Context: In this thesis subimage matching in historical handwritten documents using SIFT (Scale-Invariant Feature Transform) keypoints was tested. SIFT features are invariant to scale and rotation and have gained a lot of interest in the research community. The historical documents used in this thesis orignates from 16th century and forward. The following steps have been executed; binarization, word segmentation, feature identification and clustering. The binarization step converts the images into binary images. The word segmentation separates the different words into individual subimages. In the feature identification SIFT keypoints was found and descriptors was computed. The last step was to cluster the images based on the distances between the set of image features identified. Objectives: The main objectives are to find a good configuration for the binarization step, implement a good word segmentation, identify image features and lastly to cluster the images based on their similarity. The context from subimages are matched to each other rather than trying to predict what the context of a subimage is, simply because the data that has been used is unlabeled. Methods: Implementation were the main methodology used combined with experimentation. Measurements were taken throughout the development and accuracy of word segmentation and the clustering is measured. Results: The word segmentation got an average accuracy of 89\% correct segmentation which is comparable to other word segmentating results. The clustering however matched 0% correctly.Conclusions: The conclusions that have been drawn from this study is that SIFT keypoints are not very well suited for this type of problem which includes a lot of handwritten text. The descriptors were not discriminative enough and different keypoints were found in different images with the same handwritten text, which lead to the bad clustering results.

As autonomous systems develop an ever expanding range of capabilities, monolithic systems (systems with multiple capabilities on a single platform) become increasingly expensive to build and vulnerable to failure. A promising alternative to these monolithic systems is a distributed team with different capabilities that can provide equivalent or greater overall functionality through cooperation. Such systems benefit from decreased individual system cost, robustness to partial system failure, and the possibility of operating over larger geographical areas. However, these benefits come at the cost of increased planning, control, perception, and computational complexity, as well as novel algorithm development. This thesis contributes to the start-of-the-art in distributed systems by drawing on techniques from the fields of formal methods to address problems in team task and motion planning, and from computer vision to address problems in multi-robot perception (specifically multi-image feature matching). These problems arise in persistent surveillance, robotic agriculture, post-disaster search and rescue, and autonomous driving applications. Overall, this work enables resilient hierarchical planning for robot teams and solves the distributed multi-image feature matching problem, both of which were previously intractable to solve in many cases. We begin by exploring distributed multi-image feature matching for distributed perception and object tracking for a robot team or camera network. We then look at homogeneous multi-agent planning from rich infinite-time specifications that includes a secondary objective of optimizing local sensor information entropy. Next, we address heterogeneous multi-agent task planning from rich, timed specifications based on agent capabilities, and then detail mechanisms for online replanning due to agent loss. Finally, we address safe, reactive, and timed motion planning subject to temporal logic constraints. Accompanying each topic are a number of simulations and experiments that demonstrate their utility on real hardware. Overall, this thesis focuses on four primary contributions: 1) distributed multi-image feature matching, 2) motion planning for a homogeneous robotic team subject to distributed sensing and temporal logic constraints, 3) task planning for a heterogeneous robotic team with reactivity to changing agent availability, and 4) safe motion planning for an individual system that is reactive to disturbances and satisfies timed temporal logic constraints. / 2022-09-30T00:00:00Z

A new method for assessing the performance of popular image matching algorithms is presented. Specifically, the method assesses the type of images under which each of the algorithms reviewed herein perform to its maximum or highest efficiency. The efficiency is measured in terms of the number of matches founds by the algorithm and the number of type I and type II errors encountered when the algorithm is tested against a specific pair of images. Current comparative studies asses the performance of the algorithms based on the results obtained in different criteria such as speed, sensitivity, occlusion, and others. These studies are an important resource to understand the behavior of the algorithms and their influence on the results obtained. But they do not account for the inherent characteristics of the algorithms that derive the process through which the matching features are evaluated, filtered, and finally selected. Moreover, these methods cannot be used to predict the efficiency or level of accuracy that could be reached by using one algorithm or the other depending on of the type of images. This ability to predict performance becomes handy in situations where time is a limiting factor in a project because it allows one to quickly predict which algorithm will save the most time and resources.

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Increasing human activities have brought about a global warming trend, and cause global sea level rise. Investigations of variations in coastal margins of Antarctica and in the glacial dynamics of the Antarctic Ice Sheet provide useful diagnostic information for understanding and predicting sea level changes. This research investigates the coastal dynamics of the Antarctic Ice Sheet in terms of changes in the coastal margin and ice flow velocities. The primary methods used in this research include image segmentation based coastline extraction and image matching based velocity derivation. The image segmentation based coastline extraction method uses a modified adaptive thresholding algorithm to derive a high-resolution, complete coastline of Antarctica from 2000 orthorectified SAR images at the continental scale. This new coastline is compared with the 1997 coastline also derived from orthorectified Radarsat SAR images, and the 1963 coastline derived from Argon Declassified Intelligence Satellite Photographs for change detection analysis of the ice margins. The analysis results indicate, in the past four decades, the Antarctic ice sheet experienced net retreat and its areal extent has been reduced significantly. Especially, the ice shelves and glaciers on the Antarctic Peninsula reveal a sustained retreating trend. In addition, the advance, retreat, and net change rates have been measured and inventoried for 200 ice shelves and glaciers. A multi-scale image matching algorithm is developed to track ice motion and to measure ice velocity for a number of sectors of the Antarctic coast based on 1997 and 2000 SAR image pairs. The results demonstrate that a multi-scale image matching algorithm is much more efficient and accurate compared with the conventional algorithm. The velocity measurements from the image matching method have been compared with those derived from InSAR techniques and those observed from conventional ground surveys during 1970-1971. The comparison reveals that the ice velocity in the front part of the Amery Ice Shelf has increased by about 50-200 m/a. The rates of ice calving and temporal variation of ice flow pattern have been also analyzed by integrating the ice margin change measurement with the ice flow velocity at the terminus of the outlet glacier.

Achieving high compression ratio without significant loss of quality is the main goal of the most standard video coding systems. Since consecutive frames of a general video sequence have high correlations, the temporal redundancy between frames is removed by using motion estimation and motion compensation techniques. In this thesis, we investigate the use of video content information within the video coding system and propose a new video coding approach that can save significant bit-rates of the compressed video. Main units of the proposed coding scheme include the scene analyzer and image interpolator. The scene analyzer at the encoder extracts scene-modeling parameters from input sequences. The image interpolator at the decoder reconstructs the video frames by using the transmitted modeling parameters. The scene analyzer consists of the camera motion detector and image-matching module. We propose a new camera motion detection method that directly analyzes the 2-D distribution of inter-frame motion fields. Experimental results show that the proposed method provides higher detection accuracy and faster computation time than the 1-D angle histogram-based method. A robust image-matching method that is invariant to scale changes, rotations, and illumination changes has been presented. Invariance to these changes is achieved by adopting mutual information as a measure of similarity and adaptively changing the size and orientation of the local matching windows. To reduce ambiguities of the local matching, a global matching technique has been combined with the local matching. To evaluate the performance of the proposed coding scheme, we have integrated the camera motion detector, the image-matching module, and the image interpolator with the standard MPEG-4 video codec. We compare our method with the standard MPEG-4 codec in terms of bit rates, computation time, and subjective and objective qualities.

The use of optical scanning methods in geomorpho-analysis Abstract Main goal of this thesis is to find out if it is possible to use Structure from motion (SfM) method for analyzing geomorphological objects. Four geomorphological features in three different places within Pilsen-North region was used for testing this method. These objects with very dissimilar dimensions and shapes was scanned for this testing in various light conditions. All used data-sets was entirely created by author of this thesis. The data was initially processed by Agisoft Photoscan Professional Ediditon v1.1.6 and VisualSFM v0.5.26 to create spatial models. These models was afterwards processed in CloudCompare v2.6.1 and MeshLab v1.3.3. This software was used for clipping and merging of 3D models and for converting 3D models in to real dimensions. These real sized spatial models was then contrasted together by creating comparing entities. Outcomes are evaluated in the thesis conclusion. Keywords: Structure from motion (SfM), SIFT, RANSAC

This degree project evaluates combinations of well-known state-of-the-art keypoint detectors and descriptors, as well as keypoint matching and robust outlier rejection methods for the purpose of estimating a homography between images produced by two fundamentally different cameras. The evaluation is perfomed on both computational efficiency and matching accuracy of each combination after a series of image deformations have been applied. The results show best performance using Brute Force search with the Hamming distance on keypoint descriptors generated by running the BRISK/BRISK combination and RANSAC for finding the subset to be used in the final homography estimation. If necessary for extra time sensitive applications, using ORB/ORB for keypoint detection and description has been shown to produce largely comparable results at a higher computational efficiency / Det här arbetet evaluerar kombinationer av välkända detektorer och deskriptorer, samt metoder för att matcha dessa och välja ut de bästa för att estimera en homografi mellan två fundamentalt olika kameror. Evalueringen baseras både på tidsåtgång och slutgiltig matchningskvalitet efter en rad bilddeformationer har applicerats. Resultaten visar bäst resultat när en totalsökning med Hammingnormen körs på ett set av punkter funna med hjälp av kombinationen BRISK/BRISK och sedan RANSAC för att hitta det bästa subsetet av dessa för att estimera homografin. Om nödvändigt för extra tidskänsliga applikationer har kombinationen ORB/ORB visat sig prestera i stort sett lika bra och med en ökad effektivitet.

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