Distance Transforms on Gray-Level Surfaces

This thesis studies gray-level distance transforms, particularly the Distance Transform on Curved Space (DTOCS). The transform is produced by calculating distances on a gray-level surface. The DTOCS is improved by definingmore accurate local distances, and developing a faster transformation algorithm. The Optimal DTOCS enhances the locally Euclidean Weighted DTOCS (WDTOCS) with local distance coefficients, which minimize the maximum error from the Euclideandistance in the image plane, and produce more accurate global distance values.Convergence properties of the traditional mask operation, or sequential localtransformation, and the ordered propagation approach are analyzed, and compared to the new efficient priority pixel queue algorithm. The Route DTOCS algorithmdeveloped in this work can be used to find and visualize shortest routes between two points, or two point sets, along a varying height surface. In a digital image, there can be several paths sharing the same minimal length, and the Route DTOCS visualizes them all. A single optimal path can be extracted from the route set using a simple backtracking algorithm. A new extension of the priority pixel queue algorithm produces the nearest neighbor transform, or Voronoi or Dirichlet tessellation, simultaneously with the distance map. The transformation divides the image into regions so that each pixel belongs to the region surrounding the reference point, which is nearest according to the distance definition used. Applications and application ideas for the DTOCS and its extensions are presented, including obstacle avoidance, image compression and surface roughness evaluation.

New Distance Transforms for Gray Level Image Compression

This thesis deals with distance transforms which are a fundamental issue in image processing and computer vision. In this thesis, two new distance transforms for gray level images are presented. As a new application for distance transforms, they are applied to gray level image compression. The new distance transforms are both new extensions of the well known distance transform algorithm developed by Rosenfeld, Pfaltz and Lay. With some modification their algorithm which calculates a distance transform on binary images with a chosen kernel has been made to calculate a chessboard like distance transform with integer numbers (DTOCS) and a real value distance transform (EDTOCS) on gray level images. Both distance transforms, the DTOCS and EDTOCS, require only two passes over the graylevel image and are extremely simple to implement. Only two image buffers are needed: The original gray level image and the binary image which defines the region(s) of calculation. No other image buffers are needed even if more than one iteration round is performed. For large neighborhoods and complicated images the two pass distance algorithm has to be applied to the image more than once, typically 3 10 times. Different types of kernels can be adopted. It is important to notice that no other existing transform calculates the same kind of distance map as the DTOCS. All the other gray weighted distance function, GRAYMAT etc. algorithms find the minimum path joining two points by the smallest sum of gray levels or weighting the distance values directly by the gray levels in some manner. The DTOCS does not weight them that way. The DTOCS gives a weighted version of the chessboard distance map. The weights are not constant, but gray value differences of the original image. The difference between the DTOCS map and other distance transforms for gray level images is shown. The difference between the DTOCS and EDTOCS is that the EDTOCS calculates these gray level differences in a different way. It propagates local Euclidean distances inside a kernel. Analytical derivations of some results concerning the DTOCS and the EDTOCS are presented. Commonly distance transforms are used for feature extraction in pattern recognition and learning. Their use in image compression is very rare. This thesis introduces a new application area for distance transforms. Three new image compression algorithms based on the DTOCS and one based on the EDTOCS are presented. Control points, i.e. points that are considered fundamental for the reconstruction of the image, are selected from the gray level image using the DTOCS and the EDTOCS. The first group of methods select the maximas of the distance image to new control points and the second group of methods compare the DTOCS distance to binary image chessboard distance. The effect of applying threshold masks of different sizes along the threshold boundaries is studied. The time complexity of the compression algorithms is analyzed both analytically and experimentally. It is shown that the time complexity of the algorithms is independent of the number of control points, i.e. the compression ratio. Also a new morphological image decompression scheme is presented, the 8 kernels' method. Several decompressed images are presented. The best results are obtained using the Delaunay triangulation. The obtained image quality equals that of the DCT images with a 4 x 4

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Minimum error contrast enhancement

Contrast enhancement is an image processing technique where the objective is to preprocess the image so that relevant information can be either seen or further processed more reliably. These techniques are typically applied when the image itself or the device used for image reproduction provides poor visibility and distinguishability of different regions of interest inthe image. In most studies, the emphasis is on the visualization of image data,but this human observer biased goal often results to images which are not optimal for automated processing. The main contribution of this study is to express the contrast enhancement as a mapping from N-channel image data to 1-channel gray-level image, and to devise a projection method which results to an image with minimal error to the correct contrast image. The projection, the minimum-error contrast image, possess the optimal contrast between the regions of interest in the image. The method is based on estimation of the probability density distributions of the region values, and it employs Bayesian inference to establish the minimum error projection.

Lossless compression of hyperspectral images

Technological progress has made a huge amount of data available at increasing spatial and spectral resolutions. Therefore, the compression of hyperspectral data is an area of active research. In somefields, the original quality of a hyperspectral image cannot be compromised andin these cases, lossless compression is mandatory. The main goal of this thesisis to provide improved methods for the lossless compression of hyperspectral images. Both prediction- and transform-based methods are studied. Two kinds of prediction based methods are being studied. In the first method the spectra of a hyperspectral image are first clustered and and an optimized linear predictor is calculated for each cluster. In the second prediction method linear prediction coefficients are not fixed but are recalculated for each pixel. A parallel implementation of the above-mentioned linear prediction method is also presented. Also,two transform-based methods are being presented. Vector Quantization (VQ) was used together with a new coding of the residual image. In addition we have developed a new back end for a compression method utilizing Principal Component Analysis (PCA) and Integer Wavelet Transform (IWT). The performance of the compressionmethods are compared to that of other compression methods. The results show that the proposed linear prediction methods outperform the previous methods. In addition, a novel fast exact nearest-neighbor search method is developed. The search method is used to speed up the Linde-Buzo-Gray (LBG) clustering method.

Lyhytsanomakeskuksen lisäarvopalvelurajapinnan suunnittelu ja toteutus

Diplomityössä tutkitaan lisäarvopalveluiden tuottamisen ongelmia, jotka liittyvät teleoperaattoreiden ja palveluntuottajien väliselle ”harmaalle alueelle”. Tyypillisiä ongelmia tällä alueella ovat palveluiden lyhytsanomakeskusliityntöjen vaikeudet, laskutusongelmat ja palveluiden tietoturvan taso. Tutkimuksen lisäksi työhön kuuluu myös toteutus, joka tuottaa ratkaisun näihin ongelmiin. Ensin työssä käsitellään yleisesti lisäarvopalveluiden tuotantoon liittyviä asioita: lyhytsanomapalvelua, lyhytsanomakeskuksia, niiden lisäarvopalvelurajapintoja ja harmaan alueen käsitettä. Sitten esitellään kolme ratkaisumallia ongelmiin operaattorin näkökulmasta, joista parhaana valitaan Intellitel™ Messaging Gateway-sanomayhdyskäytävään toteutettava lisäarvopalvelurajapinta. Rajapinnan toteutus kuvataan suunnittelun, arkkitehtuurin ja toiminnallisuuksien osalta. Diplomityön tuloksena saadaan uusi testattu lisäarvopalvelurajapinta, joka täyttää sille asetetut vaatimukset. Työn lopuksi pohditaan rajapinnan jatkokehitystä ja kiinnostavia käyttökohteita. Näitä ovat rajapinnan käyttö protokollamuuntimen tai sanomayhdyskäytävän osana.

Kohinan poisto spektrikuvista epälineaaristen suodattimien avulla

Tietokoneiden vuosi vuodelta kasvanut prosessointikyky mahdollistaa spektrikuvien hyö- dyntämisen harmaasävy- ja RGB-värikuvien sijaan yhä useampien ongelmien ratkaisemi- sessa. Valitettavasti häiriöiden suodatuksen tutkimus on jäänyt jälkeen tästä kehityksestä. Useimmat menetelmät on testattu vain harmaasävy- tai RGB-värikuvien yhteydessä, mut- ta niiden toimivuutta ei ole testattu spektrikuvien suhteen. Tässä diplomityössä tutkitaan erilaisia menetelmiä bittivirheiden poistamisessa spektrikuvista. Uutena menetelmänä työssä käytetään kuutiomediaanisuodatinta ja monivaiheista kuutio- mediaanisuodatinta. Muita tutkittuja menetelmiä olivat vektorimediaanisuodatus, moni- vaiheinen vektorimediaanisuodatus, sekä rajattu keskiarvosuodatus. Kuutiosuodattimilla pyrittiin hyödyntämään spektrikuvien kaistojen välillä olevaa korrelaatiota ja niillä pääs- tiinkin kokonaisuuden kannalta parhaisiin tuloksiin. Kaikkien suodattimien toimintaa tutkittiin kahdella eri 224 komponenttisella spektriku- valla lisäämällä kuviin satunnaisia bittivirheitä.

Offset-print mottling measurement using machine vision

Mottling is one of the key defects in offset-printing. Mottling can be defined as unwanted unevenness of print. In this work, diameter of a mottle spot is defined between 0.5-10.0 mm. There are several types of mottling, but the reason behind the problem is still not fully understood. Several commercial machine vision products for the evaluation of print unevenness have been presented. Two of these methods used in these products have been implemented in this thesis. The one is the cluster method and the other is the band-pass method. The properties of human vision system have been taken into account in the implementation of these two methods. An index produced by the cluster method is a weighted sum of the number of found spots, and an index produced by band-pass method is a weighted sum of coefficients of variations of gray-levels for each spatial band. Both methods produce larger indices for visually poor samples, so they can discern good samples from the poor ones. The difference between the indices for good and poor samples is slightly larger produced by the cluster method. 11 However, without the samples evaluated by human experts, the goodness of these results is still questionable. This comparison will be left to the next phase of the project.

Osake- ja henkilöyhtiöiden sulautuminen vero-oikeudessa, yhtiöoikeudessa ja kirjanpidossa

Sulautuminen on yksi yleisimmistä yritysjärjestelyvaihtoehdoista. Sulautumisessa on otettava huomioon vero- ja yhtiöoikeudelliset sekä kirjanpidolliset seikat. Ne ohjaavat sulautumisen toteuttamista ja säätelevät sallittuja menettelytapoja. Tutkimuksen päätavoitteena on analysoida osakeyhtiön, avoimen ja kommandiittiyhtiön vero-oikeudellisen, yhtiöoikeudellisen ja kirjanpidollisen sulautumisen käsittelyn eroja. Tutkimuksessa on aluksi selvitetty syitä yritysjärjestelyihin ja sulautumista yritysjärjestelyvaihtoehtona. Sulautumisen käsittely aloitetaan osakeyhtiön sulautumisen yhtiöoikeudellisen sääntelyn käsittelemisellä. Seuraavaksi tutkittiin kirjanpidollista sääntelyä sulautumisen yhteydessä. Tämän jälkeen määritettiin verotuksen sääntelyä sulautumisen yhteydessä. Viimeisessä teoriakappaleessa käsitellään henkilöyhtiöiden sulautumista. Tutkimuksessa selvitettiin sulautumisen oikeudellinen sääntely ja havaittiin säännöksissä olevan ristiriitaisuuksia, jotka vaikuttavat sulautumisessa tehtäviin päätöksiin.

Reconstruction and analysis of surface variation using photometric stereo

In many industrial applications, accurate and fast surface reconstruction is essential for quality control. Variation in surface finishing parameters, such as surface roughness, can reflect defects in a manufacturing process, non-optimal product operational efficiency, and reduced life expectancy of the product. This thesis considers reconstruction and analysis of high-frequency variation, that is roughness, on planar surfaces. Standard roughness measures in industry are calculated from surface topography. A fast and non-contact method to obtain surface topography is to apply photometric stereo in the estimation of surface gradients and to reconstruct the surface by integrating the gradient fields. Alternatively, visual methods, such as statistical measures, fractal dimension and distance transforms, can be used to characterize surface roughness directly from gray-scale images. In this thesis, the accuracy of distance transforms, statistical measures, and fractal dimension are evaluated in the estimation of surface roughness from gray-scale images and topographies. The results are contrasted to standard industry roughness measures. In distance transforms, the key idea is that distance values calculated along a highly varying surface are greater than distances calculated along a smoother surface. Statistical measures and fractal dimension are common surface roughness measures. In the experiments, skewness and variance of brightness distribution, fractal dimension, and distance transforms exhibited strong linear correlations to standard industry roughness measures. One of the key strengths of photometric stereo method is the acquisition of higher frequency variation of surfaces. In this thesis, the reconstruction of planar high-frequency varying surfaces is studied in the presence of imaging noise and blur. Two Wiener filterbased methods are proposed of which one is optimal in the sense of surface power spectral density given the spectral properties of the imaging noise and blur. Experiments show that the proposed methods preserve the inherent high-frequency variation in the reconstructed surfaces, whereas traditional reconstruction methods typically handle incorrect measurements by smoothing, which dampens the high-frequency variation.

Color ad spectral image assessment using novel quality and fidelity techniques

The ongoing development of the digital media has brought a new set of challenges with it. As images containing more than three wavelength bands, often called spectral images, are becoming a more integral part of everyday life, problems in the quality of the RGB reproduction from the spectral images have turned into an important area of research. The notion of image quality is often thought to comprise two distinctive areas – image quality itself and image fidelity, both dealing with similar questions, image quality being the degree of excellence of the image, and image fidelity the measure of the match of the image under study to the original. In this thesis, both image fidelity and image quality are considered, with an emphasis on the influence of color and spectral image features on both. There are very few works dedicated to the quality and fidelity of spectral images. Several novel image fidelity measures were developed in this study, which include kernel similarity measures and 3D-SSIM (structural similarity index). The kernel measures incorporate the polynomial, Gaussian radial basis function (RBF) and sigmoid kernels. The 3D-SSIM is an extension of a traditional gray-scale SSIM measure developed to incorporate spectral data. The novel image quality model presented in this study is based on the assumption that the statistical parameters of the spectra of an image influence the overall appearance. The spectral image quality model comprises three parameters of quality: colorfulness, vividness and naturalness. The quality prediction is done by modeling the preference function expressed in JNDs (just noticeable difference). Both image fidelity measures and the image quality model have proven to be effective in the respective experiments.

Muotin suunnittelu- ja valmistusprossin parantaminen solumuoviteollisuudessa

Diplomityö tehtiin Pohjoismainen Solumuovi Oy:lle. Tutkimusongelmaksi määriteltiin hyvin selkeästi tiedon puute muottien suunnittelusta sekä valmistuksesta. Työn tavoite oli parantaa solumuovituotteiden valmistuksessa käytettävien muottien laatua. Työn rajaus asettuu uusien muottien suunnittelu- ja valmistusprosessin parantamiseen. Työssä käsiteltiin tämän prosessin lisäksi mm. mallisuunnittelua, sekä koneen ja prosessien ominaisuuksia niiden muoteille asettamien vaatimusten pohjalta. Muottien ominaisuuksien oikeaoppinen suunnittelu on erittäin tärkeässä roolissa tulevien tuotantokustannusten muodostumisessa. Oikeaoppisella suunnittelulla tarkoitetaan työssä muottien eri osa-alueiden toisiinsa kytkeytymisen ymmärtämistä, niin muotin toimivuuden, lopputuotteen laadun sekä muotin tehokkuuden kannalta. Muottien ominaisuudet määräävät ideaalitilanteessa pitkälti koneiden sykliaikoja käytettävän raaka-aineen ja kappaleen dimensioiden lisäksi. Tämän vuoksi tilanteessa, jossa itse kone toimii moitteettomasti vakuumin, materiaalinsyötön, jäähdytyksen ja höyrytyksen osalta, saadaan muotin oikeaoppisella suunnittelulla lyhennettyä sykliaikoja ja vähennettyä virhekappaleiden määrää. Solumuovimuotin suunnitteluperiaatteita on lähes yhtä monta kuin suunnittelijoita. Mitään yksittäistä periaatetta ei voida suoraan tuomita vääränä, koska näillä muoteilla pystytään valmistamaan laadun kriteerit täyttäviä tuotteita. Useimmat suunnitteluperiaatteet pohjautuvat kuitenkin vain tietyn osa-alueen hallintaan, jonka jälkeen loput osa-alueet suunnitellaan tämän ehdoilla. Työssä saatujen tulosten pohjalta voidaan sanoa, että muotin tuottavuus on erityisesti kiinni kokonaisuudesta, ei yksittäisestä osa-alueesta.