Chapter 7. Object Recognition ❖1. Introduction ❖2. Pattern Matching ❖3. Feature-based Methods ❖4. Artificial Neural Networks Simon Achatz, State of the art of object recognition techniques, Technische Universitat Muchen. 2 1. Introduction Object recognition: localize and to classify objects. General concept: ➢ training datasets containing images with known and labelled objects; ➢ extracts different types of information (colours, edges, geometric forms) based on the chosen algorithm ➢ for any new image the same information is gathered and compared to the training dataset to find the most suitable classification. Simon Achatz, State of the art of object recognition techniques, Technische Universitat Muchen. 3 1. Introduction Applications: ➢ robots in industrial environments, ➢ face or handwriting recognition ➢ autonomous systems such as modern cars which use object recognition for pedestrian detection, emergency brake assistant and so on. ➢… Simon Achatz, State of the art of object recognition techniques, Technische Universitat Muchen. 4 1. Introduction General Object Recognition Strategies ➢ Appearance-based method ➢ Feature-based method ➢ Interpretation Tree ➢ Pattern Matching ➢ Artificial Neural Networks Simon Achatz, State of the art of object recognition techniques, Technische Universitat Muchen. 5 1. Introduction General Object Recognition Strategies: Appearance-based method ➢ Face or handwriting recognition ➢ Reference training images ➢ This dataset is compressed to obtain a lower dimension subspace, also called eigenspace. ➢ Parts of the new input images are projected on the eigenspace and then correspondence is examined. Simon Achatz, State of the art of object recognition techniques, Technische Universitat Muchen. 6 1. Introduction General Object Recognition Strategies: Feature-based Method ➢ Characteristic for each object ➢ Colours, contour lines, geometric forms or edges ➢ The basic concept of feature-based object recognition

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