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Article
Image Compression Using Improved Ridgelet Transform

Author: Mohammed Hussien Miry
Journal: IRAQI JOURNAL OF COMPUTERS,COMMUNICATION AND CONTROL & SYSTEMS ENGINEERING المجلة العراقية لهندسة الحاسبات والاتصالات والسيطرة والنظم ISSN: 18119212 Year: 2008 Volume: 8 Issue: 1 Pages: 58-66
Publisher: University of Technology الجامعة التكنولوجية

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Abstract

Abstract:The paper describe approach to the image compression using new hybrid Transforms ,namely, the improvement ridgelet transform that has proven to show promising results over ridgelet transform. The hybrid transform based of replacing the wavelet transform with the slantlet transform, the slantlet transform is a discrete wavelet transform with two zero moments and with improved time localization. A comparison was made with compression using ridgelet transform for different images. A high quality image compression has been achieved for natural images. Computer simulation results indicate that the improvement ridgelet transform offers superior and faster compression performance compared to the ridgelet transform based approaches.

الخلاصة :في هذا البحث طريقة تقدم لضغط الصور وذلك باستخدام تحويل مركب جديد يدعى improvement ridgelet transform لتقديم نتائج واعدة افضل . هذا التحويل يعتمد على استبدال تحويل المويجة بتحويل المويل . تحويل المويل هو عبارة عن تحويل المويجة مع العزميين الصفريين وتحسين للزمن المكاني . تمت المقارنة بين تحويل المركب وتحويل المركب المحسن باستخدام صور مختلفة .استرجاع عالي للصور تمثلت للصور الطبيعية . نتائج المحاكاة بالحاسبة اظهرت ان هذا التحويل اعطى نتائج افضل واسرع من تحويل المركب بعد تطبيقهما على ضغط الصور .


Article
Edge Detection Based on Standard Deviation Value and Back Propagation Algorithm of Artificial Neural Network

Authors: Ammar Sabr Majed --- Mohammed Hussien Miry --- Ali Hussien Miry
Journal: Engineering and Technology Journal مجلة الهندسة والتكنولوجيا ISSN: 16816900 24120758 Year: 2011 Volume: 29 Issue: 3 Pages: 462-469
Publisher: University of Technology الجامعة التكنولوجية

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Abstract

This paper presents a proposed neural network based edge detectionalgorithm. we have used artificial neural network system to decide about whethereach pixel is edge or not. First standard deviation values are computed for mask(3*3), Then after training a neural network system to recognize structural patterns(these pattern represents edges), it decides on each pixel if its edge or not. Finallywe have test the proposed method on different images. Experimental results showthe ability and high performance of proposed algorithm.

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