Skeleton extraction of boiler flame images based on mathematical morphology

In this paper, a method and algorithm of skeleton extraction based on binary mathematical morphology is presented. Sequential structuring elements (SEs) is also studied, which is the key problem of skeleton extraction. The examples of boiler flame image processing show that the detected skeletons can present the geometric shape of flame images well.

Skeleton extraction and pose estimation of piglets using ZS-DLC-PAF

The accurate identification of various postures in the daily life of piglets that are directly reflected by their skeleton morphology is necessary to study the behavioral characteristics of pigs.Accordingly,this study proposed a novel approach for the skeleton extraction and pose estimation of piglets.First,an improved Zhang-Suen(ZS)thinning algorithm based on morphology was used to establish the chain code mechanism of the burr and the redundant information deletion templates to achieve a single-pixel width extraction of pig skeletons.Then,body nodes were extracted on the basis of the improved DeepLabCut(DLC)algorithm,and a part affinity field(PAF)was added to realize the connection of body nodes,and consequently,construct a database of pig behavior and postures.Finally,a support vector machine was used for pose matching to recognize the main behavior of piglets.In this study,14000 images of piglets with different types of behavior were used in posture recognition experiments.Results showed that the improved algorithm based on ZS-DLC-PAF achieved the best thinning rate compared with those of distance transformation,medial axis transformation,morphology refinement,and the traditional ZS algorithm.The node tracking accuracy reached 85.08%,and the pressure test could accurately detect up to 35 nodes of 5 pigs.The average accuracy of posture matching was 89.60%.This study not only realized the single-pixel extraction of piglets’skeletons but also the connection among the different behavior body nodes of individual sows and multiple piglets.Furthermore,this study established a database of pig posture behavior,which provides a reference for studying animal behavior identification and classification and anomaly detection.

SlowFast Based Real-Time Human Motion Recognition with Action Localization

Artificial intelligence is increasingly being applied in the field of video analysis,particularly in the area of public safety where video surveillance equipment such as closed-circuit television(CCTV)is used and automated analysis of video information is required.However,various issues such as data size limitations and low processing speeds make real-time extraction of video data challenging.Video analysis technology applies object classification,detection,and relationship analysis to continuous 2D frame data,and the various meanings within the video are thus analyzed based on the extracted basic data.Motion recognition is key in this analysis.Motion recognition is a challenging field that analyzes human body movements,requiring the interpretation of complex movements of human joints and the relationships between various objects.The deep learning-based human skeleton detection algorithm is a representative motion recognition algorithm.Recently,motion analysis models such as the SlowFast network algorithm,have also been developed with excellent performance.However,these models do not operate properly in most wide-angle video environments outdoors,displaying low response speed,as expected from motion classification extraction in environments associated with high-resolution images.The proposed method achieves high level of extraction and accuracy by improving SlowFast’s input data preprocessing and data structure methods.The input data are preprocessed through object tracking and background removal using YOLO and DeepSORT.A higher performance than that of a single model is achieved by improving the existing SlowFast’s data structure into a frame unit structure.Based on the confusion matrix,accuracies of 70.16%and 70.74%were obtained for the existing SlowFast and proposed model,respectively,indicating a 0.58%increase in accuracy.Comparing detection,based on behavioral classification,the existing SlowFast detected 2,341,164 cases,whereas the proposed model detected 3,119,323 cases,which is an increase of 33.23%.

A novel method for extracting skeleton of fruit treefrom 3D point clouds

Tree skeleton could be useful to agronomy researchers because the skeleton describes the shape and topological structure of a tree.The phenomenon of organs’mutual occlusion in fruit tree canopy is usually very serious,this should result in a large amount of data missing in directed laser scanning 3D point clouds from a fruit tree.However,traditional approaches can be ineffective and problematic in extracting the tree skeleton correctly when the tree point clouds contain occlusions and missing points.To overcome this limitation,we present a method for accurate and fast extracting the skeleton of fruit tree from laser scanner measured 3D point clouds.The proposed method selects the start point and endpoint of a branch from the point clouds by user’s manual interaction,then a backward searching is used to find a path from the 3D point cloud with a radius parameter as a restriction.The experimental results in several kinds of fruit trees demonstrate that our method can extract the skeleton of a leafy fruit tree with highly accuracy.