Synced
Synced
Sep 5 · 3 min read

Snow White’s evil stepmother utters one of animation’s classic lines: “Mirror, mirror, on the wall, who’s the fairest of them all?” If the old hag were an artificial intelligence, she’d probably spend the entire film just attempting to locate that mirror — which, as it happens, is a relatively difficult computer vision challenge.

A group of Chinese researchers have come up with a novel method for identifying mirrors in images which outperforms state-of-the-art detection and segmentation methods on targeted baselines.

Mirrors reflect content in their own environment, and that continuity makes recognizing mirrors a formidable task even for cutting-edge computer vision systems. The researchers note that no previous study has put a specific focus on the mirror segmentation problem, and that theirs is the first automatic method developed for the task.

Because model training can’t proceed without data, researchers first built a mirror dataset that includes 4,018 pairs of images (original image and segmentation image) containing mirrors and manually annotated masks.

The proposed “MirrorNet” network is a cascading framework that contains three modules:

  • a pre-trained Feature Extraction Network (ResNeXt101 network) that extracts multi-scale feature maps from input images;
  • Contextual Contrasted Feature Extraction (CCFE) modules connected to a Feature Extraction Network, which learn different scales of contextual contrasted features for localizing mirrors of different sizes;
  • a mirror map that coarsely highlights the dividing boundaries of the mirror and progressively refines itself by helping the upper CCFE layers focus on learning finer contextual contrasted features.

In their experiments, researchers adopted five evaluation metrics commonly used in similarly detection and segmentation tasks, and compared MirrorNet with 11 state-of-the-art methods such as PSPNet, ICNet and Mask RCNN. MirrorNet achieved the best performance, with a large margin over the other methods.

Researchers focused their study on interior mirrors and did not include outdoor mirrors such as the reflective glass walls of skyscrapers or large mirrors in public places or outside shops, etc. They suggest that extending MirrorNet to a wider range of scenarios in the future may benefit for example autonomous driving and drone navigation research.

The authors are from the Dalian University of Technology, Peng Cheng Laboratory, and City University of Hong Kong. The paper Where Is My Mirror? has been accepted by ICCV 2019 and is on arXiv.


Journalist: Tony Peng | Editor: Michael Sarazen


We know you don’t want to miss any stories. Subscribe to our popular Synced Global AI Weekly to get weekly AI updates.


Need a comprehensive review of the past, present and future of modern AI research development? Trends of AI Technology Development Report is out!


2018 Fortune Global 500 Public Company AI Adaptivity Report is out!
Purchase a Kindle-formatted report on Amazon.
Apply for Insight Partner Program to get a complimentary full PDF report

SyncedReview

We produce professional, authoritative, and thought-provoking content relating to artificial intelligence, machine intelligence, emerging technologies and industrial insights.

Synced

Written by

Synced

AI Technology & Industry Review — syncedreview.com | Newsletter: goo.gl/Q4cP3B | Become Synced Insight Partner: goo.gl/ucXZDw | Twitter: @Synced_Global

SyncedReview

We produce professional, authoritative, and thought-provoking content relating to artificial intelligence, machine intelligence, emerging technologies and industrial insights.

Welcome to a place where words matter. On Medium, smart voices and original ideas take center stage - with no ads in sight. Watch
Follow all the topics you care about, and we’ll deliver the best stories for you to your homepage and inbox. Explore
Get unlimited access to the best stories on Medium — and support writers while you’re at it. Just $5/month. Upgrade