Machine Learning
Periodicity, Directionality, and Randomness: Wold Features for Image Modeling and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Photorealistic Scene Reconstruction by Voxel Coloring
International Journal of Computer Vision
Image quilting for texture synthesis and transfer
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
A Statistical Approach to Texture Classification from Single Images
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
Segmentation and Recognition Using Structure from Motion Point Clouds
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
A Statistical Approach to Material Classification Using Image Patch Exemplars
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classifying materials in the real world
Image and Vision Computing
Toward automated generation of parametric BIMs based on hybrid video and laser scanning data
Advanced Engineering Informatics
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Automated sparse 3D point cloud generation of infrastructure using its distinctive visual features
Advanced Engineering Informatics
Discrete-continuous optimization for large-scale structure from motion
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Fast anisotropic Gauss filtering
IEEE Transactions on Image Processing
Optimized selection of key frames for monocular videogrammetric surveying of civil infrastructure
Advanced Engineering Informatics
| Hi-index | 0.00 |
Automatically monitoring construction progress or generating Building Information Models using site images collections - beyond point cloud data - requires semantic information such as construction materials and inter-connectivity to be recognized for building elements. In the case of materials such information can only be derived from appearance-based data contained in 2D imagery. Currently, the state-of-the-art texture recognition algorithms which are often used for recognizing materials are very promising (reaching over 95% average accuracy), yet they have mainly been tested in strictly controlled conditions and often do not perform well with images collected from construction sites (dropping to 70% accuracy and lower). In addition, there is no benchmark that validates their performance under real-world construction site conditions. To overcome these limitations, we propose a new vision-based method for material classification from single images taken under unknown viewpoint and site illumination conditions. In the proposed algorithm, material appearance is modeled by a joint probability distribution of responses from a filter bank and principal Hue-Saturation-Value color values and classified using a multiple one-vs.-all @g^2 kernel Support Vector Machine classifier. Classification performance is compared with the state-of-the-art algorithms both in computer vision and AEC communities. For experimental studies, a new database containing 20 typical construction materials with more than 150 images per category is assembled and used for validation. Overall, for material classification an average accuracy of 97.1% for 200x200 pixel image patches are reported. In cases where image patches are smaller, our method can synthetically generate additional pixels and maintain a competitive accuracy to those reported above (90.8% for 30x30 pixel patches). The results show the promise of the applicability of the proposed method and expose the limitations of the state-of-the-art classification algorithms under real world conditions. It further defines a new benchmark that could be used to measure the performance of future algorithms.