Abstract
In this paper, we present a novel feature detection approach designed for mobile devices, showing optimized solutions for both detection and description. It is based on FAST (Features from Accelerated Segment Test) and named 3D FAST. Being robust, scale-invariant and easy to compute, it is a candidate for augmented reality (AR) applications running on low performance platforms. Using simple calculations and machine learning, FAST is a feature detection algorithm known to be efficient but not very robust in addition to its lack of scale information. Our approach relies on gradient images calculated for different scale levels on which a modified9 FAST algorithm operates to obtain the values of the corner response function. We combine the detection with an adapted version of SURF (Speed Up Robust Features) descriptors, providing a system with all means to implement feature matching and object detection. Experimental evaluation on a Symbian OS device using a standard image set and comparison with SURF using Hessian matrix-based detector is included in this paper, showing improvements in speed (compared to SURF) and robustness (compared to FAST)
Highlights
Feature localization and feature matching is a common task in many computer vision applications
Our goal is to develop a novel detector that combines SURF and FAST, creating an algorithm called 3D FAST that extends traditional FAST to provide scale level and directional information
Scale Invariant Feature Transform (SIFT) and SURF both take advantage of scale level information to account for robustness through scale invariance
Summary
Feature localization and feature matching is a common task in many computer vision applications. It is frequently used in tracking, image mosaicing and object recognition. Our goal is to develop a novel detector that combines SURF and FAST, creating an algorithm called 3D FAST that extends traditional FAST to provide scale level and directional information. As camera-equipped mobile phones become more powerful and ubiquitously available, they are a perfect platform for AR systems. They are portable, low-cost, and more important, consumers are already getting used to this kind of devices. Users can be equipped with personal information and entertainment systems, allowing everyday usages that have not been possible before
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