Essentials of Image Processing and Pattern Recognition (PC147)

SynopsisWith rapid advancement of electronics, computers and computing technologies, image processing and pattern recognition has become a common tool in modern society. Applications of image processing and pattern recognition include industrial inspection, medical diagnostic imaging, traffic monitoring, security surveillance, robotics, biometric, remote sensing, and multimedia computing.

It is essential that engineers and personnel involved in using and developing products related to image processing system have a firm grasp of the fundamentals concept in image processing and pattern recognition.

With this in mind, this course is designed to provide the participants with a broad understanding of the practical aspects and common techniques of image processing and pattern recognition to better apply these techniques in real applications.

Image processing is the employment of various techniques to enhance, manipulate and extract information from images. Pattern recognition is related to techniques used to identify and interpret the extracted visual information. The human perception has the capability to acquire, integrate, and interpret all the abundant visual information around us. It is challenging to impart to a machine such capability in order to interpret the visual information embedded in images. This course is developed to provide a better understanding of the field of image processing and pattern recognition to fill the needs of engineers involved in research, product development, manufacturing, technical, and customer support of their products, with specifications related to image processing and pattern recognition.

Course Highlight
This course comprises two sections i.e. image processing and pattern recognition. In the section on image processing, it starts by introducing the fundamental concept and various terminologies in image processing. The participants are also introduced to basic techniques used in image enhancement, segmentation, and feature extraction. In the section on pattern recognition, it covers the fundamental concepts of Bayesian decision theory, maximum likelihood estimation, nonparametric estimations, and discriminant functions. The topics on the design and development of pattern recognition and learning systems using the latest technologies in computational intelligence and machine learning will also be covered.

What You Will Learn

• Fundamental concepts and terminologies employed in digital image processing such as pixels, contrast, resolution, image histogram, color models and etc
• Basic techniques employed in image enhancement for contrast manipulation, sharpening and noise removal
• Basic techniques employed in image segmentation
• Basic techniques employed for feature detection
• Fundamental concepts and terminologies employed in pattern recognition and classification such as recognition, classification, clustering, regression, estimation, etc
• Basic techniques employed in decision theory and discriminant analysis
• Basic techniques employed in parametric and nonparametric estimation
• Basic techniques employed in discriminant analysis
• Basic techniques employed in machine learning and intelligent systems for pattern recognition

Who Should AttendThe primary target audiences for this course are technicians and engineers with little or no background in digital image processing and pattern recognition who would like to learn about fundamentals and state-of-the-art algorithms in this area. The course will also be of interest to technical managers who supervise research or development in image processing and pattern recognition. This course is also applicable to a broad audience from various industries that need to have strong basic understanding of the various concepts, terms and technologies of image processing and pattern recognition.

Examples of target audience:

• Project Managers
• Program Managers
• Test Engineers
• Mechanical Engineers
• Quality and Reliability Engineers
• Vision system development Engineers
• Technical Marketing and Sales Engineers

PrerequisiteInterest in image processing and pattern recognition and desire to acquire better conceptual understanding in this area. Technical background in electrical/electronic/ mechanical engineering or physics degree would be desirable.

Course MethodologyClass room training.

Course Duration3 days, 9am - 5pm

Course Structure1) Image Processing

• Introduction to digital image processing
• Image properties
• Color and color models
• Noise and noise models
• Image enhancement
• Linear Image Processing Operations(Point processing/Neighborhood processing/Histogram-based)
• Image Transform
• Image Segmentation
• Feature extraction

• Introduction to pattern recognition systems and statistical classification
• Feature choices and model choices
• Bayesian decision theorem and minimum error rate classification
• Discriminant functions and decision surfaces
• Maximum likelihood estimation and Bayesian estimation
• Kernel estimation and nearest neighbour estimation
• Machine learning systems: supervised learning, unsupervised learning
• Computer simulations Optional Topics
• Affine Transform (Alignment of boards)
• Basic concepts of bit depth, resolution and Modulation Transfer Function (MTF)
• Morphological operations