Lectures of Pattern Recognition: Sohag University PDF

Lectures of Pattern Recognition: Rania Ramadan Mohamed Department of Computer Science Faculty of Computers and Artificial Intelligence Sohag University [email protected] Course code: Math 459  Course information Bene...

Lectures of Pattern Recognition: Rania Ramadan Mohamed Department of Computer Science Faculty of Computers and Artificial Intelligence Sohag University [email protected] Course code: Math 459  Course information Beneficiaries: fourth-level Place: Lecture students from Time duration: Time: from Hall 5 at the the 2 theoretical 11:00 -1:00 mathematical faculty of hours every pm every Science statistics and week. Sunday building programming program  ‫قواعد توزيع درجات التقييم‬ ‫تقييم امتحان المقرر من (‪ )100‬مئة درجة كرقم صحيح في الفترة من ‪ 100-0‬بناء‬ ‫على الجدول المرفق‪-:‬‬ ‫المقرر نظري‬ ‫نوع االمتحان‬ ‫وعملي‬ ‫‪%50‬‬ ‫امتحان نظري نهائي‬ ‫‪%10‬‬ ‫امتحان شفوي نهائي‬ ‫‪%20‬‬ ‫امتحان منتصف الفصل‬ ‫‪%20‬‬ ‫اختبارات دورية وتمارين‬ ‫‪100‬‬ ‫المجموع‬ Pattern Recognition and Application Pattern recognition is a fundamental concept in computer science and artificial intelligence, enabling machines to analyze data and identify meaningful patterns. It has wide-ranging applications across various domains, from image analysis to medical diagnosis. Pattern Recognition: Unveiling the Mystery of Intelligence If we draw a simple diagram of something. This is nothing but a pattern. Pattern recognition involves analyzing whether a signal is one-dimensional or two-dimensional. We want the machine to recognize that signal, so that is the problem of pattern recognition. The pattern recognition problem started within the early years with the effort to understand intelligence. What do we mean by the ability to understand and benefit from the experience? Ex1: Ex2: sharp object. If we take the tip of a sharp pin,, if we take the example of fire, we know if we we know that if we touch or hit the tip of the put our finger in the fire, the fire will burn our pin, in that case, it will hurt our finger. finger. Pattern Recognition: A Journey from Ancient Philosophy to Modern Applications Pattern recognition, a fundamental concept in artificial intelligence, explores the ability of machines to identify and classify patterns in data. This journey delves into the history of pattern recognition, tracing its roots back to ancient philosophers like Plato and Aristotle, and then explores its modern applications in various fields. When and How do we recognize these pictures? Plato's Insight: Recognizing Patterns Through Abstract Ideas Abstract Ideas Plato claimed that our understanding of abstract ideas comes from a mystical connection to the world known to us as a priori A Priori Knowledge Plato believed that we have some knowledge innately, which he called "a priori" knowledge. This knowledge is not acquired through experience but is inherent to our minds. The Mystical Connection Plato's theory suggests a mystical connection between our minds and the world that stores information in a way that hasn’t been completely comprehended to grasp abstract ideas and use them to recognize patterns. what is this picture? A priori knowledge, which has been proved by Plato is not sufficient, We should be able to adapt our knowledge. Aristotle's Insight: He did not fully agree on the concept of a priori knowledge not only the priori knowledge is very important Aristotle said that it is not only the a priori knowledge is very important but also the ability to learn and adapt to the changing world, that is also equally important. adapt to the changing environment That means we should be able to adapt to the changing environment and our learning process must be adaptive. The learn new and new things and modify the knowledge we should be able to learn new and new things and we should be able to modify the knowledge that we have already acquired.  what is the problem with pattern recognition in that case? 1 Many Representation 2 identify the underlying structure within data More than one figure can if we have a given set of data, we want to identify the represent a certain structure structures within that data and what are the structures that we want to identify? It is the structures, which are known to me a priori what is the importance of pattern recognition? 1 The purpose of implementing pattern 2 we want machines should be equally intelligent recognition is to grant machines the as a human being and whatever work that we do, ability to function like humans. a machine or a computer should be able to do the same things. Real-World Applications of Pattern Recognition medical signal analysis If a doctor or medical practitioner has any doubt about the functioning of the heart, whether our heart is functioning properly or no, they prescribe us to go for an ECG checkup. Real-World Applications of Pattern Recognition Pattern recognition finds practical applications in diverse domains. For instance, in automated assembly shops, robots equipped with vision systems utilize pattern recognition to identify and manipulate objects on the shop floor. This involves recognizing objects based on their visual patterns and then picking them up and placing them in specific positions. Voice Recognition Machine Vision Pattern recognition is used to identify individuals based on In machine vision, pattern recognition enables machines to their unique voice patterns. Various applications employ "see" and interpret images. This technology is used in self- this technology, such as unlocking devices and verifying driving cars, medical imaging, and industrial automation. identities. Pattern Recognition Techniques Supervised Learning: Learning from Labeled Data Supervised learning is a type of pattern recognition where the machine is trained on labeled data. This means that the machine is provided with examples of objects or patterns along with their corresponding labels. The machine then learns to associate these patterns with their labels and can subsequently classify new, unseen patterns. 1 A Priori Knowledge 2 Similarity Measure Supervised learning relies on a priori knowledge, The machine uses a similarity measure to compare which is knowledge acquired through experience, unknown objects or patterns to the labeled examples observation, and instruction. This knowledge is used it has been trained on. This measure helps determine to classify unknown objects or patterns. the most likely class for the unknown object. Unsupervised Learning: Discovering Patterns in Unlabeled Data Unsupervised learning is a type of pattern recognition where the machine is not provided with labeled data. Instead, the machine must discover patterns and relationships within the data itself. This approach is often used for tasks like clustering, anomaly detection. Grouping Similar Objects Unsupervised learning algorithms group similar objects together based on their inherent characteristics. This process does not rely on prior knowledge or labels. Identifying Dissimilarities The algorithms also identify objects that are dissimilar to others, potentially highlighting outliers or anomalies within the data. Feature Extraction: A Visual Explanation Feature extraction is a fundamental concept in pattern recognition and machine learning. It involves transforming raw data into a set of meaningful features that can be used to classify or analyze patterns. Imagine taking a USB drive and a pen, both objects with distinct boundaries and regions. By extracting features from these boundaries and regions, we can differentiate them. Types of Features Features are typically categorized into two types: boundary features and region features. Boundary features are derived from the object's outline, capturing information about its shape and form. Region features, on the other hand, are extracted from the area enclosed by the boundary, providing insights into the object's color, texture, and other internal properties. Boundary Features Region Features Features extracted from the object's boundary, such as Features extracted from the area enclosed by the boundary, shape and form. such as color and texture. Feature Extraction: Describing Objects and Patterns Feature extraction is a crucial step in pattern recognition. It involves extracting relevant features or characteristics from objects or patterns that can be used for classification or other tasks. These features provide a concise representation of the data, enabling the machine to make accurate predictions. Feature Type Description Color The dominant colors present in an image or object. Shape The geometric form of an object, such as a circle, square, or triangle. Texture The surface properties of an object, such as smooth, rough, or bumpy. Boundary-Based Features Boundary features can be extracted by representing the boundary as a set of points in a digital domain. For example, in a two-dimensional space, the boundary can be represented as a sequence of numbers, where each number corresponds to a point on the boundary. Number Representation Fast or Discrete Fourier Transform Each point on the boundary is represented by a complex The Discrete Fourier Transform (DFT) can be applied to the number, sk = uk + jvk, where uk and vk are the real and sequence of numbers representing the boundary to extract imaginary components, respectively. features. Shape Features Another way to extract features from the boundary is by calculating the moments of the shape around different axes. The moments capture information about the shape's distribution and can be used to differentiate between different shapes. 1 Moments 2 Shape Differentiation Moments are calculated around the principal axis and Moments can be used to differentiate between an orthogonal axis passing through the center of different shapes based on their distribution of mass. gravity of the shape. Regional Features Region features provide information about the object's internal properties, such as color and texture. Color features can be extracted by analyzing the distribution of colors within the region, while texture features can be obtained by analyzing the spatial arrangement of pixels within the region. Feature Type Description Color Distribution of colors within the region. Texture Spatial arrangement of pixels within the region. Feature Vector Representation Once various features are extracted from an object, they are combined into a feature vector. Each component of the feature ve ctor represents a specific feature, and the order of the components is important. This vector provides a numerical representation of the object , capturing its essential characteristics. Feature Extraction Feature Vector Extract features from the object's boundary, region, or other Combine the extracted features into a vector, where each properties. component represents a specific feature. Feature Space The feature vector transforms the object from its original domain (e.g., spatial or temporal) into a feature space. This space is defined by the number of features in the feature vector, and each point in this space represents a unique combination of feature values. Feature Vector A numerical representation of an object in the feature space. Dimensions The number of features in the feature vector determines the dimensionality of the feature space. Pattern Similarity in Feature Space Once objects are represented as points in the feature space, their similarity or dissimilarity can be measured by calculating the distance between their corresponding feature vectors. Similar objects will have feature vectors that are close together in the feature space, while dissimilar objects will have feature vectors that are far apart. 1 Distance Calculation The distance between feature vectors is used to measure the similarity or dissimilarity between objects. 2 Pattern Similarity Similar objects have feature vectors that are close together in the feature space. 3 Pattern Dissimilarity Dissimilar objects have feature vectors that are far apart in the feature space. Advantages of Feature Space Representation Representing objects in a feature space offers several advantages for pattern recognition and machine learning tasks. By transforming data into a feature space, we can simplify complex patterns and make them easier to analyze and classify. This representation also allows us to measure similarity and dissimilarity between objects based on their feature values. 1 Pattern Simplification Feature space representation simplifies complex patterns, making them easier to analyze and classify. 2 Similarity Measurement Distance between feature vectors allows for easy measurement of similarity and dissimilarity between objects. Error Considerations It's important to consider the impact of errors on feature extraction and representation. Measurement errors, fabrication errors, and other sources of noise can affect the accuracy of feature vectors. However, even with errors, similar objects should have feature vectors that are relatively close together in the feature space. Measurement Errors Fabrication Errors Errors introduced during data acquisition or measurement. Errors introduced during the manufacturing or production process. preencoded.png The Importance of Feature Extraction Feature extraction plays a vital role in pattern recognition by enabling machines to understand and interpret data. By extracting relevant features, machines can make more accurate predictions and classifications. This process is essential for various applications, including image recognition, speech recognition, and natural language processing. Data Reduction Feature extraction reduces the dimensionality of data by focusing on the most important characteristics, simplifying the analysis process. Improved Accuracy By extracting relevant features, machines can make more accurate predictions and classifications, leading to better performance in various tasks. Enhanced Efficiency Feature extraction reduces the computational complexity of pattern recognition algorithms, leading to faster processing times and improved efficiency. The Role of Feature Extraction in Supervised Learning In supervised learning, feature extraction is crucial for training models to accurately classify new data. By extracting relevant features from labeled examples, the model learns to associate these features with specific classes. This enables the model to generalize its knowledge to unseen data and make accurate predictions. Feature Selection Feature Engineering Selecting the most relevant features for a given task is essential for Feature engineering involves creating new features from existing ones to improving model performance. This process involves identifying features improve the model's ability to capture complex relationships within the that contribute most to the classification accuracy. data. Feature Extraction in Unsupervised Learning In unsupervised learning, feature extraction plays a crucial role in discovering hidden patterns and relationships within unlabeled data. By extracting relevant features, the algorithms can group similar objects together and identify outliers or anomalies. 1 2 3 Dimensionality Reduction Clustering Anomaly Detection Feature extraction techniques like principal By extracting features that capture the Feature extraction can help identify objects component analysis (PCA) can reduce the inherent characteristics of objects, that deviate significantly from the norm, dimensionality of data, making it easier to unsupervised learning algorithms can potentially indicating anomalies or outliers visualize and analyze patterns. effectively cluster similar objects together. within the data. The Future of Pattern Recognition Pattern recognition is a rapidly evolving field with significant potential for future advancements. As technology continues to progress, we can expect to see even more sophisticated pattern recognition algorithms and applications. Human-Robot Collaboration Virtual and Augmented Reality Pattern recognition will play a crucial role in enabling seamless Pattern recognition will enhance virtual and augmented reality collaboration between humans and robots, leading to more efficient and experiences, enabling more realistic and immersive interactions with productive work environments. virtual environments. Conclusion: The Power of Pattern Recognition Pattern recognition is a powerful tool that enables machines to understand and interpret data, leading to significant advancements in various fields. From automated assembly lines to voice recognition systems, pattern recognition is transforming the way we interact with technology and the world around us. preencoded.png Thank you