Weekly Report 3

We have worked on the configuration management plan report in this week. In addition, I have researched about the image processing part. The k-means clustering algorithm is commonly used in computer vision as a form of image segmentation. The results of the segmentation are used to aid border detection and object recognition. We can use this algorithm to detect object for feature extraction. However, the standard Euclidean distance is insufficient in forming the clusters in our case due to space dimension histogram. Therefore, Mahalanobis distance can be used instead of Euclidean distance to form clusters better in large histogram space dimensions. ISODATA algorithm is a good modified version of k-mean clustering algorithm. It uses both an iterative version of native k-mean clustering algorithm to find the best number of clusters (K) and Mahalanobis distance.

Reference:

Fatos T. Yarman-Vural, Ergin Ataman: Noise, histogram and cluster validity for Gaussian-mixtured data. Pattern Recognition 20(4): 385-401 (1987)

Configuration Management Report - Weekly Report 3

This week we were dealing with the configuration management plan report. When we build computer software, change happens. And because it happens, you need to control it effectively. So, with configuration management we tried to provide a set of activities that are designed to control change by identifying the work products that are likely to change, establishing relationships among them, defining mechanisms for managing different versions of these work products, controlling changes that are imposed, and auditing and reporting on the changes that are made.

Weekly Report 3

This week, all members of our group worked on the Configuration Management Plan Report.
The purpose of this Configuration Management (CM) Plan is to provide an overview of the organization, activities, overall tasks, and objectives of Configuration Management for our application. It addresses configuration item (CI) identification, change control and configuration audits at a high level; additional details regarding CM activities, techniques, and tools should be provided in the CM-related procedures.

We specified the tools we are using, and explained the workload further.

Weekly Report 2

Last week, I worked on the problem of sending large image files to the client from Android part. We haven't come up with a solution yet, but still working on it.

GoogleMaps Api problem and alternatives

There is a problem with the authorization system of Google Maps API. It requires a domain name to activate the product. In the web site it is said that

"For most applications we recommend that you simply register your domain name. Your key will be valid for that domain, its subdomains, all URLs on hosts in those domains, and all ports on those hosts.

For example, if you sign up for a key using http://www.mygooglemapssite.com/, your key is valid for:

• http://www.mygooglemapssite.com/
• http://www.mygooglemapssite.com/mysite/

However this key will not be valid for:

• http://mygooglemapssite.com/
• http://host1.mygooglemapssite.com/
• http://host2.mygooglemapssite.com/mysite

If as recommended you request a key for http://mygooglemapssite.com/ your key will be valid for all of the URLs listed above.

Note that a key for http://www.mygooglemapssite.com/ will only be accepted when the site is accessed using this address. It will not be accepted if the site is accessed by IP address (eg. http://10.1.2.3/) or by a hostname that is aliased to www.mygooglemapssite.com using a DNS CNAME record"

Therefore, we need to search other alternatives to Google map or find a solution to that authorization problem. JXMapViewer can be an alternative to Google Maps API. The JXMapViewer is an open source (LGPL) Swing component created by the developers at SwingLab. It is used for displaying maps on swing panels. On the other hand, Google Earth application already uses Google Maps API as a native PC program, so there should be a way to use it without a domain. This fact encourages us to search more about this problem.

First Demo Contents

This demo consists of extracting the most similar image to a reference image from between a set of subject images.

Entering the image names as command line input:

Most similar container image to the given image of an object - Weekly Report 2

Last week, I started the image processing part of the project and used SURF method which is implemented in OpenCV library (2.0). I prepared the proper environment to develop the sample code which was presented with OpenCV version 2.0.

What Does This Program Actually Do?

This week I had the opportunity of examining and improving that sample. The code begins with specifying parameters for extracting Speeded Up Robust Features using the line