Understanding leaves in natural images – A model-based approach for tree species identification

Cerutti, Guillaume; Tougne, Laure; Mille, Julien; Vacavant, Antoine; Coquin, Didier

doi:10.1016/j.cviu.2013.07.003

Cited by 106 publications

(52 citation statements)

References 27 publications

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“…Research on these areas has been motivated by several datasets showing leaves in isolation cut from plants and imaged individually, or showing leaves on the plant but with a leaf encompassing a large field of view (e.g., by imaging via a smart phone application). This problem has been addressed in an unsupervised [50,59], shape-based [13,14,30], and interactive [12][13][14] fashion.…”

Section: Related Workmentioning

confidence: 99%

Leaf segmentation in plant phenotyping: a collation study

Scharr

Minervini

French

et al. 2015

Machine Vision and Applications

241

181

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Image-based plant phenotyping is a growing application domain of computer vision in agriculture. A key task is the segmentation of all individual leaves in images. Here we focus on the most common rosette model plants Arabidopsis and young tobacco. Although leaves do share appearance and shape characteristics, the presence of occlusions and variability in leaf shape and pose, as well as imaging conditions, render this problem challenging. The aim of this paper is to compare several leaf segmentation solutions on a unique and first of its kind dataset containing images from typical phenotyping experiments. In particular, we report and discuss methods and findings of a collection of submissions for the first Leaf Segmentation Challenge (LSC) of the Computer Vision Problems in Plant Phenotyping (CVPPP) workshop in 2014. Four methods are presented: three segment leaves via processing the distance transform in an unsupervised fashion, and the other via optimal template selection and Chamfer matching. Overall, we find that although separating plant from background can be achieved with satisfactory accuracy (>90% Dice score), individual leaf segmentation and counting remain challenging when leaves overlap. Besides, accuracy is lower for younger leaves. We find also that variability in datasets does affect outcomes. Our findings motivate further investigations and development of specialized algorithms for this particular application, and that challenges of this form are ideally suited for advancing the state of the art. Data are publicly available (http://www.plantphenotyping.org/CVPPP2014-dataset) to support future challenges beyond segmentation within this application domain.

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Section: Related Workmentioning

confidence: 99%

Leaf segmentation in plant phenotyping: a collation study

Scharr

Minervini

French

et al. 2015

Machine Vision and Applications

241

181

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“…In accordance with our previous work [4], our system extracts 4 compact sets of attributes to describe the morphological properties of a segmented leaf on a high-level of interpretation. Those sets are designed to capture the information relatively to 4 supposedly independent shape criteria used by botanists to describe leaves, namely the global shape (11 attributes), the basal and apical shapes (5 attributes each), and the margin shape (13 attributes).…”

Section: A Leaf Datasetmentioning

confidence: 85%

Leaf Species Classification Based on a Botanical Shape Sub-classifier Strategy

Liu¹,

Coquin²,

Valet³

et al. 2014

2014 22nd International Conference on Pattern Recognition

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Within the framework of a smartphone-based application, helping people to identify plant species in the wild, a sub-classifier strategy has been introduced. It aims at recognizing the botanical properties of a leaf, relatively to various global and local shape criteria used in flora books. A decision function is applied on these classified shape categories to produce a final decision on the species of the leaf. In this paper, the fusion strategy and its corresponding Random-Forest-based sub-classifiers are described. The results of these algorithms for botanical leaf shape recognition demonstrate that our classification algorithm can achieve good performance on leaf species identification while providing the user with relevant information for educational purposes.

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“…If it is available, shape information can be used to reduce the computational complexity when finding leaves. However, leaf shape descriptors are not designed to consider the nature of objects (Cerutti et al, 2013a). Thus, due to shape deformation and variation within an object class, a simple rigid model-based approach will usually fail (Sclaroff and Liu, 2001).…”

Section: Introductionmentioning

confidence: 98%