Name
Abstract | ||
|---|---|---|
This paper presents a multi-class image segmentation approach to automate fruit segmentation. A feature learning algorithm combined with a conditional random field is applied to multi-spectral image data. Current classification methods used in agriculture scenarios tend to use hand crafted application-based features. In contrast, our approach uses unsupervised feature learning to automatically capture most relevant features from the data. This property makes our approach robust against variance in canopy trees and therefore has the potential to be applied to different domains. The proposed algorithm is applied to a fruit segmentation problem for a robotic agricultural surveillance mission, aiming to provide yield estimation with high accuracy and robustness against fruit variance. Experimental results with data collected in an almond farm are shown. The segmentation is performed with features extracted from multi-spectral (colour and infrared) data. We achieve a global classification accuracy of 88%. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/IROS.2013.6697125 | 2013 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) |
Keywords | Field | DocType |
image segmentation,learning artificial intelligence,image classification,mobile robots | Scale-space segmentation,Computer science,Segmentation-based object categorization,Robustness (computer science),Image segmentation,Artificial intelligence,Contextual image classification,Conditional random field,Computer vision,Pattern recognition,Segmentation,Machine learning,Feature learning | Conference |
ISSN | Citations | PageRank |
2153-0858 | 15 | 0.95 |
References | Authors | |
11 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Calvin Hung | 1 | 61 | 6.01 |
| Juan I. Nieto | 2 | 939 | 88.52 |
| Zachary Taylor | 3 | 46 | 4.95 |
| James Patrick Underwood | 4 | 442 | 39.37 |
| Salah Sukkarieh | 5 | 1142 | 141.84 |