TY - GEN
T1 - A wavelet-based approach to image feature stability assessment
AU - Robles-Kelly, Antonio
AU - Goecke, Roland
PY - 2006
Y1 - 2006
N2 - In this paper, we present a novel method for assessing image-feature stability. The method hinges on applying the discrete wavelet transform to the image features under study throughout a number of video frames in an image sequence. For purposes of stability assessment, we recover the image-feature vectors for each video frame and then track them trough a series of consecutive frames in the image sequence. We apply the discrete wavelet transform to the time series constructed from the pairwise Euclidean distances for each of the image features under study and use the wavelet transform coefficients to assess their stability. We then recover the stable features by clustering together those time series which exhibit largely constant low-pass wavelet coefficients. We present results of the stability analysis for Harris corners, Maximally Stable Extremal Regions, and Scale Invariant Feature Transform regions extracted from two real-world video sequences. We also elaborate on the applications of our method to indexing, retrieval, and compression of stable image feature vectors.
AB - In this paper, we present a novel method for assessing image-feature stability. The method hinges on applying the discrete wavelet transform to the image features under study throughout a number of video frames in an image sequence. For purposes of stability assessment, we recover the image-feature vectors for each video frame and then track them trough a series of consecutive frames in the image sequence. We apply the discrete wavelet transform to the time series constructed from the pairwise Euclidean distances for each of the image features under study and use the wavelet transform coefficients to assess their stability. We then recover the stable features by clustering together those time series which exhibit largely constant low-pass wavelet coefficients. We present results of the stability analysis for Harris corners, Maximally Stable Extremal Regions, and Scale Invariant Feature Transform regions extracted from two real-world video sequences. We also elaborate on the applications of our method to indexing, retrieval, and compression of stable image feature vectors.
UR - http://www.scopus.com/inward/record.url?scp=33845513741&partnerID=8YFLogxK
U2 - 10.1109/CVPRW.2006.22
DO - 10.1109/CVPRW.2006.22
M3 - Conference contribution
SN - 0769526462
SN - 9780769526461
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
BT - 2006 Conference on Computer Vision and Pattern Recognition Workshop
T2 - 2006 Conference on Computer Vision and Pattern Recognition Workshops
Y2 - 17 June 2006 through 22 June 2006
ER -