DIGITAL IMAGE PROCESSING ,Computer science

Texture-patters and pattern classes ,Digital image processing

1. DIGITAL IMAGE PROCESSING C.NIVETHA II MSc.cs Nadar saraswathi college of arts and science Texture , patterns and pattern classes

2. TEXTURE " texture" refers to the perceived surface quality or pattern of an image that can be used to describe and distinguish different areas within an image. Texture plays a crucial role in visual perception and can be crucial for tasks such as image classification, object recognition, and segmentation. Definition :

3. Texture Features : Statistical Features : These describe the statistical distribution of pixel values or the relationships between pixels. Common methods include : Gray-Level Co-occurrence Matrix (GLCM ) : Captures spatial relationships between pixel values at a certain distance and angle. Features derived from GLCM include contrast, correlation, and homogeneity . Histogram-Based Methods : Analyze the distribution of pixel values or gradients within a region.

4. Filtering : Applying filters like Gaussian or Laplacian to enhance or detect texture features . Segmentation : Dividing an image into regions based on texture properties. Techniques include clustering methods (e.g., k-means) and region growing . Feature Extraction : Deriving quantitative descriptors from texture patterns, which can then be used for classification or analysis. Applications Texture Analysis Techniques

5. PATTERNS AND PATTERN CLASSES Definition : Patterns are recognizable arrangements or sequences of elements within an image. They can be simple, like repeated shapes or textures, or complex, such as intricate structures or combinations of features. Types of Patterns : Geometric Patterns : Shapes or arrangements like lines, circles, and polygons. Texture Patterns : Repeated or systematic variations in pixel intensity or color, which give rise to textures (e.g., stripes, spots). Structural Patterns : Complex arrangements involving multiple features or objects (e.g., a face or a car).

6. Pattern Classes Pattern classes refer to categories or groups that share similar characteristics based on their patterns. Classifying patterns into these classes is essential for many image processing tasks. Here’s how pattern classes are generally used: Classification : Training and Testing : Patterns are classified into different classes using labeled training data. Algorithms learn the distinguishing features of each class and then apply this knowledge to new, unlabeled data . Examples of Pattern Classes : In a facial recognition system, pattern classes might include different types of faces (e.g., male, female, age groups). In satellite imagery, classes could represent various land cover types (e.g., forest, water, urban).

7. Pattern Recognition Techniques : Machine Learning : Algorithms like Support Vector Machines (SVM), Neural Networks, and Decision Trees classify patterns into predefined classes based on learned features . Deep Learning : Convolutional Neural Networks (CNNs) are especially effective for pattern recognition tasks, automatically learning features and classes from large datasets. Segmentation : Region-Based Segmentation : Divides an image into regions or segments based on pattern similarities . Edge-Based Segmentation : Identifies boundaries between different pattern classes.

8. Pattern Recognition Techniques:

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