Log-Opponent Chromaticity Coding of Color Space
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
On the Removal of Shadows from Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Make3D: Learning 3D Scene Structure from a Single Still Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
General road detection from a single image
IEEE Transactions on Image Processing
Robotics and Autonomous Systems
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This paper investigates the problem of detecting vegetation in unstructured environments for guiding an autonomous robot safely, exploiting its mobility capability in a cluttered outdoor environment. The aim is to create an adaptive learning algorithm which performs a quantitatively accurate detection that is fast enough for a real-time application. Chlorophyll-rich vegetation pixels are selected by thresholding vegetation indices, and then are considered as the seeds of a ''spread vegetation''. For each seed pixel, a convex combination of color and texture dissimilarities is used to infer the difference between the pixel and its neighbors. The convex combination, trained via semi-supervised learning, models either the difference of vegetation pixels or the difference between a vegetation pixel and a non-vegetation pixel, and thus allows a greedy decision-making process to expand the spread vegetation, so-called vision-based spreading. To avoid overspreading, especially in the case of noise, a spreading scale is set. On the other hand, another vegetation spreading based on spectral reflectance is carried out in parallel. Finally, the intersection part resulting from both the vision-based and spectral reflectance-based methods is added to the spread vegetation. The approach takes into account both vision and chlorophyll light absorption properties. This enables the algorithm to capture much more detailed vegetation features than does prior art, and also give a much richer experience in the interpretation of vegetation representation, even for scenes with significant overexposure or underexposure as well as with the presence of shadow and sunshine. In all real-world experiments we carried out, our approach yields a detection accuracy of over 90%.