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標題: Computer vision cracks the leaf code  
 
Wong
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Computer vision cracks the leaf code

Go to PNAS Homepage
>  Current Issue
> vol. 113 no. 12
> Peter Wilf,  3305–3310, doi: 10.1073/pnas.1524473113

Computer vision cracks the leaf code

Peter Wilfa,1,
Shengping Zhangb,c,1,
Sharat Chikkerurd,
Stefan A. Littlea,e,
Scott L. Wingf, and
Thomas Serreb,1

Author Affiliations

Edited by Andrew H. Knoll, Harvard University, Cambridge, MA, and approved February 1, 2016 (received for review December 14, 2015)

Significance

The botanical value of angiosperm leaf shape and venation (“leaf architecture”) is well known, but the astounding complexity and variation of leaves have thwarted efforts to access this underused resource. This challenge is central for paleobotany because most angiosperm fossils are isolated, unidentified leaves. We here demonstrate that a computer vision algorithm trained on several thousand images of diverse cleared leaves successfully learns leaf-architectural features, then categorizes novel specimens into natural botanical groups above the species level. The system also produces heat maps to display the locations of numerous novel, informative leaf characters in a visually intuitive way. With assistance from computer vision, the systematic and paleobotanical value of leaves is ready to increase significantly.


Abstract

Understanding the extremely variable, complex shape and venation characters of angiosperm leaves is one of the most challenging problems in botany. Machine learning offers opportunities to analyze large numbers of specimens, to discover novel leaf features of angiosperm clades that may have phylogenetic significance, and to use those characters to classify unknowns. Previous computer vision approaches have primarily focused on leaf identification at the species level. It remains an open question whether learning and classification are possible among major evolutionary groups such as families and orders, which usually contain hundreds to thousands of species each and exhibit many times the foliar variation of individual species. Here, we tested whether a computer vision algorithm could use a database of 7,597 leaf images from 2,001 genera to learn features of botanical families and orders, then classify novel images. The images are of cleared leaves, specimens that are chemically bleached, then stained to reveal venation. Machine learning was used to learn a codebook of visual elements representing leaf shape and venation patterns. The resulting automated system learned to classify images into families and orders with a success rate many times greater than chance. Of direct botanical interest, the responses of diagnostic features can be visualized on leaf images as heat maps, which are likely to prompt recognition and evolutionary interpretation of a wealth of novel morphological characters. With assistance from computer vision, leaves are poised to make numerous new contributions to systematic and paleobotanical studies.
leaf architecture
leaf venation
computer vision
sparse coding


圖片附件: 未标题-1 拷贝.jpg (2016-4-17 08:56 AM, 170.54 K)





古植物是化石的歌!
2016-4-17 08:56 AM#1
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oviraptor (偷蛋龍)
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Thanks for sharing. A living plants?



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2016-4-17 02:40 PM#2
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Wong
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计算机视觉破解叶子编码

    认识被子植物叶子多变而复杂的形状和脉式特征是植物学中最具挑战性的问题之一。机器学习提供了机会用来分析大量的标本,从而发现具有系统发育含义的被子植物支系的革新特征,据此来分类未知的标本。先前的计算机可视方法主要用于叶片种级水平的鉴定。机器是否可能学习和分类仍待商榷,特别是针对大的进化类群(科和目),通常包含成百上千的具有多次叶变异的种。本文中我们测试了一种计算机可视算法是否能利用一个由2001个属的7597幅叶图像数据库来学习植物学科和目的特征,进而分类新的图像。图像是通过化学漂洗处理过的透明叶标本,然后染色后显示了叶脉。机器学习可用来学习一种代表了叶形状和脉式的编码本。最终成功率远大于偶然性,自动化系统能学着把图像分类成科和目。其中最饶有植物学兴趣是,对叶图像上鉴别特征的反应能以热感应图的形式看见,它们可能促进对大量新形态特征的识别和演化解释。通过计算机视图的帮助,叶子对系统学和古植物学研究一定能做出很多新的贡献。

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[ 本帖最後由 Wong 於 2016-4-17 08:48 PM 編輯 ]




古植物是化石的歌!
2016-4-17 08:09 PM#3
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Wong
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回復 #2 oviraptor 的帖子

Here I grossly translated this English abstract into Chinese. The authors should make use of the cleared leaves of living angiosperms. The result will be promising and helpful for us to study fossil leaves. The first author Peter Wilf is a paleobotanist.



古植物是化石的歌!
2016-4-17 08:15 PM#4
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oviraptor (偷蛋龍)
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回復 #4 Wong 的帖子

Thanks



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2016-5-4 09:14 PM#5
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