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.

Microscale shaping and rounding of ridge arrays and star pattern features on nickel mould via electrochemical polishing

High quality micro mould tools are critical for ensuring defect-free production of micro injection moulded products.The demoulding stage of the micro injection moulding can adversely affect the surface integrity due to friction,adhesion and thermal stresses between the metallic mould and polymeric replicated part.In the present work,we propose the use of precision electropolishing(EP)as a shaping and polishing process to control the draft angle and fillet radius of micro features in order to ease demoulding.Typical defects that occur in replicated polymer parts include cracks,burrs and distorted features.A nickel mould having multiple linear ridges and star shape patterns was designed for the present investigation to have characteristic dimensions ranging from 10μm to 150μm and with various aspect ratios to study the effect of electropolishing on modifying the shape of micro features and surface morphology.A transient 2D computational analysis has been conducted to anticipate the effect of shaping on the Ni mould after electrochemical polishing with non-uniform material removal rates,based on the distribution of current density.The experimental results indicate that after shaping using EP,the draft angle of star-patterns and linear patterns can be effectively increased by approximately 3.6°,while the fillet radius increases by up to 5.0μm.By controlling the electropolishing process,the surface roughness can be maintained under 50 nm.This work uses a green and environmental friendly nickel sulfamate electrolyte which can be effective for shaping of nickel micro features without causing any surface deposition.

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).

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.

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.

A Deep Learning Approach to Mesh Segmentation

In the shape analysis community,decomposing a 3D shape intomeaningful parts has become a topic of interest.3D model segmentation is largely used in tasks such as shape deformation,shape partial matching,skeleton extraction,shape correspondence,shape annotation and texture mapping.Numerous approaches have attempted to provide better segmentation solutions;however,the majority of the previous techniques used handcrafted features,which are usually focused on a particular attribute of 3Dobjects and so are difficult to generalize.In this paper,we propose a three-stage approach for using Multi-view recurrent neural network to automatically segment a 3D shape into visually meaningful sub-meshes.The first stage involves normalizing and scaling a 3D model to fit within the unit sphere and rendering the object into different views.Contrasting viewpoints,on the other hand,might not have been associated,and a 3D region could correlate into totally distinct outcomes depending on the viewpoint.To address this,we ran each view through(shared weights)CNN and Bolster block in order to create a probability boundary map.The Bolster block simulates the area relationships between different views,which helps to improve and refine the data.In stage two,the feature maps generated in the previous step are correlated using a Recurrent Neural network to obtain compatible fine detail responses for each view.Finally,a layer that is fully connected is used to return coherent edges,which are then back project to 3D objects to produce the final segmentation.Experiments on the Princeton Segmentation Benchmark dataset show that our proposed method is effective for mesh segmentation tasks.

Liver Tumor Decision Support System on Human Magnetic Resonance Images:A Comparative Study

Liver cancer is the second leading cause of cancer death worldwide.Early tumor detection may help identify suitable treatment and increase the survival rate.Medical imaging is a non-invasive tool that can help uncover abnormalities in human organs.Magnetic Resonance Imaging(MRI),in particular,uses magnetic fields and radio waves to differentiate internal human organs tissue.However,the interpretation of medical images requires the subjective expertise of a radiologist and oncologist.Thus,building an automated diagnosis computer-based system can help specialists reduce incorrect diagnoses.This paper proposes a hybrid automated system to compare the performance of 3D features and 2D features in classifying magnetic resonance liver tumor images.This paper proposed two models;the first one employed the 3D features while the second exploited the 2D features.The first system uses 3D texture attributes,3D shape features,and 3D graphical deep descriptors beside an ensemble classifier to differentiate between four 3D tumor categories.On top of that,the proposed method is applied to 2D slices for comparison purposes.The proposed approach attained 100%accuracy in discriminating between all types of tumors,100%Area Under the Curve(AUC),100%sensitivity,and 100%specificity and precision as well in 3D liver tumors.On the other hand,the performance is lower in 2D classification.The maximum accuracy reached 96.4%for two classes and 92.1%for four classes.The top-class performance of the proposed system can be attributed to the exploitation of various types of feature selection methods besides utilizing the ReliefF features selection technique to choose the most relevant features associated with different classes.The novelty of this work appeared in building a highly accurate system under specific circumstances without any processing for the images and human input,besides comparing the performance between 2D and 3D classification.In the future,the presented work can be extended to be used in the huge dataset.Then,it can be a reliable,efficient Computer Aided Diagnosis(CAD)system employed in hospitals in rural areas.

Surface reconstruction of complex contour lines based on chain code matching technique

A new method for solving the tiling problem of surface reconstruction is proposed. The proposed method uses a snake algorithm to segment the original images, the contours are then transformed into strings by Freeman＇ s code. Symbolic string matching technique is applied to establish a correspondence between the two consecutive contours. The surface is composed of the pieces reconstructed from the correspondence points. Experimental results show that the proposed method exhibits a good behavior for the quality of surface reconstruction and its time complexity is proportional to mn where m and n are the numbers of vertices of the two consecutive slices, respectively.

The computer-aided design method of cabinet based on style imagery

Due to the practical problems of the high costs and the long development cycle of China’s cabinet production,a computer-aided design method of the cabinet based on style imagery is proposed.According to the principle of the conjoint analysis method, the rough set theory and the weight coefficient of different components of the cabinet,a multi-dimensional model of style imagery to evaluate the cabinet is built. Then the related constants of style imagery are calculated and the cabinet components library is also built by the three-dimensional modeling.Finally,with recombinant technology and the mapping model between cabinet style and external characteristics,the prototype system based on Visual Studio is proposed.This system actualizes the bidirectional reasoning between product style imagery and the shape features,which can assist designers to produce more creative designs,greatly improve the efficiency of cabinet development and increase the profits of companies.

Analysis of The Shape and Architectural Characteristics of The Song Family Courtyard in Yuzhou,Henan

Due to the special geographical location and environment of Yuzhou and the convenient transportation conditions,the progress of political,economic,and cultural exchanges,has led to a wide variety of residential buildings and even prototypes of residential buildings in various parts of Henan and even in the middle reaches of the Yellow River are not uncommon in Yuzhou.Therefore,Yuzhou folk houses can be called one of the typical representatives of the traditional residential architectural culture in the Central Plains.Taking the Song Family Courtyard in Qianjing Village in Yuzhou Region as the research object,this paper obtains first-hand data and materials from fieldwork method and literature analysis methods.Besides,this paper not only comprehensively analyzes the shape system,architectural characteristics,and decorative art content of residential buildings through data measurement,on-site mapping,etc.,but also sorts out and demonstrates the characteristics of residential buildings under the influence of the traditional religious ritual law system and social hierarchy.This paper provides theoretical support for further improving and enriching the theoretical achievements of traditional residential dwellings in the Yuzhou area and also provides a theoretical basis for the protection,inheritance,and reuse of residential dwellings in the Yuzhou area,aiming to lay a foundation for subsequent research.

Broccoli seedling pest damage degree evaluation based on machine learning combined with color and shape features

The degree of pest damage evaluation on corps in the field environment is very important for precision spraying pesticides.In this paper,we proposed an image processing method to identify the wormholes in the image of broccoli seedlings,and then to evaluate the damage of the broccoli seedlings by pests.The broccoli seedlings were taken as the research object.The ratio of wormhole areas to broccoli seedling leaves areas(Rw)was used to describe the pest damage degree.An algorithm was developed to calculate the ratio of wormhole areas to broccoli seedling leaves areas.Firstly,broccoli seedling leaves were segmented from the background and the area of the leaves was obtained.There were some holes in segmentation results due to pest damage and other reasons.Then,a classifier based on machine learning was developed to classify the wormholes and other holes.Twenty-four features,including color features and shape features of the holes,were used to develop classifiers.After identifying wormholes from images,the area of the wormholes was obtained and the degree of pest damage to broccoli seedling was calculated.The determination coefficient(R2)between the algorithm calculated pest damage degree and manually labeled pest damage degree was 0.85.The root-mean-square error(d)was 0.02.Results demonstrated that the color and shape were able to effectively segment wormholes from leaves of broccoli seedlings and evaluate the degree of pest damage.This method could provide references for precision spraying pesticides.

BLNet:Bidirectional learning network for point clouds

The key challenge in processing point clouds lies in the inherent lack of ordering and irregularity of the 3D points.By relying on per-point multi-layer perceptions(MLPs),most existing point-based approaches only address the first issue yet ignore the second one.Directly convolving kernels with irregular points will result in loss of shape information.This paper introduces a novel point-based bidirectional learning network(BLNet)to analyze irregular 3D points.BLNet optimizes the learning of 3D points through two iterative operations:feature-guided point shifting and feature learning from shifted points,so as to minimise intra-class variances,leading to a more regular distribution.On the other hand,explicitly modeling point positions leads to a new feature encoding with increased structure-awareness.Then,an attention pooling unit selectively combines important features.This bidirectional learning alternately regularizes the point cloud and learns its geometric features,with these two procedures iteratively promoting each other for more effective feature learning.Experiments show that BLNet is able to learn deep point features robustly and efficiently,and outperforms the prior state-of-the-art on multiple challenging tasks.

3D Ear Shape Matching Using Joint -Entropy

In this article, we investigate the use of joint a-entropy for 3D ear matching by incorporating the local shape feature of 3D ears into the joint a-entropy. First, we extract a sut^cient number of key points from the 3D ear point cloud, and fit the neighborhood of each key point to a single-value quadric surface on product parameter regions. Second, we define the local shape feature vector of each key point as the sampling depth set on the parametric node of the quadric surface. Third, for every pair of gallery ear and probe ear, we construct the minimum spanning tree （MST） on their matched key points. Finally, we minimize the total edge weight of MST to estimate its joint a-entropy the smaller the entropy is, the more similar the ear pair is. We present several examples to demonstrate the advantages of our algorithm, including low time complexity, high recognition rate, and high robustness. To the best of our knowledge, it is the first time that, in computer graphics, the classical information theory of joint a-entropy is used to deal with 3D ear shape recognition.

Efficient shape matching for Chinese calligraphic character retrieval

An efficient search method is desired for calligraphic characters due to the explosive growth of calligraphy works in digital libraries. However, traditional optical character recognition (OCR) and handwritten character recognition (HCR) technologies are not suitable for calligraphic character retrieval. In this paper, a novel shape descriptor called SC-HoG is proposed by integrating global and local features for more discriminability, where a gradient descent algorithm is used to learn the optimal combining parameter. Then two efficient methods, keypoint-based method and locality sensitive hashing (LSH) based method, are proposed to accelerate the retrieval by reducing the feature set and converting the feature set to a feature vector. Finally, a re-ranking method is described for practicability. The approach filters query-dissimilar characters using the LSH-based method to obtain candidates first, and then re-ranks the candidates using the keypointor sample-based method. Experimental results demonstrate that our approaches are effective and efficient for calligraphic character retrieval.