eCAD System Design - Applications in Architecture

The rapid advances in learning technologies, computer modeling, multimedia and spatial sciences, as well as the availability of many powerful graphics PCs and workstations, make 3-D modeling-based methods for personalized e-learning with eCAD (modeling) functionality feasible. Personalized eCAD learning is a new term in engineering, environment and architecture education, related to the development of learning educational units (3-D learning objects) with re-usable digital architecture functionality, and introduced to literature for the first time within this paper. In particular, for university education courses in eCAD, digital architecture, design computing and CAAD (reagarding spatial information systems, architectures, monuments, cultural heritage sites, etc.), such a e-learning methodolgy must be able to derive spatial, pictorial, geometric, spatial, topological, learning and semantic information from the target object (a 3-D model) or scene (a 3-D landscape environment) or procedure (a 3-D simulation approach to a phenomenon), in such a way that it can be directly used for e-learning purposes regarding the spatial topology, the history, the architecture, the structure and the temporal (time-based) 3-D geometry of the projected object, scene or procedure. This paper is about the system design of such a e-learning method. For this purpose, the requirements, objectives and pedagogical extensions are presented and discussed. Finaly, a practical project is used to demonstrate the functionality and the performance of the proposed methodology in architectur

RECONSTRUCTION OF ARCHITECTURAL HERITAGE WITH SYMMETRICAL COMPONENTS

Data capturing through either Lidar or photogrammetry, often results in incomplete and partial information related to a surface due to occlusion or inaccessibility of the clear object vision. In case of asymmetrical objects yet the reconstruction is unattainable by any means, meanwhile the approach for the development of the missing information could be done in cases of symmetrical objects. In this paper we have advised a semi-automatic approach for recreating missing or incomplete information from the partially captured data using space sub-division and 3D transformation. The study has been done on a 175 year-old building whose scanned information is available for only one side and captures a façade with four columns. The idea is to first extract the symmetrical parts through segmentation of different building parts. Then the columns with partial information have been oriented as per a reference plane based on the pose and centre computed from the horizontal parts. The instance is then used to fill in the lost information through duplication and transformation. This approach can be used to recreate structures with symmetrical elements, which are partially destroyed from withering, disaster, or any human intervention

Low-rank Based Algorithms for Rectification, Repetition Detection and De-noising in Urban Images

In this thesis, we aim to solve the problem of automatic image rectification and repeated patterns detection on 2D urban images, using novel low-rank based techniques. Repeated patterns (such as windows, tiles, balconies and doors) are prominent and significant features in urban scenes. Detection of the periodic structures is useful in many applications such as photorealistic 3D reconstruction, 2D-to-3D alignment, facade parsing, city modeling, classification, navigation, visualization in 3D map environments, shape completion, cinematography and 3D games. However both of the image rectification and repeated patterns detection problems are challenging due to scene occlusions, varying illumination, pose variation and sensor noise. Therefore, detection of these repeated patterns becomes very important for city scene analysis. Given a 2D image of urban scene, we automatically rectify a facade image and extract facade textures first. Based on the rectified facade texture, we exploit novel algorithms that extract repeated patterns by using Kronecker product based modeling that is based on a solid theoretical foundation. We have tested our algorithms in a large set of images, which includes building facades from Paris, Hong Kong and New York

Unconstrained Road Sign Recognition

There are many types of road signs, each of which carries a different meaning and function: some signs regulate traffic, others indicate the state of the road or guide and warn drivers and pedestrians. Existent image-based road sign recognition systems work well under ideal conditions, but experience problems when the lighting conditions are poor or the signs are partially occluded. The aim of this research is to propose techniques to recognize road signs in a real outdoor environment, especially to deal with poor lighting and partially occluded road signs. To achieve this, hybrid segmentation and classification algorithms are proposed. In the first part of the thesis, we propose a hybrid dynamic threshold colour segmentation algorithm based on histogram analysis. A dynamic threshold is very important in road sign segmentation, since road sign colours may change throughout the day due to environmental conditions. In the second part, we propose a geometrical shape symmetry detection and reconstruction algorithm to detect and reconstruct the shape of the sign when it is partially occluded. This algorithm is robust to scale changes and rotations. The last part of this thesis deals with feature extraction and classification. We propose a hybrid feature vector based on histograms of oriented gradients, local binary patterns, and the scale-invariant feature transform. This vector is fed into a classifier that combines a Support Vector Machine (SVM) using a Random Forest and a hybrid SVM k-Nearest Neighbours (kNN) classifier. The overall method proposed in this thesis shows a high accuracy rate of 99.4% in ideal conditions, 98.6% in noisy and fading conditions, 98.4% in poor lighting conditions, and 92.5% for partially occluded road signs on the GRAMUAH traffic signs dataset

Modeling Based on Perspective Images and Application in Cultural Heritage

U ovom radu kreiran je novi poluautomatski normativni sistem za generisanje prostora na osnovu perspektivnih slika. Sistem obuhvata niz postupaka čijim korišćenjem se na osnovu dvodimenzionalnih medijuma, najčešće fotografija, generiše trodimenzionalna struktura. Pristup je prilagođen rešavanju složenih problema iz oblasti vizuelizacije graditeljskog nasleđa, što je u radu potkrepljeno praktičnom primenom sistema.In this research a new semi-automated normative image-based modelling system is created. The system includes number of procedures that are used to transform two-dimensional medium, such as photographs, to threedimensional structure. The used approach is adjusted to the properties of complex projects in the domain of visualization of cultural heritage. An application of the system is given demonstrating its practical value

Symmetry-Based 3-D Reconstruction from Perspective Images. Part I: Detection and Segmentation. Part II: Matching and Reconstruction.

DARPA / N00014-01-1-0576Ope