Abstract: A system or method (collectively “selection system”) is disclosed for selecting attributes for a classifier in a sensor system. The selection system selects attribute types using statistical distributions of the attribute values associated with those attribute types. Attribute types not within the selectively identified subset of attribute types can be filtered out before such data is sent to a classifier. The system can use a test data subsystem for storing and accessing actual sensor data. A distribution analysis subsystem can perform statistical analyses on the test data to identify underlying distributions, and to compare individual attribute types to such distributions. An attribute selection subsystem, wherein said attribute selection subsystem selectively identifies a subset of attribute types from said subset of attribute types.

Abstract: The present invention relates in general to systems used to process images. In particular, the present invention is an image segmentation system and method used to isolate the segmented image of a target person, animal, or object from an ambient image which includes the target person, animal, or object, in addition to the area surrounding the target. The invention supports the ability of an airbag deployment system to distinguish between different types of occupants by providing such deployment systems with a segmented image of the occupant. The invention is particularly useful at night or in other environments involving inadequate light or undesirable shadows. The invention can use histograms and cumulative distribution functions to perform image thresholding. Morphological erosion and dilation can be used to eliminate optical “noise” from the image. Gap filling is performed on the basis of the “momentum” and “gravity” of regions of similar pixel values.

Abstract: The present invention relates in general to systems used to determine whether airbag deployment should be disabled or modified due to the proximity of the occupant to the airbag. In particular, the present invention is an image processing system that uses an image and signal process that utilizes real-time streaming video-images from a video camera to determine if the occupant is too close to the air bag, or will be to close the air bag by the time that the airbag deploys. In a crash situation, the system quickly determines whether or not the airbag should be disabled or modified. The process uses a multiple-model Kalman filter to infer three-dimensional information from a sequence of two-dimensional images. The system predicts the position and shape of the occupant at a faster rate than the rate in which the camera collects data.

Abstract: A system or method (collectively “selection system”) is disclosed for selecting attributes for a classifier in a sensor system. The selection system selects attribute types using statistical distributions of the attribute values associated with those attribute types. Attribute types not within the selectively identified subset of attribute types can be filtered out before such data is sent to a classifier. The system can use a test data subsystem for storing and accessing actual sensor data. A distribution analysis subsystem can perform statistical analyses on the test data to identify underlying distributions, and to compare individual attribute types to such distributions. An attribute selection subsystem, wherein said attribute selection subsystem selectively identifies a subset of attribute types from said subset of attribute types.

Abstract: The invention can determine whether a collision has occurred, whether an airbag should be deployed, and what the desirable strength of an airbag deployment should be based on occupant characteristics obtained from the segmented image of the occupant. Using interacting multiple model Kalman filters, the invention can infer three-dimensional characteristics from a two-dimensional image, and predict the location, velocity, and acceleration of the occupant. Kalman filters are used with respect to all measurements to incorporate past predictions and measurements into the most recent estimates and predictions in order to eliminate the “noise” associated with any particular measurement. The system can predict the position and shape of the occupant at a faster rate than the rate at which the sensor collects data. Occupant characteristics can be compared to corresponding predetermined data associated with various occupant modes, including the mode of crashing, to determine if a collision has occurred.

Abstract: The present invention relates in general to systems used to determine whether an airbag should be deployed at full or only partial strength. In particular, the present invention is an image processing system that utilizes real-time streaming video-images from a video camera or other sensor to determine the mass, velocity, and kinetic energy of the occupant at the time that the occupant comes into contact with the deploying airbag. By predicting the kinetic energy of the occupant at the time of impact, an airbag can be deployed at an appropriate strength corresponding to the kinetic energy of the occupant. The kinetic energy of the deploying back at the moment of impact should be equal to the kinetic energy of the occupant. The invention captures the volume of the occupant from an image, and uses volume to calculate the mass of the occupant.

Abstract: The invention can determine whether a collision has occurred, whether an airbag should be deployed, and what the desirable strength of an airbag deployment should be based on occupant characteristics obtained from the segmented image of the occupant. Using interacting multiple model Kalman filters, the invention can infer three-dimensional characteristics from a two-dimensional image, and predict the location, velocity, and acceleration of the occupant. Kalman filters are used with respect to all measurements to incorporate past predictions and measurements into the most recent estimates and predictions in order to eliminate the “noise” associated with any particular measurement. The system can predict the position and shape of the occupant at a faster rate than the rate at which the sensor collects data. Occupant characteristics can be compared to corresponding predetermined data associated with various occupant modes, including the mode of crashing, to determine if a collision has occurred.

Abstract: The present invention relates in general to systems used to determine whether an airbag should be deployed at full or only partial strength. In particular, the present invention is an image processing system that utilizes real-time streaming video-images from a video camera or other sensor to determine the mass, velocity, and kinetic energy of the occupant at the time that the occupant comes into contact with the deploying airbag. By predicting the kinetic energy of the occupant at the time of impact, an airbag can be deployed at an appropriate strength corresponding to the kinetic energy of the occupant. The kinetic energy of the deploying back at the moment of impact should be equal to the kinetic energy of the occupant. The invention captures the volume of the occupant from an image, and uses volume to calculate the mass of the occupant.

Abstract: The present invention relates in general to systems used to process images. In particular, the present invention is an image segmentation system and method used to isolate the segmented image of a target person, animal, or object from an ambient image which includes the target person, animal, or object, in addition to the area surrounding the target. The invention supports the ability of an airbag deployment system to distinguish between different types of occupants by providing such deployment systems with a segmented image of the occupant. The invention is particularly useful at night or in other environments involving inadequate light or undesirable shadows. The invention can use histograms and cumulative distribution functions to perform image thresholding. Morphological erosion and dilation can be used to eliminate optical “noise” from the image. Gap filling is performed on the basis of the “momentum” and “gravity” of regions of similar pixel values.

Abstract: The present invention relates in general to systems used to determine whether airbag deployment should be disabled or modified due to the proximity of the occupant to the airbag. In particular, the present invention is an image processing system that uses an image and signal process that utilizes real-time streaming video-images from a video camera to determine if the occupant is too close to the air bag, or will be to close the air bag by the time that the airbag deploys. In a crash situation, the system quickly determines whether or not the airbag should be disabled or modified. The process uses a multiple-model Kalman filter to infer three-dimensional information from a sequence of two-dimensional images. The system predicts the position and shape of the occupant at a faster rate than the rate in which the camera collects data.

Abstract: A deadlock avoidance system for avoiding interconnection deadlocks between a plurality of data transfer devices includes a controller, a switch interconnector coupled to all of said data transfer devices for interconnecting on a one-to-one basis selected ones of the data transfer device as requesting units to selected ones of said data transfer devices as receiving units. A transfer queue is employed that includes a master transfer register and a slave transfer register, a master register, a slave register and a target register.