Abstract: Certain aspects provide a method, including: receiving a depth image from a depth sensor; receiving a segmentation mask corresponding to the depth image and segmenting the depth image into a set of foreground pixels and a set of background pixels; determining a set of seed pixels in the depth image; for each respective seed pixel of the set of seed pixels: determining a sampling line in the depth image that starts at the respective seed pixel and passes through a portion of the depth image; for each respective sampling line pixel in the sampling line having a value in the segmentation mask indicating a foreground object in the depth image: determining one or more data attribute values based on a depth value for the respective sampling line pixel in the depth image; and adding the one or more data attribute values to a feature vector.

Abstract: Certain aspects provide a method, including: receiving a depth image from a depth sensor; receiving a segmentation mask corresponding to the depth image and segmenting the depth image into a set of foreground pixels and a set of background pixels; determining a set of seed pixels in the depth image; for each respective seed pixel of the set of seed pixels: determining a sampling line in the depth image that starts at the respective seed pixel and passes through a portion of the depth image; for each respective sampling line pixel in the sampling line having a value in the segmentation mask indicating a foreground object in the depth image: determining one or more data attribute values based on a depth value for the respective sampling line pixel in the depth image; and adding the one or more data attribute values to a feature vector.

Abstract: Certain aspects provide a method, including: receiving a depth image from a depth sensor; receiving a segmentation mask corresponding to the depth image and segmenting the depth image into a set of foreground pixels and a set of background pixels; determining a set of seed pixels in the depth image; for each respective seed pixel of the set of seed pixels: determining a sampling line in the depth image that starts at the respective seed pixel and passes through a portion of the depth image; for each respective sampling line pixel in the sampling line having a value in the segmentation mask indicating a foreground object in the depth image: determining one or more data attribute values based on a depth value for the respective sampling line pixel in the depth image; and adding the one or more data attribute values to a feature vector.

Abstract: Certain aspects provide a method, including: receiving a depth image from a depth sensor; receiving a segmentation mask corresponding to the depth image and segmenting the depth image into a set of foreground pixels and a set of background pixels; determining a set of seed pixels in the depth image; for each respective seed pixel of the set of seed pixels: determining a sampling line in the depth image that starts at the respective seed pixel and passes through a portion of the depth image; for each respective sampling line pixel in the sampling line having a value in the segmentation mask indicating a foreground object in the depth image: determining one or more data attribute values based on a depth value for the respective sampling line pixel in the depth image; and adding the one or more data attribute values to a feature vector.

Abstract: Certain aspects provide a method, including: receiving a depth image from a depth sensor; receiving a segmentation mask corresponding to the depth image and segmenting the depth image into a set of foreground pixels and a set of background pixels; determining a set of seed pixels in the depth image; for each respective seed pixel of the set of seed pixels: determining a sampling line in the depth image that starts at the respective seed pixel and passes through a portion of the depth image; for each respective sampling line pixel in the sampling line having a value in the segmentation mask indicating a foreground object in the depth image: determining one or more data attribute values based on a depth value for the respective sampling line pixel in the depth image; and adding the one or more data attribute values to a feature vector.

Abstract: Certain aspects provide a method, including: receiving a depth image from a depth sensor; receiving a segmentation mask corresponding to the depth image and segmenting the depth image into a set of foreground pixels and a set of background pixels; determining a set of seed pixels in the depth image; for each respective seed pixel of the set of seed pixels: determining a sampling line in the depth image that starts at the respective seed pixel and passes through a portion of the depth image; for each respective sampling line pixel in the sampling line having a value in the segmentation mask indicating a foreground object in the depth image: determining one or more data attribute values based on a depth value for the respective sampling line pixel in the depth image; and adding the one or more data attribute values to a feature vector.

Abstract: Useful and meaningful machine characteristic information may be derived through analysis of oversampled digital data collected using dynamic signal analyzers, such as vibration analyzers. Such data have generally been discarded in prior art systems. In addition to peak values and decimated values, other oversampled values are used that are associated with characteristics of the machine being monitored and the sensors and circuits that gather the data. This provides more useful information than has previously been derived from oversampled data within a sampling interval.

Abstract: Useful and meaningful machine characteristic information may be derived through analysis of oversampled digital data collected using dynamic signal analyzers, such as vibration analyzers. Such data have generally been discarded in prior art systems. In addition to peak values and decimated values, other oversampled values are used that are associated with characteristics of the machine being monitored and the sensors and circuits that gather the data. This provides more useful information than has previously been derived from oversampled data within a sampling interval.

Abstract: A kit for manufacturing a stitch-bound printed book comprises a book cover (10), a collection of pre-perforated sheets (15) that can be printed to make up a printed bookblock (18), and endpapers (20a,20b) for assembling the printed bookblock in the cover. The pre-perforated loose sheets (15) have along one edge that corresponds to the book's spine a series of binding perforations (16). The pre-perforated loose sheets (15) are printable on one or both sides in an A4 printer to constitute printed pages of the book. A bookblock (18) is formed by reconstituting the printed pre-perforated pages as a collection with their perforations (16) aligned and by sewing through the perforations (16). This involves stitch-binding of individual sheets (15) instead of the usual stitch-binding of folded sheets, which makes it feasible to print the prepared collection of pre-perforated sheets using desktop publishing equipment.