Difference between revisions of "Image Processing"
From Bambi
(Added paper on edge detection (reference to start with)) |
(Description of Neural Network Image Processing approach) |
||
| Line 1: | Line 1: | ||
| − | A | + | A research group from the university of California used Neuronal Networks for EDGE detection: [https://github.com/s9xie/hed github repository]. |
| + | They used [http://caffe.berkeleyvision.org/ Caffe Deep learning framework]. The same concepts can be applied to our case, maybe using [https://caffe2.ai/ Caffe2]. | ||
| − | + | ||
| + | So basically we need an Image to Image neural network that recognizes the parts of the agricultural fields black and the rest white (or the opposite). | ||
| + | To simplify the training of the network it could be a good idea to develop an iterative image recognition algorithm, based on color, texture, ... first and use it to provide learning data with google maps. The automatically elaborated pictures can then just be corrected with human interaction (smooth workflow needed!) to provide the needed gain in quality. | ||
Revision as of 11:49, 23 September 2017
A research group from the university of California used Neuronal Networks for EDGE detection: github repository. They used Caffe Deep learning framework. The same concepts can be applied to our case, maybe using Caffe2.
So basically we need an Image to Image neural network that recognizes the parts of the agricultural fields black and the rest white (or the opposite).
To simplify the training of the network it could be a good idea to develop an iterative image recognition algorithm, based on color, texture, ... first and use it to provide learning data with google maps. The automatically elaborated pictures can then just be corrected with human interaction (smooth workflow needed!) to provide the needed gain in quality.
