Tree leaves extraction in natural images: Comparative study of pre-processing tools and segmentation methods

International audienceIn this paper, we propose a comparative study of various segmentation methods applied to the extraction of tree leaves from natural images. This study follows the design of a mobile application, developed by Cerutti et al. (published in ReVeS Participation-Tree Species Classification Using Random Forests and Botanical Features. CLEF 2012), to highlight the impact of the choices made for segmentation aspects. All the tests are based on a database of 232 images of tree leaves depicted on natural background from smartphones acquisitions. We also propose to study the improvements, in terms of performance, by using pre-processing tools such as the interaction between the user and the application through an input stroke, as well as the use of color distance maps. The results presented in this paper shows that the method developed by Cerutti et al. (denoted Guided Active Contour), obtains the best score for almost all observation criteria. Finally we detail our online benchmark composed of 14 unsupervised methods and 6 supervised ones

The ImageCLEF 2012 Plant Identification Task

International audienceThe ImageCLEF's plant identification task provides a testbed for the system-oriented evaluation of plant identification, more precisely on the 126 tree species identification based on leaf images. Three types of image content are considered: Scan, Scan-like (leaf photographs with a white uniform background), and Photograph (unconstrained leaf with natural background). The main originality of this data is that it was specifically built through a citizen sciences initiative conducted by Tela Botanica, a French social network of amateur and expert botanists. This makes the task closer to the conditions of a real-world application. This overview presents more precisely the resources and assessments of task, summarizes the retrieval approaches employed by the participating groups, and provides an analysis of the main evaluation results. With a total of eleven groups from eight countries and with a total of 30 runs submitted, involving distinct and original methods, this second year pilot task confirms Image Retrieval community interest for biodiversity and botany, and highlights further challenging studies in plant identification

A Model-Based Approach for Compound Leaves Understanding and Identification

International audienceIn this paper, we propose a specific method for the identification of compound-leaved tree species, with the aim of integrating it in an educational smartphone application. Our work is based on dedicated shape models for compound leaves, designed to estimate the number and shape of leaflets. A deformable template approach is used to fit these models and produce a high-level interpretation of the image content. The resulting models are later used for the segmentation of leaves in both plain and natural background images, by the use of multiple region-based active contours. Combined with other botany-inspired descriptors accounting for the morphological properties of the leaves, we propose a classification method that makes a semantic interpretation possible. Results are presented over a set of more than 1000 images from 17 European tree species, and an integration in the existing mobile application Folia is considered

Reconnaissance de feuilles d'arbres par fusion de décisions partielles

National audienceDans le cadre du développement d'une application Smartphone destinée à la reconnaissance des espèces d'arbres, une stratégie basée sur des sous-classifieurs a été mise en place pour reconnaître les feuilles à partir des caractéristiques liées à la base, au sommet et au contour. La théorie des fonctions de croyance est appliquée sur la sortie de chaque sous-classifieur afin de raffiner les résultats en diminuant l'effet de l'incertitude qui existe sur les caractéristiques des feuilles. La décision finale sur l'espèce de feuille est prise en transformant la croyance en probabilité pignistique et en accumulant les probabilités issues de chaque sous-classifieur pour chaque espèce. Les résultats démontrent que notre méthode de sous-classification et de décision obtient de bonnes performances

Efficient Image Segmentation and Segment-Based Analysis in Computer Vision Applications

This dissertation focuses on efficient image segmentation and segment-based object recognition in computer vision applications. Special attention is devoted to analyzing shape, of particular importance for our two applications: plant species identification from leaf photos, and object classification in remote sensing images. Additionally, both problems are bound by efficiency, constraining the choice of applicable methods: leaf recognition results are to be used within an interactive system, while remote sensing image analysis must scale well over very large image sets. Leafsnap was the first mobile app to provide automatic recognition of tree species, currently counting with over 1.7 million downloads. We present an overview of the mobile app and corresponding back end recognition system, as well as a preliminary analysis of user-submitted data. More than 1.7 million valid leaf photos have been uploaded by users, 1.3 million of which are GPS-tagged. We then focus on the problem of segmenting photos of leaves taken against plain light-colored backgrounds. These types of photos are used in practice within Leafsnap for tree species recognition. A good segmentation is essential in order to make use of the distinctive shape of leaves for recognition. We present a comparative experimental evaluation of several segmentation methods, including quantitative and qualitative results. We then introduce a custom-tailored leaf segmentation method that shows superior performance while maintaining computational efficiency. The other contribution of this work is a set of attributes for analysis of image segments. The set of attributes is designed for use in knowledge-based systems, so they are selected to be intuitive and easily describable. The attributes can also be computed efficiently, to allow applicability across different problems. We experiment with several descriptive measures from the literature and encounter certain limitations, leading us to introduce new attribute formulations and more efficient computational methods. Finally, we experiment with the attribute set on our two applications: plant species identification from leaf photos and object recognition in remote sensing images

Computer Vision Techniques for Ambient Intelligence Applications

Ambient Intelligence (AmI) is a muldisciplinary area which refers to environments that are sensitive and responsive to the presence of people and objects. The rapid progress of technology and simultaneous reduction of hardware costs characterizing the recent years have enlarged the number of possible AmI applications, thus raising at the same time new research challenges. In particular, one important requirement in AmI is providing a proactive support to people in their everyday working and free-time activities. To this aim, Computer Vision represents a core research track since only through suitable vision devices and techniques it is possible to detect elements of interest and understand the occurring events. The goal of this thesis is presenting and demonstrating efficacy of novel machine vision research contributes for different AmI scenarios: object keypoints analysis for Augmented Reality purpose, segmentation of natural images for plant species recognition and heterogeneous people identification in unconstrained environments

Ames Forester Vol. 52

Published Annually by the Ames Forestry Clu

ReVeS Participation - Tree Species Classification using Random Forests and Botanical Features

International audienceThis paper summarizes the participation of the ReVeS project to the ImageCLEF 2012 Plant Identification task. Aiming to develop a system for tree leaf identification on mobile devices, our method is designed to cope with the challenges of complex natural images and to enable a didactic interaction with the user. The approach relies on a two step model-driven segmentation and on the evaluation of high-level characteristics that make a semantic interpretation possible, as well as more generic shape features. All these descriptors are combined in a random forest classification algorithm, and their significance evaluated. Our team ranks 4th overall, 3rd on natural images, which constitutes a very satisfying performance with respect to the project's objectives

ReVeS Participation - Tree Species Classification using Random Forests and Botanical Features

International audienceThis paper summarizes the participation of the ReVeS project to the ImageCLEF 2012 Plant Identification task. Aiming to develop a system for tree leaf identification on mobile devices, our method is designed to cope with the challenges of complex natural images and to enable a didactic interaction with the user. The approach relies on a two step model-driven segmentation and on the evaluation of high-level characteristics that make a semantic interpretation possible, as well as more generic shape features. All these descriptors are combined in a random forest classification algorithm, and their significance evaluated. Our team ranks 4th overall, 3rd on natural images, which constitutes a very satisfying performance with respect to the project's objectives