عنوان البحث(Papers / Research Title)
New Method for Image Features Extracting Based on Enhanced Chain Code
الناشر \ المحرر \ الكاتب (Author / Editor / Publisher)
اسراء هادي علي الشمري
Citation Information
اسراء,هادي,علي,الشمري ,New Method for Image Features Extracting Based on Enhanced Chain Code , Time 07/07/2014 07:42:39 : كلية تكنولوجيا المعلومات
وصف الابستركت (Abstract)
New Method for Image Features Extracting Based on Enhanced Chain Code
الوصف الكامل (Full Abstract)
New Method for Image Features Extracting
Based on Enhanced Chain Code
Israa Hadi Ali
University of Babylon- College of Information Technology, Iraq.
(Received: December 10, 2013; Accepted: December 15, 2013)
Abstract:-
In this paper, we have proposed image features based on chain code approach and RLE compression method for enhancing the image and proposed new method for extracting the features. The proposed method consist of the many steps, First is to apply the Chain code algorithm by using new chain code window, Second is to apply the Chain code Enhancement, in this step the chain code is first extracted and then enhanced by applying the RLE compression on the result, the purpose for this operation is to approximate the image by eliminate the small part of image and added it to the largest nearest part and the third is is to extract of chain code features by proposing a new algorithm.
Key words: Chin Code, Image features, RLE Compression.
Introduction:-
Image features are useful extractable attributes of images. Examples of image features are the histogram and the symmetry of a region of interest. Histograms are considered a low-level feature, while geometric descriptors as symmetry or orientation of regions of interest provide examples of high-level features.Some features, such as histograms and texture features, are statistical in nature, while others, such as the Euler number or boundary descriptors of regions, fall in the geometric domain. Geometric features are key in providing structural descriptors of images. Structural descriptions of images are of significant interest.
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