Apple Ripeness Level Detection Based On Skin Color Features With Convolutional Neural Network Classification Method

2021 ◽  
Author(s):  
Kevin Cliff Gunawan ◽  
Zener Sukra Lie
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Skin Color ◽  
Classification Method ◽  
Neural Network Classification ◽  
Color Features

scholarly journals Segmentation and Multi-Scale Convolutional Neural Network-Based Classification of Airborne Laser Scanner Data

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3347 ◽  
Author(s):  
Zhishuang Yang ◽  
Bo Tan ◽  
Huikun Pei ◽  
Wanshou Jiang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Region Growing ◽  
Point Clouds ◽  
Classification Method ◽  
Multi Scale ◽  
Airborne Laser

The classification of point clouds is a basic task in airborne laser scanning (ALS) point cloud processing. It is quite a challenge when facing complex observed scenes and irregular point distributions. In order to reduce the computational burden of the point-based classification method and improve the classification accuracy, we present a segmentation and multi-scale convolutional neural network-based classification method. Firstly, a three-step region-growing segmentation method was proposed to reduce both under-segmentation and over-segmentation. Then, a feature image generation method was used to transform the 3D neighborhood features of a point into a 2D image. Finally, feature images were treated as the input of a multi-scale convolutional neural network for training and testing tasks. In order to obtain performance comparisons with existing approaches, we evaluated our framework using the International Society for Photogrammetry and Remote Sensing Working Groups II/4 (ISPRS WG II/4) 3D labeling benchmark tests. The experiment result, which achieved 84.9% overall accuracy and 69.2% of average F1 scores, has a satisfactory performance over all participating approaches analyzed.

A Novel Liver Image Classification Method Using Perceptual Hash-Based Convolutional Neural Network

2018 ◽  
Vol 44 (4) ◽  
pp. 3173-3182 ◽  
Author(s):  
Fatih Özyurt ◽  
Türker Tuncer ◽  
Engin Avci ◽  
Mustafa Koç ◽  
İhsan Serhatlioğlu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Classification Method ◽  
Perceptual Hash ◽  
Liver Image
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