In this paper, a systematic review of various contour functions and methods of their analysis, as applied in the field of shape description and characterization, is presented. Contour functions are derived from planar object outlines and are used as an intermediate representation from which various shape properties can be obtained. All the functions are introduced and analyzed following the same scheme, thus making it possible to compare various representations. Although only a small subset of contour functions is included in the survey (cross-section, radius-vector, support, width, parametric, complex, tangent-angle, curvature, polynomial, and parametric cubic), the paper demonstrates a multitude of techniques for shape description that are based on this approach. Several analysis tools, such as statistics, line moments and invariants, Fourier and other series expansions, curvature scale space image, wavelet, and Radon transform are described.

/pdf/on-using-functions-to-describe-the-shape-4swhx3er2y.pdf

On Using Functions to Describe the Shape

Automatically finding out existing building layouts from a repository is always helpful for an architect to ensure reuse of design and timely completion of projects. In this paper, we propose Deep Architecture for fiNdIng alikE Layouts (DANIEL). Using DANIEL, an architect can search from the existing projects repository of layouts (floor plan), and give accurate recommendation to the buyers. DANIEL is also capable of recommending the property buyers, having a floor plan image, the corresponding rank ordered list of alike layouts. DANIEL is based on the deep learning paradigm to extract both low and high level semantic features from a layout image. The key contributions in the proposed approach are: (i) novel deep learning framework to retrieve similar floor plan layouts from repository; (ii) analysing the effect of individual deep convolutional neural network layers for floor plan retrieval task; and (iii) creation of a new complex dataset ROBIN (Repository Of BuildIng plaNs), having three broad dataset categories with 510 real world floor plans.We have evaluated DANIEL by performing extensive experiments on ROBIN and compared our results with eight different state-of-the-art methods to demonstrate DANIEL’s effectiveness on challenging scenarios.

DANIEL: A Deep Architecture for Automatic Analysis and Retrieval of Building Floor Plans

http://www.cvc.uab.es/~marcal/pdfs/PRL09.pdf

Relational indexing of vectorial primitives for symbol spotting in line-drawing images

A symbol spotting approach in graphical documents by hashing serialized graphs

In this paper an approach to the automatic quality assessment of existing geo-spatial data is presented. The necessary reference information is derived automatically from up-to-date digital remotely sensed images using image analysis methods. The focus is on the quality assessment of roads as these are among the most frequently changing objects in the landscape. In contrast to existing approaches for quality control of road data, the data to be assessed and the objects extracted from the images are modelled and processed together. A geometric-topologic relationship model for the roads and their surroundings is defined. Context objects such as rows of trees support the quality assessment of road vector data as they may explain gaps in road extraction. The extraction and explicit incorporation of these objects in the assessment of a given road database give stronger support for or against its correctness. During the assessment existing relations between road objects from the database and extracted objects are compared to the modelled relations. The certainty measures of the objects are integrated into this comparison. Normally, more than one extracted object gives evidence for a road database object; therefore, a reasoning algorithm which combines evidence given by the extracted objects is used. If the majority of the total evidence argues for the database object and if a certain amount of this database object is covered by extracted objects, the database object is assumed to be correct, i.e. it is accepted, otherwise it is rejected. The procedure is embedded into a two-stage graph-based approach which exploits the connectivity of roads and results in a reduction of false alarms. The algorithms may be incorporated into a semi-automatic environment, where a human operator only checks those objects that have been rejected. The experimental results confirm the importance of the employed advanced statistical modelling. The overall approach can reliably assess the roads from the given database, using road and context objects which have been automatically extracted from remotely sensed imagery. Sensitivity analysis shows that in most cases the chosen two-stage graph-approach reduces the number of false decisions. Approximately 66% of the road objects have been accepted by the developed approach in an extended test area, 1% has been accepted though incorrect. Those false decisions are mainly related to the lack of modelling road junction areas.

Image-based quality assessment of road databases

In this article a new concept for real time analysis of images based on the vectorized contours is presented. Given the polygone approximation of contours the geometric moments can be formulated for line contours. The resulting line moments (LM's) can be calculated for an arbitrary collection of contour fragments. Neither the existence of an area nor a closed contour is prerequisite to characterize an object by line moments. Due to these properties of the LM's time consuming preprocessing of noisy object contours is not required. Even objects with structured surfaces can be characterized by LM's. The formalism to apply the geometric moments to contour patterns is derived. A direct and a recursive algorithm to efficiently compute the LM's is given. As an application of the LM's an analysis system for structured textures is presented and results are discussed.

/pdf/line-moments-and-invariants-for-real-time-processing-of-30wnlyyzyg.pdf

Georg Lambert

Papers

Line Moments and Invariants for Real Time Processing of Vectorized Contour Data