MBR-SIFT: A mirror reflected invariant feature descriptor using a binary representation for image matching
نویسندگان
چکیده
منابع مشابه
MBR-SIFT: A mirror reflected invariant feature descriptor using a binary representation for image matching
The traditional scale invariant feature transform (SIFT) method can extract distinctive features for image matching. However, it is extremely time-consuming in SIFT matching because of the use of the Euclidean distance measure. Recently, many binary SIFT (BSIFT) methods have been developed to improve matching efficiency; however, none of them is invariant to mirror reflection. To address these ...
متن کاملMIFT: A Mirror Reflection Invariant Feature Descriptor
In this paper, we present a mirror reflection invariant descriptor which is inspired from SIFT. While preserving tolerance to scale, rotation and even affine transformation, the proposed descriptor, MIFT, is also invariant to mirror reflection. We analyze the structure of MIFT and show how MIFT outperforms SIFT in the context of mirror reflection while performs as well as SIFT when there is no ...
متن کاملVariation of SIFT Descriptor for Affine Invariant Object Matching
In this paper, a novel affine invariant descriptor for object matching is proposed. The advantage of Maximally Stable Extremal Regions (MSER) method is applied to get the most stable regions in the image. Inside each region, we pick the seeds as keypoints since MSER regions are invariant to affine transformation. Besides that, Voronoi diagram is employed to split the image into small Voronoi ce...
متن کاملSIFT Descriptor for Binary Shape Discrimination, Classification and Matching
In this work, we study efficiency of SIFT descriptor in discrimination of binary shapes. We also analyze how the use of 2 − tuples of SIFT keypoints can affect discrimination of shapes. The study is divided into two parts, the first part serves as a primary analysis where we propose to compute overlap of classes using SIFT and a majority vote of keypoints. In the second part, we analyze both cl...
متن کاملA Novel Binary Feature Descriptor for Accelerated Robust Matching
Stereo matching between images is at the base of many computer vision applications. Many traditional image features for matching focus on being robust to view-point changes with huge amount of calculation and memory storage, such as Scale Invariant Feature Transform (SIFT). Binary Robust Independent Elementary Features (BRIEF) is an efficient alternative to traditional SIFT-like features, but i...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: PLOS ONE
سال: 2017
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0178090