Abstract: A system and method for establishing a chain of trust from a registrant to a registry. A registrant request to a registrar to change a domain name record includes at least one registrant factor, such as a one time password. The registrar can formulate an extended EPP command that includes the factor to effectuate the change and send it to a registry. The registry can verify the at least one factor using at least one validation server. If the factor is successfully verified, the EPP can be processed by the registry. If the factor is not verified, the EPP command may not be processed and an error message may be generated and sent to the registrar.

Abstract: A system and method for establishing a chain of trust from a registrant to a registry. A registrant request to a registrar to change a domain name record includes at least one registrant factor, such as a one time password. The registrar can formulate an extended EPP command that includes the factor to effectuate the change and send it to a registry. The registry can verify the at least one factor using at least one validation server. If the factor is successfully verified, the EPP can be processed by the registry. If the factor is not verified, the EPP command may not be processed and an error message may be generated and sent to the registrar.

Abstract: Automatic use (102) of a disjoint probabilistic analysis of captured temporally parsed data (101) regarding at least a first and a second item serves to facilitate disambiguating state information as pertains to the first item from information as pertains to the second item. This can also comprise, for example, using a joint probability as pertains to the temporally parsed data for the first item and the temporally parsed data for the second item, by using, for example, a Bayesian-based probabilistic analysis of the temporally parsed data.

Abstract: An object in a video sequence is tracked by object masks generated for frames in the sequence. Macroblocks are motion compensated to predict the new object mask. Large differences between the next frame and the current frame detect suspect regions that may be obscured in the next frame. The motion vectors in the object are clustered using a K-means algorithm. The cluster centroid motion vectors are compared to an average motion vector of each suspect region. When the motion differences are small, the suspect region is considered part of the object and removed from the object mask as an occlusion. Large differences between the prior frame and the current frame detect suspected newly-uncovered regions. The average motion vector of each suspect region is compared to cluster centroid motion vectors. When the motion differences are small, the suspect region is added to the object mask as a disocclusion.

Abstract: An object in a video sequence of frames is tracked by object masks generated for frames in the sequence. Macroblocks are motion compensated. Blocks matching entirely within a prior-frame object mask are used to generate an average object motion. When the average motion is below a motion threshold, frames are skipped at larger intervals, but more frequent frames are processed when high motion occurs. When the macroblock best matches a prior-frame block that has the object's boundary passing through the block, the macroblock is uncertain and is sub-divided into smaller sub-blocks that are again motion compensated. Sub-blocks matching blocks within the object mask in the base frame are added to the new object mask for the current frame while sub-blocks matching a block containing the object boundary are uncertain and can again be sub-divided to further refine the object boundary. Frame skipping and adaptive-size blocks on the object boundary reduce computational load.

Abstract: An object in a video sequence is tracked by object masks generated for frames in the sequence. Macroblocks are motion compensated to predict the new object mask. Large differences between the next frame and the current frame detect suspect regions that may be obscured in the next frame. The motion vectors in the object are clustered using a K-means algorithm. The cluster centroid motion vectors are compared to an average motion vector of each suspect region. When the motion differences are small, the suspect region is considered part of the object and removed from the object mask as an occlusion. Large differences between the prior frame and the current frame detect suspected newly-uncovered regions. The average motion vector of each suspect region is compared to cluster centroid motion vectors. When the motion differences are small, the suspect region is added to the object mask as a disocclusion.