Target Distance Calculation Method Using Image Segmentation

The accurate prediction and control of burning through point are the keys of improving the quantity and quality of sinter. The position of burning through point can be determined by identifying the flame image features of the tail through the sinter. How to effectively segment the image of the flame is the key to identify the characteristics of the flame. By using the approach of the c-means clustering flame image segmentation based on particle-pair optimizer, the flame image of the sintering machine plant-tail section will be segmented in this paper. Frequently, the standard c-means clustering algorithm may easily immerse in partial minimum and slow converging speed. However, this new calculation method can overcome these shortcomings. Besides, the results of the experiment also show that this method has many other advantages, such as effectively removing the halo of the sintering machine plant-tail section, high segmenting speed and obvious segmenting effects. This approach will lay a good foundation for the extraction and identification of image features in the following stages.

Download Full-text

MULTI-CLASS REGION MERGING FOR INTERACTIVE IMAGE SEGMENTATION USING HIERARCHICAL CLUSTERING ANALYSIS

Jurnal Ilmu Komputer dan Informasi ◽

10.21609/jiki.v12i2.757 ◽

2019 ◽

Vol 12 (2) ◽

pp. 113

Author(s):

Khairiyyah Nur Aisyah ◽

Syadza Anggraini ◽

Novi Nur Putriwijaya ◽

Agus Zainal Arifin ◽

Rarasmaya Indraswari ◽

...

Keyword(s):

Image Segmentation ◽

Hierarchical Clustering ◽

Clustering Analysis ◽

Mean Value ◽

Hierarchical Clustering Analysis ◽

Region Merging ◽

Interactive Image Segmentation ◽

Distance Calculation ◽

The Mean ◽

Merging Strategy

In interactive image segmentation, distance calculation between regions and sequence of region merging is being an important thing that needs to be considered to obtain accurate segmentation results. Region merging without regard to label in Hierarchical Clustering Analysis causes the possibility of two different labels merged into a cluster and resulting errors in segmentation. This study proposes a new multi-class region merging strategy for interactive image segmentation using the Hierarchical Clustering Analysis. Marking is given to regions that are considered as objects and background, which are then referred as classes. A different label for each class is given to prevent any classes with different label merged into a cluster. Based on experiment, the mean value of ME and RAE for the results of segmentation using the proposed method are 0.035 and 0.083, respectively. Experimental results show that giving the label on each class is effectively used in multi-class region merging.

Download Full-text

PENCARIAN OBAT PADA APOTEK TERDEKAT DENGAN MEMANFAATKAN GOOGLE MAP

Compiler ◽

10.28989/compiler.v3i2.79 ◽

2014 ◽

Vol 3 (2) ◽

Author(s):

Digga Anggriawan ◽

Nurcahyani Dewi Retnowati

Keyword(s):

Open Source ◽

Calculation Method ◽

Web Server ◽

Distance Calculation ◽

Shortest Distance ◽

Function Code

The availability of drugs at any pharmacy vary different so make customers is not easy get drugs that needed when we comes to pharmacy. We need a system to assist search for drugs. This study use ssimulation of three pharmacy integrated to web server for synchronize drug stocks. This system needed the coordinates pharmacy and the coordinates customers to perform distance calculations. A data coordinates pharmacy contained in web server while the customer location input to system later request to google map for get the coordinates. Distance calculation method use calculation function code program google map is open source based on the coordinates pharmacy and the coordinates customers who later produce distance is 7,1 Km, 2,1 Km, and 5,8 Km. Than we get produce shortest distance using min method in math object provided the carrier object javascript with percentace is 43,3%, 14%, and 36,6%, and than shortest distance is 2,1 Km with percentace is 14%.

Download Full-text

An Approach to Dynamic Distance Calculations for Obstacle Avoidance Problems

3rd Conference on Flexible Assembly Systems ◽

10.1115/detc1991-0165 ◽

1991 ◽

Author(s):

C. Y. Liu ◽

W. R. Chen ◽

R. W. Mayne

Keyword(s):

Motion Planning ◽

Calculation Method ◽

Two Dimensional ◽

Robot Path Planning ◽

Distance Calculation ◽

Approach Distance ◽

Quadratic Programming Method ◽

Swept Volume ◽

Planning Problems ◽

Robot Path

Abstract This paper presents a distance calculation method which can be used in machine motion planning optimizations where interference is a concern. Dynamic distance calculations are discussed which use the quadratic programming method combined with an approximate swept volume approach. Distance-to-contact calculations can be obtained for both interference and non-interference situations. The swept volume of a moving polygon is constructed through a series of overlapped swept volume segments. Each of the swept volume segments is efficiently developed by checking the inner products of polygon outward boundary normals with velocity vectors for polygon vertices. Two dimensional examples of distance-to-contact computations and robot path planning problems are presented for a sample three link robot with three rotational joints.

Download Full-text

Image segmentation by using K-means clustering algorithm in Euclidean and Mahalanobis distance calculation in camouflage images

2020 28th Signal Processing and Communications Applications Conference (SIU) ◽

10.1109/siu49456.2020.9302320 ◽

2020 ◽

Keyword(s):

Image Segmentation ◽

Mahalanobis Distance ◽

Clustering Algorithm ◽

Distance Calculation

Download Full-text

FIXED VOTERS CLUSTERING TO DETERMINE THE LEVEL OF BEGINNER VOTERS USING DATA MINING TECHNIQUES

Engineering and Technology Journal ◽

10.47191/etj/v5i11.02 ◽

2020 ◽

Vol 5 (11) ◽

Author(s):

Ummul Hairah ◽

Keyword(s):

Data Mining ◽

Decision Making ◽

Error Rate ◽

Calculation Method ◽

Manhattan Distance ◽

Minkowski Distance ◽

Distance Calculation ◽

Distance Model ◽

Calculation Results ◽

Using Data

Data mining clustering technique is used to classify the level of beginner voters using the K-Means method. Fixed voter clusters are used for decision making for stakeholders regarding information on beginner voters in each district and sub-district. The error calculation method is used to measure the level of error value for each distance calculation used, the distance calculation method used ie Euclidean, Manhattan, and Minkowski Distance with the Means Square Error (MSE) approach to measure the level of the error value. The calculation results show that the lowest error occurs in the calculation of the Minkowski Distance model 3 cluster, where the error rate is 11%, while the highest error rate occurs in the calculation of the Manhattan Distance model 5 cluster, which is 38%.

Download Full-text