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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
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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
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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
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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
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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
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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 strategies is following: • Every input image is searched for a specific type of feature, • This feature is then compared to a database containing models of the objects in order to verify if there are recognised objects. Simon Achatz, State of the art of object recognition techniques, Technische Universitat Muchen. 7
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1. Introduction ▪ General Object Recognition Strategies: Feature-based method ➢ Features and their descriptors can be either found considering the whole image (global feature) or after observing just small parts of the image (local feature). ➢ An histogram of the pixel intensity or colour are simple examples for global features. ➢ It is not always reasonable to compare the whole image, as already slight changes in illumination, position (occlusion) or rotation lead to significant differences and a correct recognition is not possible anymore. Simon Achatz, State of the art of object recognition techniques, Technische Universitat Muchen. 8
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1. Introduction ▪ General Object Recognition Strategies: Feature-based method ➢ Descriptors of local features are more robust against these problems and therefore algorithms with local features often outperform global feature-based methods. Two patches of different images are cut and compared if the error between the patches is below a certain threshold. Simon Achatz, State of the art of object recognition techniques, Technische Universitat Muchen. 9
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1. Introduction ▪ General Object Recognition Strategies: Interpretation Tree ➢ Interpretation tree is a depth first search algorithm for model matching. ➢ Algorithms based on this approach often try to recognise n-dimensional geometric objects, therefore a database containing models with known features is necessary. ➢ The feature set might consist of distance, angle and direction constraints between points on the surface of the objects. Simon Achatz, State of the art of object recognition techniques, Technische Universitat Muchen. 10
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1. Introduction ▪ General Object Recognition Strategies: Interpretation Tree Procedure of an interpretation tree algorithm Simon Achatz, State of the art of object recognition techniques, Technische Universitat Muchen. 11
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