Watershed segmentation based on hierarchical multi-scale modification of morphological gradient

Watershed segmentation is sensitive to noises and irregular details within the image,which frequently leads to a serious over-segmentation Linear filtering before watershed segmentation can reduce over-segmentation to some extent,however,it often causes the position offset of object contours.For the purpose of reducing over-segmentation to preserve the location of object contours,the watershed segmentation based on the hierarchical multi-scale modification of morphological gradient is proposed.Firstly,multi-scale morphological filtering was employed to smooth the original image.Then,the gradient image was divided into multi-levels by the volume of three-dimension topographic relief,where the lower gradient layers were further modifiedby morphological closing with larger-sized structuring-elements,and the higher layers with the smaller one.In this way,most local minimums caused by irregular details and noises can be removed,while region contour positions corresponding to the target area were largely preserved.Finally,morphological watershed algorithm was employed to implement segmentation on the modified gradient image.The experimental results show that the proposed method can greatly reduce the over-segmentation of the watershed and avoid the position offset of the object contours.

Feature extraction of jujube fruit wrinkle based on the watershed segmentation

The degree of surface wrinkles on a dried jujube fruit(Ziziphus jujuba Mill.)is an important quality grading criterion.The aim of this research was to propose an image processing method based on the watershed segmentation to extract the wrinkle features of jujube fruits.Original images of jujube fruit taken under cyan light were transformed into grayscale images.The noise in these images was then removed by morphological reconstruction.The H-minima extended transformation was used to label the foreground of jujube fruit images after reconstruction,and the labeled foreground regions were segmented by a distance transform-based watershed algorithm.Then,the grayscale images were filtered with a local range filter.The segmentation function was obtained using the minima imposition method.Finally,a watershed segmentation was used to extract the wrinkle features of jujube fruits.Experiments on 304 images of jujube fruit showed that the accuracy of wrinkle-based grading obtained by the algorithm was 92.11%,which proved that this method could be used to classify jujube wrinkles.

Image Segmentation Algorithm Based on Improved Watershed Algorithm for Young Leaves of Tea

Aiming at the problem that the traditional watershed image segmentation algorithm is sensitive to noise and prone to ＂over-segmentation＂, an image segmentation method based on improved watershed algorithm is proposed. First, the denoising method is used to denoise the tea image by using the differential equation denoising model The interference of the image on the image segmentation, the protection of the tea image of the edge of the tea information; and then use the watershed algorithm to denoise the tea image after the split. The simulation results show that this method can effectively avoid the influence of noise on image segmentation, and get a good image of ,louug leaves of tea image.

Automated lung segmentation algorithm for CAD system of thoracic CT

Objective: To design and test the accuracy and efficiency of our lung segmentation algorithm on thoracic CT image in computer-aided diagnostic (CAD) system, especially on the segmentation between left and right lungs. Methods: We put forward the base frame of our lung segmentation firstly. Then, using optimal thresholding and mathematical morphologic methods, we acquired the rough image of lung segmentation. Finally, we presented a fast self-fit segmentation refinement algorithm, adapting to the unsuccessful left-right lung segmentation of thredsholding. Then our algorithm was used to CT scan images of 30 patients and the results were compared with those made by experts. Results: Experiments on clinical 2-D pulmonary images showed the results of our algorithm were very close to the expert’s manual outlines, and it was very effective for the separation of left and right lungs with a successful segmentation ratio 94.8%. Conclusion: It is a practicable fast lung segmentation algorithm for CAD system on thoracic CT image.

An Intelligent Framework for Recognizing Social Human-Object Interactions

Human object interaction(HOI)recognition plays an important role in the designing of surveillance and monitoring systems for healthcare,sports,education,and public areas.It involves localizing the human and object targets and then identifying the interactions between them.However,it is a challenging task that highly depends on the extraction of robust and distinctive features from the targets and the use of fast and efficient classifiers.Hence,the proposed system offers an automated body-parts-based solution for HOI recognition.This system uses RGB(red,green,blue)images as input and segments the desired parts of the images through a segmentation technique based on the watershed algorithm.Furthermore,a convex hullbased approach for extracting key body parts has also been introduced.After identifying the key body parts,two types of features are extracted.Moreover,the entire feature vector is reduced using a dimensionality reduction technique called t-SNE(t-distributed stochastic neighbor embedding).Finally,a multinomial logistic regression classifier is utilized for identifying class labels.A large publicly available dataset,MPII(Max Planck Institute Informatics)Human Pose,has been used for system evaluation.The results prove the validity of the proposed system as it achieved 87.5%class recognition accuracy.

A Simple Computational Approach for the Texture Analysis of CT Scan Images Using Orthogonal Moments

This paper is a study on texture analysis of Computer Tomography (CT) liver images using orthogonal moment features. Orthogonal moments are used as image feature representation in many applications like invariant pattern recognition of images. Orthogonal moments are proposed here for the diagnosis of any abnormalities on the CT images. The objective of the proposed work is to carry out the comparative study of the performance of orthogonal moments like Zernike, Racah and Legendre moments for the detection of abnormal tissue on CT liver images. The Region of Interest (ROI) based segmentation and watershed segmentation are applied to the input image and the features are extracted with the orthogonal moments and analyses are made with the combination of orthogonal moment with segmentation that provides better accuracy while detecting the tumor. This computational model is tested with many inputs and the performance of the orthogonal moments with segmentation for the texture analysis of CT scan images is computed and compared.

A method for analyzing on-line video images of crystallization at high-solid concentrations

Recent research has demonstrated that on-line video imaging is a very promising technique for monitoring crystallization processes. The bottleneck in applying the technique for real-time closed-loop control is considered as image analysis that needs to be robust, fast and able to handle varied image qualities due to temporal variations of operating conditions such as mixing and solid concentrations. Image analysis at highsolid concentrations turns out to be extremely challenging because crystals tend to overlap or attach to each other and the boundaries between the crystals are usually ambiguous. This paper presents an image segmentation algorithm that can effectively deal with images taken at high-solid concentrations. The method segments crystals attached to each other along the mostly related concave points on the contours of crystal blocks. The detailed procedure is introduced with application to crystallization of L-glutamic acid in a hot-stage reactor.

A new framework for GEOBIA: accurate individual plant extraction and detection using high-resolution RGB data from UAVs

Citrus(Citrus reticulata),which is an important economic crop worldwide,is often managed in a labor-intensive and inefficient manner in developing countries,thereby necessitating more rapid and accurate alternatives tofield surveys for improved crop management.In this study,we propose a novel method for individual tree segmentation from unmanned aerial vehicle remote sensing(RS)using a combination of geographic object-based image analysis(GEOBIA)and layer-adaptive Euclidean distance transformation-based watershed segmentation(LAEDT-WS).First,we use a GEOBIA support vector machine classifier that is optimized for features and parameters to identify the boundaries of citrus tree canopies accurately by generating mask images.Thereafter,our LAEDT workflow separates connected canopies and facilitates the accurate segmentation of individual canopies using WS.Our method exhibited an F1-score improvement of 10.75%compared to the traditional WS method based on the canopy height model.Furthermore,it achieved 0.01%and 1.38%higher F1-scores than the state-of-the-art deep learning detection networks YOLOX and YOLACT,respectively,on the test plot.Our method can be extended to detect larger-scale or more complex structured crops or economic plants by introducing morefinely detailed and transferable RS images,such as high-resolution or LiDAR-derived images,to improve the mask base map.