TY - GEN
T1 - On computing visual flows with boundaries
T2 - 2nd International Workshop on Biologically Motivated Computer Vision, BMCV 2002
AU - Ben-Shahar, Ohad
AU - Huggins, Patrick S.
AU - Zucker, Steven W.
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2002.
PY - 2002/1/1
Y1 - 2002/1/1
N2 - Many visual tasks depend upon the interpretation of visual structures that are flow fields, such as optical flow, oriented texture, and shading. The computation of these visual flows involves a delicate tradeoff: imaging imperfections lead to noisy and sparse initial flow measurements, necessitating further processing to infer dense coherent flows; this processing typically entails interpolation and smoothing, both of which are prone to destroy visual flow discontinuities. However, discontinuities in visual flows signal corresponding discontinuities in the physical world, thus it is critical to preserve them while processing the flow. In this paper we present a computational approach motivated by the architecture of primary visual cortex that directly incorporates boundary information into a flow relaxation network. The result is a robust computation of visual flows with the capacity to handle noisy or sparse data sets while providing stability along flow boundaries. We demonstrate the effectiveness of our approach by computing shading flows in images with intensity edges.
AB - Many visual tasks depend upon the interpretation of visual structures that are flow fields, such as optical flow, oriented texture, and shading. The computation of these visual flows involves a delicate tradeoff: imaging imperfections lead to noisy and sparse initial flow measurements, necessitating further processing to infer dense coherent flows; this processing typically entails interpolation and smoothing, both of which are prone to destroy visual flow discontinuities. However, discontinuities in visual flows signal corresponding discontinuities in the physical world, thus it is critical to preserve them while processing the flow. In this paper we present a computational approach motivated by the architecture of primary visual cortex that directly incorporates boundary information into a flow relaxation network. The result is a robust computation of visual flows with the capacity to handle noisy or sparse data sets while providing stability along flow boundaries. We demonstrate the effectiveness of our approach by computing shading flows in images with intensity edges.
UR - https://www.scopus.com/pages/publications/67349170736
U2 - 10.1007/3-540-36181-2_19
DO - 10.1007/3-540-36181-2_19
M3 - Conference contribution
AN - SCOPUS:67349170736
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 189
EP - 198
BT - Biologically Motivated Computer Vision - 2nd International Workshop, BMCV 2002, Proceedings
A2 - Bulthoff, Heinrich H.
A2 - Wallraven, Christian
A2 - Lee, Seong-Whan
A2 - Poggio, Tomaso A.
PB - Springer Verlag
Y2 - 22 November 2002 through 24 November 2002
ER -