Fast Skeleton Research Articles

Long non-coding RNA (LncRNA) HOTAIR regulates BMP9-induced osteogenic differentiation by targeting the proliferation of mesenchymal stem cells (MSCs).

Long non-coding RNAs are important regulators of biological processes, but their roles in the osteogenic differentiation of mesenchymal stem cells (MSCs) remain unclear. Here we investigated the role of murine HOX transcript antisense RNA (mHotair) in BMP9-induced osteogenic differentiation of MSCs using immortalized mouse adipose-derived cells (iMADs). Touchdown quantitative polymerase chain reaction analysis found increased mHotair expression in bones in comparison with most other tissues. Moreover, the level of mHotair in femurs peaked at the age of week-4, a period of fast skeleton development. BMP9 could induce earlier peak expression of mHotair during in vitro iMAD osteogenesis. Silencing mHotair diminished BMP9-induced ALP activity, matrix mineralization, and expression of osteogenic, chondrogenic and adipogenic markers. Cell implantation experiments further confirmed that knockdown of mHotair attenuated BMP9-induced ectopic bone formation and mineralization of iMADs, leading to more undifferentiated cells. Crystal violet staining and cell cycle analysis revealed that silencing of mHotair promoted the proliferation of iMAD cells regardless of BMP9 induction. Moreover, ectopic bone masses developed from mHotair-knockdown iMAD cells exhibited higher expression of PCNA than the control group. Taken together, our results demonstrated that murine mHotair is an important regulator of BMP9-induced MSC osteogenesis by targeting cell cycle and proliferation.

GAN-Based Unpaired Chinese Character Image Translation via Skeleton Transformation and Stroke Rendering

The automatic style translation of Chinese characters (CH-Char) is a challenging problem. Different from English or general artistic style transfer, Chinese characters contain a large number of glyphs with the complicated content and characteristic style. Early methods on CH-Char synthesis are inefficient and require manual intervention. Recently some GAN-based methods are proposed for font generation. The supervised GAN-based methods require numerous image pairs, which is difficult for many chirography styles. In addition, unsupervised methods often cause the blurred and incorrect strokes. Therefore, in this work, we propose a three-stage Generative Adversarial Network (GAN) architecture for multi-chirography image translation, which is divided into skeleton extraction, skeleton transformation and stroke rendering with unpaired training data. Specifically, we first propose a fast skeleton extraction method (ENet). Secondly, we utilize the extracted skeleton and the original image to train a GAN model, RNet (a stroke rendering network), to learn how to render the skeleton with stroke details in target style. Finally, the pre-trained model RNet is employed to assist another GAN model, TNet (a skeleton transformation network), to learn to transform the skeleton structure on the unlabeled skeleton set. We demonstrate the validity of our method on two chirography datasets we established.

Image detection and basketball training performance simulation based on improved machine learning

Basketball player detection technology is an important subject in the field of computer vision and the basis of related image processing research. This study uses machine learning technology to build a basketball sport feature recognition model. Moreover, this research mainly takes the characteristic information of basketball in the state of basketball goals as the starting point and compares and analyzes the detection methods by detecting the targets in the environment. By comprehensively considering the advantages and disadvantages of various methods, a method suitable for the subject is proposed, namely, a fast skeleton extraction and model segmentation method. The fitting effect of this method, whether in terms of compactness or quantity, has greater advantages than traditional bounding boxes, and realizes the construction of dynamic ellipsoidal bounding boxes in a moving state. In addition, this study designs a controlled trial to verify the analysis of this research model. The research results show that the model proposed in this paper has certain effects and can improve practical guidance for competitions and basketball players training.

ALGORITHMS FOR ADJUSTMENT OF SYMMETRY AXIS FOUND FOR 2D SHAPES BY THE SKELETON COMPARISON METHOD

Abstract. Reflection symmetry detection for 2D shapes is a well-known task in Computer Vision, but there is a limited number of efficient and effective methods for its solution. Our previously proposed approach based on pair-wise comparison of sub-sequences of skeleton primitives finds the axis of symmetry within few seconds. In order to evaluate the value of symmetry relative to the found axis we use the Jaccard similarity measure. It is applied to the pixels subsets of a shape which are split by the axis. Often an axis found by the skeleton comparison method diverges more or less from the ground-truth axis found by the method of exhaustive search among all the potential candidates. That is why the algorithms that allow adjusting the axis found by the fast skeleton method are proposed. They are based on the idea of searching the axis which is located near the seed skeleton axis and has greater Jaccard similarity measure. The experimental study on the ”Flavia” and ”Butterflies” datasets shows that proposed algorithms find the ground-truth axis (or the axis which has slightly less Jaccard similarity value than the ground-truth axis) in near real time. It is considerably faster than any of the optimized brute-force methods.

The Research on Behavioral Status Judgment Method Based on Skeleton Modeling and Vector Processing

Fast, simple and reliable method for judging the behavioral status of video object is the public research base of many application fields, including public safety monitoring, national defense, aeronautics, sports and so on. This paper focuses on the fast parametric skeleton modeling and simple process of vectorization. It proposes an improved thinning algorithm for fast skeleton modeling and then locates the key points by a new scanning and marketing method, ultimately, according to the coordinate of the key points, it constructs a matrix of pose estimation and formulates the behavioral status judgment as a problem of calculating the angle between vectors. This method not only resolves the problems of large volumes of data and high complexity of algorithm in the traditional approach, but also lays the foundation for the extended researches and applications of multi-field intelligent video behavior analysis.

基于快速骨架提取的三维目标识别

Aiming at the IR 3D target recognition question,a method based on fast skeleton extraction is proposed.Based on the topology structure of target reflected by the skeleton,the correspondence between different targets' structural portions are established.Within the corresponding curve segments,curves are matched based on curve alignment,and the similarity is evaluated according to the sum of all the segments' cost.This method performs well in the present of commonly occurring visual transformations.Moreover,incorrect correspondence introduced by curve match's drawback,that is,curve matching tends to match local parts,and ignores the global view of how these parts are spatial arranged with respect to each other,can be avoided.Experimental results show that this algorithm has a rather good effect on 3D target recognition.

Width-independent fast skeletonization algorithm for binary pictures

An effective algorithm of skeletonization for digital binary pictures based on input-time-tracing principle and sequential decomposition of input picture into simple parts is described. The proposed algorithm is fast and requires no image memory. Its computing complexity does not depend on the thickness of picture patterns and is determined not by the number of picture pixels but by the essentially small number of horizontal and vertical lines constituting the contour of the picture.