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This paper presents an approach for a special class of objects.Objects with elastic static characteristic change their characteristic depending on the rate of change of the object's input coordinates. An example of objects with elastic characteristic would be an inflated construction. The motion trajectory of the material point that possesses weight on this construction depends on the rate of change of the coordinates. A control system for similar objects must be supplemented with a special correction element that changes the parameters of the main system depending on the rate of change of the main system's outputs.Objects with plastic static characteristic change their characteristic in the zone where the objects' input signals coordinates are located. An example of such objects is objects that are sensitive to wear, e.g. working zone of mechanical objects and senescent objects, biological objects, etc. A control system for such objects must be supplemented with the plastic static characteristic model. This subsystem will change the control system's main parameters based on the input signals' trails depth.

Abstract. We present two novel contributions to the problem of region classification in scenery/landscape images. The first is a model that incorporates local cues with global layout cues, following the statistical characteristics recently suggested in [1]. The observation that background regions in scenery images tend to horizontally span the image allows us to represent the contextual dependencies between background region labels with a simple graphical model, on which exact inference is possible. While background is traditionally classified using only local color and textural features, we show that using new layout cues significantly improves background region classification. Our second contribution addresses the problem of correct results being considered as errors in cases where the ground truth provides the structural class of a land region (e.g., mountain), while the classifier provides its coverage class (e.g., grass), or vice versa. We suggest an alternative labeling method that, while trained using ground truth that describes each region with one label, assigns both a structural and a coverage label for each land region in the validation set. By suggesting multiple labels, each describing a different aspect of the region, the method provides more information than that available in the ground truth.