Background: In recent years, computer vision has undergone significant advancements, revolutionizing fields such as robotics, augmented reality, and autonomoussystems. Key to this transformation is Simultaneous Localization and Mapping(SLAM), a fundamental technology that allows machines to navigate and interactintelligently with their surroundings. Challenges persist in harmonizing spatial andsemantic understanding, as conventional methods often treat these tasks separately,limiting comprehensive evaluations with shared datasets. As applications continueto evolve, the demand for accurate and efficient image segmentation ground truthbecomes paramount. Manual annotation, a traditional approach, proves to be bothcostly and resource-intensive, hindering the scalability of computer vision systems.This thesis addresses the urgent need for a cost-effective and scalable solution byfocusing on the creation of accurate and efficient image segmentation ground truth,bridging the gap between spatial and semantic tasks. Objective: This thesis addresses the challenge of creating an efficient image segmentation ground truth to complement datasets with spatial ground truth. Theprimary objective is to reduce the time and effort taken for annotation of datasets. Method: Our methodology adopts a systematic approach to evaluate and combineexisting annotation techniques, focusing on precise object detection and robust segmentation. By merging these approaches, we aim to enhance annotation accuracywhile streamlining the annotation process. This approach is systematically appliedand evaluated across multiple datasets, including the NYU V2 dataset(consists ofover 1449 images), ARID(real-world sequential dataset), and Italian flats(sequentialdataset created in blender). Results: The developed pipeline demonstrates promising outcomes, showcasing asubstantial reduction in annotation time compared to manual annotation, thereby addressing the challenges posed by the cost and resource intensiveness of the traditionalapproach. We observe that although not initially optimized for SLAM datasets, thepipeline performs exceptionally well on both ARID and Italian flats datasets, highlighting its adaptability to real-world scenarios. Conclusion: In conclusion, this research introduces an innovative annotation pipeline,offering a systematic and efficient approach to annotation. It tries to bridge the gapbetween spatial and semantic tasks, addressing the pressing need for comprehensiveannotation tools in this domain.

Comparative anatomical studies of diverse plant species are vital for the understanding of changes in gene functions such as those involved in solute transport and hormone signaling in plant roots. The state-of-the-art method for confocal image analysis called PlantSeg utilized U-Net for cell wall segmentation. U-Net is a neural network model that requires training with a large amount of manually labeled confocal images and lacks generalizability. In this research, we test a foundation model called the Segment Anything Model (SAM) to evaluate its zero-shot learning capability and whether prompt engineering can reduce the effort and time consumed in dataset annotation, facilitating a semi-automated training process. Our proposed method improved the detection rate of cells and reduced the error rate as compared to state-of-the-art segmentation tools. We also estimated the IoU scores between the proposed method and PlantSeg to reveal the trade-off between accuracy and detection rate for different quality of data. By addressing the challenges specific to confocal images, our approach offers a robust solution for studying plant structure. Our findings demonstrated the efficiency of SAM in confocal image segmentation, showcasing its adaptability and performance as compared to existing tools. Overall, our research highlights the potential of foundation models like SAM in specialized domains and underscores the importance of tailored approaches for achieving accurate semantic segmentation in confocal imaging. / Master of Science / Studying different plant species' anatomy is crucial for understanding how genes work, especially those related to moving substances and signaling in plant roots. Scientists often use advanced techniques like confocal microscopy to examine plant tissues in detail. Traditional techniques like PlantSeg in automatically segmenting plant cells require a lot of computational resources and manual effort in preparing the dataset and training the model. In this study, we develop a novel technique using Segment-Anything-Model that could learn to identify cells without needing as much training data. We found that SAM performed better than other methods, detecting cells more accurately and making fewer mistakes. By comparing SAM with PlantSeg, we could see how well they worked with different types of images. Our results show that SAM is a reliable option for studying plant structures using confocal imaging. This research highlights the importance of using tailored approaches like SAM to get accurate results from complex images, offering a promising solution for plant scientists.

The advancement of foundation models have opened up new possibilities in deep learning. These models can be adapted to new tasks and unseen domains from minimal or even no training data, making them well-suited for applications where labelled data is scarce or costly to collect. Lack of data has meant that deep learning for change detection in sonar imagery has not been used. Reliable methods for change detection of underwater environments is critical for a range of fields such as marine research and object search. Previous work in change detection for sonar imagery focus on non-deep learning methods. In this paper, we explore the use of a foundation model (Segment Anything Model) for performing change detection in imagery collected with synthetic aperture sonar (SAS). This thesis is the first case of applying Segment Anything Model to change detection in SAS imagery. The proposed method segments bi-temporal images, and change detection is then performed on the segments. In addition to a set of bi-temporal images containing real change, the model is also tested on a set of synthetic images. The proposed method shows promising results on both a real and synthetic data set.

Background:The dynamic field of computer vision and artificial intelligence has continually evolved, pushing the boundaries in areas like semantic segmentation andenvironmental recognition, pivotal for indoor scene analysis. This research investigates the integration of these two technologies, examining their synergy and implicayions for enhancing indoor scene understanding. The application of this integrationspans across various domains, including smart home systems for enhanced ambientliving, navigation assistance for Cleaning robots, and advanced surveillance for security. Objectives: The primary goal is to assess the impact of integrating semantic segmentation data on the accuracy of environmental recognition algorithms in indoor environments. Additionally, the study explores how environmental context can enhance the precision and accuracy of contour-aware semantic segmentation. Methods: The research employed an extensive methodology, utilizing various machine learning models, including standard algorithms, Long Short-Term Memorynetworks, and ensemble methods. Transfer learning with models like EfficientNet B3, MobileNetV3 and Vision Tranformer was a key aspect of the experimentation. The experiments were designed to measure the effect of semantic segmentation on environmental recognition and its reciprocal influence. Results: The findings indicated that the integration of semantic segmentation data significantly enhanced the accuracy of environmental recognition algorithms. Conversely, incorporating environmental context into contour-aware semantic segmentation led to notable improvements in precision and accuracy, reflected in metrics such as Mean Intersection over Union(MIoU). Conclusion: This research underscores the mutual enhancement between semantic segmentation and environmental recognition, demonstrating how each technology significantly boosts the effectiveness of the other in indoor scene analysis. The integration of semantic segmentation data notably elevates the accuracy of environmental recognition algorithms, while the incorporation of environmental context into contour-aware semantic segmentation substantially improves its precision and accuracy.The results also open avenues for advancements in automated annotation processes, paving the way for smarter environmental interaction.