Comparison of Local Feature Detectors and Descriptors for Visual Object Categorization

Local features are used in many computer vision tasks including visual object categorization, content-based image retrieval and object recognition to mention a few. Local features are points, blobs or regions in images that are extracted using a local feature detector. To make use of extracted local features the localized interest points are described using a local feature descriptor. A descriptor histogram vector is a compact representation of an image and can be used for searching and matching images in databases. In this thesis the performance of local feature detectors and descriptors is evaluated for object class detection task. Features are extracted from image samples belonging to several object classes. Matching features are then searched using random image pairs of a same class. The goal of this thesis is to find out what are the best detector and descriptor methods for such task in terms of detector repeatability and descriptor matching rate.

Local Binary Patterns in Focal-Plane Processing. Analysis and Applications

Feature extraction is the part of pattern recognition, where the sensor data is transformed into a more suitable form for the machine to interpret. The purpose of this step is also to reduce the amount of information passed to the next stages of the system, and to preserve the essential information in the view of discriminating the data into different classes. For instance, in the case of image analysis the actual image intensities are vulnerable to various environmental effects, such as lighting changes and the feature extraction can be used as means for detecting features, which are invariant to certain types of illumination changes. Finally, classification tries to make decisions based on the previously transformed data. The main focus of this thesis is on developing new methods for the embedded feature extraction based on local non-parametric image descriptors. Also, feature analysis is carried out for the selected image features. Low-level Local Binary Pattern (LBP) based features are in a main role in the analysis. In the embedded domain, the pattern recognition system must usually meet strict performance constraints, such as high speed, compact size and low power consumption. The characteristics of the final system can be seen as a trade-off between these metrics, which is largely affected by the decisions made during the implementation phase. The implementation alternatives of the LBP based feature extraction are explored in the embedded domain in the context of focal-plane vision processors. In particular, the thesis demonstrates the LBP extraction with MIPA4k massively parallel focal-plane processor IC. Also higher level processing is incorporated to this framework, by means of a framework for implementing a single chip face recognition system. Furthermore, a new method for determining optical flow based on LBPs, designed in particular to the embedded domain is presented. Inspired by some of the principles observed through the feature analysis of the Local Binary Patterns, an extension to the well known non-parametric rank transform is proposed, and its performance is evaluated in face recognition experiments with a standard dataset. Finally, an a priori model where the LBPs are seen as combinations of n-tuples is also presented

A Local DLP-GIS-LP system for geographically decentralized wood procurement planning and decision making

Summary

Local and Regional Scale Determinants of Biodiversity Patterns in Boreal Agricultural Landscapes

This thesis examines the local and regional scale determinants of biodiversity patterns using existing species and environmental data. The research focuses on agricultural environments that have experienced rapid declines of biodiversity during past decades. Existing digital databases provide vast opportunities for habitat mapping, predictive mapping of species occurrences and richness and understanding the speciesenvironment relationships. The applicability of these databases depends on the required accuracy and quality of the data needed to answer the landscape ecological and biogeographical questions in hand. Patterns of biodiversity arise from confounded effects of different factors, such as climate, land cover and geographical location. Complementary statistical approaches that can show the relative effects of different factors are needed in biodiversity analyses in addition to classical multivariate models. Better understanding of the key factors underlying the variation in diversity requires the analyses of multiple taxonomic groups from different perspectives, such as richness, occurrence, threat status and population trends. The geographical coincidence of species richness of different taxonomic groups can be rather limited. This implies that multiple geographical regions should be taken into account in order to preserve various groups of species. Boreal agricultural biodiversity and in particular, distribution and richness of threatened species is strongly associated with various grasslands. Further, heterogeneous agricultural landscapes characterized by moderate field size, forest patches and non-crop agricultural habitats enhance the biodiversity of rural environments. From the landscape ecological perspective, the major threats to Finnish agricultural biodiversity are the decline of connected grassland habitat networks, and general homogenization of landscape structure resulting from both intensification and marginalization of agriculture. The maintenance of key habitats, such as meadows and pastures is an essential task in conservation of agricultural biodiversity. Furthermore, a larger landscape context should be incorporated in conservation planning and decision making processes in order to respond to the needs of different species and to maintain heterogeneous rural landscapes and viable agricultural diversity in the future.

Coarse Global Visual Localization of a Mobile Robot

Localization, which is the ability of a mobile robot to estimate its position within its environment, is a key capability for autonomous operation of any mobile robot. This thesis presents a system for indoor coarse and global localization of a mobile robot based on visual information. The system is based on image matching and uses SIFT features as natural landmarks. Features extracted from training images arestored in a database for use in localization later. During localization an image of the scene is captured using the on-board camera of the robot, features are extracted from the image and the best match is searched from the database. Feature matching is done using the k-d tree algorithm. Experimental results showed that localization accuracy increases with the number of training features used in the training database, while, on the other hand, increasing number of features tended to have a negative impact on the computational time. For some parts of the environment the error rate was relatively high due to a strong correlation of features taken from those places across the environment.

Supervised localimage feature detection

This thesis is about detection of local image features. The research topic belongs to the wider area of object detection, which is a machine vision and pattern recognition problem where an object must be detected (located) in an image. State-of-the-art object detection methods often divide the problem into separate interest point detection and local image description steps, but in this thesis a different technique is used, leading to higher quality image features which enable more precise localization. Instead of using interest point detection the landmark positions are marked manually. Therefore, the quality of the image features is not limited by the interest point detection phase and the learning of image features is simplified. The approach combines both interest point detection and local description into one phase for detection. Computational efficiency of the descriptor is therefore important, leaving out many of the commonly used descriptors as unsuitably heavy. Multiresolution Gabor features has been the main descriptor in this thesis and improving their efficiency is a significant part. Actual image features are formed from descriptors by using a classifierwhich can then recognize similar looking patches in new images. The main classifier is based on Gaussian mixture models. Classifiers are used in one-class classifier configuration where there are only positive training samples without explicit background class. The local image feature detection method has been tested with two freely available face detection databases and a proprietary license plate database. The localization performance was very good in these experiments. Other applications applying the same under-lying techniques are also presented, including object categorization and fault detection.

Local and Global Feature Extraction for Invariant Object Recognition

Perceiving the world visually is a basic act for humans, but for computers it is still an unsolved problem. The variability present innatural environments is an obstacle for effective computer vision. The goal of invariant object recognition is to recognise objects in a digital image despite variations in, for example, pose, lighting or occlusion. In this study, invariant object recognition is considered from the viewpoint of feature extraction. Thedifferences between local and global features are studied with emphasis on Hough transform and Gabor filtering based feature extraction. The methods are examined with respect to four capabilities: generality, invariance, stability, and efficiency. Invariant features are presented using both Hough transform and Gabor filtering. A modified Hough transform technique is also presented where the distortion tolerance is increased by incorporating local information. In addition, methods for decreasing the computational costs of the Hough transform employing parallel processing and local information are introduced.

Harmonisation of Inbound Logistics with Local Suppliers

Nokian Matkapuhelimien tuotantoprosessit on suunniteltu tukemaan massaräätälöintiä perustuen assembly-to-order tekniikkaan (ATO). Tämän työn tehtävänä oli kehittää toimitusmallit ATO-materiaaleille paikallisilta alihankkijoilta matkapuhelintehtaalle. Aluksi analysoitiin nykyiset toimintamallit perustun Nokia Komarom Kft:n työntekijöiden ja toimittajien haastatteluihin. Ongelmiksi havaittiin toimintatapojen monimuotoisuus, dokumentoinnin puute, puutteelliset laskut ja riittämätön toimitusseuranta.Malleille asetettavien joustavuusvaatimusten selvittämiseksi suoritettiin laajamittainen näkyvyysvälineiden ja tuotantokapasiteetin tutkimus. Vaatimukset tilauksesta toimitukseen läpimenoajoille ja kuljetustoiminnoille johdettiin joustavuusvaatimuksista. Työn lopussa muodostetaan toimitusmallit perustuen nykytilan analyysiin, havaittuihin ongelmiin ja malleille asetettuihin vaatimuksiin. Materiaalit jaetaan eri malleilla ohjattaviksi niiden ominaisuuksiin perustuen. Ensimmäinen malleista implementoidaan kahden pilottitoimittajan kanssa ja esitetään tulokset. Toista mallia hyödynnetään lähitulevaisuudessa Nokia Komarom Kft:n aloittaessa yhteistyön uusien paikallistoimittajien kanssa.

Business potential evaluation of wireless local area networks: Teleoperator’s view

The objective of the thesis was to evaluate business potential of wireless local area networks (WLAN, Wireless LAN). At first, the scope of business potential evaluation of technology was introduced. Next, a general framework of business potential evaluation of technology based on literature was presented. In addition, convergence of cellular networks and data networks was studied in order to get an insight of current situation of mobile telecommunications industry. Finally, wireless local area networks business potential was evaluated. A wireless local area network is a data communication system, which combines data connectivity with mobility and is implemented in unlicensed frequency bands, allowing new business opportunities to emerge. The main markets of WLAN are corporate networks, public area networks and access networks. At the moment the penetration of WLAN terminals is low which derives to low demand of wireless LAN services. In addition, unlicensed spectrum forces the teleoperators to set the service price relatively low. The business potential is in integrating wireless LANs to cellular networks and in offering value added services to end users. The future of wireless LAN is to be complementary network to cellular networks. In this vision cellular networks provide voice and low data services and broadband wireless networks enable multimedia services.

Local Services in a Fourth Generation Mobile Network

Neljännen sukupolven mobiiliverkot kokoaa kaikki tietoliikenneverkot ja palvelut Internetin ympärille. Tämä mullistus muuttaa vanhat vertikaaliset tietoliikenneverkot joissa yhden tietoliikenneverkon palvelut ovat saatavissa vain kyseisen verkon päätelaitteille horisontaaliseksi malliksi jossa päätelaitteet käyttävät omaa verkkoansa pääsynä Internetin palveluihin. Tämä diplomityö esittelee idean paikallisista palveluista neljännen sukupolven mobiiliverkossa. Neljännen sukupolven mobiiliverkko yhdistää perinteiset televerkkojen palvelut ja Internet palvelut sekä mahdollistaa uuden tyyppisten palveluiden luonnin. TCP/IP protokollien ja Internetin evoluutio on esitelty. Laajakaistaiset, lyhyen kantaman radiotekniikat joita käytetään langattomana yhteytenä Internetiin on käsitelty. Evoluutio kohti neljännen sukupolven mobiiliverkkoja on kuvattu esittelemällä vanhat, nykyiset ja tulevat mobiiliverkot sekä niiden palvelut. Ennustukset palveluiden ja markkinoiden tulevaisuuden kehityksestä on käsitelty. Neljännen sukupolven mobiiliverkon arkkitehtuuri mahdollistaa paikalliset palvelut jotka ovat saatavilla vain yhdessä paikallisessa 4G verkossa. Paikalliset palvelut voidaan muunnella jokaiselle käyttäjälle erikseen käyttäen profiili-informaatiota ja paikkatietoa. Työssä on pohdittu paikallisten palveluiden käyttökelpoisuutta ja mahdollisuuksia käyttäen Lappeenrannan teknillisen korkeakoulun 4G projektin palvelupilotin tuloksia.

Software methods for HTML files protection in local / corporate networks: review and implementation

Tässä luomistyössä on esitetty tutkimus informaation suojaamisen menetelmien osalta paikallisissa ja ryhmäkuntaisissa verkoissa. Tutkimukseen kuuluu nykyaikaisten kryptagraafisten järjestelmien, Internetin/Intranetin ohjelmointikeinojen ja pääsyoikeuksien jakelumenetelmien analyysi. Tutkimusten perusteella on laadittu ohjelmiston prototyyppi HTML-tiedostojen suojaamista varten. Ohjelmiston laatimisprosessi on sisältänyt vaatimusten, järjestelmän ja suojelukomponenttien suunnittelun ja protytyypin testauksen. Ohjelmiston realisoinnin jälkeen kirjoitettiin käyttöohjeet. Ohjelmiston prototyyppi suojaa informaatiota HTML-tiedoston koko käytön aikana ja eri yrityksissä voidaan käyttää sitä pienien laajennuksien jälkeen.