Fine-grained Recognition of Plants from Images

Result code in 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>

Result on the web

<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

—

Result language

angličtina

Original language name

Fine-grained Recognition of Plants from Images

Original language description

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.

Czech name

—

Czech description

—

Type

O - Miscellaneous

CEP classification

JD - Use of computers, robotics and its application

OECD FORD branch

—

Project

<a href="/en/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Center for Large Scale Multi-modal Data Interpretation</a><br>

Continuities

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

Publication year

2016

Confidentiality

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