Shadows Sample Clauses

Shadows. Any shifts not filled due to the temporary absence of a full-time employee will be assigned to that employee’s shadow.

Shadows. 1. As set forth in the FEIS and LOR, prior to the time when the Project casts shadows on the stained glass windows of the Church of the Redeemer (the “Church”), FCRC shall develop and implement measures to offset the impacts of the shadows comprised of: (i) removing the existing protective coverings from all of the stained glass windows, including any patching and repair associated with the removal;

Shadows. It is nearly impossible to obtain ortho imagery without the shadows of buildings or cliffs. However, if the digital imagery is processed correctly, it should have acceptable detail in shadow and bright areas. If a seam line runs through a shadow, the shadow may change position or stop at the seam line. This may draw attention to the shadow, but is not grounds for rejection of data. ▇▇▇▇▇▇▇ does not route seamlines around specific shadows to allow for ground features to come from the “best” image. ▇▇▇▇▇▇▇ does route seamlines to avoid buildings and ground features Typical features that can be seen in the imagery are sidewalks, painted lines on the street, cars, houses, swimming pools outlines, porches, outdoor furniture, etc. There are instances when a shadow will be cast on areas that essentially will lack detail. Features such as grass, bushes, water, black automobiles or other very dark colored features will not be very distinguishable in shadows. Features like this are not grounds for rejection.

Shadows shadows constitute a major problem for all outdoor, and to a lesser extent indoor classification tasks. Shadows change the size of the tracked object and add extra regions to classify, which in fact hold little usable data. Shadows are also hard to suppress, as they do not hold a uniform shape or size. Efforts are being made within the AVITRACK project to minimise these problems. Occlusion – when an object passes in front of any tracked object, the tracked object is said to be occluded. The occluding object may be stationary or in motion, and may take any form. When this occurs, the overall appearance of the tracked object changes in very unpredictable ways. This problem can be minimised through the recognition of subsections of the image to be classified instead of the image as a whole. Image Transformations – this concerns translation and rotations to the image. Images can differ greatly dependant on the rotation and position within the objects bounding box. The global position of the object is resolved by the current implementation of the system, however, the objects bounding box returned by the system may only have part of the object – in the case of an object entering the scene – and may present the object at any rotation. Availability of Exemplar Objects – it is often difficult to find objects, which exemplify their respective class, especially within object classes that can deform, for example people, articulated vehicles and vehicles with external moving parts. The objects are also, as mentioned above, often miss­tracked or the bounding boxes contain large amounts of unwanted background pixels.