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A neural network based method to determine initial object positions for segmentation

conference contribution
posted on 06.12.2017, 00:00 authored by Paul MoorePaul Moore, Brijesh VermaBrijesh Verma, Minmei LiMinmei Li
This paper presents a new neural network based method for the segmentation of sky from video data collected from a vehicle with a fixed camera position. Using a combination of spatial information, a neural network and thresholding, a high degree of success has been achieved with the images tested. Having the approximate location of the sky allows for an initial starting point for segmentation to be determined. By training aneural network on various sky pixel data, it is possible to find starting locations for thresholding despite the effects of different lighting conditions which significantly affect the colour of the sky in an image. Using this information, thresholds based on colour difference can be employed to discover sky connected pixels. Due to the similar colour of poles to sky, these must then be subtracted from the discovered sky pixels using an edge detection algorithm. The results have been compared with both an SVM and exclusive thresholding technique and comparative analysis is presented in this paper.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Start Page

622

End Page

628

Number of Pages

7

Start Date

01/01/2015

Finish Date

01/01/2015

ISBN-13

9781467376785

Location

Zhangjiajie, China

Publisher

IEEE

Place of Publication

Piscataway, NJ.

Peer Reviewed

Yes

Open Access

No

Era Eligible

Yes

Name of Conference

International Conference on Natural Computation