Constitution identification model in traditional Chinese medicine based on multiple features

Objective To construct a precise model for identifying traditional Chinese medicine(TCM)constitutions;thereby offering optimized guidance for clinical diagnosis and treatment plan-ning;and ultimately enhancing medical efficiency and treatment outcomes.Methods First;TCM full-body inspection data acquisition equipment was employed to col-lect full-body standing images of healthy people;from which the constitutions were labelled and defined in accordance with the Constitution in Chinese Medicine Questionnaire(CCMQ);and a dataset encompassing labelled constitutions was constructed.Second;heat-suppres-sion valve(HSV)color space and improved local binary patterns(LBP)algorithm were lever-aged for the extraction of features such as facial complexion and body shape.In addition;a dual-branch deep network was employed to collect deep features from the full-body standing images.Last;the random forest(RF)algorithm was utilized to learn the extracted multifea-tures;which were subsequently employed to establish a TCM constitution identification mod-el.Accuracy;precision;and F1 score were the three measures selected to assess the perfor-mance of the model.Results It was found that the accuracy;precision;and F1 score of the proposed model based on multifeatures for identifying TCM constitutions were 0.842;0.868;and 0.790;respectively.In comparison with the identification models that encompass a single feature;either a single facial complexion feature;a body shape feature;or deep features;the accuracy of the model that incorporating all the aforementioned features was elevated by 0.105;0.105;and 0.079;the precision increased by 0.164;0.164;and 0.211;and the F1 score rose by 0.071;0.071;and 0.084;respectively.Conclusion The research findings affirmed the viability of the proposed model;which incor-porated multifeatures;including the facial complexion feature;the body shape feature;and the deep feature.In addition;by employing the proposed model;the objectification and intel-ligence of identifying constitutions in TCM practices could be optimized.

Mesh representation matters:investigating the influence of different mesh features on perceptual and spatial fidelity of deep 3D morphable models

Background Deep 3D morphable models(deep 3DMMs)play an essential role in computer vision.They are used in facial synthesis,compression,reconstruction and animation,avatar creation,virtual try-on,facial recognition systems and medical imaging.These applications require high spatial and perceptual quality of synthesised meshes.Despite their significance,these models have not been compared with different mesh representations and evaluated jointly with point-wise distance and perceptual metrics.Methods We compare the influence of different mesh representation features to various deep 3DMMs on spatial and perceptual fidelity of the reconstructed meshes.This paper proves the hypothesis that building deep 3DMMs from meshes represented with global representations leads to lower spatial reconstruction error measured with L_(1) and L_(2) norm metrics and underperforms on perceptual metrics.In contrast,using differential mesh representations which describe differential surface properties yields lower perceptual FMPD and DAME and higher spatial fidelity error.The influence of mesh feature normalisation and standardisation is also compared and analysed from perceptual and spatial fidelity perspectives.Results The results presented in this paper provide guidance in selecting mesh representations to build deep 3DMMs accordingly to spatial and perceptual quality objectives and propose combinations of mesh representations and deep 3DMMs which improve either perceptual or spatial fidelity of existing methods.

Robust Symmetry Prediction with Multi-Modal Feature Fusion for Partial Shapes

In geometry processing,symmetry research benefits from global geo-metric features of complete shapes,but the shape of an object captured in real-world applications is often incomplete due to the limited sensor resolution,single viewpoint,and occlusion.Different from the existing works predicting symmetry from the complete shape,we propose a learning approach for symmetry predic-tion based on a single RGB-D image.Instead of directly predicting the symmetry from incomplete shapes,our method consists of two modules,i.e.,the multi-mod-al feature fusion module and the detection-by-reconstruction module.Firstly,we build a channel-transformer network(CTN)to extract cross-fusion features from the RGB-D as the multi-modal feature fusion module,which helps us aggregate features from the color and the depth separately.Then,our self-reconstruction net-work based on a 3D variational auto-encoder(3D-VAE)takes the global geo-metric features as input,followed by a prediction symmetry network to detect the symmetry.Our experiments are conducted on three public datasets:ShapeNet,YCB,and ScanNet,we demonstrate that our method can produce reliable and accurate results.

Active Shape Models Using Scale Invariant Feature Transform

A new active shape models (ASMs) was presented, which is driven by scale invariant feature transform (SIFT) local descriptor instead of normalizing first order derivative profiles in the original formulation, to segment lung fields from chest radiographs. The modified SIFT local descriptor, more distinctive than the general intensity and gradient features, is used to characterize the image features in the vicinity of each pixel at each resolution level during the segmentation optimization procedure. Experimental results show that the proposed method is more robust and accurate than the original ASMs in terms of an average overlap percentage and average contour distance in segmenting the lung fields from an available public database.

混合U型网络与Transformer的图像去模糊

针对现有去模糊方法不能有效地恢复图像精细细节的问题,提出了一种混合U型网络与Transformer的图像去模糊方法。首先,使用一个多尺度特征提取模块提取图像的浅层特征信息。然后,通过一个含逐级特征增强模块的层级嵌套U型子网络,在保留图像细节信息的同时获取图像深层特征信息。再次,构建了一个局部-全局残差细化模块,通过卷积神经网络和SwinTransformer之间的信息交互充分提取全局和局部信息,并实现特征信息的进一步细化。最后,使用一个1×1卷积层进行特征重建。所提方法在GoPro数据集上的实验结果显示,图像的峰值信噪比和结构相似度均值分别为32.92和0.964,均优于其他对比方法。实验结果表明,所提方法可以有效地去除模糊,重建出具有丰富细节的潜在清晰图像。

A Topology Mapping Method for Feature Extraction of Irregular Curve Shape

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Evolutionary Computation Based Optimization of Image Zernike Moments Shape Feature Vector

The image shape feature can be described by the image Zernike moments. In this paper, we points out the problem that the high dimension image Zernike moments shape feature vector can describe more detail of the original image but has too many elements making trouble for the next image analysis phases. Then the low dimension image Zernike moments shape feature vector should be improved and optimized to describe more detail of the original image. So the optimization algorithm based on evolutionary computation is designed and implemented in this paper to solve this problem. The experimental results demonstrate the feasibility of the optimization algorithm.

Carcinoma cell identification via optical microscopy and shape feature analysis

Optical microscopy is commonly used for cancer cell detection. Focusing on carcinoma cell identification via optical microscopy, a proof-of-concept study was performed at Laboratory of Design, Optimization and Modeling (LCOMS) to determine the grade of cancer cells. This paper focuses on three types of abnormal cells;namely, Benign Hyperplasia (BH), Intraepithelial Neoplasia (IN), which is a precursor state for cancer, and Carcinoma (Ca), which corresponds to abnormal tissue proliferation cancer. These types of cells were used to assess the efficiency of using shape features to identify carcinoma cells. A comparative study based on performance indicator concludes that three features, Area, Xor-Convex, and Solidity, were found to be effective in identifying the Carcinoma grade of cancer cells.

Modified Fourier descriptor for shape feature extraction

A modified Fourier descriptor was presented. Information from a local space can be used more efficiently. After the boundary pixel set of an object was computed, centroid distance approach was used to compute shape signature in the local space. A pair of shape signature and boundary pixel gray was used as a point in a feature space. Then, Fourier transform was used for composition of point information in the feature space so that the shape features could be computed. It is proved theoretically that the shape features from modified Fourier descriptors are invariant to translation, rotation, scaling, and change of start point. It is also testified by measuring the retrieval performance of the systems that the shape features from modified Fourier oescriptors are more discriminative than those from other Fourier descriptors.

Active Shape Model of Combining Pca and Ica: Application to Facial Feature Extraction

Active Shape Model （ASM） is a powerful statistical tool to extract the facial features of a face image under frontal view. It mainly relies on Principle Component Analysis （PCA） to statistically model the variability in the training set of example shapes. Independent Component Analysis （ICA） has been proven to be more efficient to extract face features than PCA. In this paper, we combine the PCA and ICA by the consecutive strategy to form a novel ASM. Firstly, an initial model, which shows the global shape variability in the training set, is generated by the PCA-based ASM. And then, the final shape model, which contains more local characters, is established by the ICA-based ASM. Experimental results verify that the accuracy of facial feature extraction is statistically significantly improved by applying the ICA modes after the PCA modes.

形状补全引导的Transformer点云目标检测方法

针对雷达传感器采集到的场景点云中存在大量远距离或位于遮挡视角的形状缺失的低质量目标,其几何信息不足难以被识别,影响检测精度的问题,本文提出一种基于形状补全引导的Transformer点云目标检测方法(shape completion-guided transformer point cloud object detection method,STDet),通过增强低质量目标形状特征来有效提升目标检测精度,利用Pointformer主干网络提取场景点云特征以生成初始候选框,基于特征分离预测的形状补全模块重构候选框中残缺目标的完整形状点云;构建Transformer几何特征增强模型,融合目标完整形状信息及空间位置信息至各目标点特征中,并感知各目标点不同邻域掩码范围内的局部结构信息与全局几何特征的注意力相关性,以获取关键几何信息增强的目标全局几何特征;基于该特征引导生成精细化的目标检测框。在KITTI数据集上的实验结果表明,该方法在存在大量形状残缺低质量目标的困难场景中检测精度较基准算法提升了4.96%,大量消融实验证明了该方法所构建的形状补全算法和Transformer几何特征增强模型的有效性。

A flower image retrieval method based on ROI feature

Flower image retrieval is a very important step for computer-aided plant species recognition. In this paper, we propose an efficient segmentation method based on color clustering and domain knowledge to extract flower regions from flower images. For flower retrieval, we use the color histogram of a flower region to characterize the color features of flower and two shape-based features sets, Centroid-Contour Distance (CCD) and Angle Code Histogram (ACH), to characterize the shape features of a flower contour. Experimental results showed that our flower region extraction method based on color clustering and domain knowledge can produce accurate flower regions. Flower retrieval results on a database of 885 flower images collected from 14 plant species showed that our Region-of-Interest (ROI) based retrieval approach using both color and shape features can perform better than a method based on the global color histogram proposed by Swain and Ballard (1991) and a method based on domain knowledge-driven segmentation and color names proposed by Das et al.(1999).

Effect of Sn Content on the Microstructural Features, Martensitic Transformation and Mechanical Properties in Ti-V-Al-Based Shape Memory Alloys

In the present study, it is expected to tailor the microstructural features, martensitic transformation temperatures and mechanical properties of Ti-V-Al shape memory alloys through adding Sn alloying elements, which further expands their applications. Sn addition results in the monotonous rising of average valence electron number (e/a). In proportion, the single α″ martensite phase directly evolves into merely β parent phase in present Ti-V-Al-based shape memory alloys, as Sn content increases from 0.5 to 5.0 at.%. Meanwhile, Sn addition causes the reduction in the grain size. Combined with transmission electron microscopy (TEM) observation and d electron theory analysis, it can be speculated that Sn addition can suppress the precipitation of ω phase. With increasing Sn content, fracture strength invariably decreases from 962 to 792 MPa, whereas the yield strength firstly decreases and then increases. The lowest yield stress for the stress-induced martensitic transformation of 220 MPa can be obtained in Ti-V-Al shape memory alloy by adding 3.0 at.% Sn. By optimizing 1.0 at.% Sn, the excellent ductility with a largest elongation of 42.1% can be gained in Ti-V-Al shape memory alloy, which is larger than that of the reported Ti-V-Al-based shape memory alloys. Besides, as a result of solution strengthening and grain refinement, Ti-V-Al-based shape memory alloy with 5.0 at.% Sn possesses the highest yield strength, further contributing to the excellent strain recovery characteristics with 4% fully recoverable strain.

Numerical Simulation for Shaping Feature of Molten Pool in Twin-Arc Submerged Arc Welding

The notion of ratio of width to length is proposed to describe the shaping feature of molten pool of twin-Arc submerged arc welding accurately, and analyze the law of molten pool variation and weld formation. The temperature field finite element numerical simulation model of twin arc movement is established. The loading form of twin-arc with double ellipsoid heat source is discussed. The molten pool temperature field of twin-arc submerged arc welding is calculated and analyzed under different process parameters. The law of molten pool characteristics influenced by the welding speed, current and voltage of twin-arc submerged arc welding parameters is analyzed. The relation between shaping feature of molten pool and weld formation is discussed according to the ratio of width to length. The results manifested that the width to length ratio of weld pool decreases with the improvement of welding speed, which result in gene- ration of weld defects. The width to length ratio of weld pool is increased by adjusting the proportion of the current, voltage and the distance of the two arcs, which avoids the generation of weld defects.

Novel Facial Features Segmentation Algorithm

An efficient algorithm for facial features extractions is proposed. The facial features we segment are the two eyes, nose and mouth. The algorithm is based on an improved Gabor wavelets edge detector, morphological approach to detect the face region and facial features regions, and an improved T-shape face mask to locate the extract location of facial features. The experimental results show that the proposed method is robust against facial expression, illumination, and can be also effective if the person wearing glasses, and so on.

基于局部形状特征和Bag-of-Feature模型的磨粒图像形状特征提取

以磨粒图像为研究对象,提出了基于局部形状特征和Bag-of-Features模型的磨粒形状特征提取方法。首先构建磨粒区域的骨架,根据骨架端点和分支得到磨粒的轮廓基元和区域基元,不同数量的相邻的轮廓和区域基元组合构成局部轮廓和局部区域,提取局部轮廓和区域的形状特征,两者融合得到磨粒的局部形状特征集合。然后根据Bag-of-Features模型的思想,以训练集所有磨粒样本的局部形状特征集合为基础,构建视觉词典,经过特征编码、特征汇集和归一化,得到磨粒形状特征的编码向量表示。最后根据形状特征的编码向量,采用多级支持向量机的方法对磨粒类型进行识别。实验结果表明,基于提出的磨粒形状特征方法能够有效、准确地识别磨粒类型。

改进形状上下文蚁群算法的边缘景象匹配方法

针对弹体低空飞越沙漠、山地等区域时,实时图与基准图之间尺度差异大,匹配误差大的问题,提出一种用改进的形状上下文结合蚁群算法对边缘特征进行部分形状匹配的方法。为克服异源图像边缘特征的形状差异,在形状上下文算法的基础上,提取边缘邻域特征,根据边缘邻域特征相似性进行粗匹配选出候选边缘,再利用候选边缘和待配准边缘点的位置关系和形状相似度使初始化后的蚂蚁遍历所有边缘点,然后利用转换后匹配点对的距离加权和计算信息素增量,由相邻匹配点对的方向约束关系计算其权重,并根据相邻匹配对的距离关系辨别出没有正确匹配关系的边缘片段,则剔除片段上的匹配点对,对应匹配对的权重为零,最后多只蚂蚁共同多次迭代得到最优匹配结果。在实测红外可见光数据集上进行景象匹配实验,结果表明,提出的算法显著优于SIFT算法,能够克服异源图像上边缘特征的形状差异,实现两个边缘特征的精确匹配,定位精度比传统的边缘特征匹配方法提高了43.71%。

基于形体特征层次分析的概念设计原型评价与演化方法

为对概念设计原型进行定量评价,实现具有形位公差需求的机械零件成形时间与制造质量之间的均衡,提出一种基于形体特征层次分析(Shape Feature Hierarchical Analysis,SFHA)的概念设计原型评价与演化方法。针对概念设计形体的孔和轴的形态特性,采用最大内切圆柱和最小外接圆柱构建特征模型,计算由分层策略所导致偏移的实际轴线位置,推算圆柱度误差大小。进行基于形位公差的自适应分层,在保证圆柱度等公差满足设计需求的情况下尽可能减少所需层数。通过改进粒子群算法进行非凸优化求解,遴选出最优的分层序列。计算特征层次化模型向正平面V(XOZ)和侧平面W(YOZ)的平行正投影面积及绝对梯度变化,实现基于SFHA的概念设计原型特征定量化描述与动态演化。以典型支座零件为例进行验证,采用熔融沉积成形设备进行物理试验,基于高精度结构光三维扫描仪获取原型零件的圆柱度误差,相较于均匀分层方法,所提出的SFHA方法制造时间可减少26.97%。