Patch Match with Multiple Descriptors for Scene Alignment
Date Issued
2014
Date
2014
Author(s)
Tsai, Han-Yi
Abstract
In this thesis, we introduce a general method for image-based scene alignment. Scene alignment aims to establish dense correspondence for a pair of images. Much research effort has been made to estimate a dense stereo and optical flow field for two given images that have the same scene but were captured from distinct viewpoints or at different time. However, to align images with different scenes or objects, it is still challenging. There are diverse image variations between general images. It is difficult to know what kind of image variations occurs across the images in advance. We hence propose to utilize multiple descriptors to deal with the problem of different image variations. A criterion is presented to select proper descriptors among SIFT, geometric blur, DAISY, and LIOP. Moreover, serious image variations as well as high image resolutions make the computational cost becomes much higher. To improve efficiency in this circumstances, we adopt a hierarchical structure to estimate the approximate correspondences in coarse-to-fine manner. This work is based on an exiting technique, ie PatchMatch filter, which is a generic and fast computational framework for general multi-labeling problems. We integrate the aforementioned criterion into the framework. Experiments on different challenging datasets show that our approach is suitable for general images by leveraging the complementary information from the different descriptors.
Subjects
場景對位
多重描述符
非監督式標號
圖像塊匹配
邊緣保持濾波
Type
thesis
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