Covariant Feature Detector on Parallel Ultra Low Power Architecture
From iis-projects
Contents
Short Description
Together with the continuous progress on Ultra Low Power (ULP) accelerators we are developing the first Computer Vision (CV) library in such domain. The development of a CV library featuring high level capabilities is key tackling real word problems such as wearable devices, smart IoT (Internet of Things) sensors and intelligent nano-vehicles. The proposed project take place in this very context and it is meant to operate on the Parallel ULP (PULP) architecture we developed here at the IIS: a scalable, clustered many-core computing platform designed to operate on a large range of operating voltages, achieving in this way a high level of energy efficiency over a wide range of application workloads. In this project, you will port a set of existing vision algorithms, based on the VlFeat library [1], on the PULP platform, dealing with parallel programming challenging, low-power computational constraints as well as limited hardware features (e.g. the absence of a Floating Point Unit). The algorithms to be ported on the target architecture refers to the Covariant Feature Detector family, where the purpose of a covariant detector is to extract from an image a set of local features in a manner which is consistent with spatial transformations of the image itself. More in detail the proposed algorithms are:
- Harris corner [2]
- Hessian blobs [3]
- Laplacian and Difference of Gaussian (LoG, DoG) [4]
The goal of the project is not only to achieve good performance in term of execution time, but also to find the best trade-off between feature's accuracy and computation time for this platform. In particular the goals of the project can be summarized as follows:
- Acquiring familiarity with development on the PULP architecture (available as virtual platform and FPGA)
- Achieving a good knowledge of the proposed algorithms
- Porting/development and optimization of Covariant Feature Detectors proposed
- Measurement of performance and accuracy of the proposed solution
Status: Available
- Looking for Interested Semester and Master Project Students
- Supervision: Daniele Palossi, Andrea Marongiu
Character
- 20-30% Theory
- 30-40% Implementation
- 30-40% Optimization
- 20-30% Testing & Evaluation
Prerequisites
- Familiarity with embedded C programming.
- Knowledge of parallel computing would be an asset.
- Basic understanding of fundamental CV concepts is favorable.
Professor
References
- http://www.vlfeat.org/
- C. Harris , M. Stephens "A combined corner and edge detector" In Proc. of Fourth Alvey Vision Conference, 1988 link
- D. G. Lowe "Distinctive Image Features from Scale-Invariant Keypoints" In International Journal of Computer Vision, 2004 link
- K. Mikolajczyk, C. Schmid "An Affine Invariant Interest Point Detector" In ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I, 2002 link