Fine-grained Recognition of Plants from Images

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F16%3A00307202" target="_blank" >RIV/68407700:21230/16:00307202 - isvavai.cz</a>

Výsledek na webu

<a href="http://cmp.felk.cvut.cz/pub/cmp/articles/sulc/Sulc-TR-2016-09.pdf" target="_blank" >http://cmp.felk.cvut.cz/pub/cmp/articles/sulc/Sulc-TR-2016-09.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Fine-grained Recognition of Plants from Images

Popis výsledku v původním jazyce

The work focuses on automatic and semi-automatic fine-grained recognition of plants from an image, set of images or videos. Computer vision based plant recognition is a challenging problem due to the variable appearance of plants, high intra-class variability and small inter-class differences, complex geometry and multi-scale hierarchical structure. Plant recognition tasks, ranging from identification of plants from specific plant organs, to general plant recognition and localization "in the wild", are discussed. State-of-the-art methods are reviewed and examples of applications are presented, such as mobile apps for plant recognition, automatic mapping of plant species distribution or automation in agriculture. Our contributions so far include the state-of-the-art methods for leaf and bark recognition using the Fast Features Invariant to Rotation and Scale of Texture, studies of texture- and color-based recognition methods, as well as a deep learning contribution to the PlantCLEF 2016 plant identification challenge, achieving very competitive results. The goals of the thesis are to further study the plant recognition tasks and propose methods suitable for detection, localization and recognition of plants from images at relatively low computational cost, making them suitable for the proposed applications. Extensions to related problems are discussed, including recognition of other natural objects and species, and other fine-grained recognition problems such as food product classification.

Název v anglickém jazyce

Fine-grained Recognition of Plants from Images

Popis výsledku anglicky

The work focuses on automatic and semi-automatic fine-grained recognition of plants from an image, set of images or videos. Computer vision based plant recognition is a challenging problem due to the variable appearance of plants, high intra-class variability and small inter-class differences, complex geometry and multi-scale hierarchical structure. Plant recognition tasks, ranging from identification of plants from specific plant organs, to general plant recognition and localization "in the wild", are discussed. State-of-the-art methods are reviewed and examples of applications are presented, such as mobile apps for plant recognition, automatic mapping of plant species distribution or automation in agriculture. Our contributions so far include the state-of-the-art methods for leaf and bark recognition using the Fast Features Invariant to Rotation and Scale of Texture, studies of texture- and color-based recognition methods, as well as a deep learning contribution to the PlantCLEF 2016 plant identification challenge, achieving very competitive results. The goals of the thesis are to further study the plant recognition tasks and propose methods suitable for detection, localization and recognition of plants from images at relatively low computational cost, making them suitable for the proposed applications. Extensions to related problems are discussed, including recognition of other natural objects and species, and other fine-grained recognition problems such as food product classification.

Druh

O - Ostatní výsledky

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

<a href="/cs/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Centrum pro multi-modální interpretaci dat velkého rozsahu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů