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Low and high-level visual feature-based apple detection from multi-modal images
Year:
2010
Source of publication :
precision agriculture (source )
Authors :
Alchanatis, Victor
;
.
Volume :
11
Co-Authors:
Wachs, J.P., School of Industrial Engineering, Purdue University, West Lafayette, IN, United States
Stern, H.I., Department of Industrial Engineering, Ben Gurion University of the Negev, Beersheva, Israel
Burks, T., Agricultural and Biological Engineering, University of Florida, Gainesville, FL, United States
Alchanatis, V., Institute of Agricultural Engineering, Agricultural Research Organization, The Volcani Center, Bet-Dagan, Israel
Facilitators :
From page:
717
To page:
735
(
Total pages:
19
)
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. © 2010 Springer Science+Business Media, LLC.
Note:
Related Files :
alternative agriculture
harvesting
infrared imagery
Multi-modal registration
recognition
sensor
Sensor fusion
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Related Content
More details
DOI :
10.1007/s11119-010-9198-x
Article number:
Affiliations:
Database:
Scopus
Publication Type:
article
;
.
Language:
English
Editors' remarks:
ID:
29320
Last updated date:
02/03/2022 17:27
Creation date:
17/04/2018 00:45
Scientific Publication
Low and high-level visual feature-based apple detection from multi-modal images
11
Wachs, J.P., School of Industrial Engineering, Purdue University, West Lafayette, IN, United States
Stern, H.I., Department of Industrial Engineering, Ben Gurion University of the Negev, Beersheva, Israel
Burks, T., Agricultural and Biological Engineering, University of Florida, Gainesville, FL, United States
Alchanatis, V., Institute of Agricultural Engineering, Agricultural Research Organization, The Volcani Center, Bet-Dagan, Israel
Low and high-level visual feature-based apple detection from multi-modal images
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. © 2010 Springer Science+Business Media, LLC.
Scientific Publication
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