We present a novel method to recognise planar structures in a single image and estimate their 3D orientation. This is done by exploiting the relationship between image appearance and 3D structure, using machine learning methods with supervised training data. As such, the method does not require specific features or use geometric cues, such as vanishing points. We employ general feature representations based on spatiograms of gradients and colour, coupled with relevance vector machines for classification and regression. We first show that using hand-labelled training data, we are able to classify pre-segmented regions as being planar or not, and estimate their 3D orientation. We then incorporate the method into a segmentation algorithm to detect multiple planar structures from a previously unseen image.
|Pages (from-to)||1849 - 1861|
|Number of pages||13|
|Journal||IEEE Transactions on Pattern Analysis and Machine Intelligence|
|Early online date||18 Dec 2014|
|Publication status||Published - 1 Sep 2015|