Language Recognition (12.4) Longin Jan Latecki Temple University
Computer Vision and Data Mining Research Projects Longin Jan Latecki Computer and Information...
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Transcript of Computer Vision and Data Mining Research Projects Longin Jan Latecki Computer and Information...
Computer Vision and Data Mining Research Projects
Longin Jan LateckiComputer and Information Sciences
Dept.Temple [email protected]
Research Projects
• Object detection and recognition in images
• Improving ranking of search queries
• Motion and activity detection in videos
• Merging laser range maps of multiple robots
Object detection and recognition based on contour parts
• Often only parts of objects are visible in images• We can detect and recognize such objects in
edge images by performing contour grouping with shape similarity
Edge image Detected object
• Probabilistic approaches are needed to address noisy sensor information in robot perception.
• We use Rao-Blackwellized particle filtering that has been successfully applied to solve the robot mapping problem (SLAM).
• We use medial axis (skeleton) as our shape representation.
Methodology
•Supported by DOE, NNSA, NA-22 •NSF, Computer Vision Program
Applications:Analysis of aerial and satellite images,
in particular object and change detection
Supported by LANL, RADIUS: Rapid Automated Decomposition of Images for Ubiquitous Sensing, PI: Lakshman Prasad, LANL
detected structures of interest at three different scales (in maroon).
the original aerial image detected parts of contours
Videos are obtained from the Temple University Police video surveillance system.
Object and activity detection results
Motion and activity detection in videos
Methodology: We use PCA to learn local background textures, and detect motion by analysisof texture trajectories.Many Video Surveillance Applications, e.g.,:Detection of moving objects and detection of abandon objects, e.g., around power plants
Improving ranking in face profile retrieval Original retrieval
Improved retrievalquery
Methodology: We use semi-supervised manifold learning to learn new distancesin the manifold spanned by the training data set.Further applications:This methods makes it possible to improve ranking of any queriesfrom images through text to concepts.
Prior based on motion model
• Our motion model is based on structure registration process between local maps which results in multi-modal prior.
Prior in odometry based motion model
Prior in our structure registration based motion
model
Merging maps of multiple robots