National Repository of Grey Literature 4 records found  Search took 0.00 seconds. 
Detection of cells in confocal microscopy images
Hubálek, Michal ; Štursa, Dominik (referee) ; Škrabánek, Pavel (advisor)
The goal of the thesis was to create an application that automatically detects healthy cardiomyocytes from images captured by a confocal microscope. The thesis was created based on the specific needs of researchers from the Slovak Academy of Sciences.The application will facilitate and increase the efficiency of their research,because until now they have to evaluate the images and search for suitable cells manually. The RetinaNet convolutional neural network is used for detection and has been implemented in a user-friendly desktop application. The application also automatically records and stores coordinates of detected cells which can be used for capturing cells in higher image quality. Another advantage of the developed application is its versatility, which allows to train detection on other data, making it applicable to other projects. The result of this work is a functional, standalone and intuitive application that is ready to be used by researchers.
Counting of characteristic scales of sand lizards in colour images
Maršala, Štěpán ; Štursa, Dominik (referee) ; Škrabánek, Pavel (advisor)
The diploma thesis describes the design and implementation of a program for counting secondary scales in the image data of the ventral sides of the bodies of sand lizards. The program respects the requirements of scientists from the Institute of Vertebrate Biology of the Czech Academy of Sciences and the Faculty of Education at Masaryk University for the controllability and accuracy of results. The program consists of several parts. In input receives photos of sand lizards, in which he cuts out an area of interest. Unifies the orientation of these sections using detected objects. Object detection is provided by YOLOv4. Another part of the program called the Centroid Detector determines the position of the centers of the secondary scales in the unified sections. This part uses the U-Net convolutional neural network, which is specially modified to detect the centers of objects in close proximity. The other parts of the program divide the detected positions of the scale centers into left and right secondary rows and write their numbers to the output file.
Counting of characteristic scales of sand lizards in colour images
Maršala, Štěpán ; Štursa, Dominik (referee) ; Škrabánek, Pavel (advisor)
The diploma thesis describes the design and implementation of a program for counting secondary scales in the image data of the ventral sides of the bodies of sand lizards. The program respects the requirements of scientists from the Institute of Vertebrate Biology of the Czech Academy of Sciences and the Faculty of Education at Masaryk University for the controllability and accuracy of results. The program consists of several parts. In input receives photos of sand lizards, in which he cuts out an area of interest. Unifies the orientation of these sections using detected objects. Object detection is provided by YOLOv4. Another part of the program called the Centroid Detector determines the position of the centers of the secondary scales in the unified sections. This part uses the U-Net convolutional neural network, which is specially modified to detect the centers of objects in close proximity. The other parts of the program divide the detected positions of the scale centers into left and right secondary rows and write their numbers to the output file.
Detection of cells in confocal microscopy images
Hubálek, Michal ; Štursa, Dominik (referee) ; Škrabánek, Pavel (advisor)
The goal of the thesis was to create an application that automatically detects healthy cardiomyocytes from images captured by a confocal microscope. The thesis was created based on the specific needs of researchers from the Slovak Academy of Sciences.The application will facilitate and increase the efficiency of their research,because until now they have to evaluate the images and search for suitable cells manually. The RetinaNet convolutional neural network is used for detection and has been implemented in a user-friendly desktop application. The application also automatically records and stores coordinates of detected cells which can be used for capturing cells in higher image quality. Another advantage of the developed application is its versatility, which allows to train detection on other data, making it applicable to other projects. The result of this work is a functional, standalone and intuitive application that is ready to be used by researchers.

Interested in being notified about new results for this query?
Subscribe to the RSS feed.