Low and high-level visual feature-based apple detection from multi-modal images |
| |
Authors: | J P Wachs H I Stern T Burks V Alchanatis |
| |
Institution: | (1) School of Industrial Engineering, Purdue University, West Lafayette, IN, USA;(2) Department of Industrial Engineering, Ben Gurion University of the Negev, Beersheva, Israel;(3) Institute of Agricultural Engineering, Agricultural Research Organization, The Volcani Center, Bet-Dagan, Israel;(4) Agricultural and Biological Engineering, University of Florida, Gainesville, FL, USA |
| |
Abstract: | Automated harvesting requires accurate detection and recognition of the fruit within a tree canopy in real-time in uncontrolled
environments. However, occlusion, variable illumination, variable appearance and texture make this task a complex challenge.
Our research discusses the development of a machine vision system, capable of recognizing occluded green apples within a tree
canopy. This involves the detection of “green” apples within scenes of “green leaves”, shadow patterns, branches and other
objects found in natural tree canopies. The system uses both thermal infra-red and color image modalities in order to achieve
improved performance. Maximization of mutual information is used to find the optimal registration parameters between images
from the two modalities. We use two approaches for apple detection based on low and high-level visual features. High-level
features are global attributes captured by image processing operations, while low-level features are strong responses to primitive
parts-based filters (such as Haar wavelets). These features are then applied separately to color and thermal infra-red images
to detect apples from the background. These two approaches are compared and it is shown that the low-level feature-based approach
is superior (74% recognition accuracy) over the high-level visual feature approach (53.16% recognition accuracy). Finally,
a voting scheme is used to improve the detection results, which drops the false alarms with little effect on the recognition
rate. The resulting classifiers acting independently can partially recognize the on-tree apples, however, when combined the
recognition accuracy is increased. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|