Abstract
Visual search is a fundamental technology in the computer vision community. It is difficult to find an object in complex scenes when there exist similar distracters in the background. We propose a target search method in rough three-dimensional-modeling scenes based on a vision salience theory and camera imaging model. We give the definition of salience of objects (or features) and explain the way that salience measurements of objects are calculated. Also, we present one type of search path that guides to the target through salience objects. Along the search path, when the previous objects are localized, the search region of each subsequent object decreases, which is calculated through imaging model and an optimization method. The experimental results indicate that the proposed method is capable of resolving the ambiguities resulting from distracters containing similar visual features with the target, leading to an improvement of search speed by over 50%.
Highlights
Visual search is one of the critical technologies in the field of computer vision; it can support high-level applications such as motion analysis, image understanding, and so on
We build an optimization model for this issue based on a camera imaging model
We propose a visual search method based on vision salience theory and a camera imaging model, which performs rapid and accurate object locating along a visual search path
Summary
Visual search is one of the critical technologies in the field of computer vision; it can support high-level applications such as motion analysis, image understanding, and so on. It is a common task to find specific objects in the scene that have been roughly three-dimensional (3-D) modeled by methods such as simultaneous localization and mapping (SLAM)[1] or structure from motion (SFM).[2] In these scenarios, location information can be supplied by sensors such as global position system in the outdoors or RGB-D in the indoors. Corresponding 3-D-coordinates of some image pixels can be calculated by triangulation methods.[3] For this case, we refer to rough 3-D-modeling scenes. Given a known point in the rough 3-D-modeling scenes, the search region in the image of the target will be decreased. We build an optimization model for this issue based on a camera imaging model. Through this optimization method, we calculate the search regions of the other points when a two-dimensional (2-D)–3-D point pair is found.
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