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image processing _Edge Detection Luc01.pdf

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ِِِAhmed R. A. Shamsan

The document discusses edge detection in image processing, defining edges as significant local changes in intensity, which can result from geometric and non-geometric events. It outlines the causes of intensity changes, such as depth, surface orientation, and illumination variations, and emphasizes the importance of detecting edges to simplify image interpretation and feature extraction. The ultimate goal is to produce a clear representation of scenes while noting that complex images may result in fragmented or erroneous edge data.

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Related topics:
Edge Detection Methods • Computer Vision Insights • Digital Image Processing
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1 Edge Detection lecture 01 BY AHMED R. A. SHAMSAN MOHAMMED ALMOHAMADI AHMED R. A. SHAMSAN & M. MOHAMADI EDGE DETECTION | LUC01
2 Definition of Edges  Edges are significant local changes of intensity in an image.  Or Edges represent boundaries or abrupt changes in intensity between adjacent pixels. 2 ‫الحواف‬ ‫تعريف‬ ‫الصورة‬ ‫في‬ ‫الشدة‬ ‫في‬ ‫مهمة‬ ‫محلية‬ ‫تغييرات‬ ‫هي‬ ‫الحواف‬ . ‫ا‬ ‫البكسﻼت‬ ‫بين‬ ‫الشدة‬ ‫في‬ ‫مفاجئة‬ ‫تغييرات‬ ‫أو‬ ‫ًا‬‫د‬‫حدو‬ ‫الحواف‬ ‫تمثل‬ ‫حيث‬ ‫لمجاورة‬ . AHMED R. A. SHAMSAN & M. MOHAMADI EDGE DETECTION | LUC01
3 What Causes Intensity Changes  Geometric events  surface orientation (boundary) discontinuities  depth discontinuities  color and texture discontinuities  Non-geometric events  illumination changes  specularities  shadows  inter-reflections DEPTH DISCONTINUITY COLOR DISCONTINUITY ILLUMINATION DISCONTINUITY SURFACE NORMAL DISCONTINUITY 3 ‫ما‬ ‫الذي‬ ‫يسبب‬ ‫تغيرات‬ ‫الشدة‬ ‫للصور؟‬ • ‫اﻷحداث‬ ‫الهندسية‬ • ‫التوجهات‬ ‫السطحية‬ ) ‫الحدود‬ ( • ‫انقطاعات‬ ‫العمق‬ • ‫انقطاع‬ ‫اللون‬ ‫والملمس‬ • ‫اﻷحداث‬ ‫غير‬ ‫الهندسية‬ • ‫تغييرات‬ ‫اﻹضاءة‬ • ‫المضاربات‬ • ‫ظﻼل‬ • ‫التأمﻼت‬ ‫المتبادلة‬ AHMED R. A. SHAMSAN & M. MOHAMADI EDGE DETECTION | LUC01
Geometric events  In image processing, surface orientation discontinuities, also known as edges, are significant geometric events that cause intensity changes in an image. These edges are curves in a digital image where the image brightness changes sharply or has discontinuities1.  Edges in an image can be caused by several factors:  Discontinuities in depth: This refers to the sudden change in distance between the camera (or viewer) and the objects in the scene.  Discontinuities in surface orientation: This refers to the changes in the angle at which a surface is oriented concerning the camera. For example, the corners of a cube cause discontinuities in surface orientation3.  Changes in material properties: This refers to the changes in the physical properties of the surface material, such as color, texture, and reflectance.  Variations in scene illumination: This refers to changes in lighting conditions, such as shadows and highlights.  The purpose of detecting these sharp changes in image brightness is to capture important events and changes in properties of the world. If the edge detection step is successful, the subsequent task of interpreting the information contents in the original image may therefore be substantially simplified1. However, it is not always possible to obtain such ideal edges from real-life images of moderate complexity 4 ‫في‬ ‫معالجة‬ ،‫الصور‬ ‫يعد‬ ‫انقطاع‬ ‫اتجاه‬ ،‫السطح‬ ‫المعروف‬ ‫ا‬ً‫ض‬‫أي‬ ‫باسﻢ‬ ،‫الحواف‬ ‫ا‬ً‫ث‬‫أحدا‬ ‫هندسية‬ ‫مهمة‬ ‫ت‬ ‫سبب‬ ‫تغيرات‬ ‫في‬ ‫الشدة‬ ‫في‬ ‫الصورة‬ . ‫هذه‬ ‫الحواف‬ ‫عبارة‬ ‫عن‬ ‫منحنيات‬ ‫في‬ ‫صورة‬ ‫رقمية‬ ‫حيث‬ ‫يتغير‬ ‫سطو‬ ‫ع‬ ‫الصورة‬ ‫بشكل‬ ‫حاد‬ ‫أو‬ ‫يتوقف‬ ‫عن‬ ‫العمل‬ . ‫يمكن‬ ‫أن‬ ‫تكون‬ ‫الحواف‬ ‫في‬ ‫الصورة‬ ‫ناتجة‬ ‫عن‬ ‫عدة‬ ‫عوامل‬ : • ‫اﻻنقطاعات‬ ‫ﻓﻲ‬ ‫العمق‬ : ‫يشير‬ ‫هذا‬ ‫إلى‬ ‫التغيير‬ ‫المفاجئ‬ ‫في‬ ‫المسافة‬ ‫بين‬ ‫الكام‬ ‫يرا‬ ) ‫أو‬ ‫المشاهد‬ ( ‫واﻷشياء‬ ‫الموجودة‬ ‫في‬ ‫المشهد‬ . • ‫اﻻنقطاعات‬ ‫ﻓﻲ‬ ‫اتجاه‬ ‫السطح‬ : ‫يشير‬ ‫هذا‬ ‫إلى‬ ‫التغيرات‬ ‫في‬ ‫الزاوية‬ ‫التي‬ ‫يتﻢ‬ ‫ف‬ ‫يها‬ ‫توجيه‬ ‫السطح‬ ‫فيما‬ ‫يتعلق‬ ‫بالكاميرا‬ . ‫على‬ ‫سبيل‬ ،‫المثال‬ ‫تسبب‬ ‫زوايا‬ ‫المكعب‬ ‫انقط‬ ‫اعات‬ ‫في‬ ‫التوجه‬ ‫السطحي‬ . • ‫التغيرات‬ ‫ﻓﻲ‬ ‫ﺧﺼاﺋﺺ‬ ‫المواد‬ : ‫يشير‬ ‫هذا‬ ‫إلى‬ ‫التغيرات‬ ‫في‬ ‫الخصائص‬ ‫الفيز‬ ‫يائية‬ ‫للمادة‬ ،‫السطحية‬ ‫مثل‬ ‫اللون‬ ‫والملمس‬ ‫واﻻنعكاس‬ . • ‫اﻻﺧتﻼﻓات‬ ‫ﻓﻲ‬ ‫إضاءة‬ ‫المشهد‬ : ‫يشير‬ ‫هذا‬ ‫إلى‬ ‫التغيرات‬ ‫في‬ ‫ظروف‬ ،‫اﻹضاءة‬ ‫مثل‬ ‫الظﻼل‬ ‫واﻹضاءة‬ . ‫الغرض‬ ‫من‬ ‫اكتشاف‬ ‫هذه‬ ‫التغييرات‬ ‫الحادة‬ ‫في‬ ‫سطوع‬ ‫الصورة‬ ‫هو‬ ‫التقاط‬ ‫اﻷحداث‬ ‫والتغيرات‬ ‫المهمة‬ ‫في‬ ‫ﺧصائص‬ ‫العالﻢ‬ . ‫إذا‬ ‫نجحﺖ‬ ‫ﺧطوة‬ ‫كشف‬ ،‫الحافة‬ ‫فإن‬ ‫المهمة‬ ‫الﻼ‬ ‫حقة‬ ‫لتفسير‬ ‫محتويات‬ ‫المعلومات‬ ‫في‬ ‫الصورة‬ ‫اﻷصلية‬ ‫قد‬ ‫تكون‬ ‫مبسطة‬ ‫إلى‬ ‫حد‬ ‫كبي‬ ‫ر‬ . ‫ومع‬ ،‫ذلك‬ ‫ليس‬ ‫من‬ ‫الممكن‬ ‫ا‬ً‫م‬‫دائ‬ ‫الحصول‬ ‫على‬ ‫مثل‬ ‫هذه‬ ‫الحواف‬ ‫المثالية‬ ‫من‬ ‫صور‬ ‫الحياة‬ ‫الواقعية‬ ‫ذات‬ ‫التعقيد‬ ‫المعتدل‬ 4 AHMED R. A. SHAMSAN & M. MOHAMADI EDGE DETECTION | LUC01
Non-geometric events  Non-geometric events: These are visual occurrences in an image that aren’t related to the shape or structure of objects within the scene. Examples include changes in lighting, reflections, and shadows which can alter the appearance of objects without changing their inherent structure.  Illumination changes: This refers to variations in lighting conditions within a scene. It can be caused by natural light sources (like the sun moving across the sky) or artificial ones (like a light being turned on/off), affecting how objects and surroundings are viewed.  Specularities: These are bright spots or streaks appearing on surfaces due to the reflection of light. They depend on the angle of incidence of light and the viewer’s perspective, often making object identification challenging.  Shadows: These are dark areas where direct light is obstructed by an object. Shadows can provide cues about an object’s shape and lighting direction but can also complicate image interpretation.  Inter-reflections: These occur when light reflects between surfaces within a scene before reaching the camera. They can introduce additional lighting variations, complicating understanding of material properties and shapes.  These factors can significantly affect the appearance of an image and are important considerations in image processing and computer vision. They can both provide valuable information and introduce challenges in interpreting the image content. 5 • ‫اﻷحداث‬ ‫غير‬ ‫الهندسية‬ ‫هذه‬ ‫أحداث‬ ‫بصرية‬ ‫في‬ ‫صورة‬ ‫ﻻ‬ ‫تتعلق‬ ‫بشكل‬ ‫أو‬ ‫بنية‬ ‫اﻷشياء‬ ‫داﺧل‬ ‫المشهد‬ . ‫تشمل‬ ‫اﻷمثلة‬ ‫التغيي‬ ‫رات‬ ‫في‬ ‫اﻹضاءة‬ ‫واﻻنعكاسات‬ ‫والظﻼل‬ ‫التي‬ ‫يمكن‬ ‫أن‬ ‫تغير‬ ‫مظهر‬ ‫اﻷشياء‬ ‫دون‬ ‫تغيير‬ ‫بنيتها‬ ‫المتأصلة‬ . .1 ‫تغييرات‬ ‫اﻹضاءة‬ : ‫يشير‬ ‫هذا‬ ‫إلى‬ ‫اﻻﺧتﻼفات‬ ‫في‬ ‫ظروف‬ ‫اﻹضاءة‬ ‫داﺧل‬ ‫المشهد‬ . ‫يمكن‬ ‫أن‬ ‫يكون‬ ‫ا‬ً‫ج‬‫نات‬ ‫عن‬ ‫مصادر‬ ‫الضوء‬ ‫الطبيعي‬ ) ‫مثل‬ ‫الشمس‬ ‫التي‬ ‫تتحرك‬ ‫عبر‬ ‫السماء‬ ( ‫أو‬ ‫تلك‬ ‫اﻻصطناعية‬ ) ‫مثل‬ ‫الضوء‬ ‫الذي‬ ‫يتﻢ‬ ‫تشغيله‬ / ‫إيقاف‬ ‫تشغيله‬ ( ، ‫مما‬ ‫يؤثر‬ ‫على‬ ‫كيفية‬ ‫رؤية‬ ‫اﻷشياء‬ ‫والمناطق‬ ‫المحيطة‬ ‫بها‬ . .2 ‫المضاربات‬ : ‫هذه‬ ‫نقاط‬ ‫مضيئة‬ ‫أو‬ ‫ﺧطوط‬ ‫تظهر‬ ‫على‬ ‫اﻷسطح‬ ‫بسبب‬ ‫انعكاس‬ ‫الض‬ ‫وء‬ . ‫فهي‬ ‫تعتمد‬ ‫على‬ ‫زاوية‬ ‫حدوث‬ ‫الضوء‬ ‫ومنظور‬ ،‫المشاهد‬ ‫مما‬ ‫يجعل‬ ‫تحديد‬ ‫الكائن‬ ‫أ‬ ‫ا‬ً‫مر‬ ‫ا‬ً‫ب‬‫صع‬ ‫في‬ ‫كثير‬ ‫من‬ ‫اﻷحيان‬ . .3 ‫الظﻼل‬ : ‫هذه‬ ‫مناطق‬ ‫مظلمة‬ ‫حيث‬ ‫يتﻢ‬ ‫عرقلة‬ ‫الضوء‬ ‫المباشر‬ ‫بواسطة‬ ‫جسﻢ‬ ‫ما‬ . ‫يمكن‬ ‫أن‬ ‫توفر‬ ‫الظﻼل‬ ‫إشارات‬ ‫حول‬ ‫شكل‬ ‫الكائن‬ ‫واتجاه‬ ‫اﻹضاءة‬ ‫ولكنها‬ ‫يمكن‬ ‫أن‬ ‫تعقد‬ ‫ا‬ً‫ض‬‫أي‬ ‫تفسير‬ ‫الصورة‬ . .4 ‫اﻻنعكاسات‬ ‫البينية‬ : ‫تحدث‬ ‫هذه‬ ‫عندما‬ ‫ينعكس‬ ‫الضوء‬ ‫بين‬ ‫اﻷسطح‬ ‫داﺧل‬ ‫المشه‬ ‫د‬ ‫قبل‬ ‫الوصول‬ ‫إلى‬ ‫الكاميرا‬ . ‫يمكنهﻢ‬ ‫إدﺧال‬ ‫اﺧتﻼفات‬ ‫إضافية‬ ‫في‬ ،‫اﻹضاءة‬ ‫مما‬ ‫يعقد‬ ‫ف‬ ‫هﻢ‬ ‫ﺧصائص‬ ‫وأشكال‬ ‫المواد‬ . ‫يمكن‬ ‫أن‬ ‫تؤثر‬ ‫هذه‬ ‫العوامل‬ ‫بشكل‬ ‫كبير‬ ‫على‬ ‫مظهر‬ ‫الصورة‬ ‫وهي‬ ‫اعتبارات‬ ‫مهمة‬ ‫في‬ ‫معالجة‬ ‫الصور‬ ‫ورؤ‬ ‫ية‬ ‫الكمبيوتر‬ . ‫يمكن‬ ‫أن‬ ‫توفر‬ ‫معلومات‬ ‫قيمة‬ ‫وتقدم‬ ‫تحديات‬ ‫في‬ ‫تفسير‬ ‫محتوى‬ ‫الصورة‬ . 5 AHMED R. A. SHAMSAN & M. MOHAMADI EDGE DETECTION | LUC01
6 Goal of Edge Detection  Produce a line “drawing” of a scene from an image of that scene. 6 AHMED R. A. SHAMSAN & M. MOHAMADI EDGE DETECTION | LUC01
The goal of edge detection  The goal of edge detection in image processing is to identify and locate sharp discontinuities in an image, known as edges. Here are the key objectives12345:  Capture important events: Edges can capture significant events and changes in properties of the world. Discontinuities in image brightness are likely to correspond to discontinuities in depth, surface orientation, changes in material properties, and variations in scene illumination1.  Reduce the amount of data: Edge detection can significantly reduce the amount of data to be processed by preserving the important structural properties of an image1.  Simplify image content interpretation: If the edge detection step is successful, the subsequent task of interpreting the information contents in the original image may be substantially simplified1.  Feature extraction: Important features can be extracted from the edges of an image (e.g., corners, lines, curves). These features are used by higher-level computer vision algorithms (e.g., recognition)2.  Image segmentation and data extraction: Edge detection is used for image segmentation and data extraction in areas such as image processing, computer vision, and machine vision5.  However, it’s worth noting that edges extracted from complex images are often hampered by fragmentation, missing edge segments, and false edges not corresponding to interesting phenomena in the image1. 7 ‫ما‬ ‫الفائدة‬ ‫من‬ ‫تحديد‬ ‫الحواف‬ ‫؟‬ ‫الهدف‬ ‫من‬ ‫اكتشاف‬ ‫الحافة‬ ‫في‬ ‫معالجة‬ ‫الصور‬ ‫هو‬ ‫تحديد‬ ‫وتحديد‬ ‫اﻻنقطاعات‬ ‫الحادة‬ ‫في‬ ‫الصور‬ ،‫ة‬ ‫والمعروفة‬ ‫باسﻢ‬ ‫الحواف‬ . ‫فيما‬ ‫يلي‬ ‫اﻷهداف‬ ‫الرئيسية‬ : .1 ‫التقاط‬ ‫اﻷحداث‬ ‫المهمة‬ : ‫يمكن‬ ‫للحواف‬ ‫التقاط‬ ‫اﻷحداث‬ ‫والتغيرات‬ ‫المهمة‬ ‫في‬ ‫ﺧصائص‬ ‫الع‬ ‫الﻢ‬ . ‫من‬ ‫المرجح‬ ‫أن‬ ‫تتوافق‬ ‫اﻻنقطاعات‬ ‫في‬ ‫سطوع‬ ‫الصورة‬ ‫مع‬ ‫اﻻنقطاعات‬ ‫في‬ ،‫العمق‬ ‫واتجاه‬ ،‫السطح‬ ‫والتغيرات‬ ‫في‬ ‫ﺧصائص‬ ،‫المواد‬ ‫واﻻﺧتﻼفات‬ ‫في‬ ‫إضاءة‬ ‫المشهد‬ . .2 ‫تقليل‬ ‫كمية‬ ‫البيانات‬ : ‫يمكن‬ ‫أن‬ ‫يقلل‬ ‫الكشف‬ ‫عن‬ ‫الحافة‬ ‫بشكل‬ ‫كبير‬ ‫من‬ ‫كمية‬ ‫البيا‬ ‫نات‬ ‫التي‬ ‫يجب‬ ‫معالجتها‬ ‫عن‬ ‫طريق‬ ‫الحفاظ‬ ‫على‬ ‫الخصائص‬ ‫الهيكلية‬ ‫المهمة‬ ‫للصورة‬ . .3 ‫تبسيط‬ ‫تفسير‬ ‫محتوى‬ ‫الﺼورة‬ : ‫إذا‬ ‫نجحﺖ‬ ‫ﺧطوة‬ ‫الكشف‬ ‫عن‬ ،‫الحافة‬ ‫فإن‬ ‫المهمة‬ ‫الﻼحق‬ ‫ة‬ ‫لتفسير‬ ‫محتويات‬ ‫المعلومات‬ ‫في‬ ‫الصورة‬ ‫اﻷصلية‬ ‫قد‬ ‫تكون‬ ‫مبسطة‬ ‫إلى‬ ‫حد‬ ‫كبير‬ . .4 ‫استخراج‬ ‫الميزات‬ : ‫يمكن‬ ‫استخراج‬ ‫الميزات‬ ‫المهمة‬ ‫من‬ ‫حواف‬ ‫الصورة‬ ) ‫على‬ ‫سبيل‬ ،‫المثال‬ ‫الزوايا‬ ‫والخطوط‬ ‫والمنحنيات‬ ( . ‫يتﻢ‬ ‫استخدام‬ ‫هذه‬ ‫الميزات‬ ‫من‬ ‫قبل‬ ‫ﺧوارزميات‬ ‫الرؤية‬ ‫الحا‬ ‫سوبية‬ ‫عالية‬ ‫المستوى‬ ) ‫على‬ ‫سبيل‬ ،‫المثال‬ ‫التعرف‬ ( . .5 ‫تجزﺋة‬ ‫الﺼور‬ ‫واستخراج‬ ‫البيانات‬ : ‫يتﻢ‬ ‫استخدام‬ ‫الكشف‬ ‫عن‬ ‫الحافة‬ ‫لتجزئة‬ ‫الصورة‬ ‫و‬ ‫استخراج‬ ‫البيانات‬ ‫في‬ ‫مجاﻻت‬ ‫مثل‬ ‫معالجة‬ ‫الصور‬ ‫ورؤية‬ ‫الكمبيوتر‬ ‫ورؤية‬ ‫اﻵلة‬ . ‫ومع‬ ،‫ذلك‬ ‫تجدر‬ ‫اﻹشارة‬ ‫إلى‬ ‫أن‬ ‫الحواف‬ ‫المستخرجة‬ ‫من‬ ‫الصور‬ ‫المعقدة‬ ‫ا‬ً‫ب‬‫غال‬ ‫ما‬ ‫يتﻢ‬ ‫إعاقتها‬ ‫ب‬ ‫سبب‬ ،‫التجزئة‬ ‫وفقدان‬ ‫أجزاء‬ ،‫الحافة‬ ‫والحواف‬ ‫الزائفة‬ ‫التي‬ ‫ﻻ‬ ‫تتوافق‬ ‫مع‬ ‫الظواهر‬ ‫المثيرة‬ ‫لﻼه‬ ‫تمام‬ ‫في‬ ‫الصورة‬ . 7 AHMED R. A. SHAMSAN & M. MOHAMADI EDGE DETECTION | LUC01
8 Why is Edge Detection Useful? 8 AHMED R. A. SHAMSAN & M. MOHAMADI EDGE DETECTION | LUC01
Description The goal of Edge Detection Edges can capture significant events and changes in the properties of the world. Discontinuities in image brightness are likely to correspond to discontinuities in depth, surface orientation, changes in material properties, and variations in scene illumination. Capture important events Edge detection can significantly reduce the amount of data to be processed by preserving the important structural properties of an image. Reduce the amount of data If the edge detection step is successful, the subsequent task of interpreting the information contents in the original image may be substantially simplified. Simplify image content interpretation Important features can be extracted from the edges of an image (e.g., corners, lines, curves). These features are used by higher-level computer vision algorithms (e.g., recognition). Feature extraction Edge detection is used for image segmentation and data extraction in areas such as image processing, computer vision, and machine vision. Image segmentation and data extraction 9 ‫من‬ ‫اهﻢ‬ ‫اهداف‬ ‫كشف‬ ‫الحافة‬ • ‫التقاط‬ ‫اﻷحداث‬ ‫المهمة‬ ‫يمكن‬ ‫للحواف‬ ‫التقاط‬ ‫اﻷحداث‬ ‫والتغيرات‬ ‫المهمة‬ ‫في‬ ‫ﺧصائص‬ ‫العالﻢ‬ . ‫من‬ ‫المرجح‬ ‫أن‬ ‫ت‬ ‫توافق‬ ‫اﻻنقطاعات‬ ‫في‬ ‫سطوع‬ ‫الصورة‬ ‫مع‬ ‫اﻻنقطاعات‬ ‫في‬ ،‫العمق‬ ‫واتجاه‬ ،‫السطح‬ ‫والتغيرات‬ ‫في‬ ‫ﺧصائص‬ ،‫المواد‬ ‫واﻻﺧتﻼفات‬ ‫في‬ ‫إضاءة‬ ‫المشهد‬ . • ‫ﺧفﺾ‬ ‫كمية‬ ‫البيانات‬ ‫يمكن‬ ‫أن‬ ‫يقلل‬ ‫اكتشاف‬ ‫الحافة‬ ‫بشكل‬ ‫كبير‬ ‫من‬ ‫كمية‬ ‫البيانات‬ ‫التي‬ ‫يجب‬ ‫معالجت‬ ‫ها‬ ‫عن‬ ‫طريق‬ ‫الحفاظ‬ ‫على‬ ‫الخصائص‬ ‫الهيكلية‬ ‫المهمة‬ ‫للصورة‬ . • ‫ابسط‬ ‫تفسير‬ ‫محتوى‬ ‫الﺼورة‬ ‫إذا‬ ‫نجحﺖ‬ ‫ﺧطوة‬ ‫الكشف‬ ‫عن‬ ،‫الحافة‬ ‫فقد‬ ‫يتﻢ‬ ‫تبسيط‬ ‫المهمة‬ ‫الﻼحقة‬ ‫لتفسير‬ ‫محتو‬ ‫يات‬ ‫المعلومات‬ ‫في‬ ‫الصورة‬ ‫اﻷصلية‬ ‫بشكل‬ ‫كبير‬ . • ‫ميزة‬ ‫اﻻستخراج‬ ‫يمكن‬ ‫استخراج‬ ‫ميزات‬ ‫مهمة‬ ‫من‬ ‫حواف‬ ‫الصورة‬ ) ‫على‬ ‫سبيل‬ ،‫المثال‬ ‫الزوايا‬ ‫والخطوط‬ ‫والمنحنيات‬ ( . ‫يتﻢ‬ ‫استخدام‬ ‫هذه‬ ‫الميزات‬ ‫من‬ ‫قبل‬ ‫ﺧوارزميات‬ ‫الرؤية‬ ‫الحاسوبية‬ ‫عالية‬ ‫المستوى‬ ) ‫على‬ ‫سبيل‬ ،‫المثال‬ ‫التعرف‬ ( . • ‫تجزﺋة‬ ‫الﺼور‬ ‫واستخراج‬ ‫البيانات‬ ‫يتﻢ‬ ‫استخدام‬ ‫الكشف‬ ‫عن‬ ‫الحافة‬ ‫لتجزئة‬ ‫الصورة‬ ‫واستخراج‬ ‫البيانات‬ ‫في‬ ‫مجاﻻت‬ ‫م‬ ‫ثل‬ ‫معالجة‬ ‫الصور‬ ‫ورؤية‬ ‫الكمبيوتر‬ ‫ورؤية‬ ‫اﻵلة‬ . 9 AHMED R. A. SHAMSAN & M. MOHAMADI EDGE DETECTION | LUC01
10 Effect of Illumination 10 AHMED R. A. SHAMSAN & M. MOHAMADI EDGE DETECTION | LUC01
The effect of illumination in edge detection refers to how different lighting conditions can impact the identification of edges in an image. Here’s a simple explanation with examples: •Under bright lighting, edges may appear more distinct and easier to detect because the contrast between different regions of the image is increased. For example, the edge of a book on a table might be very clear under a bright light because the shadows cast by the book create a stark contrast Bright Lighting: • In low lighting conditions, edges can become less visible and harder to identify. This is because there’s less contrast between different regions of the image. For example, if you’re trying to detect the edge of a dark-colored object in a dimly lit room, it might be difficult because the object and its surroundings may blend together Low Lighting: •If the lighting is uneven, it can create false edges or miss real ones. For example, a shiny object might reflect light and create bright spots that could be mistaken for edges. Conversely, shadows might obscure real edges Uneven Lighting: •In situations where the lighting conditions change dynamically, edge detection can become challenging. For example, if a cloud moves and changes the lighting on a scene, the edges in the image can appear or disappear Dynamic Lighting: In summary, illumination plays a crucial role in edge detection. It can both aid and complicate the process of identifying edges, which is why many edge detection algorithms try to account for variations in illumination 11 ‫يشير‬ ‫تأثير‬ ‫اﻹضاءة‬ ‫في‬ ‫اكتشاف‬ ‫الحافة‬ ‫إلى‬ ‫كيفية‬ ‫تأثير‬ ‫ظروف‬ ‫اﻹضاءة‬ ‫المختلفة‬ ‫على‬ ‫تحد‬ ‫يد‬ ‫الحواف‬ ‫في‬ ‫الصورة‬ . ‫إليك‬ ‫شرح‬ ‫بسيط‬ ‫مع‬ ‫أمثلة‬ : ‫اﻹضاءة‬ ‫الساطعة‬ : ‫تحﺖ‬ ‫اﻹضاءة‬ ،‫الساطعة‬ ‫قد‬ ‫تبدو‬ ‫الحواف‬ ‫أكثر‬ ‫ا‬ً‫تميز‬ ‫وأسهل‬ ‫في‬ ‫اكتشافها‬ ‫بسب‬ ‫ب‬ ‫زيادة‬ ‫التباين‬ ‫بين‬ ‫مناطق‬ ‫مختلفة‬ ‫من‬ ‫الصورة‬ . ‫على‬ ‫سبيل‬ ،‫المثال‬ ‫قد‬ ‫تكون‬ ‫حافة‬ ‫كتاب‬ ‫على‬ ‫طاولة‬ ‫واضحة‬ ‫ًا‬‫د‬‫ج‬ ‫ت‬ ‫حﺖ‬ ‫ضوء‬ ‫ساطع‬ ‫ﻷن‬ ‫الظﻼل‬ ‫التي‬ ‫يلقيها‬ ‫الكتاب‬ ‫تخلق‬ ‫ا‬ً‫ن‬‫تباي‬ ‫ا‬ً‫ﺧ‬‫صار‬ . ‫اﻹضاءة‬ ‫المنخفضة‬ : ‫ﻓﻲ‬ ‫ظروف‬ ‫اﻹضاءة‬ ،‫المنخفضة‬ ‫يمكن‬ ‫أن‬ ‫تصبح‬ ‫الحواف‬ ‫أقل‬ ‫ا‬ً‫ح‬‫وضو‬ ‫ويصعب‬ ‫تحديدها‬ . ‫هذا‬ ‫بسبب‬ ‫وجود‬ ‫تباين‬ ‫أقل‬ ‫بين‬ ‫مناطق‬ ‫مختلفة‬ ‫من‬ ‫الصورة‬ . ‫على‬ ‫سبيل‬ ،‫المثال‬ ‫إذا‬ ‫كنﺖ‬ ‫تحاول‬ ‫اكت‬ ‫شاف‬ ‫حافة‬ ‫جسﻢ‬ ‫داكن‬ ‫اللون‬ ‫في‬ ‫غرفة‬ ‫مضاءة‬ ‫بشكل‬ ،‫ﺧافﺖ‬ ‫فقد‬ ‫يكون‬ ‫اﻷمر‬ ‫ا‬ً‫ب‬‫صع‬ ‫ﻷن‬ ‫الجسﻢ‬ ‫ومحيطه‬ ‫قد‬ ‫يمت‬ ‫زجان‬ ‫ا‬ً‫ع‬‫م‬ . ‫اﻹضاءة‬ ‫غير‬ ‫المتكاﻓئة‬ : ‫إذا‬ ‫كانﺖ‬ ‫اﻹضاءة‬ ‫غير‬ ،‫متساوية‬ ‫فيمكنها‬ ‫إنشاء‬ ‫حواف‬ ‫ﺧاطئة‬ ‫أو‬ ‫تفويﺖ‬ ‫حو‬ ‫اف‬ ‫حقيقية‬ . ‫على‬ ‫سبيل‬ ،‫المثال‬ ‫قد‬ ‫يعكس‬ ‫الجسﻢ‬ ‫الﻼمع‬ ‫الضوء‬ ‫ويخلق‬ ‫نقاط‬ ‫مضيئة‬ ‫يمكن‬ ‫الخلط‬ ‫بينها‬ ‫وبين‬ ‫الحواف‬ . ‫على‬ ‫العكس‬ ‫من‬ ،‫ذلك‬ ‫قد‬ ‫تحجب‬ ‫الظﻼل‬ ‫الحواف‬ ‫الحقيقية‬ . ‫اﻹضاءة‬ ‫الديناميكية‬ : ‫ﻓﻲ‬ ‫الحاﻻت‬ ‫التي‬ ‫تتغير‬ ‫فيها‬ ‫ظروف‬ ‫اﻹضاءة‬ ،‫ا‬ً‫ي‬‫ديناميك‬ ‫يمكن‬ ‫أن‬ ‫يصبح‬ ‫اكتشاف‬ ‫الحافة‬ ‫ا‬ً‫أمر‬ ‫ا‬ً‫ب‬‫صع‬ . ‫على‬ ‫سبيل‬ ،‫المثال‬ ‫إذا‬ ‫تحركﺖ‬ ‫السحابة‬ ‫وغيرت‬ ‫اﻹضاءة‬ ‫على‬ ‫مشهد‬ ،‫ما‬ ‫يمكن‬ ‫أن‬ ‫تظهر‬ ‫الحواف‬ ‫الموجودة‬ ‫في‬ ‫الصورة‬ ‫أو‬ ‫تختفي‬ . ،‫باﺧتصار‬ ‫يلعب‬ ‫اﻹضاءة‬ ‫ا‬ً‫دور‬ ‫ا‬ً‫م‬‫مه‬ ‫في‬ ‫اكتشاف‬ ‫الحافة‬ . ‫يمكن‬ ‫أن‬ ‫يساعد‬ ‫ويعقد‬ ‫عملية‬ ‫تحديد‬ ‫الح‬ ،‫واف‬ ‫ولهذا‬ ‫السبب‬ ‫تحاول‬ ‫العديد‬ ‫من‬ ‫ﺧوارزميات‬ ‫الكشف‬ ‫عن‬ ‫الحافة‬ ‫حساب‬ ‫اﻻﺧتﻼفات‬ ‫في‬ ‫اﻹضاءة‬ . 11 AHMED R. A. SHAMSAN & M. MOHAMADI EDGE DETECTION | LUC01

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image processing _Edge Detection Luc01.pdf

  • 1. 1 Edge Detection lecture 01 BY AHMED R. A. SHAMSAN MOHAMMED ALMOHAMADI AHMED R. A. SHAMSAN & M. MOHAMADI EDGE DETECTION | LUC01
  • 2. 2 Definition of Edges  Edges are significant local changes of intensity in an image.  Or Edges represent boundaries or abrupt changes in intensity between adjacent pixels. 2 ‫الحواف‬ ‫تعريف‬ ‫الصورة‬ ‫في‬ ‫الشدة‬ ‫في‬ ‫مهمة‬ ‫محلية‬ ‫تغييرات‬ ‫هي‬ ‫الحواف‬ . ‫ا‬ ‫البكسﻼت‬ ‫بين‬ ‫الشدة‬ ‫في‬ ‫مفاجئة‬ ‫تغييرات‬ ‫أو‬ ‫ًا‬‫د‬‫حدو‬ ‫الحواف‬ ‫تمثل‬ ‫حيث‬ ‫لمجاورة‬ . AHMED R. A. SHAMSAN & M. MOHAMADI EDGE DETECTION | LUC01
  • 3. 3 What Causes Intensity Changes  Geometric events  surface orientation (boundary) discontinuities  depth discontinuities  color and texture discontinuities  Non-geometric events  illumination changes  specularities  shadows  inter-reflections DEPTH DISCONTINUITY COLOR DISCONTINUITY ILLUMINATION DISCONTINUITY SURFACE NORMAL DISCONTINUITY 3 ‫ما‬ ‫الذي‬ ‫يسبب‬ ‫تغيرات‬ ‫الشدة‬ ‫للصور؟‬ • ‫اﻷحداث‬ ‫الهندسية‬ • ‫التوجهات‬ ‫السطحية‬ ) ‫الحدود‬ ( • ‫انقطاعات‬ ‫العمق‬ • ‫انقطاع‬ ‫اللون‬ ‫والملمس‬ • ‫اﻷحداث‬ ‫غير‬ ‫الهندسية‬ • ‫تغييرات‬ ‫اﻹضاءة‬ • ‫المضاربات‬ • ‫ظﻼل‬ • ‫التأمﻼت‬ ‫المتبادلة‬ AHMED R. A. SHAMSAN & M. MOHAMADI EDGE DETECTION | LUC01
  • 4. Geometric events  In image processing, surface orientation discontinuities, also known as edges, are significant geometric events that cause intensity changes in an image. These edges are curves in a digital image where the image brightness changes sharply or has discontinuities1.  Edges in an image can be caused by several factors:  Discontinuities in depth: This refers to the sudden change in distance between the camera (or viewer) and the objects in the scene.  Discontinuities in surface orientation: This refers to the changes in the angle at which a surface is oriented concerning the camera. For example, the corners of a cube cause discontinuities in surface orientation3.  Changes in material properties: This refers to the changes in the physical properties of the surface material, such as color, texture, and reflectance.  Variations in scene illumination: This refers to changes in lighting conditions, such as shadows and highlights.  The purpose of detecting these sharp changes in image brightness is to capture important events and changes in properties of the world. If the edge detection step is successful, the subsequent task of interpreting the information contents in the original image may therefore be substantially simplified1. However, it is not always possible to obtain such ideal edges from real-life images of moderate complexity 4 ‫في‬ ‫معالجة‬ ،‫الصور‬ ‫يعد‬ ‫انقطاع‬ ‫اتجاه‬ ،‫السطح‬ ‫المعروف‬ ‫ا‬ً‫ض‬‫أي‬ ‫باسﻢ‬ ،‫الحواف‬ ‫ا‬ً‫ث‬‫أحدا‬ ‫هندسية‬ ‫مهمة‬ ‫ت‬ ‫سبب‬ ‫تغيرات‬ ‫في‬ ‫الشدة‬ ‫في‬ ‫الصورة‬ . ‫هذه‬ ‫الحواف‬ ‫عبارة‬ ‫عن‬ ‫منحنيات‬ ‫في‬ ‫صورة‬ ‫رقمية‬ ‫حيث‬ ‫يتغير‬ ‫سطو‬ ‫ع‬ ‫الصورة‬ ‫بشكل‬ ‫حاد‬ ‫أو‬ ‫يتوقف‬ ‫عن‬ ‫العمل‬ . ‫يمكن‬ ‫أن‬ ‫تكون‬ ‫الحواف‬ ‫في‬ ‫الصورة‬ ‫ناتجة‬ ‫عن‬ ‫عدة‬ ‫عوامل‬ : • ‫اﻻنقطاعات‬ ‫ﻓﻲ‬ ‫العمق‬ : ‫يشير‬ ‫هذا‬ ‫إلى‬ ‫التغيير‬ ‫المفاجئ‬ ‫في‬ ‫المسافة‬ ‫بين‬ ‫الكام‬ ‫يرا‬ ) ‫أو‬ ‫المشاهد‬ ( ‫واﻷشياء‬ ‫الموجودة‬ ‫في‬ ‫المشهد‬ . • ‫اﻻنقطاعات‬ ‫ﻓﻲ‬ ‫اتجاه‬ ‫السطح‬ : ‫يشير‬ ‫هذا‬ ‫إلى‬ ‫التغيرات‬ ‫في‬ ‫الزاوية‬ ‫التي‬ ‫يتﻢ‬ ‫ف‬ ‫يها‬ ‫توجيه‬ ‫السطح‬ ‫فيما‬ ‫يتعلق‬ ‫بالكاميرا‬ . ‫على‬ ‫سبيل‬ ،‫المثال‬ ‫تسبب‬ ‫زوايا‬ ‫المكعب‬ ‫انقط‬ ‫اعات‬ ‫في‬ ‫التوجه‬ ‫السطحي‬ . • ‫التغيرات‬ ‫ﻓﻲ‬ ‫ﺧﺼاﺋﺺ‬ ‫المواد‬ : ‫يشير‬ ‫هذا‬ ‫إلى‬ ‫التغيرات‬ ‫في‬ ‫الخصائص‬ ‫الفيز‬ ‫يائية‬ ‫للمادة‬ ،‫السطحية‬ ‫مثل‬ ‫اللون‬ ‫والملمس‬ ‫واﻻنعكاس‬ . • ‫اﻻﺧتﻼﻓات‬ ‫ﻓﻲ‬ ‫إضاءة‬ ‫المشهد‬ : ‫يشير‬ ‫هذا‬ ‫إلى‬ ‫التغيرات‬ ‫في‬ ‫ظروف‬ ،‫اﻹضاءة‬ ‫مثل‬ ‫الظﻼل‬ ‫واﻹضاءة‬ . ‫الغرض‬ ‫من‬ ‫اكتشاف‬ ‫هذه‬ ‫التغييرات‬ ‫الحادة‬ ‫في‬ ‫سطوع‬ ‫الصورة‬ ‫هو‬ ‫التقاط‬ ‫اﻷحداث‬ ‫والتغيرات‬ ‫المهمة‬ ‫في‬ ‫ﺧصائص‬ ‫العالﻢ‬ . ‫إذا‬ ‫نجحﺖ‬ ‫ﺧطوة‬ ‫كشف‬ ،‫الحافة‬ ‫فإن‬ ‫المهمة‬ ‫الﻼ‬ ‫حقة‬ ‫لتفسير‬ ‫محتويات‬ ‫المعلومات‬ ‫في‬ ‫الصورة‬ ‫اﻷصلية‬ ‫قد‬ ‫تكون‬ ‫مبسطة‬ ‫إلى‬ ‫حد‬ ‫كبي‬ ‫ر‬ . ‫ومع‬ ،‫ذلك‬ ‫ليس‬ ‫من‬ ‫الممكن‬ ‫ا‬ً‫م‬‫دائ‬ ‫الحصول‬ ‫على‬ ‫مثل‬ ‫هذه‬ ‫الحواف‬ ‫المثالية‬ ‫من‬ ‫صور‬ ‫الحياة‬ ‫الواقعية‬ ‫ذات‬ ‫التعقيد‬ ‫المعتدل‬ 4 AHMED R. A. SHAMSAN & M. MOHAMADI EDGE DETECTION | LUC01
  • 5. Non-geometric events  Non-geometric events: These are visual occurrences in an image that aren’t related to the shape or structure of objects within the scene. Examples include changes in lighting, reflections, and shadows which can alter the appearance of objects without changing their inherent structure.  Illumination changes: This refers to variations in lighting conditions within a scene. It can be caused by natural light sources (like the sun moving across the sky) or artificial ones (like a light being turned on/off), affecting how objects and surroundings are viewed.  Specularities: These are bright spots or streaks appearing on surfaces due to the reflection of light. They depend on the angle of incidence of light and the viewer’s perspective, often making object identification challenging.  Shadows: These are dark areas where direct light is obstructed by an object. Shadows can provide cues about an object’s shape and lighting direction but can also complicate image interpretation.  Inter-reflections: These occur when light reflects between surfaces within a scene before reaching the camera. They can introduce additional lighting variations, complicating understanding of material properties and shapes.  These factors can significantly affect the appearance of an image and are important considerations in image processing and computer vision. They can both provide valuable information and introduce challenges in interpreting the image content. 5 • ‫اﻷحداث‬ ‫غير‬ ‫الهندسية‬ ‫هذه‬ ‫أحداث‬ ‫بصرية‬ ‫في‬ ‫صورة‬ ‫ﻻ‬ ‫تتعلق‬ ‫بشكل‬ ‫أو‬ ‫بنية‬ ‫اﻷشياء‬ ‫داﺧل‬ ‫المشهد‬ . ‫تشمل‬ ‫اﻷمثلة‬ ‫التغيي‬ ‫رات‬ ‫في‬ ‫اﻹضاءة‬ ‫واﻻنعكاسات‬ ‫والظﻼل‬ ‫التي‬ ‫يمكن‬ ‫أن‬ ‫تغير‬ ‫مظهر‬ ‫اﻷشياء‬ ‫دون‬ ‫تغيير‬ ‫بنيتها‬ ‫المتأصلة‬ . .1 ‫تغييرات‬ ‫اﻹضاءة‬ : ‫يشير‬ ‫هذا‬ ‫إلى‬ ‫اﻻﺧتﻼفات‬ ‫في‬ ‫ظروف‬ ‫اﻹضاءة‬ ‫داﺧل‬ ‫المشهد‬ . ‫يمكن‬ ‫أن‬ ‫يكون‬ ‫ا‬ً‫ج‬‫نات‬ ‫عن‬ ‫مصادر‬ ‫الضوء‬ ‫الطبيعي‬ ) ‫مثل‬ ‫الشمس‬ ‫التي‬ ‫تتحرك‬ ‫عبر‬ ‫السماء‬ ( ‫أو‬ ‫تلك‬ ‫اﻻصطناعية‬ ) ‫مثل‬ ‫الضوء‬ ‫الذي‬ ‫يتﻢ‬ ‫تشغيله‬ / ‫إيقاف‬ ‫تشغيله‬ ( ، ‫مما‬ ‫يؤثر‬ ‫على‬ ‫كيفية‬ ‫رؤية‬ ‫اﻷشياء‬ ‫والمناطق‬ ‫المحيطة‬ ‫بها‬ . .2 ‫المضاربات‬ : ‫هذه‬ ‫نقاط‬ ‫مضيئة‬ ‫أو‬ ‫ﺧطوط‬ ‫تظهر‬ ‫على‬ ‫اﻷسطح‬ ‫بسبب‬ ‫انعكاس‬ ‫الض‬ ‫وء‬ . ‫فهي‬ ‫تعتمد‬ ‫على‬ ‫زاوية‬ ‫حدوث‬ ‫الضوء‬ ‫ومنظور‬ ،‫المشاهد‬ ‫مما‬ ‫يجعل‬ ‫تحديد‬ ‫الكائن‬ ‫أ‬ ‫ا‬ً‫مر‬ ‫ا‬ً‫ب‬‫صع‬ ‫في‬ ‫كثير‬ ‫من‬ ‫اﻷحيان‬ . .3 ‫الظﻼل‬ : ‫هذه‬ ‫مناطق‬ ‫مظلمة‬ ‫حيث‬ ‫يتﻢ‬ ‫عرقلة‬ ‫الضوء‬ ‫المباشر‬ ‫بواسطة‬ ‫جسﻢ‬ ‫ما‬ . ‫يمكن‬ ‫أن‬ ‫توفر‬ ‫الظﻼل‬ ‫إشارات‬ ‫حول‬ ‫شكل‬ ‫الكائن‬ ‫واتجاه‬ ‫اﻹضاءة‬ ‫ولكنها‬ ‫يمكن‬ ‫أن‬ ‫تعقد‬ ‫ا‬ً‫ض‬‫أي‬ ‫تفسير‬ ‫الصورة‬ . .4 ‫اﻻنعكاسات‬ ‫البينية‬ : ‫تحدث‬ ‫هذه‬ ‫عندما‬ ‫ينعكس‬ ‫الضوء‬ ‫بين‬ ‫اﻷسطح‬ ‫داﺧل‬ ‫المشه‬ ‫د‬ ‫قبل‬ ‫الوصول‬ ‫إلى‬ ‫الكاميرا‬ . ‫يمكنهﻢ‬ ‫إدﺧال‬ ‫اﺧتﻼفات‬ ‫إضافية‬ ‫في‬ ،‫اﻹضاءة‬ ‫مما‬ ‫يعقد‬ ‫ف‬ ‫هﻢ‬ ‫ﺧصائص‬ ‫وأشكال‬ ‫المواد‬ . ‫يمكن‬ ‫أن‬ ‫تؤثر‬ ‫هذه‬ ‫العوامل‬ ‫بشكل‬ ‫كبير‬ ‫على‬ ‫مظهر‬ ‫الصورة‬ ‫وهي‬ ‫اعتبارات‬ ‫مهمة‬ ‫في‬ ‫معالجة‬ ‫الصور‬ ‫ورؤ‬ ‫ية‬ ‫الكمبيوتر‬ . ‫يمكن‬ ‫أن‬ ‫توفر‬ ‫معلومات‬ ‫قيمة‬ ‫وتقدم‬ ‫تحديات‬ ‫في‬ ‫تفسير‬ ‫محتوى‬ ‫الصورة‬ . 5 AHMED R. A. SHAMSAN & M. MOHAMADI EDGE DETECTION | LUC01
  • 6. 6 Goal of Edge Detection  Produce a line “drawing” of a scene from an image of that scene. 6 AHMED R. A. SHAMSAN & M. MOHAMADI EDGE DETECTION | LUC01
  • 7. The goal of edge detection  The goal of edge detection in image processing is to identify and locate sharp discontinuities in an image, known as edges. Here are the key objectives12345:  Capture important events: Edges can capture significant events and changes in properties of the world. Discontinuities in image brightness are likely to correspond to discontinuities in depth, surface orientation, changes in material properties, and variations in scene illumination1.  Reduce the amount of data: Edge detection can significantly reduce the amount of data to be processed by preserving the important structural properties of an image1.  Simplify image content interpretation: If the edge detection step is successful, the subsequent task of interpreting the information contents in the original image may be substantially simplified1.  Feature extraction: Important features can be extracted from the edges of an image (e.g., corners, lines, curves). These features are used by higher-level computer vision algorithms (e.g., recognition)2.  Image segmentation and data extraction: Edge detection is used for image segmentation and data extraction in areas such as image processing, computer vision, and machine vision5.  However, it’s worth noting that edges extracted from complex images are often hampered by fragmentation, missing edge segments, and false edges not corresponding to interesting phenomena in the image1. 7 ‫ما‬ ‫الفائدة‬ ‫من‬ ‫تحديد‬ ‫الحواف‬ ‫؟‬ ‫الهدف‬ ‫من‬ ‫اكتشاف‬ ‫الحافة‬ ‫في‬ ‫معالجة‬ ‫الصور‬ ‫هو‬ ‫تحديد‬ ‫وتحديد‬ ‫اﻻنقطاعات‬ ‫الحادة‬ ‫في‬ ‫الصور‬ ،‫ة‬ ‫والمعروفة‬ ‫باسﻢ‬ ‫الحواف‬ . ‫فيما‬ ‫يلي‬ ‫اﻷهداف‬ ‫الرئيسية‬ : .1 ‫التقاط‬ ‫اﻷحداث‬ ‫المهمة‬ : ‫يمكن‬ ‫للحواف‬ ‫التقاط‬ ‫اﻷحداث‬ ‫والتغيرات‬ ‫المهمة‬ ‫في‬ ‫ﺧصائص‬ ‫الع‬ ‫الﻢ‬ . ‫من‬ ‫المرجح‬ ‫أن‬ ‫تتوافق‬ ‫اﻻنقطاعات‬ ‫في‬ ‫سطوع‬ ‫الصورة‬ ‫مع‬ ‫اﻻنقطاعات‬ ‫في‬ ،‫العمق‬ ‫واتجاه‬ ،‫السطح‬ ‫والتغيرات‬ ‫في‬ ‫ﺧصائص‬ ،‫المواد‬ ‫واﻻﺧتﻼفات‬ ‫في‬ ‫إضاءة‬ ‫المشهد‬ . .2 ‫تقليل‬ ‫كمية‬ ‫البيانات‬ : ‫يمكن‬ ‫أن‬ ‫يقلل‬ ‫الكشف‬ ‫عن‬ ‫الحافة‬ ‫بشكل‬ ‫كبير‬ ‫من‬ ‫كمية‬ ‫البيا‬ ‫نات‬ ‫التي‬ ‫يجب‬ ‫معالجتها‬ ‫عن‬ ‫طريق‬ ‫الحفاظ‬ ‫على‬ ‫الخصائص‬ ‫الهيكلية‬ ‫المهمة‬ ‫للصورة‬ . .3 ‫تبسيط‬ ‫تفسير‬ ‫محتوى‬ ‫الﺼورة‬ : ‫إذا‬ ‫نجحﺖ‬ ‫ﺧطوة‬ ‫الكشف‬ ‫عن‬ ،‫الحافة‬ ‫فإن‬ ‫المهمة‬ ‫الﻼحق‬ ‫ة‬ ‫لتفسير‬ ‫محتويات‬ ‫المعلومات‬ ‫في‬ ‫الصورة‬ ‫اﻷصلية‬ ‫قد‬ ‫تكون‬ ‫مبسطة‬ ‫إلى‬ ‫حد‬ ‫كبير‬ . .4 ‫استخراج‬ ‫الميزات‬ : ‫يمكن‬ ‫استخراج‬ ‫الميزات‬ ‫المهمة‬ ‫من‬ ‫حواف‬ ‫الصورة‬ ) ‫على‬ ‫سبيل‬ ،‫المثال‬ ‫الزوايا‬ ‫والخطوط‬ ‫والمنحنيات‬ ( . ‫يتﻢ‬ ‫استخدام‬ ‫هذه‬ ‫الميزات‬ ‫من‬ ‫قبل‬ ‫ﺧوارزميات‬ ‫الرؤية‬ ‫الحا‬ ‫سوبية‬ ‫عالية‬ ‫المستوى‬ ) ‫على‬ ‫سبيل‬ ،‫المثال‬ ‫التعرف‬ ( . .5 ‫تجزﺋة‬ ‫الﺼور‬ ‫واستخراج‬ ‫البيانات‬ : ‫يتﻢ‬ ‫استخدام‬ ‫الكشف‬ ‫عن‬ ‫الحافة‬ ‫لتجزئة‬ ‫الصورة‬ ‫و‬ ‫استخراج‬ ‫البيانات‬ ‫في‬ ‫مجاﻻت‬ ‫مثل‬ ‫معالجة‬ ‫الصور‬ ‫ورؤية‬ ‫الكمبيوتر‬ ‫ورؤية‬ ‫اﻵلة‬ . ‫ومع‬ ،‫ذلك‬ ‫تجدر‬ ‫اﻹشارة‬ ‫إلى‬ ‫أن‬ ‫الحواف‬ ‫المستخرجة‬ ‫من‬ ‫الصور‬ ‫المعقدة‬ ‫ا‬ً‫ب‬‫غال‬ ‫ما‬ ‫يتﻢ‬ ‫إعاقتها‬ ‫ب‬ ‫سبب‬ ،‫التجزئة‬ ‫وفقدان‬ ‫أجزاء‬ ،‫الحافة‬ ‫والحواف‬ ‫الزائفة‬ ‫التي‬ ‫ﻻ‬ ‫تتوافق‬ ‫مع‬ ‫الظواهر‬ ‫المثيرة‬ ‫لﻼه‬ ‫تمام‬ ‫في‬ ‫الصورة‬ . 7 AHMED R. A. SHAMSAN & M. MOHAMADI EDGE DETECTION | LUC01
  • 8. 8 Why is Edge Detection Useful? 8 AHMED R. A. SHAMSAN & M. MOHAMADI EDGE DETECTION | LUC01
  • 9. Description The goal of Edge Detection Edges can capture significant events and changes in the properties of the world. Discontinuities in image brightness are likely to correspond to discontinuities in depth, surface orientation, changes in material properties, and variations in scene illumination. Capture important events Edge detection can significantly reduce the amount of data to be processed by preserving the important structural properties of an image. Reduce the amount of data If the edge detection step is successful, the subsequent task of interpreting the information contents in the original image may be substantially simplified. Simplify image content interpretation Important features can be extracted from the edges of an image (e.g., corners, lines, curves). These features are used by higher-level computer vision algorithms (e.g., recognition). Feature extraction Edge detection is used for image segmentation and data extraction in areas such as image processing, computer vision, and machine vision. Image segmentation and data extraction 9 ‫من‬ ‫اهﻢ‬ ‫اهداف‬ ‫كشف‬ ‫الحافة‬ • ‫التقاط‬ ‫اﻷحداث‬ ‫المهمة‬ ‫يمكن‬ ‫للحواف‬ ‫التقاط‬ ‫اﻷحداث‬ ‫والتغيرات‬ ‫المهمة‬ ‫في‬ ‫ﺧصائص‬ ‫العالﻢ‬ . ‫من‬ ‫المرجح‬ ‫أن‬ ‫ت‬ ‫توافق‬ ‫اﻻنقطاعات‬ ‫في‬ ‫سطوع‬ ‫الصورة‬ ‫مع‬ ‫اﻻنقطاعات‬ ‫في‬ ،‫العمق‬ ‫واتجاه‬ ،‫السطح‬ ‫والتغيرات‬ ‫في‬ ‫ﺧصائص‬ ،‫المواد‬ ‫واﻻﺧتﻼفات‬ ‫في‬ ‫إضاءة‬ ‫المشهد‬ . • ‫ﺧفﺾ‬ ‫كمية‬ ‫البيانات‬ ‫يمكن‬ ‫أن‬ ‫يقلل‬ ‫اكتشاف‬ ‫الحافة‬ ‫بشكل‬ ‫كبير‬ ‫من‬ ‫كمية‬ ‫البيانات‬ ‫التي‬ ‫يجب‬ ‫معالجت‬ ‫ها‬ ‫عن‬ ‫طريق‬ ‫الحفاظ‬ ‫على‬ ‫الخصائص‬ ‫الهيكلية‬ ‫المهمة‬ ‫للصورة‬ . • ‫ابسط‬ ‫تفسير‬ ‫محتوى‬ ‫الﺼورة‬ ‫إذا‬ ‫نجحﺖ‬ ‫ﺧطوة‬ ‫الكشف‬ ‫عن‬ ،‫الحافة‬ ‫فقد‬ ‫يتﻢ‬ ‫تبسيط‬ ‫المهمة‬ ‫الﻼحقة‬ ‫لتفسير‬ ‫محتو‬ ‫يات‬ ‫المعلومات‬ ‫في‬ ‫الصورة‬ ‫اﻷصلية‬ ‫بشكل‬ ‫كبير‬ . • ‫ميزة‬ ‫اﻻستخراج‬ ‫يمكن‬ ‫استخراج‬ ‫ميزات‬ ‫مهمة‬ ‫من‬ ‫حواف‬ ‫الصورة‬ ) ‫على‬ ‫سبيل‬ ،‫المثال‬ ‫الزوايا‬ ‫والخطوط‬ ‫والمنحنيات‬ ( . ‫يتﻢ‬ ‫استخدام‬ ‫هذه‬ ‫الميزات‬ ‫من‬ ‫قبل‬ ‫ﺧوارزميات‬ ‫الرؤية‬ ‫الحاسوبية‬ ‫عالية‬ ‫المستوى‬ ) ‫على‬ ‫سبيل‬ ،‫المثال‬ ‫التعرف‬ ( . • ‫تجزﺋة‬ ‫الﺼور‬ ‫واستخراج‬ ‫البيانات‬ ‫يتﻢ‬ ‫استخدام‬ ‫الكشف‬ ‫عن‬ ‫الحافة‬ ‫لتجزئة‬ ‫الصورة‬ ‫واستخراج‬ ‫البيانات‬ ‫في‬ ‫مجاﻻت‬ ‫م‬ ‫ثل‬ ‫معالجة‬ ‫الصور‬ ‫ورؤية‬ ‫الكمبيوتر‬ ‫ورؤية‬ ‫اﻵلة‬ . 9 AHMED R. A. SHAMSAN & M. MOHAMADI EDGE DETECTION | LUC01
  • 10. 10 Effect of Illumination 10 AHMED R. A. SHAMSAN & M. MOHAMADI EDGE DETECTION | LUC01
  • 11. The effect of illumination in edge detection refers to how different lighting conditions can impact the identification of edges in an image. Here’s a simple explanation with examples: •Under bright lighting, edges may appear more distinct and easier to detect because the contrast between different regions of the image is increased. For example, the edge of a book on a table might be very clear under a bright light because the shadows cast by the book create a stark contrast Bright Lighting: • In low lighting conditions, edges can become less visible and harder to identify. This is because there’s less contrast between different regions of the image. For example, if you’re trying to detect the edge of a dark-colored object in a dimly lit room, it might be difficult because the object and its surroundings may blend together Low Lighting: •If the lighting is uneven, it can create false edges or miss real ones. For example, a shiny object might reflect light and create bright spots that could be mistaken for edges. Conversely, shadows might obscure real edges Uneven Lighting: •In situations where the lighting conditions change dynamically, edge detection can become challenging. For example, if a cloud moves and changes the lighting on a scene, the edges in the image can appear or disappear Dynamic Lighting: In summary, illumination plays a crucial role in edge detection. It can both aid and complicate the process of identifying edges, which is why many edge detection algorithms try to account for variations in illumination 11 ‫يشير‬ ‫تأثير‬ ‫اﻹضاءة‬ ‫في‬ ‫اكتشاف‬ ‫الحافة‬ ‫إلى‬ ‫كيفية‬ ‫تأثير‬ ‫ظروف‬ ‫اﻹضاءة‬ ‫المختلفة‬ ‫على‬ ‫تحد‬ ‫يد‬ ‫الحواف‬ ‫في‬ ‫الصورة‬ . ‫إليك‬ ‫شرح‬ ‫بسيط‬ ‫مع‬ ‫أمثلة‬ : ‫اﻹضاءة‬ ‫الساطعة‬ : ‫تحﺖ‬ ‫اﻹضاءة‬ ،‫الساطعة‬ ‫قد‬ ‫تبدو‬ ‫الحواف‬ ‫أكثر‬ ‫ا‬ً‫تميز‬ ‫وأسهل‬ ‫في‬ ‫اكتشافها‬ ‫بسب‬ ‫ب‬ ‫زيادة‬ ‫التباين‬ ‫بين‬ ‫مناطق‬ ‫مختلفة‬ ‫من‬ ‫الصورة‬ . ‫على‬ ‫سبيل‬ ،‫المثال‬ ‫قد‬ ‫تكون‬ ‫حافة‬ ‫كتاب‬ ‫على‬ ‫طاولة‬ ‫واضحة‬ ‫ًا‬‫د‬‫ج‬ ‫ت‬ ‫حﺖ‬ ‫ضوء‬ ‫ساطع‬ ‫ﻷن‬ ‫الظﻼل‬ ‫التي‬ ‫يلقيها‬ ‫الكتاب‬ ‫تخلق‬ ‫ا‬ً‫ن‬‫تباي‬ ‫ا‬ً‫ﺧ‬‫صار‬ . ‫اﻹضاءة‬ ‫المنخفضة‬ : ‫ﻓﻲ‬ ‫ظروف‬ ‫اﻹضاءة‬ ،‫المنخفضة‬ ‫يمكن‬ ‫أن‬ ‫تصبح‬ ‫الحواف‬ ‫أقل‬ ‫ا‬ً‫ح‬‫وضو‬ ‫ويصعب‬ ‫تحديدها‬ . ‫هذا‬ ‫بسبب‬ ‫وجود‬ ‫تباين‬ ‫أقل‬ ‫بين‬ ‫مناطق‬ ‫مختلفة‬ ‫من‬ ‫الصورة‬ . ‫على‬ ‫سبيل‬ ،‫المثال‬ ‫إذا‬ ‫كنﺖ‬ ‫تحاول‬ ‫اكت‬ ‫شاف‬ ‫حافة‬ ‫جسﻢ‬ ‫داكن‬ ‫اللون‬ ‫في‬ ‫غرفة‬ ‫مضاءة‬ ‫بشكل‬ ،‫ﺧافﺖ‬ ‫فقد‬ ‫يكون‬ ‫اﻷمر‬ ‫ا‬ً‫ب‬‫صع‬ ‫ﻷن‬ ‫الجسﻢ‬ ‫ومحيطه‬ ‫قد‬ ‫يمت‬ ‫زجان‬ ‫ا‬ً‫ع‬‫م‬ . ‫اﻹضاءة‬ ‫غير‬ ‫المتكاﻓئة‬ : ‫إذا‬ ‫كانﺖ‬ ‫اﻹضاءة‬ ‫غير‬ ،‫متساوية‬ ‫فيمكنها‬ ‫إنشاء‬ ‫حواف‬ ‫ﺧاطئة‬ ‫أو‬ ‫تفويﺖ‬ ‫حو‬ ‫اف‬ ‫حقيقية‬ . ‫على‬ ‫سبيل‬ ،‫المثال‬ ‫قد‬ ‫يعكس‬ ‫الجسﻢ‬ ‫الﻼمع‬ ‫الضوء‬ ‫ويخلق‬ ‫نقاط‬ ‫مضيئة‬ ‫يمكن‬ ‫الخلط‬ ‫بينها‬ ‫وبين‬ ‫الحواف‬ . ‫على‬ ‫العكس‬ ‫من‬ ،‫ذلك‬ ‫قد‬ ‫تحجب‬ ‫الظﻼل‬ ‫الحواف‬ ‫الحقيقية‬ . ‫اﻹضاءة‬ ‫الديناميكية‬ : ‫ﻓﻲ‬ ‫الحاﻻت‬ ‫التي‬ ‫تتغير‬ ‫فيها‬ ‫ظروف‬ ‫اﻹضاءة‬ ،‫ا‬ً‫ي‬‫ديناميك‬ ‫يمكن‬ ‫أن‬ ‫يصبح‬ ‫اكتشاف‬ ‫الحافة‬ ‫ا‬ً‫أمر‬ ‫ا‬ً‫ب‬‫صع‬ . ‫على‬ ‫سبيل‬ ،‫المثال‬ ‫إذا‬ ‫تحركﺖ‬ ‫السحابة‬ ‫وغيرت‬ ‫اﻹضاءة‬ ‫على‬ ‫مشهد‬ ،‫ما‬ ‫يمكن‬ ‫أن‬ ‫تظهر‬ ‫الحواف‬ ‫الموجودة‬ ‫في‬ ‫الصورة‬ ‫أو‬ ‫تختفي‬ . ،‫باﺧتصار‬ ‫يلعب‬ ‫اﻹضاءة‬ ‫ا‬ً‫دور‬ ‫ا‬ً‫م‬‫مه‬ ‫في‬ ‫اكتشاف‬ ‫الحافة‬ . ‫يمكن‬ ‫أن‬ ‫يساعد‬ ‫ويعقد‬ ‫عملية‬ ‫تحديد‬ ‫الح‬ ،‫واف‬ ‫ولهذا‬ ‫السبب‬ ‫تحاول‬ ‫العديد‬ ‫من‬ ‫ﺧوارزميات‬ ‫الكشف‬ ‫عن‬ ‫الحافة‬ ‫حساب‬ ‫اﻻﺧتﻼفات‬ ‫في‬ ‫اﻹضاءة‬ . 11 AHMED R. A. SHAMSAN & M. MOHAMADI EDGE DETECTION | LUC01