Comparison of Neural Network and Maximum Likelihood High Resolution Image Classification for Weed Detection in Crops: Applications in Precision Agriculture

2006 ◽  
Author(s):  
P. Eddy ◽  
A. Smith ◽  
B. Hill ◽  
D. Peddle ◽  
C. Coburn ◽  
...  
Keyword(s):  
Neural Network ◽  
Maximum Likelihood ◽  
High Resolution ◽  
Image Classification ◽  
Precision Agriculture ◽  
Weed Detection ◽  
Resolution Image ◽  
High Resolution Image

High Resolution Image Classification Method Combining with Minimum Noise Fraction Rotation and Convolution Neural Network

2017 ◽  
Vol 54 (10) ◽  
pp. 102801
Author(s):  
陈 洋 Chen Yang ◽  
范荣双 Fan Rongshuang ◽  
王竞雪 Wang Jingxue ◽  
吴增林 Wu Zenglin ◽  
孙汝星 Sun Ruxing
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Image Classification ◽  
Convolution Neural Network ◽  
Classification Method ◽  
Resolution Image ◽  
High Resolution Image ◽  
Minimum Noise Fraction ◽  
Noise Fraction ◽  
Minimum Noise

scholarly journals High-Resolution Image Inpainting Based on Multi-Scale Neural Network

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1370 ◽  
Author(s):  
Tingzhu Sun ◽  
Weidong Fang ◽  
Wei Chen ◽  
Yanxin Yao ◽  
Fangming Bi ◽  
...  
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Pedestrian Detection ◽  
Image Inpainting ◽  
Resolution Image ◽  
Multi Scale ◽  
High Resolution Image ◽  
Adversarial Network ◽  
Generation Network ◽  
High Resolution Images

Although image inpainting based on the generated adversarial network (GAN) has made great breakthroughs in accuracy and speed in recent years, they can only process low-resolution images because of memory limitations and difficulty in training. For high-resolution images, the inpainted regions become blurred and the unpleasant boundaries become visible. Based on the current advanced image generation network, we proposed a novel high-resolution image inpainting method based on multi-scale neural network. This method is a two-stage network including content reconstruction and texture detail restoration. After holding the visually believable fuzzy texture, we further restore the finer details to produce a smoother, clearer, and more coherent inpainting result. Then we propose a special application scene of image inpainting, that is, to delete the redundant pedestrians in the image and ensure the reality of background restoration. It involves pedestrian detection, identifying redundant pedestrians and filling in them with the seemingly correct content. To improve the accuracy of image inpainting in the application scene, we proposed a new mask dataset, which collected the characters in COCO dataset as a mask. Finally, we evaluated our method on COCO and VOC dataset. the experimental results show that our method can produce clearer and more coherent inpainting results, especially for high-resolution images, and the proposed mask dataset can produce better inpainting results in the special application scene.

Sign in / Sign up

Export Citation Format

Share Document