https://www.selleckchem.com/products/MLN8237.html Coherent point drift is a well-known algorithm for non-rigid registration, i.e., a procedure for deforming a shape to match another shape. Despite its prevalence, the algorithm has a major drawback that remains unsolved It unnaturally deforms the different parts of a shape, e.g., human legs, when they are neighboring each other. The inappropriate deformations originate from a proximity-based deformation constraint, called motion coherence. This study proposes a non-rigid registration method that addresses the drawback. The key to solving the problem is to redefine the motion coherence using a geodesic, i.e., the shortest route between points on a shape's surface. We also propose the accelerated variant of the registration method. In numerical studies, we demonstrate that the algorithms can circumvent the drawback of coherent point drift. We also show that the accelerated algorithm can be applied to shapes comprising several millions of points.Supervised salient object detection (SOD) methods achieve state-of-the-art performance by relying on human-annotated saliency maps, while unsupervised methods attempt to achieve SOD by not using any annotations. In unsupervised SOD, how to obtain saliency in a completely unsupervised manner is a huge challenge. Existing unsupervised methods usually gain saliency by introducing other handcrafted feature-based saliency methods. In general, the location information of salient objects is included in the feature maps. If the features belonging to salient objects are called salient features and the features that do not belong to salient objects, such as background, are called nonsalient features, by dividing the feature maps into salient features and nonsalient features in an unsupervised way, then the object at the location of the salient feature is the salient object. Based on the above motivation, a novel method called learning salient feature (LSF) is proposed, which achieves unsu