The need for faster feature matching has left as a result a new set of feature descriptors to the computer vision community, ORB, BRISK and FREAK amongst others. These new descriptors allow reduced time and memory consumption on the processing and storage stages, mitigating the implementation of more complex tasks. The problem is now the lack of fast interest point detectors with good repeatability to use with these new descriptors. A blob-detection algorithm was recently presented that uses an innovative non-deterministic low-level operator called the Brightness Clustering Transform (BCT) (Lomeli-R. and Nixon in The brightness clustering transform and locally contrasting keypoints. In CAIP. Springer, Berlin, pp [362][363][364][365][366][367][368][369][370][371][372][373] 2015). This algorithm is easy to implement and is faster than most of the currently used feature detectors. The BCT can be thought as a coarse-to-fine search through scale spaces for the true derivative of the image. The new algorithm is called Locally Contrasting Keypoints detector (LOCKY). Showing good robustness to image transformations included in the Oxford affine-covariant regions dataset, LOCKY is amongst the fastest affine-covariant feature detectors. In this paper, we present an extension of the BCT that detects larger structures maintaining timing and repeatability; this extension is called the BCT-S.
Abstract. In recent years a new wave of feature descriptors has been presented to the computer vision community, ORB, BRISK and FREAK amongst others. These new descriptors allow reduced time and memory consumption on the processing and storage stages of tasks such as image matching or visual odometry, enabling real time applications. The problem is now the lack of fast interest point detectors with good repeatability to use with these new descriptors. We present a new blobdetector which can be implemented in real time and is faster than most of the currently used feature-detectors. The detection is achieved with an innovative non-deterministic low-level operator called the Brightness Clustering Transform (BCT). The BCT can be thought as a coarse-tofine search through scale spaces for the true derivative of the image; it also mimics trans-saccadic perception of human vision. We call the new algorithm Locally Contrasting Keypoints detector or LOCKY. Showing good repeatability and robustness to image transformations included in the Oxford dataset, LOCKY is amongst the fastest affine-covariant feature detectors.
Abstract. Feature-based symmetry detection algorithms have become popular amongst researchers due to their dominance in performance, nevertheless, these approaches are computationally demanding. Also they are reliant on the presence of matched features, therefore they benefit from the abundance of detected keypoints; this implies that a trade-off between performance and computation time must be found. In this paper both issues are addressed, the detection of large sets of keypoints and the computation time for feature-based symmetry detection algorithms. We present an innovative process to learn rotation-invariant salient structures by clustering self-similarities. Keypoints are detected as local maxima in feature-maps computed using the learnt structures. Keypoints are described using BRISK. We consider an axis of symmetry to be a dense cloud of points in a parameter-space, a density-based clustering algorithm is used to find such clouds. Computing times are drastically shortened taking an average of 0.619 seconds to process an image. Detection results for single and multiple, straight and curved, reflection and glide-reflection symmetries are similar to the current state of the art.
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