Detection of objects based on color is not commonly used method of computer vision. There are many methods thats deals with the detection of significant points, but the color information has been omitted. The goal of this thesis is to design method of the detection significant color image areas and these areas match up with areas detected in another image. I analyzed features of detectors required to identify the reciprocal correspondence of images, defined the color significance concept, described basic color models and their properties, and a design of statistically compiled data - based method was described. Algorithms for the detection of color use color models RGB and HSV. Correspondence of areas detected in different images is performedy Kohonen neural network. The first input vector can teach Kohonen neural network and the second vector is classified by this network. To remove erroneous classifications RANSAC method is used. As a result, the method can be used for basic and rapid determination of correspondence between images, or to speed up commonly used methods for detection of significant points. At the end of the thesis are presented programs, showing functionality and options of design methods. The designed algorithms have been developed in MATLAB.

This master thesis, conducted at SAAB Dynamics Linköping, studies Visual Odometry (VO), or camera pose estimation, using a monocular event camera. Event cameras are not yet widely used in the industry, and there is significant interest in understanding the methodological differences between performing Visual Odometry (VO) with event cameras compared to traditional frame cameras. Event cameras have the potential to capture information between frames, which may include data that is lost or not captured by frame cameras. This thesis compares two different feature detectors and evaluates their performance against a frame-based method. Visual Odometry was conducted both with and without known 3D points. However, attempts to perform VO without known 3D points did not yield a robust pipeline within the limited time frame of this thesis, and thus was not further developed to function with a purely event-based method. On the other hand, VO performance with known 3D points achieved continues 6-DoF pose estimation. The results demonstrate that event cameras have the potential to detect and track features in challenging scenes, such as those with dark or bright lighting conditions, for example, objects passing by the sun. This thesis suggests that implementing a robust 6-DoF pose estimation would be feasible with a more reliable 3D-2D point pair matching technique or a more sophisticated VO pipeline.

This master’s thesis describes the design and implementation of the application that created the basis set pieces "puzzle" according to the shape of the folded edges of the original image. This application is developed using Matlab. The work also describes how to create a database of actual pieces of the puzzle composite photo image. Closer was also focused on finding the characteristic section points, their segmentation and appropriate description. There is dismantled procedure for selecting the types of symptoms and their extraction. On the basis of suitably described pieces of segmented parts is designed and implemented the algorithm of comparing and grouping into clusters. Using the proposed method of visualization is then displayed in the resulting composite picture puzzle.

This master’s thesis describes the design and implementation of the application that created the basis set pieces "puzzle" according to the shape of the folded edges of the original image. This application is developed using Matlab. The work also describes how to create a database of actual pieces of the puzzle composite photo image. Closer was also focused on finding the characteristic section points, their segmentation and appropriate description. There is dismantled procedure for selecting the types of symptoms and their extraction. On the basis of suitably described pieces of segmented parts is designed and implemented the algorithm of comparing and grouping into clusters. Using the proposed method of visualization is then displayed in the resulting composite picture puzzle.