The first step to accomplish panoramic or omnidirectional image stitching often rely on how well the feature detector extract sufficient number of matchable corresponding interest points between images. This step remains a challenging problem to many renowned feature detectors, especially when the image content is fairly near-uniform. The awful impacts are often reflected in obvious misaligned and projective distortion. To address this problem, we propose an effective feature detection method based on Log-Gabor feature transform. Instead of depending on image intensity for feature point detection, the proposed method utilizes log-Gabor function to construct a number of scaled and oriented log-Gabor scale spaces from which more distinctive and matchable features can be extracted effectively. In addition, we also introduce new evaluation metrics which are more relevant for evaluating the performance of image stitching. Our method has a better feature matching performance than other state-of-the-art feature extraction methods, particularly for near-uniform scenes.

完成全景或全向图像拼接的第一步通常取决于特征检测器在图像之间提取足够数量的可匹配对应兴趣点的能力。这一步对于许多著名的特征检测器来说仍然是一个具有挑战性的问题，尤其是当图像内容相当接近均匀时。可怕的影响通常反映在明显的错位和投影失真上。为了解决这个问题，我们提出了一种基于Log-Gabor特征变换的有效特征检测方法。所提出的方法不依赖于图像强度进行特征点检测，而是利用 log-Gabor 函数来构建许多缩放和定向的 log-Gabor 尺度空间，从中可以有效地提取出更独特和匹配的特征。此外，我们还引入了与评估图像拼接性能更相关的新评估指标。我们的方法比其他最先进的特征提取方法具有更好的特征匹配性能，特别是对于接近均匀的场景。