We present a method for structural summarization and abstraction of complex spatial arrangements found in architectural drawings. The method is based on the well-known Gestalt rules, which summarize how forms, patterns, and semantics are perceived by humans from bits and pieces of geometric information. Although defining a computational model for each rule alone has been extensively studied, modeling a conjoint of Gestalt rules remains a challenge. In this work, we develop a computational framework which models Gestalt rules and more importantly, their complex interactions. We apply conjoining rules to line drawings, to detect groups of objects and repetitions that conform to Gestalt principles. We summarize and abstract such groups in ways that maintain structural semantics by displaying only a reduced number of repeated elements, or by replacing them with simpler shapes. We show an application of our method to line drawings of architectural models of various styles, and the potential of extending the technique to other computer-generated illustrations, and three-dimensional models.

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The human eye can perceive a vast array of colors. Whether light or dark, the colors that our eyes can see are supposedly unlimited. However, this is not the case. In reality, every image has two aspects or descriptors to it, a reflectance and an illumination. While the reflectance essentially shows an images true color, the illumination is what causes the colors to seem different to the human eye. This effect, originally discovered by Helmholtz, is known as Color Constancy. Color Constancy ensures that the color the Human Visual System (HVS) receives is the true color of the image, regardless of illumination. As a result of this effect, in 1971, Land and McCann created the Retinex theory. Using the pixels in the image, Land tried to estimate the value of the reflectances and thus reveal the true color of the image. This theory was basically a color constancy algorithm that tried to explain why colors look different when exposed to lighting. By calculating the pixels, Land was able to depict the sameness in a gradient of colors in an image. However, the algorithm is both inefficient and complicated. Following their footsteps, many other people have tried to formulate new algorithms around the Lands original Retinex algorithm. In this paper, different methods such as least squares and discrete cosine transform are explained as well as how to enhance images using both Lands idea and histogram equalization.

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This paper presents a new approach for reconstructing solids with planar, quadric and toroidal surfaces from three-view engineering drawings. By applying geometric theory to 3-D reconstruction, our method is able to remove restrictions placed on the axes of curved surfaces by existing methods. The main feature of our algorithm is that it combines the geometric properties of conics with affine properties to recover a wider range of 3-D edges. First, the algorithm determines the type of each 3-D candidate conic edge based on its projections in three orthographic views, and then generates that candidate edge using the conjugate diameter method. This step produces a wire-frame model that contains all candidate vertices and candidate edges. Next, a maximum turning angle method is developed to find all the candidate faces in the wire-frame model. Finally, a general and efficient searching technique is proposed for finding valid solids from the candidate faces; the technique greatly reduces the searching space and the backtracking incidents. Several examples are given to demonstrate the efficiency and capability of the proposed algorithm.