Corner is widely utilized in computer vision and image processing. As a representative contourbased corner detection algorithm, RJ detector is first proposed to use the K-cosine to estimate curvature of digital curves for corner finding. However, such influential approach is quite sensitive to the geometric transformations and noise due to its dynamic smoothing scale. To overcome this drawback and enhance its performance further, this paper presents a multi-scale version of RJ detector. First, we adopt fixed region of radius (RoS) to avoid its sensitiveness to geometric transformations; second, the technique of scale product is employed to enhance curvature extreme peaks and suppress noise for improving localization. Extensive experiments on several corner detection datasets are conducted for evaluating its performances. And the experimental results demonstrate that such simple idea endows RJ an incredible improvement and MSRJ achieves the competitive performance compared with state-of-the-arts corner detectors under measure metrics of average repeatability and localization error. INDEX TERMS Corner detection, image processing, multi-scale product, average repeatability. I. INTRODUCTION Corners are the outstanding local features of image and have played important roles on many applications in image processing and computer vision, such as robot navigation, video retrieval and intelligent transportation systems and so on. By now, plenty of corner detectors have been proposed, which can be broadly separated into two kinds: intensity-based detectors [1]-[9] and contour-based detectors [11]-[29]. With respect to contour-based methods, corners are mainly detected on the boundaries extracted from images among which estimating the digital curvature of the boundaries is the critical step. Rosenfeld and Johnston (RJ) [11] first considered angle as the discrete curvature to find corners for digital curves and later they presented its improved version which used average angle instead [12]. In RJ algorithm and its improved version, the angles of a digital curve are calculated by K-cosine, where K is the radius of the region of support (RoS). Since the angle The associate editor coordinating the review of this manuscript and approving it for publication was Zhihan Lv .
In recent years, deep learning has made successful applications and remarkable achievements in the field of medical image registration, and the method of medical image registration based on deep learning has become the current research hotspot. However, the performance of convolutional neural networks may not be fully exploited due to neglect of spatial relationships between distant locations in the image and incomplete updates of network parameters. To avoid this phenomenon, MHNet, a multiscale hierarchical deformable registration network for 3D brain MR images, was proposed in this paper. This network was an unsupervised end-to-end convolutional neural network. After training, the dense displacement vector field can be predicted almost in real-time for the unseen input image pairs, which saves a lot of time compared with the traditional algorithms of independent iterative optimization for each pair of images. On the basis of the encoder-decoder structure, this network introduced the improved Inception module for multiscale feature extraction and expanding the receptive field and the hierarchical forecast structure to promote the update of the parameters of the middle layers, which achieved the best performance on the augmented public dataset compared with the existing four excellent registration methods.
Corner is an important local feature of image which has been widely applied on various computer vision and image processing tasks. Here, a contour-based corner detector is developed by using the ratio of parallelogram diagonals (RPD) to estimate the curve curvature. The main advantage of RPD detector is that only once square root operation is required to calculate the curvature value at each point on a contour while maintaining good noise robustness. The contributions of this paper include the following three aspects: First, the motivation of the proposed RPD curvature is illustrated by means of parallelogram theory; second, a complete corner detector is proposed based on RPD curvature; third, comprehensive experiments are carried out and the experimental results show superior performances of the proposed method against another five strong baselines. In these experiments, RPD runs 100% faster than the prior works. Moreover, a mean accuracy of 83.87% is reported on GCM dataset which is an improvement of about 0.9% and a mean accuracy of 74.21% is reported on CPDA dataset which is an improvement of about 0.2%.
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