Seeds Microscopic Image Edge Detection Based on the Clustering Method and Applied in Andriod Platform

2014 ◽  
Vol 644-650 ◽  
pp. 1100-1103
Author(s):  
Xiao Fen Guo ◽  
Yan Li Zuo
Keyword(s):  
Clustering Algorithm ◽  
Median Filter ◽  
Threshold Value ◽  
Microscopic Image ◽  
Image Edge Detection ◽  
Image Edge ◽  
Hierarchical Clustering Algorithm ◽  
Edge Based ◽  
Roberts Operator ◽  
Suppress Noise

Sobel, Roberts operator is derived based on the differential. As a result of the template and fixed threshold value, it lacks adaptability. Median filter on images of the collected cardiac seeds maturity, use split clustering algorithm on cardiac seeds maturity for the first image gradient value clustering, condensed cluster on the result of the first split the clustering results, then secondly division cluster, and finally work out the image edge based on the second clustering results and realize on the FPGA implementation. At last the method is Applied in Andriod Platform. The experimental results show that it is more delicate to use the hierarchical clustering algorithm to detect the edge and it has stronger ability to suppress noise.

Application of Petrochemical Heat Equipments' State Inspection Based on the Edge Detection of Infrared Image Segmentation

2013 ◽  
Vol 303-306 ◽  
pp. 970-974
Author(s):  
Lin Lin Cui ◽  
Hua Lai ◽  
Yong Wang Tang ◽  
Ming Jie Qi
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Infrared Image ◽  
Threshold Value ◽  
State Inspection ◽  
Experimental Result ◽  
Target Area ◽  
Image Edge ◽  
Roberts Operator ◽  
Infrared Image Segmentation

According to the problem of petrochemical heat equipment status inspection and fault diagnosis, a method based on edge detection of infrared image segmentation was presented studying the infrared image segmentation based on edge detection and combining Roberts operator into best threshold segmentation method to do simulation of buoyant, medium and heavy damaged equipments. Experimental result shows that edge detection operator of best threshold value has ideal effects to the image edge extraction's target area of thermal infrared equipment.

Algorithm for Improving Image Edge Detection Based on Directional Wavelet Transform

2014 ◽  
Vol 543-547 ◽  
pp. 2763-2765
Author(s):  
Xiang Shi Wang ◽  
Gui Feng Liu
Keyword(s):  
Wavelet Transform ◽  
Edge Detection ◽  
Median Filter ◽  
Image Data ◽  
Edge Image ◽  
Image Edge Detection ◽  
Image Edge ◽  
Directional Wavelet

The information of the image edge is the important parameters in identifying, segmenting and compressing image. The performance of the algorithms about edge image algorithms closely relies on the noises generally included in the image. The main goal of this paper is firstly to eliminate the false edges by the median filter and extract the information of the edge image by directional wavelet transform. Application on image data shows that the proposed tool can enhance the direction edge images which is fused to form the complete image edge.

Edge Detection of Inner Crack Defects Based on Improved Sobel Operator and Clustering Algorithm

2011 ◽  
Vol 55-57 ◽  
pp. 467-471 ◽  
Author(s):  
Ke Fei Wang
Keyword(s):  
Edge Detection ◽  
Clustering Algorithm ◽  
Detection Methods ◽  
Experimental Result ◽  
Edge Image ◽  
Positioning Accuracy ◽  
Sobel Operator ◽  
Image Edge Detection ◽  
Image Edge ◽  
Sobel Edge Detection

The classical Sobel edge detection operator has the shortcomings of low edge positioning accuracy and coarse edge, image edge detection based on improved Sobel operator and clustering algorithm was proposed. Four Sobel-like edge operators are used to improve the edge positioning accuracy and clustering algorithm are used to edge thinning. The experimental result demonstrates that the effect of the edge detection is greatly improved comparing with the traditional edge detection methods.

Study on the Method of Feature Extraction of Vegetable Diseases Based on Image Processing

2014 ◽  
Vol 1044-1045 ◽  
pp. 1366-1369 ◽  
Author(s):  
Zhan Wu Peng ◽  
Xue Wang
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Computer Image ◽  
Median Filter ◽  
Diseased Leaf ◽  
Image Edge Detection ◽  
Computer Image Processing ◽  
Geometrical Features ◽  
Greenhouse Vegetable ◽  
Image Edge

Using computer image processing technology, greenhouse vegetable diseases were recognized intelligently, and taking the cucumber mildew as an example, the method of image pre-processing and feature extraction for intelligently recognizing greenhouse vegetable diseases was studied. White was chosen as the background of diseased leaf, median filter was utilized to effectively wipe out the disturbance of noise, and two-apex method was applied to separate the disease images from the background. Geometrical features of the disease spots were accurately extracted by performing image edge detection. The results suggest that the method can pre-process images and precisely extract the features of the diseases.

Hierarchical Clustering Algorithm of the Minimum Risk

2013 ◽  
Vol 333-335 ◽  
pp. 1410-1413
Author(s):  
De Xing Wang ◽  
Jie Long Xu ◽  
Hong Chun Yuan
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Analysis ◽  
Clustering Algorithm ◽  
Strong Support ◽  
Threshold Value ◽  
Boundary Region ◽  
Minimum Risk ◽  
Hierarchical Clustering Algorithm ◽  
Similar Class ◽  
Marine Environment Monitoring

Clustering analysis is grouping a set of physical or abstract objects into the similar class. In traditional clustering algorithm, objects are usually divided into a certain cluster. This paper applies the risk evaluation of decision-theoretic rough set model in clustering analysis which solves the problem of uncertain boundary region, and proposes a hierarchical clustering algorithm of the minimum risk which can adjust threshold value to construct a clustering evaluation function in order to find the solution to optimize the result. At last, the case analysis shows the algorithm is feasible. It could provide a strong support for marine environment monitoring system and so on.

Surface Quality Detection of Welding Nuts Based on Machine Vision

2014 ◽  
Vol 1039 ◽  
pp. 237-241
Author(s):  
Zheng Li ◽  
Li Xin Lu ◽  
Gui Qin Li ◽  
Yan Zhao ◽  
Hong Bo Li ◽  
...  
Keyword(s):  
Machine Vision ◽  
Surface Quality ◽  
Automotive Industry ◽  
Canny Operator ◽  
Threaded Fasteners ◽  
Image Edge Detection ◽  
Quality Detection ◽  
Image Edge ◽  
Security And Reliability ◽  
Suppress Noise

Welding nuts are being used widely in automotive industry as threaded fasteners, its security and reliability requests are increasingly high. Accurate and quick detection results for surface quality can be obtained by using machine vision. Image edge detection by Canny operator has stronger acclimatization in anti-jamming and accurate edge localization. In order to suppress noise in maximum, an improved method is put forward to calculate the gradient of Canny operator, introducing Robert cross gradient in 3×3 neighborhood when calculating the gradient magnitude in finite difference method. The experimental results show that the improved Canny operator makes more effectively on the surface detection of welding nuts.

Image Edge Detection Based on Multi-Structure Elements and Nonsubsambled Contourlet Transform

2012 ◽  
Vol 591-593 ◽  
pp. 1822-1826
Author(s):  
Kun Xian He ◽  
Qing Wang ◽  
Fan He
Keyword(s):  
Edge Detection ◽  
Mathematical Morphology ◽  
Fusion Algorithm ◽  
Edge Image ◽  
Multi Scale ◽  
Image Edge Detection ◽  
Image Edge ◽  
Modulus Maxima ◽  
Single Pixel ◽  
Suppress Noise

This paper presents a fusion algorithm for image edge detection based on the mathematical morphology and the NSCT. First the de-noised image is processed by the multi-structure elements of the mathematical morphology. And then the processed image is decomposed by the NSCT into multi-scale and multi-directional sub-bands. Edges in the high-frequency sub-bands are extracted with the dual-threshold modulus maxima method. Finally the edges of the de-noised image are refined into a single pixel edge image. The simulation results show that this method can effectively suppress noise, eliminate pseudo-edges, locate accurately and detect the complete outline.

A New Method of SAR Airdrome Image Edge Detection Based on Image Character

2013 ◽  
Vol 347-350 ◽  
pp. 3560-3564
Author(s):  
De Jun Li ◽  
He Nan Wu ◽  
Guan Zhong Li ◽  
Guang Yang ◽  
Li Cheng ◽  
...  
Keyword(s):  
Edge Detection ◽  
Median Filter ◽  
Detection Methods ◽  
Sar Image ◽  
Basic Character ◽  
Image Edge Detection ◽  
Imaging Theory ◽  
Image Edge ◽  
And Mathematics ◽  
Image Pretreatment

SAR image edge detection is one of the basic contents for SAR image processing and analysis, one of SAR airdrome image edge detection methods was advanced in the paper based on the basic character of SAR airdrome image through imaging theory and target statistical trait, SAR image pretreatment was realized by synchronously using median filter, image division and mathematics morphologic, at last SAR image was edge detected using Canny arithmetic operators.

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

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