TreeLogy
2,0star
127 пікір
10 мың+
Жүктеп алынғандар
Барлығы үшін
info
Осы қолданба туралы
“Treelogy” is a mobile application, which can perform leaf-based tree identification among tree species in Turkey using one picture of a given leaf.
There is only handful of applications interested in tree identification and they are designed primarily for detecting North American and European tree species. There is no application which has a good performance and localization support for identifying tree species native to Turkey. This project is aimed to fill this gap.
While we are constructing this application, we worked on supervised learning for classification task. We focused on both Deep Learning (specifically Deep Convolutional Neural Networks) and Support Vector Machines. Tree identification process uses leaf image features gathered from Caffe, a convolutional neural network framework, and our image processing module.
After several experiments, we reached the optimal classification accuracy of 93.59% for 57 tree species. Experiments involve 16096 training and 3020 testing leaf images. According to our findings, we come to the following conclusion. Certain image processing procedures for extracting features such as shape and texture descriptors, which we have used in our project, does not produce features as feasible as convolutional neural networks.
"Created by group paY inekereG" => METU Students
There is only handful of applications interested in tree identification and they are designed primarily for detecting North American and European tree species. There is no application which has a good performance and localization support for identifying tree species native to Turkey. This project is aimed to fill this gap.
While we are constructing this application, we worked on supervised learning for classification task. We focused on both Deep Learning (specifically Deep Convolutional Neural Networks) and Support Vector Machines. Tree identification process uses leaf image features gathered from Caffe, a convolutional neural network framework, and our image processing module.
After several experiments, we reached the optimal classification accuracy of 93.59% for 57 tree species. Experiments involve 16096 training and 3020 testing leaf images. According to our findings, we come to the following conclusion. Certain image processing procedures for extracting features such as shape and texture descriptors, which we have used in our project, does not produce features as feasible as convolutional neural networks.
"Created by group paY inekereG" => METU Students
Жаңартылған күні
Қауіпсіздік ұғымы әзірлеушілердің деректеріңізді қалай жинап, бөлісетінін түсінуден басталады. Дерек құпиялығы мен қауіпсіздік шаралары қолданбаңыздың пайдаланылуына, аймағыңыз бен жасыңызға байланысты әртүрлі болуы мүмкін. Бұл ақпаратты әзірлеуші ұсынды және оны өзгертіп тұруы мүмкін.
Үшінші тараппен ешбір дерек бөлісілмейді.
Әзірлеушілердің деректерді бөлісу бойынша мәлімдемесі туралы толық ақпарат алыңыз.
Деректер жиналмады.
Әзірлеушілердің деректерді жинау бойынша мәлімдемесі туралы толық ақпарат алыңыз.
Бағалар мен пікірлер
2,0
121 пікір
Жаңалықтар
Sunucu güncellemesi, hata düzeltmeleri
