TY - GEN
T1 - Optical Snow Analysis Using the 3D-Xray Transform
AU - Keller, Y.
AU - Peled, D.
AU - Averbuch, A.
AU - Shkolnisky, Y.
N1 - Place of conference:USA
PY - 2006
Y1 - 2006
N2 - There are many methods to analyze motion in computer vision. Most of the classical methods
use optical flow, layered motion or segmentation. Optical snow is a complex motion estimation
scenario which analyzes motions such as snowfall, tree movements, cars traffic and people walking.
These scenes are made of many different objects, moving in different speeds in various directions.
While analyzing these scenes, we have to recover both the motion and object segmentation. In
this paper, we detect optical snow motion in video by using the discrete 3D-Xray transform. The
algorithm uses the discrete 3D-Xray transform, which is situated as a core algorithm in medical
imaging when 3D reconstructions from projections are needed, to partition the captured data and
to assemble 3D space into specific planes. The detection of optical snow motion with object tracking
are achieved through analysis of the energy distributions on these 3D-Xray planes. The output from
this analysis contains local and global directions of the movements of these objects. The algorithm
identifies and preserves the path of tracked objects even when there are multiple objects, or they are
partially covered (occluded) by other objects or there is a compound object of merge and split as
players in a soccer game or as in surveillance applications. The algorithm analyzes frame by frame a
video sequence without the need to have neither object segmentation nor clustering. The algorithm
is exact and geometrically faithful as it uses summation along straight geometric lines without any
interpolation schemes. In addition, it detects the direction of each object and computes its relative
velocity. The algorithm has two limitations; the moving objects have to move in straight lines with
constant velocity. The algorithm is computationally efficient since the computation utilizes only
certain planes without the need to have an exhaustive search and computations of of the activities
in all planes.
AB - There are many methods to analyze motion in computer vision. Most of the classical methods
use optical flow, layered motion or segmentation. Optical snow is a complex motion estimation
scenario which analyzes motions such as snowfall, tree movements, cars traffic and people walking.
These scenes are made of many different objects, moving in different speeds in various directions.
While analyzing these scenes, we have to recover both the motion and object segmentation. In
this paper, we detect optical snow motion in video by using the discrete 3D-Xray transform. The
algorithm uses the discrete 3D-Xray transform, which is situated as a core algorithm in medical
imaging when 3D reconstructions from projections are needed, to partition the captured data and
to assemble 3D space into specific planes. The detection of optical snow motion with object tracking
are achieved through analysis of the energy distributions on these 3D-Xray planes. The output from
this analysis contains local and global directions of the movements of these objects. The algorithm
identifies and preserves the path of tracked objects even when there are multiple objects, or they are
partially covered (occluded) by other objects or there is a compound object of merge and split as
players in a soccer game or as in surveillance applications. The algorithm analyzes frame by frame a
video sequence without the need to have neither object segmentation nor clustering. The algorithm
is exact and geometrically faithful as it uses summation along straight geometric lines without any
interpolation schemes. In addition, it detects the direction of each object and computes its relative
velocity. The algorithm has two limitations; the moving objects have to move in straight lines with
constant velocity. The algorithm is computationally efficient since the computation utilizes only
certain planes without the need to have an exhaustive search and computations of of the activities
in all planes.
UR - https://scholar.google.co.il/scholar?q=Optical+Snow+Analysis+Using+the+3D-Xray+Transform&btnG=&hl=en&as_sdt=0%2C5
M3 - Conference contribution
BT - SIAM Conference on Imaging Science
ER -