Conceptual spatial representations for indoor mobile robots

We present an approach for creating conceptual representations of human-made indoor environments using mobile robots. The concepts refer to spatial and functional properties of typical indoor environments. Following findings in cognitive psychology, our model is composed of layers representing maps at different levels of abstraction. The complete system is integrated in a mobile robot endowed with laser and vision sensors for place and object recognition. The system also incorporates a linguistic framework that actively supports the map acquisition process, and which is used for situated dialogue. Finally, we discuss the capabilities of the integrated system

Workflow for reducing semantic segmentation annotation time

Abstract. Semantic segmentation is a challenging task within the field of pattern recognition from digital images. Current semantic segmentation methods that are based on neural networks show great promise in accurate pixel-level classification, but the methods seem to be limited at least to some extent by the availability of accurate training data. Semantic segmentation training data is typically curated by humans, but the task is rather slow and tedious even for humans. While humans are fast at checking whether a segmentation is accurate or not, creating segmentations is rather slow as the human visual system becomes limited by physical interfaces such as hand coordination for drawing segmentations by hand. This thesis evaluates a workflow that aims to reduce the need for drawing segmentations by hand to create an accurate set of training data. A publicly available dataset is used as the starting-point for the annotation process, and four different evaluation sets are used to evaluate the introduced annotation workflow in labour efficiency and annotation accuracy. Evaluation of the results indicates that the workflow can produce annotations that are comparable to manually corrected annotations in accuracy while requiring significantly less manual labour to produce annotations.Työnkulku semanttisen segmentoinnin annotointiajan vähentämiseen. Tiivistelmä. Semanttinen segmentointi on haastava osa-alue hahmontunnistusta digitaalisista kuvista. Tämänhetkiset semanttiset segmentaatiomenetelmät, jotka perustuvat neuroverkkoihin, osoittavat suurta potentiaalia tarkassa pikselitason luokittelussa, mutta ovat ainakin osittain tarkan koulutusdatan saatavuuden rajoittamia. Semanttisen segmentaation koulutusdata on tyypillisesti täysin ihmisten annotoimaa, mutta segmentaatioiden annotointi on hidasta ja pitkäveteistä. Vaikka ihmiset ovat nopeita tarkistamaan ovatko annotaatiot tarkkoja, niiden luonti on hidasta, koska ihmisen visuaalisen järjestelmän nopeuden ja tarkkuuden rajoittavaksi tekijäksi lisätään fyysinen rajapinta, kuten silmä-käsi-koordinaatio piirtäessä segmentaatioita käsin. Tämä opinnäytetyö arvioi kokonaisvaltaisen semanttisten segmentaatioiden annotointitavan, joka pyrkii vähentämään käsin piirtämisen tarvetta tarkan koulutusdatan luomiseksi. Julkisesti saatavilla olevaa datajoukkoa käytetään annotoinnin lähtökohtana, ja neljää erilaista evaluointijoukkoa käytetään esitetyn annotointitavan työtehokkuuden sekä annotaatiotarkkuuden arviointiin. Evaluaatiotulokset osoittavat, että esitetty tapa kykenee tuottamaan annotaatioita jotka ovat yhtä tarkkoja kuin käsin korjatut annotaatiot samalla merkittävästi vähentäen käsin tehtävän työn määrää

Image processing and understanding based on graph similarity testing: algorithm design and software development

Image processing and understanding is a key task in the human visual system. Among all related topics, content based image retrieval and classification is the most typical and important problem. Successful image retrieval/classification models require an effective fundamental step of image representation and feature extraction. While traditional methods are not capable of capturing all structural information on the image, using graph to represent the image is not only biologically plausible but also has certain advantages. Graphs have been widely used in image related applications. Traditional graph-based image analysis models include pixel-based graph-cut techniques for image segmentation, low-level and high-level image feature extraction based on graph statistics and other related approaches which utilize the idea of graph similarity testing. To compare the images through their graph representations, a graph similarity testing algorithm is essential. Most of the existing graph similarity measurement tools are not designed for generic tasks such as image classification and retrieval, and some other models are either not scalable or not always effective. Graph spectral theory is a powerful analytical tool for capturing and representing structural information of the graph, but to use it on image understanding remains a challenge. In this dissertation, we focus on developing fast and effective image analysis models based on the spectral graph theory and other graph related mathematical tools. We first propose a fast graph similarity testing method based on the idea of the heat content and the mathematical theory of diffusion over manifolds. We then demonstrate the ability of our similarity testing model by comparing random graphs and power law graphs. Based on our graph analysis model, we develop a graph-based image representation and understanding framework. We propose the image heat content feature at first and then discuss several approaches to further improve the model. The first component in our improved framework is a novel graph generation model. The proposed model greatly reduces the size of the traditional pixel-based image graph representation and is shown to still be effective in representing an image. Meanwhile, we propose and discuss several low-level and high-level image features based on spectral graph information, including oscillatory image heat content, weighted eigenvalues and weighted heat content spectrum. Experiments show that the proposed models are invariant to non-structural changes on images and perform well in standard image classification benchmarks. Furthermore, our image features are robust to small distortions and changes of viewpoint. The model is also capable of capturing important image structural information on the image and performs well alone or in combination with other traditional techniques. We then introduce two real world software development projects using graph-based image processing techniques in this dissertation. Finally, we discuss the pros, cons and the intuition of our proposed model by demonstrating the properties of the proposed image feature and the correlation between different image features