Abstract: Computer-implemented method and system (100) for estimating vegetation coverage in a real-world environment. The system receives an RGB image (91) of a real-world scenery (1) with one or more plant elements (10) of one or more plant species. At least one channel of the RGB image (91) is provided to a semantic regression neural network (120) which is trained to estimate at least a near-infrared channel (NIR) from the RGB image. The system obtains an estimate of the near-infrared channel (NIR) by applying the semantic regression neural network (120) to the at least one RGB channel (91). A multi-channel image (92) comprising at least one of the R-, G-, B-channels (R, G, B) of the RGB image and the estimated near-infrared channel (NIR), is provided as test input (TI1) to a semantic segmentation neural network (130) trained with multi-channel images to segment the test input (TI1) into pixels associated with plant elements and pixels not associated with plant elements.

Abstract: A computer generates a training set with annotated images (473) to train a convolutional neural network (CNN). The computer receives leaf-images showing leaves and biological objects such as insects, in a first color-coding (413-A), changes the color-coding of the pixels to a second color-coding and thereby enhances the contrast (413-C), assigns pixels in the second color-coding to binary values (413-D), differentiates areas with contiguous pixels in the first binary value into non-insect areas and insect areas by an area size criterion (413-E), identifies pixel-coordinates of the insect areas with rectangular tile-areas (413-F), and annotates the leaf-images in the first color-coding by assigning the pixel-coordinates to corresponding tile-areas. The annotated image is then used to train the CNN for quantifying plant infestation by estimating the number of biological object such as insects on the leaves of plants.

Abstract: Disclosed is a device for use in the oral cavity that includes a (1) handle, (2) camera, scanner, or other imaging device (collectively, “camera”), and (3) flexible stem having a first end attached to the handle and a second end attached to the camera. The stem can be moved by an operator into a bent (or curved) position suitable for taking images in various locations in the oral cavity, such as in the back of the mouth and/or behind the teeth. The stem requires an appropriate amount of bending force to be moved into a curved position and then out of the curved position into another position. The flexible stem is configured to require enough bending force so that it does not relax sufficiently to move out of position during normal use.

Abstract: A system and method is provided to manage different UI language settings in a central place by merging different UI language settings and related dependency data to an integrated UI language preference list. A set of API functions is provided that enables each resource loader to gain consistent UI language support by interacting with the integrated UI language preference list.

Abstract: An improved application architecture comprises a bifurcated structure having a language neutral portion and a localizable portion, compacted for efficiency into a smaller set of larger files. This bifurcated structure allows for easier distribution and updating of the application, while the reduced set of files provides for more efficient file management. A resource manifest can be specified during the compile stage to identify elements that are language specific and elements that remain language neutral. Furthermore, additional software can be used after compilation for compacting multiple localizable elements into a single file. Such compacting software can receive inputs specifying the language specific resources which are to be compacted into larger files, and the identity of those larger files.