ORB Descriptor Research Articles

Low-dimensional superpixel descriptor and its application in visual correspondence estimation

Establishing local visual correspondence between video frames is an important and challenging problem in many vision based applications. Local keypoint detection and description based pixel-level matching is a typical way for visual correspondence estimation. Unlike traditional local keypoint descriptor based methods, this paper proposes a comprehensive yet low-dimensional local feature descriptor based on superpixels generated by over segmentation. The proposed local feature descriptor extracts shape feature, texture feature, and color feature from superpixels by orientated center-boundary distance (OCBD), gray-level co-occurrence matrix (GLCM), and saturation histogram (SHIST), respectively. The types of features are more comprehensive than existing descriptors which extract only one specific kind of feature. Experimental results on the widely used Middlebury optical flow dataset prove that the proposed superpixel descriptor achieves triple accuracy compared with the state-of-the-art ORB descriptor which has the same dimension of features with the proposed one. In addition, since the dimension of the proposed superpixel descriptor is low, it is convenient for matching and memory-efficient for hardware implementation.

Improving the construction of ORB through FPGA-based acceleration

Binary descriptors have won their place as efficient and effective visual descriptors in several vision tasks. In this context, one of the most widely used binary descriptors to date is the ORB descriptor. ORB is robust against rotation changes, and it uses a learning procedure to generate sampling pairwise tests to construct the descriptor. However, this construction involves a sequential memory access of as many steps as the binary string size. From the latter and motivated by the fact that modern computer vision tasks may require the construction of thousands, if not millions of binary descriptors, we propose to accelerate the construction process of the ORB descriptor via an FPGA-based hardware architecture. The latter is leveraged with a novel arrangement of pairwise tests, which takes advantage of a dual random access memory scheme achieving an acceleration of up to 17 times when compared against the sequential way. The empirical assessment indicates that ORB descriptors obtained from the proposed approach keep a similar performance to that of the original ORB.

Local image features matching for real-time seabed tracking applications

ABSTRACTReal-time seabed tracking applications play an important role in underwater systems. A lot of them use computer vision for servoing, positioning, navigation, odometry and simultaneous localisation and mapping. They are mostly based on local image features, therefore feature detection, description and matching are crucial for their efficient operations. The aim of this study was to investigate the most popular feature detection and description algorithms such as SIFT, SURF, FAST, STAR, HARRIS, ORB, BRISK and FREAK. Additionally, the image correction technique was presented and image enhancement methods were analysed in order to increase efficiency of image features matching. The matching algorithm was based on the homography matrix and random sample consensus technique. Our results indicate that the combination of the histogram equalisation technique and ORB detector and descriptor enables real-time seabed tracking with sufficient efficiency.

Keypoint descriptor fusion with Dempster–Shafer theory

Keypoint matching is the task of accurately finding the location of a scene point in two images. Many keypoint descriptors have been proposed in the literature aiming at providing robustness against scale, translation and rotation transformations, each having advantages and disadvantages. This paper proposes a novel approach to fuse the information from multiple keypoint descriptors using Dempster–Shafer theory of evidence [1] , which has proven particularly efficient in combining sources of information providing incomplete, imprecise, biased, and conflictive knowledge. The matching results of each descriptor are transformed into an evidence distribution on which a confidence factor is computed making use of its entropy. Then, the evidence distributions are fused using Dempster–Shafer Theory (DST), considering its confidence. As a result of the fusion, a new evidence distribution that improves the result of the best descriptor is obtained. Our method has been tested with SIFT, SURF, ORB, BRISK and FREAK descriptors using all possible their combinations. Results on the Oxford keypoint dataset [2] show that the proposed approach obtains an improvement of up to 10 % compared to the best one (FREAK). • Novel approach fusing information from multiple keypoint descriptors using the Dempster–Shafer Theory of evidence. • Descriptors matches are transformed in evidence distributions assigning a confidence factor using Shannon's entropy. • Results on the Oxford keypoint dataset show improvements of up to 10% compared to the best keypoint descriptor.

Real-Time and Robust Stereo Visual Navigation Localization Algorithm Based on ORB

The biggest challenge of visual navigation localization is feature extraction and association. Currently, the most widely used method is simple corner feature and simple matching strategy based on SAD or NCC. Another option is scale invariant feature and rotation invariant descriptor, typically as SIFT, SURF. Feature extraction and matching methods based on the SIFT or SURF are accurate and robust. However, its computational complexity is too high and not suitable for the real-time navigation localization task. This paper presents a new fast, accurate, robust stereo vision navigation localization method, based on a new developed ORB feature and descriptor. First, we presented our matching method based on ORB. Then, we obtained matching inliers and an initial motion estimation parameters using RANSAC and three points motion estimation method. Finally, nonlinear motion refinement method was used to polish the solution. Experimental results show that our method is robust, accurate and real-time.