Abstract: Seat belts, seat belt assemblies, seat belt systems, and methods for incorporating the seat belts, seat belt assemblies, and seat belt assembly systems are disclosed, with the seat belt including a seat belt locking mechanism sensor that, in tandem with a seat belt thermal sensor provides confirmatory information as to a definitive locking condition or locking state of a seat belt.

Abstract: Seat belts, seat belt assemblies, seat belt systems, and methods for incorporating the seat belts, seat belt assemblies, and seat belt assembly systems are disclosed, with the seat belt including a seat belt locking mechanism sensor that, in tandem with a seat belt thermal sensor provides confirmatory information as to a definitive locking condition or locking state of a seat belt.

Abstract: Certain embodiments of these teachings take inputs of a) an image to be segmented and b) a set of user input markers that identify at least a foreground of said image. From these inputs are determined strong foreground regions Sf, strong background regions Sb and ambiguous regions G. Each ambiguous region Gi is classified as either a weak foreground region Wf or a weak background region Wb by comparing color of each ambiguous region Gi against color of regions adjacent thereto and merging the respective ambiguous region Gi with its adjacent region having a most similar color. The strong foreground regions Sf are also merged with the weak foreground regions Wf. What is output therefrom is a foreground image of the image to be segmented, separate and distinct from any region that is classified as one of the strong background regions or weak background regions.

Abstract: A computer-implemented method for tracking an object in a video including, in a first video frame: filtering the first video frame to delineate foreground from background and to select from the foreground an object to be tracked, said object identified at least by contour, center and color; constructing a first virtual bounding box minimally enclosing said object; using a tracking algorithm to establish a target location for the object in the second video frame based on similarity to the object identified in the first video frame; constructing a second virtual bounding box minimally enclosing the target shape at the target location; extending the second virtual bounding box; filtering the second video frame to delineate foreground from background using weighted values propagated from at least the first frame; scrutinizing the output of the second video frame by matching at least shape and color of the target shape at the target location.

Abstract: Certain embodiments of these teachings take inputs of a) an image to be segmented and b) a set of user input markers that identify at least a foreground of said image. From these inputs are determined strong foreground regions Sf, strong background regions Sb and ambiguous regions G. Each ambiguous region Gi is classified as either a weak foreground region Wf or a weak background region Wb by comparing color of each ambiguous region Gi against color of regions adjacent thereto and merging the respective ambiguous region Gi with its adjacent region having a most similar color. The strong foreground regions Sf are also merged with the weak foreground regions Wf. What is output therefrom is a foreground image of the image to be segmented, separate and distinct from any region that is classified as one of the strong background regions or weak background regions.