Revealing object and surface materials in natural environments is beneficial to a broad range of applications such as robotic manipulation and navigation, visual quality control in manufacturing and waste sorting. For example, knowing an objectâ€™s material, a robot could adjust its grasping strategy according to the friction of its surface. For this reason, material categorisation from real-world images has attracted growing interests from both the theoretical and practical points of view. In this project, we aim to design and develop a method for material detection and classification in natural images. In practice, the robustness of such a method is challenged by variations presented real-world environments such as illumination conditions, viewpoints and occlusions. Recently, computer vision researchers have contributed a number of benchmark databases for material classification in the wild, such as the CUReT , KTH-TIPS , Flickr Material Dataset (FMD) , UIUC , UMD , Outex , and Drexel Texture Database . However, these datasets are not realistic as a single material usually fills the entire image. To support applications of material recognition in realistic scenarios, there is a need for a dataset of images, each of which capturing multiple materials in cluttered backgrounds. The construction of such a dataset would open up opportunities to examine the extent of applicability of existing algorithms for material detection and classification in the wild.
â€¢ A real-world image dataset for material segmentation and classification. The images should be composed of multiple materials in a cluttered background. â€¢ Experimentation and performance reporting for the newly built dataset with combinations of existing segmentation and material classification methods.
This project is suitable for candidates with a strong undergraduate background in mathematics, computer science and software/computer engineering. The following skills are essential for a successful undertaking of the proposed project â€¢ A strong foundation in mathematics (linear algebra, calculus and optimisation). â€¢ Familiarity with computer vision, pattern recognition and image processing (desirable). â€¢ Good software design and programming skills (especially in C++ and Matlab).
1. 1. K. J. Dana, B. van Ginneken, S. K. Nayar, and J. J. Koenderink. Reflectance and texture of real world surfaces. ACM Transactions on Graphics, 18(1):1â€“34, 1999. 2. B. Caputo, E. Hayman, and P. Mallikarjuna. Class-specific material categorisation. In ICCV, 2005. 3. L. Sharan, R. Rosenholtz, and E. H. Adelson. Material perception: What can you see in a brief glance? Journal of Vision, 9:784(8), 2009. 4. S. Lazebnik, C. Schmid, and J. Ponce. A sparse texture representation using local affine regions. PAMI, 28(8):2169â€“2178, 2005. 5. Y. Xu, H. Ji, and C. Fermuller. Viewpoint invariant texture description using fractal analysis. IJCV, 83(1):85â€“100, June 2009. 6. T. Ojala, M. Pietikainen, and T. Maenpaa. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. PAMI, 24(7):971â€“987, 2002. 7. G. Oxholm, P. Bariya, and K. Nishino. The scale of geometric texture. In European Conference on Computer Vision, pages 58â€“71. Springer Berlin/Heidelberg, 2012.
The student will gain a practical experience through working on real-world research problems with experienced researchers in the areas of computer vision and pattern recognition. He/she will receive technical support in terms of hardware equipment/software for data collection and processing. The student will gain the working knowledge applicable to the above and other related real-world applications.