIP:Image Understanding.1993,58(2):221-234 -
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