In this paper, we address the problem of constraint detection for layout regularization. The layout we consider is a set of two-dimensional elements where each element is represented by its bounding box. Layout regula...
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In this paper, we address the problem of constraint detection for layout regularization. The layout we consider is a set of two-dimensional elements where each element is represented by its bounding box. Layout regularization is important in digitizing plans or images, such as floor plans and facade images, and in the improvement of user-created contents, such as architectural drawings and slide layouts. To regularize a layout, we aim to improve the input by detecting and subsequently enforcing alignment, size, and distance constraints between layout elements. Similar to previous work, we formulate layout regularization as a quadratic programming problem. In addition, we propose a novel optimization algorithm that automatically detects constraints. We evaluate the proposed framework using a variety of input layouts from different applications. Our results demonstrate that our method has superior performance to the state of the art.
Linear programming is a method for solving linear optimization problems with constraints, widely met in real-world applications. In the vast majority of these applications, the number of constraints is significantly l...
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Linear programming is a method for solving linear optimization problems with constraints, widely met in real-world applications. In the vast majority of these applications, the number of constraints is significantly larger than the number of variables. Since the crucial subject of these problems is to detect the constraints that will be verified as equality in an optimal solution, there are methods for investigating such constraints to accelerate the whole process. In this paper, a technique named proximity technique is addressed, which under a proposed theoretical framework gives an ascending order to the constraints in such a way that those with low ranking are characterized of high priority to be binding. Under this framework, two new Linear programming optimization algorithms are introduced, based on a proposed Utility matrix and a utility vector accordingly. For testing the addressed algorithms firstly a generator of 10,000 random linear programming problems of dimension n with m constraints, where , is introduced in order to simulate as many as possible real-world problems, and secondly, real-life linear programming examples from the NETLIB repository are tested. A discussion of the numerical results is given. Furthermore, already known methods for solving linear programming problems are suggested to be fitted under the proposed framework.
Modelling with constraints is a modem approach to product modelling. Engineering knowledge is associated with geometry and topology in the product model. Together with the technique of feature based modelling, modelli...
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Modelling with constraints is a modem approach to product modelling. Engineering knowledge is associated with geometry and topology in the product model. Together with the technique of feature based modelling, modelling with constraints has widely affected the development of new CAD-systems. The architecture of so called parametric CAD-systems reflects the impact of these new technologies. Besides the new hybrid modellers of geometry and topology, the sketcher and the constraint solver have become key components in a parametric CAD-system. The sketcher may be regarded as a designer interface for modelling with constraints. Moreover rule-based methods of automatic constraint detection are applied in sketchers. constraint solvers evaluate the network of constraints formulated by the sketcher as a set of equations or predicates using numeric or symbolic algorithms or rule based approaches. The article closes with a summary of the advantages and risks of designing with constraints, showing typical application fields of this modelling technique and discussing some open issues.
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