The content of this course is complementary to the Machine Learning course offered by Joschka Boedecker and Frank Hutter. It absolutely makes sense to attend both courses if you want to specialize in Machine Learning.
It also complements the Deep Learning course. Remarks: Full completion of all relevant theoretical and programming assignments is highly recommended. The exam will consist of a mixture of binary choice questions and fields, in which you must fill your solution.
To get an idea of the style of the exam you can have a look at the test exam for image processing and the test exam for optimization. Class 1: Introduction MachineLearning Class 2: Probability distributions MachineLearning Class 4: Nonparametric methods MachineLearning Class 5: Regression MachineLearning Class 6: Gaussian processes MachineLearning Class 7: Classification MachineLearning Class 8: Support vector machines MachineLearning Class 9: Projection methods MachineLearning The performance of enhanced statistics is investigated in terms of feature extraction for the statistical classifiers.
The feature extraction methods reviewed and applied are decision boundary feature extraction and discriminant analysis.
Computer Vision Group, Freiburg
The classification results obtained by using enhanced statistics in classification of hyperdimensional data are excellent and show the classifiers to be able to distinguish between classes with similar spectral properties. Classification methods based on consensus from several data sources are also considered with respect to classification of hyperdimensional data. The consensus theoretic methods need weighting mechanisms to control the influence of each data source in the combined classification.
The weights are optimized in order to improve the combined classification accuracies. A nonlinear method which utilizes a neural network is used and gives excellent results in experiments. Optical pattern recognition and neural associative memory are important research topics for optical computing. Optical techniques, in particular, those based on holographic principle, are useful for associative memory because of its massive parallelism and high information throughput. The objective of this chapter is to discuss system issues including the design and fabrication of a multi-sensory opto-electronic feature extraction neural associative retriever MOFENAR.
A set of Fourier transforms of reference inputs can be selectively recorded in the hologram. When convergence is reached after iterations, the output can either be displayed or used for post-processing computations. Methods for combining multiple classifiers have been developed for improved performance in pattern recognition. This paper examines nine correlated classifiers from the perspective of majority voting. It demonstrates that relationships between the classifiers can be observed from the voting results, that the error reduction ability of a combination varies inversely with the correlation among the classifiers to be combined, and that the correlation coefficient is an effective measure for selecting a subset of classifiers for combination to achieve the best results.
Surveys of the basic concepts and underlying techniques are presented in this chapter.
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A basic model for document processing is described. In this model, document processing can be divided into two phases: document analysis and document understanding. A document has two structures: geometric layout structure and logical structure. Extraction of the geometric structure from a document refers to document analysis; mapping the geometric structure into logical structure deals with document understanding.
Both types of document structures and the two areas of document processing are discussed in this chapter.
Two categories of methods have been used in document analysis, namely, 1 hierarchical methods including top-down and bottom-up approaches, 2 no-hierarchical methods including modified fractal signatures. Tree transform, formatting knowledge and description language approaches have been used in document understanding. All the above approaches are presented in this chapter. A particular case — form document processing is discussed. Form description and form registration approaches are presented.
A form processing system is also introduced. Finally, many techniques, such as skew detection, Hough transform, Gabor filters, projection, crossing counts, form definition language, etc. A variety of biomedical signals such as hormonal concentrations in peripheral blood can only be sampled and measured infrequently over a limited period of time; hence, they are considered as sparsely sampled non-stationary short time series. Discrete pseudo Wigner distribution is a transform which can be applied to such signals to provide time-dependent spectral information at an improved frequency resolution in comparison to the short-time Fourier transform.
When appropriately clipped and scaled, it can be visualized as an image showing the characteristic pattern of the signal. Spectral features can be extracted from the Wigner distribution for use in automatic pattern recognition. The basic technique is described in this article along with an example of its application to cortisol time series.
This chapter provides a summary of recent developments in two connected fields: remotely sensed image analysis and geographic map data analysis, with emphasis on spatial pattern extraction and description as well as on segmentation and categorisation of the geographic space. Then the application of pattern recognition, computer vision and spatial analysis principles to image data analysis and map data analysis are examined.
Finally, examples are given to illustrate the possibilities of achieving greater synergy in the use of both image and map data sets. Such use will be essential to fully exploit images which would be provided by new satellite sensors with spatial resolution ranging form a meter to a kilometer. This chapter surveys the progress in the application area of face recognition, a task posing a challenging mix of problems in object recognition.
Face recognition promises to contribute to solutions in such diverse application areas as multimedia e. Contributing to the growing interest in face recognition is the fact that humans readily relate to the results " … but I am terrible with names. The Encyclopedia Britannica defines recognition as a "form of remembering characterized by a feeling of familiarity" under the usage of face recognition: " … Recognizing a familiar face without being able to recall the person's name is a common example …".
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The chapter starts with a task statement distinguishing classes of applications, continues with pointers supporting the competitive testing of face recognition and provides examples for image standards and publicly available databases. The remainder of the chapter describes as an example the paradigm based on elastic matching of attributed graphs. The need for automation in the food industry is growing. Some industries such as the poultry industry are now highly automated whereas others such as the fishing industry are still highly dependent on human operators.
At the same time consumers are demanding increased quality of the products. In the food industry the objects are often of varying size and shape, and often flexible and randomly oriented when presented to the automation system. To automate handling of these objects, an intelligent system such as a vision system is needed to control the mechanical operations to ensure optimum performance and quality.
Data Analysis and Pattern Classification
This chapter describes vision techniques that can be used to detect and measure shape and quality of food products. It stresses the specific implementation context, needed performance, sensors, optics, illumination as well as vision algorithms. Algorithms include those for the size measurement of flexible objects and for the colour measurement of objects with nonuniform colour. Some results are given. Over the last few years significant progress has been made in applying methods using distributions of feature values to texture analysis.
Pattern Recognition and Machine Learning
Very good performance has been obtained in various texture classification and segmentation problems. This chapter overviews recent progress and presents some examples to demonstrate the efficiency of the approach. Problems of analyzing textured surfaces in industrial applications are also discussed.
A general overview of the problem space is given, presenting sets of solutions proposed and their prerequisites. This chapter gives a formal model for scene understanding, as well as for context information; it helps in adapting image understanding procedures and software to varying contexts, when some formal assumptions are satisfied. We have defined and formalized context separation and context adaptation, which are essential for many applications, including to achieve the robustness of the understanding results in changing sensing environments. This model uses constraint logic programming and specialized models for the various interactions between the objects in the scene and the context.
A comparison is made with, and examples are given of, the context models in more classical frameworks such as multilevel understanding structures, object based design in scenes, knowledge based approach, and perceptual context separation. In this paper, we review various methods and techniques for estimating the position and pose of an autonomous mobile robot.
The techniques vary depending on the kind of environment in which the robot navigates, the known conditions of the environment, and the type of sensors with which the robot is equipped. The methods studied so far are broadly classified into four categories, landmark-based methods, methods using trajectory integration and dead reckoning, methods using a standard reference pattern, and methods using a priori knowledge of a world model which is matched the sensor data for position estimation.
Each of these methods is considered and its relative merits and drawbacks are discussed. The objective of this chapter is to provide a critical analysis of Computer Vision within the context of Postal Automation services. The main functional requirements of this application field are briefly referred, as well as the involved Vision functions, which are considered here in a broad sense, including Pattern Recognition, Image Processing and understanding, Signal Processing and Robot Vision. New trends as well as new services emerging in Postal Automation are also discussed, in an attempt to highlight the expected impact on the development of Computer Vision technology.
The aim of the chapter is also to refer to the most relevant chievements of Computer Vision as well as to discuss why other promising techniques did not succeed, in spite of the advanced results obtained at prototypical level in laboratory experiments. The ultimate goal is to provide a contribution to stimulate the basic and applied research efforts in this important field of industrial automation and possibly support the recent initiatives of technology transfer from research to industry.
During the last decade, significant progress has been made towards the goal of using machine vision as an aid to highway driving. This chapter describes a few pieces of representative work which have been done in the area.