1
Wesley Kenneth Cobb, David Friedlander, Rajkiran Kumar Gottumukkal, Ming Jung Seow, Gang Xu: Identifying anomalous object types during classification. Behavioral Recognition Systems, Patterson & Sheridan, September 18, 2012: US08270733 (5 worldwide citation)

Techniques are disclosed for identifying anomaly object types during classification of foreground objects extracted from image data. A self-organizing map and adaptive resonance theory (SOM-ART) network is used to discover object type clusters and classify objects depicted in the image data based on ...


2
Wesley Kenneth Cobb, David Friedlander, Kishor Adinath Saitwal, Ming Jung Seow, Gang Xu: Video surveillance system configured to analyze complex behaviors using alternating layers of clustering and sequencing. Behavioral Recognition Systems, Patterson & Sheridan, May 1, 2012: US08170283 (2 worldwide citation)

Techniques are disclosed for a video surveillance system to learn to recognize complex behaviors by analyzing pixel data using alternating layers of clustering and sequencing. A video surveillance system may be configured to observe a scene (as depicted in a sequence of video frames) and, over time, ...


3
Wesley Kenneth Cobb, David Friedlander, Kishor Adinath Saitwal, Ming Jung Seow, Gang Xu: Classifier anomalies for observed behaviors in a video surveillance system. Behavioral Recognition Systems, Patterson & Sheridan, May 15, 2012: US08180105 (1 worldwide citation)

Techniques are disclosed for a video surveillance system to learn to recognize complex behaviors by analyzing pixel data using alternating layers of clustering and sequencing. A combination of a self organizing map (SOM) and an adaptive resonance theory (ART) network may be used to identify a variet ...


4
Wesley Kenneth Cobb, David Friedlander, Kishor Adinath Saitwal, Ming Jung Seow, Gang Xu: Clustering nodes in a self-organizing map using an adaptive resonance theory network. Behavioral Recognition Systems, Patterson & Sheridan, September 18, 2012: US08270732 (1 worldwide citation)

Techniques are disclosed for discovering object type clusters using pixel-level micro-features extracted from image data. A self-organizing map and adaptive resonance theory (SOM-ART) network is used to classify objects depicted in the image data based on the pixel-level micro-features. Importantly, ...


5
Wesley Kenneth Cobb, David Friedlander, Kishor Adinath Saitwal, Ming Jung Seow, Gang Xu: Classifier anomalies for observed behaviors in a video surveillance system. Behavioral Recognition Systems, Patterson & Sheridan, July 23, 2013: US08494222

Techniques are disclosed for a video surveillance system to learn to recognize complex behaviors by analyzing pixel data using alternating layers of clustering and sequencing. A combination of a self organizing map (SOM) and an adaptive resonance theory (ART) network may be used to identify a variet ...


6
Wesley Kenneth Cobb, David Friedlander, Rajkiran Kumar Gottumukkal, Ming Jung Seow, Gang Xu: Identifying anomalous object types during classification. Behavioral Recognition Systems, Patterson & Sheridan, October 1, 2013: US08548198

Techniques are disclosed for identifying anomaly object types during classification of foreground objects extracted from image data. A self-organizing map and adaptive resonance theory (SOM-ART) network is used to discover object type clusters and classify objects depicted in the image data based on ...


7
Wesley Kenneth Cobb, David Friedlander, Kishor Adinath Saitwal, Ming Jung Seow, Gang Xu: Classifier anomalies for observed behaviors in a video surveillance system. March 17, 2011: US20110064267-A1

Techniques are disclosed for a video surveillance system to learn to recognize complex behaviors by analyzing pixel data using alternating layers of clustering and sequencing. A combination of a self organizing map (SOM) and an adaptive resonance theory (ART) network may be used to identify a variet ...


8
Wesley Kenneth Cobb, David Friedlander, Kishor Adinath Saitwal, Ming Jung Seow, Gang Xu: Video surveillance system configured to analyze complex behaviors using alternating layers of clustering and sequencing. March 17, 2011: US20110064268-A1

Techniques are disclosed for a video surveillance system to learn to recognize complex behaviors by analyzing pixel data using alternating layers of clustering and sequencing. A video surveillance system may be configured to observe a scene (as depicted in a sequence of video frames) and, over time, ...


9
Wesley Kenneth Cobb, David Friedlander, Kishor Adinath Saitwal, Ming Jung Seow, Gang Xu: Clustering nodes in a self-organizing map using an adaptive resonance theory network. March 3, 2011: US20110052067-A1

Techniques are disclosed for discovering object type clusters using pixel-level micro-features extracted from image data. A self-organizing map and adaptive resonance theory (SOM-ART) network is used to classify objects depicted in the image data based on the pixel-level micro-features. Importantly, ...


10
Wesley Kenneth Cobb, David FRIEDLANDER, Rajkiran Kumar Gottumukkal, Ming Jung Seow, Gang Xu: Identifying anomalous object types during classification. March 3, 2011: US20110052068-A1

Techniques are disclosed for identifying anomaly object types during classification of foreground objects extracted from image data. A self-organizing map and adaptive resonance theory (SOM-ART) network is used to discover object type clusters and classify objects depicted in the image data based on ...