TY - GEN
T1 - Kona
T2 - International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 1997
AU - Parida, Laxmi
AU - Geiger, Davi
AU - Hummel, Robert
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 1997.
PY - 1997
Y1 - 1997
N2 - Corners, T-, Y-, X-junctions give vital depth cues which is a critical aspect of image understanding tasks like object recognition: junctions form an important class of features invaluable in most vision systems. The three main issues in a junction (or any feature) detector are: scale, location, and, the junction (feature) parameters. The junction parameters are (1) the radius, or size, of the junction, (2) the kind of junction: lines, corners, 3-junctions such as T or Y, or, 4-junction such as X-junction, etcetera, (3) angles of the wedges, and, (4) intensity in each of the wedges. Our main contribution in this paper is a modeling of the junction (using the minimum description length principle), which is complex enough to handle all the three issues and simple enough to admit an effective dynamic programming solution. Kona is an implementation of this model. A similar approach can be used to model other features like thick edges, blobs and end-points.
AB - Corners, T-, Y-, X-junctions give vital depth cues which is a critical aspect of image understanding tasks like object recognition: junctions form an important class of features invaluable in most vision systems. The three main issues in a junction (or any feature) detector are: scale, location, and, the junction (feature) parameters. The junction parameters are (1) the radius, or size, of the junction, (2) the kind of junction: lines, corners, 3-junctions such as T or Y, or, 4-junction such as X-junction, etcetera, (3) angles of the wedges, and, (4) intensity in each of the wedges. Our main contribution in this paper is a modeling of the junction (using the minimum description length principle), which is complex enough to handle all the three issues and simple enough to admit an effective dynamic programming solution. Kona is an implementation of this model. A similar approach can be used to model other features like thick edges, blobs and end-points.
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U2 - 10.1007/3-540-62909-2_72
DO - 10.1007/3-540-62909-2_72
M3 - Conference contribution
AN - SCOPUS:33747805205
SN - 3540629092
SN - 9783540629092
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 51
EP - 65
BT - Energy Minimization Methods in Computer Vision and Pattern Recognition - International Workshop EMMCVPR 1997, Proceedings
A2 - Hancock, Edwin R.
A2 - Pelillo, Marcello
PB - Springer Verlag
Y2 - 21 May 1997 through 23 May 1997
ER -