Multi-Scale Mixed Attention Tea Shoot Instance Segmentation Model

Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea.Traditional tea-picking machines may compromise the quality of the tea leaves.High-quality teas are often handpicked and need more delicate operations in intelligent picking machines.Compared with traditional image processing techniques,deep learning models have stronger feature extraction capabilities,and better generalization and are more suitable for practical tea shoot harvesting.However,current research mostly focuses on shoot detection and cannot directly accomplish end-to-end shoot segmentation tasks.We propose a tea shoot instance segmentation model based on multi-scale mixed attention(Mask2FusionNet)using a dataset from the tea garden in Hangzhou.We further analyzed the characteristics of the tea shoot dataset,where the proportion of small to medium-sized targets is 89.9%.Our algorithm is compared with several mainstream object segmentation algorithms,and the results demonstrate that our model achieves an accuracy of 82%in recognizing the tea shoots,showing a better performance compared to other models.Through ablation experiments,we found that ResNet50,PointRend strategy,and the Feature Pyramid Network(FPN)architecture can improve performance by 1.6%,1.4%,and 2.4%,respectively.These experiments demonstrated that our proposed multi-scale and point selection strategy optimizes the feature extraction capability for overlapping small targets.The results indicate that the proposed Mask2FusionNet model can perform the shoot segmentation in unstructured environments,realizing the individual distinction of tea shoots,and complete extraction of the shoot edge contours with a segmentation accuracy of 82.0%.The research results can provide algorithmic support for the segmentation and intelligent harvesting of premium tea shoots at different scales.

A froth velocity measurement method based on improved U-Net++semantic segmentation in flotation process

During flotation,the features of the froth image are highly correlated with the concentrate grade and the corresponding working conditions.The static features such as color and size of the bubbles and the dynamic features such as velocity have obvious differences between different working conditions.The extraction of these features is typically relied on the outcomes of image segmentation at the froth edge,making the segmentation of froth image the basis for studying its visual information.Meanwhile,the absence of scientifically reliable training data with label and the necessity to manually construct dataset and label make the study difficult in the mineral flotation.To solve this problem,this paper constructs a tungsten concentrate froth image dataset,and proposes a data augmentation network based on Conditional Generative Adversarial Nets(cGAN)and a U-Net++-based edge segmentation network.The performance of this algorithm is also evaluated and contrasted with other algorithms in this paper.On the results of semantic segmentation,a phase-correlationbased velocity extraction method is finally suggested.

AWeb Application Fingerprint Recognition Method Based on Machine Learning

Web application fingerprint recognition is an effective security technology designed to identify and classify web applications,thereby enhancing the detection of potential threats and attacks.Traditional fingerprint recognition methods,which rely on preannotated feature matching,face inherent limitations due to the ever-evolving nature and diverse landscape of web applications.In response to these challenges,this work proposes an innovative web application fingerprint recognition method founded on clustering techniques.The method involves extensive data collection from the Tranco List,employing adjusted feature selection built upon Wappalyzer and noise reduction through truncated SVD dimensionality reduction.The core of the methodology lies in the application of the unsupervised OPTICS clustering algorithm,eliminating the need for preannotated labels.By transforming web applications into feature vectors and leveraging clustering algorithms,our approach accurately categorizes diverse web applications,providing comprehensive and precise fingerprint recognition.The experimental results,which are obtained on a dataset featuring various web application types,affirm the efficacy of the method,demonstrating its ability to achieve high accuracy and broad coverage.This novel approach not only distinguishes between different web application types effectively but also demonstrates superiority in terms of classification accuracy and coverage,offering a robust solution to the challenges of web application fingerprint recognition.

DR-XGBoost: An XGBoost model for field-road segmentation based on dual feature extraction and recursive feature elimination

Field-road segmentation is one of the key tasks in the processing of the trajectory of agricultural machinery.To improve the accuracy of the field-road segmentation,this study proposed an XGBoost model based on dual feature extraction and recursive feature elimination called DR-XGBoost.DR-XGBoost takes only a small amount of agricultural machine trajectory features as input.Firstly,the model adopted the dual feature extraction method we designed to rapidly expand the number of features and then adequately extract local trajectory features by the time window and feature extraction operator.Secondly,the model applies the recursive feature elimination algorithm to eliminate redundant features from the perspective of the model segmentation effect and thus reduce the computational consumption of model training.Thirdly,it trains XGBoost to complete the trajectory segmentation.To evaluate the effectiveness of DR-XGBoost,we conducted a series of experiments on a real trajectory dataset of agricultural machines.The model achieves a 98.2%Macro-F1 score on the dataset,which is 10.9%higher than the previous state-of-art.The proposal of DR-XGBoost fills the knowledge gap of trajectory feature extraction for agricultural machinery and provides a reasonable and effective feature selection scheme for the field-road segmentation problem.

Hybrid Segmentation Scheme for Skin Features Extraction Using Dermoscopy Images

Objective and quantitative assessment of skin conditions is essential for cosmeceutical studies and research on skin aging and skin regeneration.Various handcraft-based image processing methods have been proposed to evaluate skin conditions objectively,but they have unavoidable disadvantages when used to analyze skin features accurately.This study proposes a hybrid segmentation scheme consisting of Deeplab v3+with an Inception-ResNet-v2 backbone,LightGBM,and morphological processing(MP)to overcome the shortcomings of handcraft-based approaches.First,we apply Deeplab v3+with an Inception-ResNet-v2 backbone for pixel segmentation of skin wrinkles and cells.Then,LightGBM and MP are used to enhance the pixel segmentation quality.Finally,we determine several skin features based on the results of wrinkle and cell segmentation.Our proposed segmentation scheme achieved a mean accuracy of 0.854,mean of intersection over union of 0.749,and mean boundary F1 score of 0.852,which achieved 1.1%,6.7%,and 14.8%improvement over the panoptic-based semantic segmentation method,respectively.

Efficient Object Segmentation and Recognition Using Multi-Layer Perceptron Networks

Object segmentation and recognition is an imperative area of computer vision andmachine learning that identifies and separates individual objects within an image or video and determines classes or categories based on their features.The proposed system presents a distinctive approach to object segmentation and recognition using Artificial Neural Networks(ANNs).The system takes RGB images as input and uses a k-means clustering-based segmentation technique to fragment the intended parts of the images into different regions and label thembased on their characteristics.Then,two distinct kinds of features are obtained from the segmented images to help identify the objects of interest.An Artificial Neural Network(ANN)is then used to recognize the objects based on their features.Experiments were carried out with three standard datasets,MSRC,MS COCO,and Caltech 101 which are extensively used in object recognition research,to measure the productivity of the suggested approach.The findings from the experiment support the suggested system’s validity,as it achieved class recognition accuracies of 89%,83%,and 90.30% on the MSRC,MS COCO,and Caltech 101 datasets,respectively.

Apriori and N-gram Based Chinese Text Feature Extraction Method

A feature extraction, which means extracting the representative words from a text, is an important issue in text mining field. This paper presented a new Apriori and N-gram based Chinese text feature extraction method, and analyzed its correctness and performance. Our method solves the question that the exist extraction methods cannot find the frequent words with arbitrary length in Chinese texts. The experimental results show this method is feasible.

A feature extraction and correspondence algorithm for laser range finder with sensor uncertainty

This paper presents a feature extraction and correspondence algorithm which employs a novel feature transform. Unlike conventional approaches such as Hough Transform, we employ a robust but simple approach to extract the geometrical feature under real dynamic world conditions. Multi-threshold segmentation and the split-and-merge method are employed to interpret geometrical features such as edge, concave corners, convex corners, and segments in a unified framework. The features are represented by feature tree (F-Tree) so as to compactly represent the environments and some important properties of the F-Tree are discussed in this paper. To demonstrate the validity of the approach, we show the actual experiment results which are based on real Laser Range Finder data and real time analysis. The comparative study with Hough Transform shows the advantages and the high performance of the proposed algorithm.

Research on Intelligent Identification of PD Patterns Based on the Fingerprint Features

Five-electrode configurations were designed to simulate the distribution inhomogeneity of electric field intensities in the air-insulating medium, and the characteristic data waveforms of partial discharge generated by different electrode configurations under the excitation of power frequency AC voltage were carefully collected in this paper. Furthermore, the feature vectors of the corresponding fingerprint, contained in partial discharge data, were extracted by rigorous mathematical algorithms, and the artificial neural network was employed to realize the pattern recognition of partial discharge caused by the inhomogeneity of electric field intensity with different electrode configurations. The results indicate that the J4 value in the space of 7 feature quantities is 1905.6, and the recognition rate is 100% when the hidden layer neuron of the network is 19. However, the J5 value of 9 feature quantities is 1589.9, and the purpose of recognition has been achieved when the number of hidden layer neurons of the network is 6. Increasing the number of hidden layer neurons will only waste computing resources. Of course, PD information collection mode, feature quantity selection, optimal feature space composition, network structure and classification algorithm are the key to realizing PD fault intelligence identification.

A Method for Head-shoulder Segmentation and Human Facial Feature Positioning

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Applying rough sets in word segmentation disambiguation based on maximum entropy model

To solve the complicated feature extraction and long distance dependency problem in Word Segmentation Disambiguation （WSD）, this paper proposes to apply rough sets ill WSD based on the Maximum Entropy model. Firstly, rough set theory is applied to extract the complicated features and long distance features, even frnm noise or inconsistent corpus. Secondly, these features are added into the Maximum Entropy model, and consequently, the feature weights can be assigned according to the performance of the whole disambiguation mnltel. Finally, tile semantic lexicou is adopted to build class-hased rough set teatures to overcome data spareness. The experiment indicated that our method performed better than previous models, which got top rank in WSD in 863 Evaluation in 2003. This system ranked first and second respcetively in MSR and PKU open test in the Second International Chinese Word Segmentation Bankeoff held in 2005.

A Transformer-Assisted Cascade Learning Network for Choroidal Vessel Segmentation

As a highly vascular eye part,the choroid is crucial in various eye disease diagnoses.However,limited research has focused on the inner structure of the choroid due to the challenges in obtaining sufficient accurate label data,particularly for the choroidal vessels.Meanwhile,the existing direct choroidal vessel segmentation methods for the intelligent diagnosis of vascular assisted ophthalmic diseases are still unsatisfactory due to noise data,while the synergistic segmentation methods compromise vessel segmentation performance for the choroid layer segmentation tasks.Common cascaded structures grapple with error propagation during training.To address these challenges,we propose a cascade learning segmentation method for the inner vessel structures of the choroid in this paper.Specifically,we propose TransformerAssisted Cascade Learning Network(TACLNet)for choroidal vessel segmentation,which comprises a two-stage training strategy:pre-training for choroid layer segmentation and joint training for choroid layer and choroidal vessel segmentation.We also enhance the skip connection structures by introducing a multi-scale subtraction connection module designated as MSC,capturing differential and detailed information simultaneously.Additionally,we implement an auxiliary Transformer branch named ATB to integrate global features into the segmentation process.Experimental results exhibit that our method achieves the state-of-the-art performance for choroidal vessel segmentation.Besides,we further validate the significant superiority of the proposed method for retinal fluid segmentation in optical coherence tomography(OCT)scans on a publicly available dataset.All these fully prove that our TACLNet contributes to the advancement of choroidal vessel segmentation and is of great significance for ophthalmic research and clinical application.

Hybrid Active Contour Mammographic Mass Segmentation and Classification

This research implements a novel segmentation of mammographic mass.Three methods are proposed,namely,segmentation of mass based on iterative active contour,automatic region growing,and fully automatic mask selectionbased active contour techniques.In the first method,iterative threshold is performed for manual cropped preprocessed image,and active contour is applied thereafter.To overcome manual cropping in the second method,an automatic seed selection followed by region growing is performed.Given that the result is only a few images owing to over segmentation,the third method uses a fully automatic active contour.Results of the segmentation techniques are compared with the manual markup by experts,specifically by taking the difference in their mean values.Accordingly,the difference in the mean value of the third method is 1.0853,which indicates the closeness of the segmentation.Moreover,the proposed method is compared with the existing fuzzy C means and level set methods.The automatic mass segmentation based on active contour technique results in segmentation with high accuracy.By using adaptive neuro fuzzy inference system,classification is done and results in a sensitivity of 94.73%,accuracy of 93.93%,and Mathew’s correlation coefficient(MCC)of 0.876.

Online Fingerprint Verification Algorithm and Distributed System

In this paper, a novel online fingerprint verification algorithm and distribution system is proposed. In the beginning, fingerprint acquisition, image preprocessing, and feature extraction are conducted on workstations. Then, the extracted feature is transmitted over the internet. Finally, fingerprint verification is processed on a server through web-based database query. For the fingerprint feature extraction, a template is imposed on the fingerprint image to calculate the type and direction of minutiae. A data structure of the feature set is designed in order to accurately match minutiae features between the testing fingerprint and the references in the database. An elastic structural feature matching algorithm is employed for feature verification. The proposed fingerprint matching algorithm is insensitive to fingerprint image distortion, scale, and rotation. Experimental results demonstrated that the matching algorithm is robust even on poor quality fingerprint images. Clients can remotely use ADO.NET on their workstations to verify the testing fingerprint and manipulate fingerprint feature database on the server through the internet. The proposed system performed well on benchmark fingerprint dataset.

Detection and Classification of Brain Tumor Based on Multilevel Segmentation with Convolutional Neural Network

Magnetic Resonance Imaging (MRI) is an important diagnostic technique for early detection of brain Tumor and the classification of brain Tumor from MRI image is a challenging research work because of its different shapes, location and image intensities. For successful classification, the segmentation method is required to separate Tumor. Then important features are extracted from the segmented Tumor that is used to classify the Tumor. In this work, an efficient multilevel segmentation method is developed combining optimal thresholding and watershed segmentation technique followed by a morphological operation to separate the Tumor. Convolutional Neural Network (CNN) is then applied for feature extraction and finally, the Kernel Support Vector Machine (KSVM) is utilized for resultant classification that is justified by our experimental evaluation. Experimental results show that the proposed method effectively detect and classify the Tumor as cancerous or non-cancerous with promising accuracy.

A Novel Method for Character Segmentation in Natural Scenes

A new approach to extract and segment characters in natural scenes was proposed in this paper. First, a set of intrinsic features were calculated based on connected components (CCs) extracted by a non-linear Nilblack algorithm. Then, feature propagation was conducted for feature enhancement, under the constraint of the layout relations. Next, candidate CCs were fed into classifiers with the enhanced feature vector. At last, a model-based hierarchical merging (MHM) procedure was presented to obtain understandable characters. The proposed merging algorithm utilized the constraint of text lines for specific languages and dynamically merges CCs into characters. The whole algorithm was evaluated at both pixel level and character level, experimental results showed that the proposed method is effective in detecting scene characters with significant geometric variations, uneven illumination, extremely low contrast and cluttered background.

Study on threshold segmentation of multi-resolution 3D human brain CT image

In order to effectively improve the pathological diagnosis capability and feature resolution of 3D human brain CT images,a threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is proposed in this paper.In this method,first,original 3D human brain image information is collected,and CT image filtering is performed to the collected information through the gradient value decomposition method,and edge contour features of the 3D human brain CT image are extracted.Then,the threshold segmentation method is adopted to segment the regional pixel feature block of the 3D human brain CT image to segment the image into block vectors with high-resolution feature points,and the 3D human brain CT image is reconstructed with the salient feature point as center.Simulation results show that the method proposed in this paper can provide accuracy up to 100%when the signal-to-noise ratio is 0,and with the increase of signal-to-noise ratio,the accuracy provided by this method is stable at 100%.Comparison results show that the threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is signicantly better than traditional methods in pathological feature estimation accuracy,and it effectively improves the rapid pathological diagnosis and positioning recognition abilities to CT images.

Hybrid Methodologies for Segmentation and Classification of Skin Diseases: A Study

Skin disorders are a serious global health problem for humans. These disorders become dangerous when they grow into the malignant stage. Hence, it is necessary to detect these diseases at their early stage. A mobile-based automated skin disease detection system is vital for detecting skin diseases. This system also offers cure or treatment plans to the affected person through the short message service (SMS) or electronic mail (e-mail). An effective skin disease detection system consists of three processes: segmentation, feature extraction, and classification. Several hybrid methodologies are already developed for the above-mentioned processes for detecting skin diseases at the initial stage. This research gives a standard hybrid framework for detecting skin diseases and highlights some design requirements for achieving high accuracy. Existing state-of-the-art hybrid methods of three processes for detecting skin diseases along with their limitations are also summarized. It also identifies the challenges for developing an effective skin disease detection system and gives future research directions.

Feature Extraction of Chinese Materia Medica Fingerprint Based on Star Plot Representation of Multivariate Data

Objective To study a novel feature extraction method of Chinese materia medica(CMM) fingerprint.Methods On the basis of the radar graphical presentation theory of multivariate,the radar map was used to figure the non-map parameters of the CMM fingerprint,then to extract the map features and to propose the feature fusion.Results Better performance was achieved when using this method to test data.Conclusion This shows that the feature extraction based on radar chart presentation can mine the valuable features that facilitate the identification of Chinese medicine.