NDLI: Performance evaluation of color based road detection using neural nets and support vector machines

Content Provider	IEEE Xplore Digital Library
Author	Conrad, P. Foedisch, M.
Copyright Year	2004
Description	Author affiliation: Nat. Inst. of Stand. & Technol., Gaithersburg, MD, USA (Conrad, P.; Foedisch, M.)
Abstract	We present a comparison of two methods for color based road segmentation. The first was implemented using a neural network, while the second approach is based on support vector machines. A large number of training images were used with varying road conditions including roads with snow, dirt or gravel surfaces, and asphalt. We experimented with grouping the training images by road condition and generating a separate model for each group. The system would automatically select the appropriate one for each novel image. Those results were compared with creating a single model with all images. In another set of experiments, we added the image coordinates of each point as an additional feature in the models. Finally, we compared the results and the efficiency of neural networks and support vector machines of segmentation with each combination of feature sets and image groups.
Sponsorship	IEEE Comput. Soc., Tech. Committee on Pattern Analysis and Machine Intelligence
Starting Page	157
Ending Page	160
File Size	688001
Page Count	4
File Format	PDF
ISBN	0769520294
DOI	10.1109/AIPR.2003.1284265
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-10-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Support vector machines Pixel Snow Road vehicles Vehicle driving Histograms Image segmentation Remotely operated vehicles Vehicle safety
Content Type	Text
Resource Type	Article

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