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Biologically Inspired Methods of Object Recognition
Vaľko, Tomáš ; Hradiš, Michal (referee) ; Juránek, Roman (advisor)
Object recognition is one of many tasks in which the computer is still behind the human. Therefore, development in this area takes inspiration from nature and especially from the function of the human brain. This work focuses on object recognition based on extracting relevant information from images, features. Features are obtained in a similar way as the human brain processes visual stimuli. Subsequently, these features are used to train classifiers for object recognition (e.g. SVM, k-NN, ANN). This work examines the feature extraction stage. Its aim is to improve the feature extraction and thereby increase performance of object recognition by computer.
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Multi Object Class Learning and Detection in Image
Chrápek, David ; Hradiš, Michal (referee) ; Beran, Vítězslav (advisor)
This paper is focused on object learning and recognizing in the image and in the image stream. More specifically on learning and recognizing humans or theirs parts in case they are partly occluded, with possible usage on robotic platforms. This task is based on features called Histogram of Oriented Gradients (HOG) which can work quite well with different poses the human can be in. The human is split into several parts and those parts are detected individually. Then a system of voting is introduced in which detected parts votes for the final positions of found people. For training the detector a linear SVM is used. Then the Kalman filter is used for stabilization of the detector in case of detecting from image stream.
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Recognition of Objects in Pictures
Nedoma, David ; Samek, Jan (referee) ; Zbořil, František (advisor)
This thesis is about solving a problem of recognition of objects in pictures. The aim was to create a program that will be able to recognize objects in an image. Describes progressively step by step processing of image data. Shortly describes preprocessing of image, after that describes in detail segmentation, description of segmented data and classification of objects. Describes algorithms and methods that are applicable for each step.
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Structural Methods of Objects Identification for Industrial Robot Operation
Minařík, Martin ; Šlapal, Josef (referee) ; Konečný, Vladimír (referee) ; Šťastný, Jiří (advisor)
This PhD thesis deals with the use of structural methods of objects identification for industrial robots operation. First, the present state of knowledge in the field is described, i.e. the whole process of objects recognition with the aid of common methods of the syntactic analysis. The main disadvantage of these methods is that is impossible to recognize objects whose digitalized image is corrupted in some ways (due to excessive noise or image disturbances), objects are therefore deformed. Further, other methods for the recognition of deformed objects are described. These methods use structural description of objects for object recognition, i.e. methods which determine the distance between attribute descriptions of images. The core part of this PhD thesis begins in Chapter 5, where deformation grammars, capable of description of all possible object deformations, are described. The only complication in the analysis is the ambiguity of the deformation grammar, which lowers the effectiveness of the analysis. Further, PhD thesis deals with the selection and modification of a proper parser, which is able to analyze a deformation grammar effectively. Three parsers are described: the modified Earley parser, the modified Tomita parser and the modified hybrid LRE(k) parser. As for the modified Earley’s parser, ways of its effective implementation are described. One of the necessary parts of the object recognition is providing the invariances, which this PhD thesis covers in detail, too. Finally, the results of described algorithms are mentioned (successfulness and speed of deformed objects recognition) and suggested testing environment and implemented algorithms are described. In conclusion, all determined possibilities of deformation grammars and their results are summarized.
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Hough's transform for circle detection
Kazík, Martin ; Burget, Radim (referee) ; Říha, Kamil (advisor)
The thesis is focused on the implementation of Hough transform algorithm for circle recognition. Algorithm is implemented in C++ language using open source library OpenCv. As a development environment was chosen Microsoft Visual Studio 2008. At first there is general description of classical Hough transform for line and circle recognition. Then thesis contains description of particular steps of Hough transform algorithm and description of OpenCv functions witch are used in these steps. There is a detail description of functions for converting image to grayscale, smoothing image by Gaussian filter and Canny edge detector for edge detecting in smoothed image. Efficiency and speed of algorithm is increased by using function for finding possible centers. This function using the fact that line perpendicular to the chord of circle and going thought its middle point at the same time have to cross the center of the circle. Results of particular stages of algorithm (converting to grayscale, smoothing by Gaussian filter, edge detection, creating of possible centers accumulator and drawing circles) are presented on ultrasonic image of collagen arterial substitute. In the second part of the thesis the algorithm is used for detection of artery in frames of video captured by ultrasound. There is a description of automatic method for evaluating of success of artery detection. Success of detection is analyzed by changing values of important algorithm parameters. From series of tests there are defined ideal parameters of algorithm for artery detection in the video.
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Design of a vision system with Kuka robot
Rusnák, Jakub ; Pochylý, Aleš (referee) ; Kubela, Tomáš (advisor)
Diploma thesis deals with applications of vision system with KUKA robot in field of identification and sorting bigger amount of different objects. Introductory and theoretical part of the thesis describes present situation on industrial vision systems market and their usage. Diploma thesis include practical application of object (coin) recognition with SICK IVC 2D vision system and their sorting by industrial robot KUKA KR 3. Application is also concerned with network communication between camera and robot via PLC, programming in KRL language and programm for object recognition in IVC Studio.
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Robot vision with industrial robots Kuka
Krutílek, Jan ; Pochylý, Aleš (referee) ; Kubela, Tomáš (advisor)
Diploma thesis deals with a robot vision and its application to the problem of manipulation of coincidentally placed objects. There is mentioned an overview of current principles of the most frequently used vision systems on the market. With regard to the required task to be solved, there are mentioned various possibilities of using basic softsensors during the recognition of different objects. The objective of this Diploma thesis is also programming and realization of a demonstration application applying knowledge of PLC programming, knowledge of expert programming KRL language (for KUKA robots), knowledge of designing scripts for smart camera in Spectation software and knowledge of network communication among all devices used in this case.
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Hand drawn objects recognition
Křístek, Jakub ; Čmiel, Vratislav (referee) ; Janoušek, Oto (advisor)
This work deals with recognition of hand-drawn objects traced by children with mental disorders. The aim is to classify object’s geometrical primitives into classes so then can be plotted along with the idealized shape of the input object. Level of mental retardation is determined by the variance of the input (drawn) object from idealized shape of the object (artwork).
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