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Machine vision implementation in the UVSSR PORTABLE CELL production system
Gómez Rojas, José Luis ; Kroupa, Jiří (referee) ; Bražina, Jakub (advisor)
This thesis investigates the integration of computer vision into Industry 4.0, utilizing the UVSSR CELL at Brno University of Technology. Focused on enhancing virtual commissioning, it introduces three innovative vision techniques linked via an OPC server to an IoT gateway. Object recognition, hand gesture control, and facial recognition are employed, improving robotic arm operations and security protocols. This integration resulted in high accuracy trained model for object detection with mAP50-90 close to 0.9, and control precision of the technologies and the virtual environment, contributing significantly to smart industry automation and setting a call for future work on top of it. The thesis covers methodology, technological implementation, and prospects for advanced, efficient machine vision systems within industry 4.0.
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Gesture Based Human-Computer Interface
Jaroň, Lukáš ; Beran, Vítězslav (referee) ; Španěl, Michal (advisor)
This masters thesis describes possibilities and principles of gesture-based computer interface. The work describes general approaches for gesture control. It also deals with implementation of the selected detection method of the hands and fingers using depth maps loaded form Kinect sensor. The implementation also deals with gesture recognition using hidden Markov models. For demonstration purposes there is also described implementation of a simple photo viewer that uses developed gesture-based computer interface. The work also focuses on quality testing and accuracy evaluation for selected gesture recognizer.
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Hand Gesture Recognition
Adámek, Jakub ; Beran, Vítězslav (referee) ; Španěl, Michal (advisor)
This thesis is focused on human hand gesture recognition. The main part of the work deals with image segmentation of videosequences for further gesture recognition. For the image segmentation, techniques such as face detection followed by skin detection in combination with background subtraction method are used. In order to eliminate noise, methods of mathematic morphology are applied. The work focuses only on dynamic hand gesture recognition. The proposed gesture recognition system is inspirited by hidden Markov model method. The last chapter of the thesis discusses the accuracy of the gesture recognition.
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Hand gesticulation recognition in image
Zlotý, Petr ; Horák, Karel (referee) ; Janáková, Ilona (advisor)
This work describes gesticulation recognition analysis and implementation. There was chosen the recognition method based on orientation histograms. This method was enhanced by combining it with two segmentation methods. First one based on interframe diferencing and the second one based on skin color detection. There is also mentioned the movement recognition method from reasonable data. It's based on finding a minimum of absolute value of diference between scalar product of picture’s bases vectors and motion vector.
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Optical methods of gesture recognition
Netopil, Jan ; Odstrčilík, Jan (referee) ; Čmiel, Vratislav (advisor)
This thesis deals with optical devices and methods image processing for recognizing hand gestures. The types of gestures, possible applications, contact based devices and vision based devices are described in thesis. Next, a review of hand detection, features extraction and gesture classification is provided. Proposed gesture recognition system consists of infrared camera FLIR A655sc, infrared FLIR Lepton module, webcam Logitech S7500, method for hand gesture analysis and a database of gestures for classification. For each of the devices, gesture recognition is evaluated in terms of speed and accuracy in different environments. The proposed method was implemented in MATLAB.
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Hand Gesture Recognition
Adámek, Jakub ; Beran, Vítězslav (referee) ; Španěl, Michal (advisor)
This thesis is focused on human hand gesture recognition. The main part of the work deals with image segmentation of videosequences for further gesture recognition. For the image segmentation, techniques such as face detection followed by skin detection in combination with background subtraction method are used. In order to eliminate noise, methods of mathematic morphology are applied. The work focuses only on dynamic hand gesture recognition. The proposed gesture recognition system is inspirited by hidden Markov model method. The last chapter of the thesis discusses the accuracy of the gesture recognition.
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Optical methods of gesture recognition
Netopil, Jan ; Odstrčilík, Jan (referee) ; Čmiel, Vratislav (advisor)
This thesis deals with optical devices and methods image processing for recognizing hand gestures. The types of gestures, possible applications, contact based devices and vision based devices are described in thesis. Next, a review of hand detection, features extraction and gesture classification is provided. Proposed gesture recognition system consists of infrared camera FLIR A655sc, infrared FLIR Lepton module, webcam Logitech S7500, method for hand gesture analysis and a database of gestures for classification. For each of the devices, gesture recognition is evaluated in terms of speed and accuracy in different environments. The proposed method was implemented in MATLAB.
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Gesture Based Human-Computer Interface
Jaroň, Lukáš ; Beran, Vítězslav (referee) ; Španěl, Michal (advisor)
This masters thesis describes possibilities and principles of gesture-based computer interface. The work describes general approaches for gesture control. It also deals with implementation of the selected detection method of the hands and fingers using depth maps loaded form Kinect sensor. The implementation also deals with gesture recognition using hidden Markov models. For demonstration purposes there is also described implementation of a simple photo viewer that uses developed gesture-based computer interface. The work also focuses on quality testing and accuracy evaluation for selected gesture recognizer.
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Hand gesticulation recognition in image
Zlotý, Petr ; Horák, Karel (referee) ; Janáková, Ilona (advisor)
This work describes gesticulation recognition analysis and implementation. There was chosen the recognition method based on orientation histograms. This method was enhanced by combining it with two segmentation methods. First one based on interframe diferencing and the second one based on skin color detection. There is also mentioned the movement recognition method from reasonable data. It's based on finding a minimum of absolute value of diference between scalar product of picture’s bases vectors and motion vector.
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