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Presenter: Sina Ghaffari
Supervisor:

Date: Thu, March 23, 2023
Time: 17:30:00 - 00:00:00
Place: ZOOM - Please see below.

ABSTRACT

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https://uvic.zoom.us/j/82473986126?pwd=SXQ0WDBCbXNCZXhSMS9mUnJHQWFaUT09 

ABSTRACT 

Image matching is one of the fundamental problems in computer vision, and has many applications such as object recognition, structure from motion, and 3D reconstruction. In this work, we aim to accelerate image matching algorithms, and improve their accuracy by proposing approaches that have a minimal impact on speed. With this in mind, we focus on handcrafted descriptor and matching algorithms. Our contributions in this research are twofold. 

The first set of contributions are related to the acceleration of descriptor algorithms and the reduction of resource utilization for image matching by proposing novel circuits based on Field Programmable Gate Arrays (FPGAs). We use FPGAs as a platform due to their features such as parallel processing, low-power computing, and flexibility in design. 

A novel hardware-software co-design of the HOG algorithm is introduced to accelerate the execution of this descriptor algorithm. Next, we focus on binary descriptors and present a novel hardware implementation of the Binary Robust Invariant Scalable Keypoints (BRISK) algorithm. BRISK is faster than non-binary descriptors but is computationally expensive with respect to other binary descriptor algorithms. A new sampling pattern for the BRISK algorithm is proposed to facilitate the hardware implementation of BRISK in multiple scales. 

The second set of contributions is related to improving image matching accuracy while maintaining performance in terms of computations. For this purpose, the focus is on handcrafted descriptor algorithms which are known to be more computationally efficient than deep learning based algorithms. We analyze and propose fusion of descriptor algorithms which extracts complementary information to attain higher accuracy. The experimental results and analysis demonstrate a higher mean Average Precision (mAP) of the fusion methods in comparison with the baseline algorithms. 

The next contribution for accuracy improvement is adding convolutional neural network (CNN) prefiltering to images prior to keypoint detection. The addition of a shallow CNN as the first step of a handcrafted algorithm to improve accuracy is proposed. The CNN is trained to filter the raw input images to achieve higher mAP. Experimental results indicate an improvement of accuracy using this method on the Hpatches dataset.