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Automated flower species detection and recognition from digital images
العناوين الأخرى
الكشف و التمييز الآلي لأنواع الزهور من الصور الرقمية
مقدم أطروحة جامعية
مشرف أطروحة جامعية
أعضاء اللجنة
Muhanna, Muhanna
Kabnah, Khalid Abd al-Hafiz
al-Halabi, Yahya
الجامعة
جامعة الأميرة سمية للتكنولوجيا
الكلية
كلية الملك الحسين لعلوم الحوسبة
القسم الأكاديمي
قسم علم الحاسوب
دولة الجامعة
الأردن
الدرجة العلمية
ماجستير
تاريخ الدرجة العلمية
2016
الملخص الإنجليزي
Automated flower species recognition has been studied for many years.
Differences between these studies come from features which were extracted from the flower image, and the recognition algorithm that was used to recognize the flower species.
A new automated system was adapted to detect the flower region from the image and recognize its species.
By using a modified segmentation method, the features were extracted from the interest part only, so this increased the recognition accuracy.
The study aims at improving the flower recognition accuracy by extracting features from flowers’ leaf image.
Leaves are served as an important addition to the flower features, which is not been addressed by most of literatures.
Two datasets have been built, the first is Aalaa19 Dataset for flower only, and contains flowers from Jordan.
Including Iris Nigricans the national flower of Jordan, which is not addressed even in the famous Oxford17 dataset.
The second is FlowerAndLeaf21 Dataset for flowers with their leaves to study their effects on recognition accuracy.
Region growing segmentation was applied to extract flower and leaf region from their images.
Features based on color, texture, and shape have been extracted and used to train the Stochastic Gradient Descent (SGD) classifier with one versus all approach.
The results showed that the recognition accuracy for Aalaa19 Dataset is 92%.
The result of the second dataset FlowerAndLeaf21 showed that considering leaf in recognition increased the recognition accuracy from 71.1% to 77.31% with SGD classifier, while using random forest classifier increased the recognition accuracy from 83.5% to 85.5%.
Our proposed system outperforms several methods on Oxfoed17 Dataset; a class average recognition accuracy of 83.52% was achieved.
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
عدد الصفحات
84
قائمة المحتويات
Table of contents.
Abstract.
Abstract in Arabic.
Chapter One : Introduction.
Chapter Two : Literature review.
Chapter Three : Background.
Chapter Four : Proposed system overview.
Chapter Five : Experiments.
Chapter Six : Results and discussion.
Chapter Seven : Conclusion and future work.
References.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
al-Badarinah, Ala Abd Allah. (2016). Automated flower species detection and recognition from digital images. (Master's theses Theses and Dissertations Master). Princess Sumaya University for Technology, Jordan
https://search.emarefa.net/detail/BIM-693652
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
al-Badarinah, Ala Abd Allah. Automated flower species detection and recognition from digital images. (Master's theses Theses and Dissertations Master). Princess Sumaya University for Technology. (2016).
https://search.emarefa.net/detail/BIM-693652
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
al-Badarinah, Ala Abd Allah. (2016). Automated flower species detection and recognition from digital images. (Master's theses Theses and Dissertations Master). Princess Sumaya University for Technology, Jordan
https://search.emarefa.net/detail/BIM-693652
لغة النص
الإنجليزية
نوع البيانات
رسائل جامعية
رقم السجل
BIM-693652
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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