Abstract
Abstract. Matching images containing large viewpoint and viewing direction changes, resulting in large perspective differences, still is a very challenging problem. Affine shape estimation, orientation assignment and feature description algorithms based on detected hand crafted features have shown to be error prone. In this paper, affine shape estimation, orientation assignment and description of local features is achieved through deep learning. Those three modules are trained based on loss functions optimizing the matching performance of input patch pairs. The trained descriptors are first evaluated on the Brown dataset (Brown et al., 2011), a standard descriptor performance benchmark. The whole pipeline is then tested on images of small blocks acquired with an aerial penta camera, to compute image orientation. The results show that learned features perform significantly better than alternatives based on hand crafted features.
Highlights
Feature based image matching aims at finding correspondences among images and is a fundamental research issue in photogrammetry and computer vision
In this paper we suggest a feature matching pipeline based on convolutional neural network (CNN) in order to derive image orientation parameters for blocks of aerial penta cameras
In our method the three steps of affine shape estimation, orientation assignment and description of local image patches are all learned based on a CNN architecture with detected feature pairs as input, which serve as training data
Summary
Feature based image matching aims at finding correspondences among images and is a fundamental research issue in photogrammetry and computer vision. The key challenge of feature based image matching frameworks is to ensure invariance against complex geometric and radiometric changes between images. As indicated in (AanÃęs et al, 2012) , the invariance of hand crafted detectors and descriptors decreases sharply for images containing 3D scenes when viewpoint and viewing direction changes increase. Feature based image matching algorithms can be designed to be invariant against certain geometric and radiometric changes. The well known SIFT operator (Lowe, 2004) is rotation and scale invariant to a certain degree. This invariance can be extended to a reasonable level of affine transformation between images, see e.g., the Hessian-Affine detector (Mikolajczyk et al, 2005). A new feature based image matching framework based on deep neural networks, including affine shape estimation, feature orientation and description is presented and results for the image orientation of small blocks of oblique aerial images are reported and analysed
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