Introduction to image processing and pattern recognitionSaibee Alam
this power point presentation provides a brief introduction to image processing and pattern recognition and its related research papers including conclusion
Introduction to image processing and pattern recognitionSaibee Alam
this power point presentation provides a brief introduction to image processing and pattern recognition and its related research papers including conclusion
Self-Organising Maps for Customer Segmentation using R - Shane Lynn - Dublin Rshanelynn
Self-Organising maps for Customer Segmentation using R.
These slides are from a talk given to the Dublin R Users group on 20th January 2014. The slides describe the uses of customer segmentation, the algorithm behind Self-Organising Maps (SOMs) and go through two use cases, with example code in R.
Accompanying code and datasets now available at http://shanelynn.ie/index.php/self-organising-maps-for-customer-segmentation-using-r/.
Cluster analysis is a data exploration (mining) tool
for dividing a multivariate dataset into “natural”
clusters (groups). We use the methods to explore
whether previously undefined clusters (groups) may
exist in the dataset.
Graph Tea: Simulating Tool for Graph Theory & AlgorithmsIJMTST Journal
Simulation in teaching has recently entered the field of education. It is used at different levels of instruction.
The teacher is trained practically and also imparted theoretical learning. In Computer Science, Graph theory
is the fundamental mathematics required for better understanding Data Structures. To Teach Graph theory &
Algorithms, We introduced Simulation as an innovative teaching methodology. Students can understand in a
better manner by using simulation. Graph Tea is one of such simulation tool for Graph Theory & Algorithms.
In this paper, we simulated Tree Traversal Techniques like Breadth First Search (BFS), Depth First Search
(DFS) and minimal cost spanning tree algorithms like Prims.
Slides from my Pittsburgh TechFest 2014 talk, "Machine Learning for Modern Developers". This talk covers basic concepts and math for statistical machine learning, focusing on the problem of classification.
Want some working code from the demos? Head over here: https://github.com/cacois/ml-classification-examples
Self-Organising Maps for Customer Segmentation using R - Shane Lynn - Dublin Rshanelynn
Self-Organising maps for Customer Segmentation using R.
These slides are from a talk given to the Dublin R Users group on 20th January 2014. The slides describe the uses of customer segmentation, the algorithm behind Self-Organising Maps (SOMs) and go through two use cases, with example code in R.
Accompanying code and datasets now available at http://shanelynn.ie/index.php/self-organising-maps-for-customer-segmentation-using-r/.
Cluster analysis is a data exploration (mining) tool
for dividing a multivariate dataset into “natural”
clusters (groups). We use the methods to explore
whether previously undefined clusters (groups) may
exist in the dataset.
Graph Tea: Simulating Tool for Graph Theory & AlgorithmsIJMTST Journal
Simulation in teaching has recently entered the field of education. It is used at different levels of instruction.
The teacher is trained practically and also imparted theoretical learning. In Computer Science, Graph theory
is the fundamental mathematics required for better understanding Data Structures. To Teach Graph theory &
Algorithms, We introduced Simulation as an innovative teaching methodology. Students can understand in a
better manner by using simulation. Graph Tea is one of such simulation tool for Graph Theory & Algorithms.
In this paper, we simulated Tree Traversal Techniques like Breadth First Search (BFS), Depth First Search
(DFS) and minimal cost spanning tree algorithms like Prims.
Slides from my Pittsburgh TechFest 2014 talk, "Machine Learning for Modern Developers". This talk covers basic concepts and math for statistical machine learning, focusing on the problem of classification.
Want some working code from the demos? Head over here: https://github.com/cacois/ml-classification-examples
Colonial Law Group | Identification of common torts (intentional and unintentional). An Introduction to the Legal Aspects of Investing and Establishing a Business in Canada.
Currently, gas demand exceeds supply by 30 per cent. While the demand for natural gas in India is 118 million metric standard cubic meter per day (MMSCMD), the current supply from various sources is 80 MMSCMD, leaving a shortfall of 28 MMSCMD. That deficiency can be covered by CBM production.
ALTASYS Conseil accompagne les collaborateurs dans l’amélioration de leurs compétences et dans la mise en pratique des actions élaborées au cours du projet d’amélioration
Il est également possible de faire intervenir ALTASYS Conseil exclusivement sur des modules de formation intra-entreprise, sans pour autant entamer une démarche de conseil
GIS Ppt 5.pptx: SPACIAL DATA ANALSYSISISmulugeta48
GIS AND REMOTE SENSINGN
In many irrigation projects, crop yields are reduced due to water logging and salinization of the land.
In some cases, there is total loss of production and therefore the land is abandoned.
Water logging may also cause human health problems, particularly malaria, because of ponded water.
Two important causes of water logging and salinization are:
Part of the water that infiltrates into the soil will be stored in the soil pores and will be used by the crop; another part of the water will be lost as deep percolation.
When the percolating water reaches that part of the soil which is saturated with water, it will cause the water table to rise .
If the water table reaches the root zone, the plants may suffer.
The soil has become waterlogged.
Drainage is needed to remove the excess water and stop the rise of the water table.
IMAGE SUBSET SELECTION USING GABOR FILTERS AND NEURAL NETWORKSijma
An automatic method for the selection of subsets of images, both modern and historic, out of a set of
landmark large images collected from the Internet is presented in this paper. This selection depends on the
extraction of dominant features using Gabor filtering. Features are selected carefully from a preliminary
image set and fed into a neural network as a training data. The method collects a large set of raw landmark
images containing modern and historic landmark images and non-landmark images. The method then
processes these images to classify them as landmark and non-landmark images. The classification
performance highly depends on the number of candidate features of the landmark.
I MAGE S UBSET S ELECTION U SING G ABOR F ILTERS A ND N EURAL N ETWORKSijma
An automatic method for the selection of subsets of
images, both modern and historic, out of a set of
landmark large images collected from the Internet i
s presented in this paper. This selection depends o
n the
extraction of dominant features using Gabor filteri
ng. Features are selected carefully from a prelimin
ary
image set and fed into a neural network as a traini
ng data. The method collects a large set of raw lan
dmark
images containing modern and historic landmark imag
es and non-landmark images. The method then
processes these images to classify them as landmark
and non-landmark images. The classification
performance highly depends on the number of candida
te features of the landmark.
1. UNIVERSITY OF GHANA
DEPARTMENT OF GEOGRAPHY & RESOURCE
DEVELOPMENT
NAME : SELASE KWAMI
I.D.NUMBER : 10452218
COURSE : GEOG 344 GEOGRAPHIC INFORMATIONS SYSTEMS
LECTURERS : DR GERALD YIRAN AND BARIMA OWUSU
PHONE NUMBER : 0264562312
2. LAB REPORT IV
Questions?
1. Choose one school or any of the data you are using ‘Extract part’ of your work and save it as a
different shape file. Explain in your own words the meaning and functions of your choice?
2. Choose one ‘Overlay’itemandperformusingyour data. Explaininyour own words,the meaning
and functions of your choice.
3. Bufferaroundapoint,aline orapolygonat adistance of 1000m anddisplayyourresults.Compare
this buffer and the buffer distance chosen in activity 2 i.e. ‘Selection by Location’. What is the
difference ?
4. Choose one ‘Statistics’ item and perform with your data. Explain your results.
3. Answers
1. Stepson how to performclip
Open ArcMap
Connect to folder and load your files
Open Arc tool box and expand Analysis tools.
Expand the extract option and click on Clip
NB : Before selecting your input feature,you may need to do a projection if the original
projection of your point or line feature does not match that of your polygon.
Choose input feature of your choice. For example AMA roads project.
Choose output feature of your choice For example Accra metro.
Click OK and wait for Clip to take place.
Essence of the Clip function : This function clips a raster using a rectangular shape according to the
extents defined or will clip a raster to the shape of an input polygon feature class. The shape defining the
clip can clip the extent of the raster or clip out an area within the raster.
The inputs for this function are the following:
Input Raster
Type—either Outside or Inside
Clip Extents
The Clip Extents can be defined by a dataset. By default, it uses the envelope of the dataset; however, if
there is a polygon feature within the dataset it will clip to the shape of the polygon. Alternatively, you can
specify the x and y minimum and maximum coordinates. If you choose the Outside clip type, then the
imagery outside the extents will be removed. If you choose the Inside clip type, then the imagery within
the extents will be removed.
4. Image of clip function
2. Steps on how to perform spatial join
Open ArcMap
Connect to folder and load your files
Open Arc tool box and expand Analysis tools.
Expand the Overlay option and click on Spatial Join.
Select target features, join features and join operation.
Click ok and wait for spatial join to take place.
Essence of Spatial join : This function joins attributes from one feature to another are
based on the spatial relationship between target and join features. The target features and
the joined attributes from the join features are written to the output feature class. A spatial
join involves matching rows from the Join Features to the Target Features based on their
relative spatial locations.
5. Image of Spatial Join function
3. Steps on how to perform Buffer/proximity analysis
Open ArcMap
Connect to folder and load your files.
Open Arc tool box and expand Analysis tools.
Perform a clip.
Expand Proximity Options
Click Buffer
Input feature must be the same input feature you used in performing your
clip.
Enter linear unit (change to metres) and select distance of your choice.
Select the (ALL) option as your dissolve type.
7. 4. Summary Statistics
Open ArcMap
Connect to folder and load your files
Open Arc tool box and expand Analysis tools.
Expand Statistics option
Click Summary Statistics
Select input table i.e. Ghana_Districts
NB: Input table must have an attribute with integer value fields.
Select statistics field and statistic type and click OK.
Summary Statistics image
Afterperformingsummarystatistics,atable containingfieldnamesObject_ID,Frequencyand
SUM_Shape_Lengthwascreated.I obtainedmyresultsbyselectingShape_length asmyStatisticsfield
and Mean as my Statistics type. Iobtainedasingle value because meanisthe average of the shape
lengthortotal sumof lengthsdividedbythe numberof individual lengths.