Search results

(1 - 2 of 2)
HARDWARE/SOFTWARE CO-DESIGN PARTITIONING ALGORITHM FOR MACHINE VISION APPLICATIONS
Title
HARDWARE/SOFTWARE CO-DESIGN PARTITIONING ALGORITHM FOR MACHINE VISION APPLICATIONS
Creator
Gonnot, Thomas
Date
2017, 2017-05
Description
Advancements in FPGA technologies now allows the implementation of machine vision using hardware component rather than processors for...
Show moreAdvancements in FPGA technologies now allows the implementation of machine vision using hardware component rather than processors for increased efficiency. The combination of hardware and software implementations, however, can provide even more efficient results by combining the advantages of both technologies. This leads to the problem of partitioning the machine vision algorithms between hardware and software. The hardware/software partition problem is NP-hard, which means that a solution to the problem can be checked in polynomial time, but the time to find the solution is not predictable. Automated methods based on a genetic algorithm or discrete particle swarm optimization algorithm allow a designer to implement computer vision algorithms without concerns for the hardware/software partitioning. Their reliance on randomness to explore different partitioning selections, however, means that the optimum result might not be reached and that the processing time cannot be predicted. This dissertation introduces a model for image processing and computer vision algorithms in a set of elementary blocks, each of which is assigned one or more configuration. This configuration can be either hardware or software and is linked to the corresponding resource utilization and performance. A procedure is also introduced to allocate the different blocks to either hardware or software, and a cost function is defined to evaluate the relevance of the generated design. The implementation of the model and procedure allows for the partitioning of any image processing in polynomial time by checking various implementations and selecting the optimum solution. This thesis includes two test cases used to test the efficiency of the method. The shift-invariant features transform is used to demonstrate the viability of the partitioning results on an algorithm containing multiple image convolution operations in parallel. The neural network, on the other hand, is used to demonstrate the performances of the procedure when machine vision algorithm contains many blocks. Finally, this dissertation present a set of machine vision applications, such as object tracking, object recognition, optical character recognition, facial recognition, and visually impaired assistance. The proposed model and procedure could be included in the design flow of hardware/software co-design tools and provide a library of image processing blocks ready to be implemented. This would allow image processing and computer vision designers would be able to implement any algorithm efficiently in hardware/software co-design without the need to know how to partition it.
Ph.D. in Electrical Engineering, May 2017
Show less
STEREO-BASED DEPTH MAP PROCESSING: ESTIMATION AND REFINEMENT
Title
STEREO-BASED DEPTH MAP PROCESSING: ESTIMATION AND REFINEMENT
Creator
Loghman, Maziar
Date
2016, 2016-12
Description
During the past decade, research in 3D video has become a hot topic owing to advancements in both hardware and software. Amongst different...
Show moreDuring the past decade, research in 3D video has become a hot topic owing to advancements in both hardware and software. Amongst different methods proposed for representing 3D data, multi-view video plus depth (MVD) format has gained a lot of attention. Most of such 3D algorithms rely on a per-pixel depth representation of the scene called a depth map. Depth maps are very useful for rendering virtual views and have lead to advancements in 3D compression algorithms. Generating an accurate and dense depth map is one of the important prerequisite for many 3D video applications. In this thesis, we highlight the following major problems in MVD. * Depth map estimation * Depth map refinement * Depth map coding In order to generate an accurate depth map, we propose a method based on Census transform with adaptive window patterns and semi-global optimization. A modified cross-based cost aggregation technique is proposed which helps to calculate a more reliable depth map. In order to further enhance the quality of the generated depth map, a novel multi-resolution anisotropic diffusion based algorithm is presented. The proposed depth refinement algorithm computes a dense depth map in which the holes have been filled and the object boundaries are sharpened. The next part of the research is based on depth map coding. In depth map coding, a considerable amount of time is required to investigate the mode decision pro- cess for every block of depth pixels. However, in real-time purposes, we can partially skip the mode selection step. In this thesis, we propose a novel depth intra-coding scheme for 3D video coding based on HEVC standard. The core idea of the proposed method is motivated by the fact that depth maps have specific characteristics that distinguish them from those of color images. By analyzing the reference depth maps based on homogeneousness of different regions, for some particular blocks, the DMM full-RD search is skipped and the mode is selected based on the previous similar tree- blocks. By this means, the time complexity of the encoding process is significantly reduced.
Ph.D. in Electrical Engineering, December 2016
Show less