Binary-tree based object depth order evaluation method in monocular image

Abstract

The invention discloses a binary-tree based object depth order evaluation method in a monocular image. The evaluation method comprises steps of 1: defining middle distances among image regions; 2: evaluating T angle point confidence for T angle points formed at junctures of the image regions; 3: according to the middle distances and the T angle point confidence, constructing a binary partitioning tree so as to obtain a regional model of the image; and 4: selecting an optimal T angle point set and finishing depth ordering so as to obtain a depth order graph. Thus, a depth levelrestoration effect is achieved.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a method for estimating the depth order of objects in a monocular image based on a binary tree. Background technique [0002] There are usually two main methods for depth-order inference of monocular images: one is based on learning, and the other is based on image structure to find low-level clues for inference. [0003] For the first type of method, D.Hoiem et al. ("Recoveringocclusionboundariesfromasingleimage", ICCV, 2007, pp.1-8) over-segment the image, and then extract color, texture, vertical and horizontal features for each region, and stack them Together, the depth order is estimated under the framework of Markov Random Field (MRF). Obviously, these methods are based on learning, and MRF needs to be trained through real depth level data. However, the main drawback of such methods is that their effect is limited to images of the same type as the training images. If the ...

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Problems solved by technology

Although the effect is good, some complex T corners are estimated incorrectly in the initial stage

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