Abstract: 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 pixel-level micro-features that are extracted from the image data. Importantly, the discovery of the object type clusters is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. The SOM-ART network is adaptive and able to learn while discovering the object type clusters and classifying objects and identifying anomaly object types.

Abstract: 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 variety of different anomalous inputs at each cluster layer. As progressively higher layers of the cortex model component represent progressively higher levels of abstraction, anomalies occurring in the higher levels of the cortex model represent observations of behavioral anomalies corresponding to progressively complex patterns of behavior.

Abstract: An apparatus, method, and system for the deployment of surgical mesh material, which are particularly suited for use in the laparoscopic surgical repair of hernias. Any suitable surgical mesh can be placed between at least two elongate retaining members; wrapped around the elongate retaining members; inserted into a patient; and then deployed using at least two elongate deploying members on either side of the mesh.

Abstract: 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 pixel-level micro-features that are extracted from the image data. Importantly, the discovery of the object type clusters is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. The SOM-ART network is adaptive and able to learn while discovering the object type clusters and classifying objects and identifying anomaly object types.

Abstract: 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, the discovery of the object type clusters is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. The SOM-ART network is adaptive and able to learn while discovering the object type clusters and classifying objects.

Abstract: 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 variety of different anomalous inputs at each cluster layer. As progressively higher layers of the cortex model component represent progressively higher levels of abstraction, anomalies occurring in the higher levels of the cortex model represent observations of behavioral anomalies corresponding to progressively complex patterns of behavior.

Abstract: 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 variety of different anomalous inputs at each cluster layer. As progressively higher layers of the cortex model component represent progressively higher levels of abstraction, anomalies occurring in the higher levels of the cortex model represent observations of behavioral anomalies corresponding to progressively complex patterns of behavior.

Abstract: 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, develop hierarchies of concepts including classes of objects, actions and behaviors. That is, the video surveillance system may develop models at progressively more complex levels of abstraction used to identify what events and behaviors are common and which are unusual. When the models have matured, the video surveillance system issues alerts on unusual events.

Abstract: 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, develop hierarchies of concepts including classes of objects, actions and behaviors. That is, the video surveillance system may develop models at progressively more complex levels of abstraction used to identify what events and behaviors are common and which are unusual. When the models have matured, the video surveillance system issues alerts on unusual events.

Abstract: 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 variety of different anomalous inputs at each cluster layer. As progressively higher layers of the cortex model component represent progressively higher levels of abstraction, anomalies occurring in the higher levels of the cortex model represent observations of behavioral anomalies corresponding to progressively complex patterns of behavior.

Abstract: 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 pixel-level micro-features that are extracted from the image data. Importantly, the discovery of the object type clusters is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. The SOM-ART network is adaptive and able to learn while discovering the object type clusters and classifying objects and identifying anomaly object types.

Abstract: 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, the discovery of the object type clusters is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. The SOM-ART network is adaptive and able to learn while discovering the object type clusters and classifying objects.