Computer Vision News - August 2017

GMS: Grid-based Motion Statistics

Every month, Computer Vision News reviews a research from our field. This

month we have chosen to review two papers. The first one is

GMS: Grid-based

Motion Statistics for Fast, Ultra-robust Feature Correspondence

. a

paperoffering a simple means to incorporate motionsmoothness in a way that

rapidly and reliablydifferentiates true and false matches in a region. Weare

indebted to the authors (

JiaWang Bian

and

Wen-Yan Lin

- as joint first authors -

as well as

Yasuyuki Matsushita

Sai-Kit Yeung

Tan-DatNguyen

and

Ming-Ming

Cheng

) for allowing us to use their images to illustrate this review. The website

of the project is

Background, motivation and novelty:

The main advantage of encapsulating smoothness constraints into feature

matching is the ultra-robustness of the generated results. However, the price to

pay is not negligible, in terms of complexity and slowness, which prevent the use

of such a technique for video. The challenge is to develop accurate and robust

matching of features between images, quickly enough and with computational

efficiency sufficient for real-time application. GMS, that is Grid-based Motion

Statistics, is the solution proposed by this paper to incorporate motion

smoothness as a statistical likelihood of having a certain number of feature

matches between a region pair by the way of differentiating true and false

matches - by evaluating the number of matches in selected neighborhood. The

assumption (largely inherited from previous works) behind this idea is that

motion smoothness induces correspondence clusters that are unlikely to occur

at random. Statistical measures (building on the law-of-large-numbers) are so

introduced to reject false matches and allow previously unthought-of results.

“GMS achieved real-time matching of features in challenging

scenarios, not yet successfully dealt with before”

Research

Research