Emerald Associates

I have been working for a large company and involved in the execution of large scale projects. As the large scale projects demands heavy work and assurance to finish the work before the deadline. The large scale project involves a lot of department from design, execution, marketing, finance , administration and human resources. The amount of data transferred between every department will be critical and to the successful completion of this project it requires a proper coordination among all the departments. Hence there is a need for the project management software. Primavera systems inc.provides project and program management software for construction, architecture and engineering industry. It is a project portfolio management makes the user to understand the complete status of project and to take a wise decision about the project. It helps to prioritize the users to take necessary decision about the type of investments and required inventory to complete the project.

The primavera software package includes inspire, evolve, suretalk, pertmaster, costmanager, contract Manager, Prosight and P6. The database for primavera should be supported by oracle 9i or 10g or Microsoft sql server 2000 and 2005. To get trained on this software we would request to visit Emerald Associates. Certified Primavera Training offers complete training solution to the people who are interested to get trained on primavera. The trainers are well trained to meet the customer expectations and have the ability to clarify all the doubts of students.

Presently the training has been offered at USA and Canada. To know the schedule of Certified Primavera Training in Canada, the people are requested to visit the website. Apart from training they do consulting to the clients. The details about latest development, upcoming training and webinars can be obtained from the website. Definitely it will be useful to everyone interested to take training on primavera.

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Content Based Image Retrieval Techniques

CBIR techniques

In contrast to the text based approach of the systems, CBIR operates on a totally different principal, retrieving stored images from a collection by comparing features automatically extracted from the images themselves. The commonest features used are mathematical measures of color, texture or shape. A typical system allows users to formulate queries by submitting an example of the type of image being sought, though some offer alternatives such as selection from a palette or sketch input. The system then identifies those stored image whose feature values match those of the query most closely, and displace thumbnails of these images on the screen.


Color Retrieval

Several methods for retrieving images on the basis of color similarity have been described in the literature, but most are variations on the same basic idea. Each image added to the collection is analyzed to compute a color histogram, which shows the proportion of pixels of each color with in the image. The color histogram for each image is then stored in the database.

Texture Retrieval

The ability to retrieve images on the basis of texture similarity may not see very useful. But the ability to match on texture similarity can often be useful in distinguishing between areas of images with similar color. The best established rely on comparing values of what are known as second order statistics calculated from query and stores images.

Shape Retrieval

The ability to retrieve by shape is perhaps the most obvious requirement at the primitive level. Unlike texture, shape is a fairly well defined concept and there is considerable evidence that natural object are primarily recognized by the shape. A number of features characteristics of object shape are computed for every object identified with in each stored image. Queries are then answered by computing the same set of features for the query image, and retrieving those stored images whose features most closely match those of the query.

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Element description of EDI segments

ISA - Interchange Contro1 Header

ISA01 - Authorization Information Qualifier
ISA02 - Authorization Information
ISA03 - Security Information Qualifier
ISA04 - Security Information
ISA05 - Interchange ID Qualifier
ISA06 - Interchange Sender ID
ISA07 - Interchange ID Qualifier
ISA08 - Interchange Receiver ID
ISA09 - Interchange Date
ISA10 - Interchange Time
ISA11 - Interchange Control Standards ID
ISA12 - Interchange Control Version number
ISA13 - Interchange Control Number
ISA14 - Acknowledgement Requested
ISA15 - Test Indicator
ISA16 - Sub element separator

GS – Functional Group Header

GS01 - Functional ID code
GS02 - Application sender Id
GS03 - Application receiver Id
GS04 - Date
GS05 - Time
GS06 - Group Control number
GS07 - Responsible agency code
GS08 - Version/Rel Ind.ID code

ST – Functional Group Header

ST01 - Transaction Set ID Code
ST02 - Transaction set Control number

SE – Functional Group Trailer

SE01 - Number of included Segments
SE02 - Transaction Set Control Number

GE – Functional Group Trailer

GE01 - Number of Included Segments
GE02 - Group Control Number

IEA - Interchange Contro1 Trailer

IEA01 - Number of Functional Groups
IEA02 - Interchange Control Number

Content based Image retrieval method

Image Retrieval has become a very active Research area. The research communities study image retrieval from different angles, which are text-based and content-based respectively. Text-based image retrieval is now used by almost all the image retrieval services. A traditional framework for text-based image retrieval is to first annotate each image by text (keyword) and then use text-based database management system to perform image retrieval. Unlike textual work that can perform almost accurate catalogue (e.g. titles, abstract of contents, etc), image catalogues are more Complex and subjective. Different people perceive images in different ways, hence annotation of images are far from accurate and standard, which can cause unrecoverable failures in image retrieval.

To overcome this problem to some extent, the Content Based Image Retrieval (CBIR) was proposed. Rather than manually annotating each image, images would be indexed based on their own visual contents. The essence of Content-based Image Retrieval is the Feature Extraction of images. Depending on the abstractions of query type; feature extraction can be divided into three levels:

Level 1: Primitive Features, such as color, shape, etc.
Level 2: Logical features, such as the identity of objects.
Level 3: Abstract Attributes, such as meaning or purpose of Object.
Here, the Geometrical shape of the object as the main searching criteria.Added to that, consider the color features to retrieve appropriate and relevant images. We has restricted the objects to a fix number of Geometrical shape. The process begins with converting the query image into a grey scale image then thresholding is performed. We adopted distance threshold method in order to segment the object from the Background. Then the edges of the object are been found by using sobel edge detection method. Thus the boundary of the object has been obtained by the edge detection method. Then the key points of the object are calculated by considering the difference in slope of the curves and lines of the object.

The key point thus detected act as a main criteria for detecting the shape. Thus the shape feature is been extracted and this Feature is been compared with the feature stored in the data base. The Numbers of key points act as a deciding factors for shape estimation. The matched images in the database are displayed in Priority. The most matched image will be displayed below. Thus the image which fits the Content Search of the given query Image were retrieved from the database and displayed after ranking.

Terms of Digital Image processing

Segmentation

Segmentation is a process of sub dividing an image into its constituent regions or objects. Image segmentation algorithms are based on two basic properties of intensity values, Discontinuity and similarity. In the first category, the approach is to partition an Image based on intensity, such as edges in an image. The second categories are based on portioning an image into regions that are similar according to a set of predefined criteria. This involves thresholding, region-growing and region splitting and merging.

Edge Detection
Point and Line detection certainly are important on segmentation. Edge detection is by far the most common approach for detecting meaningful discontinuities in gray level. An edge is a set of connected pixel that lies on the boundary between two regions. In practice optics, sampling and other image acquisition imperfections yield edges that are blurred, with the degree of blurring being determined by factors such as the quality of the image acquisition system, the sampling rate and illumination conditions under which the image is acquired.

Key-point Detection
Key point is the point where the change in the slope curve is different from its surroundings. Key point act as major shape feature in determining the shape of the object. After the number of Key points in an object is been detected, it will be optimized in order to eliminate the false key points considered during evaluation.