90 research outputs found
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Image based human body rendering via regression & MRF energy minimization
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A machine learning method for synthesising human images is explored to create new images without relying on 3D modelling. Machine learning allows the creation of new images through prediction from existing data based on the use of training images. In the present study, image synthesis is performed at two levels: contour and pixel. A class of learning-based methods is formulated to create object contours from the training image for the synthetic image that allow pixel synthesis within the contours in the second level. The methods rely on applying robust object descriptions, dynamic learning models after appropriate motion segmentation, and machine learning-based frameworks.
Image-based human image synthesis using machine learning is a research focus that has recently gained considerable attention in the field of computer graphics. It makes use of techniques from image/motion analysis in computer vision. The problem lies in the estimation of methods for image-based object configuration (i.e. segmentation, contour outline). Using the results of these analysis methods as bases, the research adopts the machine learning approach, in which human images are synthesised by executing the synthesis of contour and pixels through the learning from training image.
Firstly, thesis shows how an accurate silhouette is distilled using developed background subtraction for accuracy and efficiency. The traditional vector machine approach is used to avoid ambiguities within the regression process. Images can be represented as a class of accurate and efficient vectors for single images as well as sequences. Secondly, the framework is explored using a unique view of machine learning methods, i.e., support vector regression (SVR), to obtain the convergence result of vectors for contour allocation. The changing relationship between the synthetic image and the training image is expressed as a vector and represented in functions. Finally, a pixel synthesis is performed based on belief propagation.
This thesis proposes a novel image-based rendering method for colour image synthesis using SVR and belief propagation for generalisation to enable the prediction of contour and colour information from input colour images. The methods rely on using appropriately defined and robust input colour images, optimising the input contour images within a sparse SVR framework. Firstly, the thesis shows how contour can effectively and efficiently be predicted from small numbers of input contour images. In addition, the thesis exploits the sparse properties of SVR efficiency, and makes use of SVR to estimate regression function. The image-based rendering method employed in this study enables contour synthesis for the prediction of small numbers of input source images. This procedure avoids the use of complex models and geometry information. Secondly, the method used for human body contour colouring is extended to define eight differently connected pixels, and construct a link distance field via the belief propagation method. The link distance, which acts as the message in propagation, is transformed by improving the low-envelope method in fast distance transform. Finally, the methodology is tested by considering human facial and human body clothing information. The accuracy of the test results for the human body model confirms the efficiency of the proposed method
Mapping Road Infrastructure in Developing Countries Applying Remote Sensing and GIS : The Case of the Taita Hills, Kenya
Road transport and infrastructure has a fundamental meaning for the developing world. Poor quality and inadequate coverage of roads, lack of maintenance operations and outdated road maps continue to hinder economic and social development in the developing countries. This thesis focuses on studying the present state of road infrastructure and its mapping in the Taita Hills, south-east Kenya. The study is included as a part of the TAITA-project by the Department of Geography, University of Helsinki.
The road infrastructure of the study area is studied by remote sensing and GIS based methodology. As the principal dataset, true colour airborne digital camera data from 2004, was used to generate an aerial image mosaic of the study area. Auxiliary data includes SPOT satellite imagery from 2003, field spectrometry data of road surfaces and relevant literature.
Road infrastructure characteristics are interpreted from three test sites using pixel-based supervised classification, object-oriented supervised classifications and visual interpretation. Road infrastructure of the test sites is interpreted visually from a SPOT image. Road centrelines are then extracted from the object-oriented classification results with an automatic vectorisation process. The road infrastructure of the entire image mosaic is mapped by applying the most appropriate assessed data and techniques. The spectral characteristics and reflectance of various road surfaces are considered with the acquired field spectra and relevant literature. The results are compared with the experimented road mapping methods.
This study concludes that classification and extraction of roads remains a difficult task, and that the accuracy of the results is inadequate regardless of the high spatial resolution of the image mosaic used in this thesis. Visual interpretation, out of all the experimented methods in this thesis is the most straightforward, accurate and valid technique for road mapping. Certain road surfaces have similar spectral characteristics and reflectance values with other land cover and land use. This has a great influence for digital analysis techniques in particular. Road mapping is made even more complicated by rich vegetation and tree canopy, clouds, shadows, low contrast between roads and surroundings and the width of narrow roads in relation to the spatial resolution of the imagery used.
The results of this thesis may be applied to road infrastructure mapping in developing countries on a more general context, although with certain limits. In particular, unclassified rural roads require updated road mapping schemas to intensify road transport possibilities and to assist in the development of the developing world.Tieliikenteellä ja -infrastruktuurilla on keskeinen merkitys kehitysmaissa. Tiestön kattavuudessa, kunnossa, tienpidossa ja kartoituksessa on puutteita, jotka rajoittavat taloudellista ja sosiaalista kehitystä. Tämä tutkimus keskittyy tieinfrastruktuurin nykytilan tutkimiseen ja kartoitukseen Taita Hillsin alueella Kaakkois-Keniassa, sekä tiekartoituksen mahdollisuuksien arviointiin yleisesti kehitysmaissa. Tutkimus on osa Helsingin yliopiston maantieteen laitoksen TAITA-projektia.
Tutkimusalueen tieinfrastruktuuria tutkitaan kaukokartoitus- ja paikkatietomenetelmien avulla. Tutkimuksen pääaineistona ovat vuoden 2004 digitaaliset oikeaväri-ilmakuvat, joista muodostetaan ilmakuvamosaiikki. Lisäaineistona ovat SPOT väärävärisatelliittikuva vuodelta 2003, tiepintojen spektrometrimittaukset, olemassa olevat kartta-aineistot sekä aihetta käsittelevä kirjallisuus.
Tiestö tulkitaan ilmakuvamosaiikin kolmelta testialueelta pikselipohjaisella ohjatulla luokituksella, objekti-orientoiduilla ohjatuilla luokituksilla sekä visuaalisella tulkinnalla. SPOT kuvalta testialueiden tiestö tulkitaan visuaalisesti. Toisen objekti-orientoidun luokituksen tuloksesta tiestön keskilinjat irrotetaan automaattisella vektoroinnilla. Lopuksi tieverkko kartoitetaan koko ilmakuvamosaiikin alueelta parhaiksi havaituilla menetelmillä ja aineistolla. Spektrometrimittauksien ja kirjallisuuden avulla tarkastellaan eri tiepintojen heijastusarvoja ja teiden spektraalisia ominaisuuksia ja tuloksia verrataan testattujen tulkintamenetelmien tuloksiin.
Yhteenvetona voidaan sanoa, että tieverkon luokittelu ja irrottaminen on digitaalisilla menetelmillä vaikeaa ja tulokset epätarkkoja ilmakuva-aineiston korkeasta spatiaalisesta resoluutiosta huolimatta. Visuaalinen tulkinta ja digitointi on toistaiseksi yksinkertaisin, tarkin ja validein tutkituista menetelmistä. Tietyillä tiepinnoilla on koostumuksen ja rakennusmateriaalien takia samankaltaiset heijastusominaisuudet muun maanpeitteen- ja maankäytön kanssa, mikä vaikuttaa etenkin digitaalisten tulkintamenetelmien tuloksiin. Teiden kartoitusta vaikeuttaa myös runsas kasvillisuus, puiden latvuskerros, pilvet, varjot, heikko kontrasti ympäristöön ja teiden kapeus suhteessa käytetyn aineiston spatiaaliseen resoluutioon.
Tämän tutkimuksen tuloksia ja menetelmiä voidaan soveltaa tietyin rajoituksin myös laajempaan, kehitysmaiden tiekartoituksen kontekstiin. Erityisesti maaseutujen luokittelemattomat tiet ovat ajantasaisen tiekartoituksen tarpeessa tieliikenteen tehostamiseksi ja kehityksen edesauttamiseksi kehitysmaissa
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