Extraction and Recognition of Planes in a Single Image Using Matlab
We present a novel method to recognize 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 color, coupled with relevance vector machines for classification and regression. We first show that using hand labeled 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. INTRODUCTION: This project is concerned with the automatic extraction of 3D structure from single images. While the creation of 3D models of real world scenes from image data has been a topic of interest for a long time, it is usual for this to involve either multiple views of a scene or video data, exploiting parallax to obtain information about scene depth. Inferring depth from only a single image is much more challenging. However, previous works have shown that a number of image cues can be exploited to extract information about depth, shape, or other 3D structure.Two prominent existing methods are those of Saxena et al. [32] and Hoiem et al. [19] The former is able to recover an approximate depth map for an image having learned the relationship between image appearance and ground truth depth maps derived from laser scanning. RELATED WORK: Here we discuss examples of prior work on extracting planar structure from single images. This can be divided into two main categories: methods which explicitly use geometric properties, such as parallel lines and texture; and work which aims to recognize structure based on learning from training examples. Given two or more sets of parallel lines lying on a plane, their respective vanishing points uniquely define the plane's 3D orientation [17] Thus, detecting such line features in an image enables the extraction of structure, providing they lie on a common plane. One approach is to detect rectangular structures, such as windows or doors, which provide orthogonal parallel lines in the same plane [22] [27] which can be used for basic camera pose recovery and wide baseline matching. However, these methods rely on orthogonal, Manhattan like structure, and reliable line detection, hence limiting applicability. Machine Learning Methods: More recent work has looked at techniques for learning the relationship between appearance and structure. A good example of such a technique is by Torralba and Oliva [35] who estimate overall depth using knowledge that certain types of structure tend to appear at particular distances. However, this work