Acupuncture, a traditional Chinese therapeutic technique, has been put into practice for more than 4000 years and widely used for pain management since 1958. However, what is the mechanism underlying the acupuncture for analgesia effects by stimulation of acupoints, what substances receive the original mechanical acupuncture signals from the acupoints, or what transforms these signals into effective biological signals are not well understood. In this work, the role of collagen fibers at acupoints during acupuncture analgesia on rats was investigated. When the structure of the collagen fibers at Zusanli (ST36) was destroyed by injection of type I collagenase, the needle force caused by the acupuncture declined and the analgesic effects of rotation or lift-thrusting manipulations was attenuated accompanying the restraint of the degranulation ratios of mast cells. We propose that collagen fibers play an important role in acupuncture-induced analgesia, and they participate in signal transmission and transform processes.
In this paper, a new image segmentation method is proposed by combining the FCM clustering algorithm with a rough set theory. First, the attribute value table is constructed based on the segmentation results of FCM under different clustering numbers, and the image is divided into several small regions based on the indistinguishable relationship of attributes. Then, the weight values of each attribute are obtained by value reduction and used as the basis to calculate the difference between regions and then the similarity evaluation of each region is realized through the equivalence relationship defined by the difference degree. Finally, the final equivalence relation defined by similarity is used to merge regions and complete image segmentation. This method is validated in the segmentation of artificially generated images, brain CT images, and MRI images. The experimental results show that compared with the FCM method, the proposed method can reduce the error rate and achieve better segmentation results for the fuzzy boundary region. And, the experimental results also prove that the algorithm has strong anti-noise ability.
We propose an edge-based method for 6DOF pose tracking of rigid objects using a monocular RGB camera. One of the critical problem for edge-based methods is to search the object contour points in the image corresponding to the known 3D model points. However, previous methods often produce false object contour points in case of cluttered backgrounds and partial occlusions. In this paper, we propose a novel edge-based 3D objects tracking method to tackle this problem. To search the object contour points, foreground and background clutter points are first filtered out using edge color cue, then object contour points are searched by maximizing their edge confidence which combines edge color and distance cues. Furthermore, the edge confidence is integrated into the edge-based energy function to reduce the influence of false contour points caused by cluttered backgrounds and partial occlusions. We also extend our method to multi-object tracking which can handle mutual occlusions. We compare our method with the recent state-of-art methods on challenging public datasets. Experiments demonstrate that our method improves robustness and accuracy against cluttered backgrounds and partial occlusions. CCS Concepts • Computing methodologies → Mixed / augmented reality; Tracking;
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