Abstract: A handrail system (10) is disclosed having an elongated handrail rod (11) coupled to handrail mounts (12) that are adapted to be mounted to a support surface. The handrail system also includes accessory devices (13) coupled to the elongated handrail. The elongated handrail rod includes a casing (16) and an internal frame (17) to form a channel (22). The accessory device includes a mounting flange (43) configured to mate with the handrail rod and an accessory holder (44). The mounting flange includes an elongated neck (45) extending to a curved top portion (46) configured to overlay the top of the handrail. The mounting flange also includes a locking member (47) configured to be received within the channel of the handrail rod.

Abstract: A handrail system (10) is disclosed having an elongated handrail rod (11) coupled to handrail mounts (12) that are adapted to be mounted to a support surface. The handrail system also includes accessory devices (13) coupled to the elongated handrail. The elongated handrail rod includes a casing (16) and an internal frame (17) to form a channel (22). The accessory device includes a mounting flange (43) configured to mate with the handrail rod and an accessory holder (44). The mounting flange includes an elongated neck (45) extending to a curved top portion (46) configured to overlay the top of the handrail. The mounting flange also includes a locking member (47) configured to be received within the channel of the handrail rod.

Abstract: An estimate of a threshold value for feature extraction from image data containing noise and quantized into a plurality of quantization levels is made by filtering, such as recursively averaging n adjacent quantization levels and counting the extrema of the result. The recursive averaging is repeated with increasing values of n until only three extrema are present in the filtered data. The estimate of the threshold value is refined by comparing averages or summations of n quantization levels above and below the estimate and altering the estimated threshold value based on a comparison of the averages or summations. By basing both the estimation and refinement processes on the original data and using a value of n which is smaller in the refinement process than in the estimation process, a highly effective threshold value for the feature extraction process is obtained. Adaptive adjustment of n during the estimation process minimizes loss of data as well as computing time.