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.

Measuring irregularities and surface defects from printed patterns

Quality inspection and assurance is a veryimportant step when today's products are sold to markets. As products are produced in vast quantities, the interest to automate quality inspection tasks has increased correspondingly. Quality inspection tasks usuallyrequire the detection of deficiencies, defined as irregularities in this thesis. Objects containing regular patterns appear quite frequently on certain industries and science, e.g. half-tone raster patterns in the printing industry, crystal lattice structures in solid state physics and solder joints and components in the electronics industry. In this thesis, the problem of regular patterns and irregularities is described in analytical form and three different detection methods are proposed. All the methods are based on characteristics of Fourier transform to represent regular information compactly. Fourier transform enables the separation of regular and irregular parts of an image but the three methods presented are shown to differ in generality and computational complexity. Need to detect fine and sparse details is common in quality inspection tasks, e.g., locating smallfractures in components in the electronics industry or detecting tearing from paper samples in the printing industry. In this thesis, a general definition of such details is given by defining sufficient statistical properties in the histogram domain. The analytical definition allowsa quantitative comparison of methods designed for detail detection. Based on the definition, the utilisation of existing thresholding methodsis shown to be well motivated. Comparison of thresholding methods shows that minimum error thresholding outperforms other standard methods. The results are successfully applied to a paper printability and runnability inspection setup. Missing dots from a repeating raster pattern are detected from Heliotest strips and small surface defects from IGT picking papers.

Towards desired crystalline product properties: In-situ monitoring of batch crystallization

The objective of industrial crystallization is to obtain a crystalline product which has the desired crystal size distribution, mean crystal size, crystal shape, purity, polymorphic and pseudopolymorphic form. Effective control of the product quality requires an understanding of the thermodynamics of the crystallizing system and the effects of operation parameters on the crystalline product properties. Therefore, obtaining reliable in-line information about crystal properties and supersaturation, which is the driving force of crystallization, would be very advantageous. Advanced techniques, such asRaman spectroscopy, attenuated total reflection Fourier transform infrared (ATR FTIR) spectroscopy, and in-line imaging techniques, offer great potential for obtaining reliable information during crystallization, and thus giving a better understanding of the fundamental mechanisms (nucleation and crystal growth) involved. In the present work, the relative stability of anhydrate and dihydrate carbamazepine in mixed solvents containing water and ethanol were investigated. The kinetics of the solvent mediated phase transformation of the anhydrate to hydrate in the mixed solvents was studied using an in-line Raman immersion probe. The effects of the operation parameters in terms of solvent composition, temperature and the use of certain additives on the phase transformation kineticswere explored. Comparison of the off-line measured solute concentration and the solid-phase composition measured by in-line Raman spectroscopy allowedthe identification of the fundamental processes during the phase transformation. The effects of thermodynamic and kinetic factors on the anhydrate/hydrate phase of carbamazepine crystals during cooling crystallization were also investigated. The effect of certain additives on the batch cooling crystallization of potassium dihydrogen phosphate (KDP) wasinvestigated. The crystal growth rate of a certain crystal face was determined from images taken with an in-line video microscope. An in-line image processing method was developed to characterize the size and shape of thecrystals. An ATR FTIR and a laser reflection particle size analyzer were used to study the effects of cooling modes and seeding parameters onthe final crystal size distribution of an organic compound C15. Based on the obtained results, an operation condition was proposed which gives improved product property in terms of increased mean crystal size and narrowersize distribution.

Spektrikuvien tiivistäminen itseorganisoituvan kartan avulla.

Vuosi vuodelta kasvava tietokoneiden prosessointikyky on mahdollistanut harmaataso- ja RGB-värikuvia tarkempien spektrikuvien käsittelyn järjellisessä ajassa ilman suuria kustannuksia. Ongelmana on kuitenkin, ettei talletus- ja tiedonsiirtomedia ole kehittynyt prosessointikyvyn vauhdissa. Ratkaisu tähän ongelmaan on spektrikuvien tiivistäminen talletuksen ja tiedonsiirron ajaksi. Tässä työssä esitellään menetelmä, jossa spektrikuva tiivistetään kahdessa vaiheessa: ensin ryhmittelemällä itseorganisoituvan kartan (SOM) avulla ja toisessa vaiheessa jatketaan tiivistämistä perinteisin menetelmin. Saadut tiivistyssuhteet ovat merkittäviä vääristymän pysyessä siedettävänä. Työ on tehty Lappeenrannan teknillisen korkeakoulun Tietotekniikan osaston Tietojenkäsittelytekniikan tutkimuslaboratoriossa osana laajempaa kuvantiivistyksen tutkimushanketta.

Reconstruction of 3D Planar Objects with Partial Occlusion

Kolmiulotteisten kappaleiden rekonstruktio on yksi konenäön haastavimmista ongelmista, koska kappaleiden kolmiulotteisia etäisyyksiä ei voida selvittää yhdestä kaksiulotteisesta kuvasta. Ongelma voidaan ratkaista stereonäön avulla, jossa näkymän kolmiulotteinen rakenne päätellään usean kuvan perusteella. Tämä lähestymistapa mahdollistaa kuitenkin vain rekonstruktion niille kappaleiden osille, jotka näkyvät vähintään kahdessa kuvassa. Piilossa olevien osien rekonstruktio ei ole mahdollista pelkästään stereonäön avulla. Tässä työssä on kehitetty uusi menetelmä osittain piilossa olevien kolmiulotteisten tasomaisten kappaleiden rekonstruktioon. Menetelmän avulla voidaan selvittää hyvällä tarkkuudella tasomaisista pinnoista koostuvan kappaleen muoto ja paikka käyttäen kahta kuvaa kappaleesta. Menetelmä perustuu epipolaarigeometriaan, jonka avulla selvitetään molemmissa kuvissa näkyvät kappaleiden osat. Osittain piilossa olevien piirteiden rekonstruointi suoritetaan käyttämäen stereonäköä sekä tietoa kappaleen rakenteesta. Esitettyä ratkaisua voitaisiin käyttää esimerkiksi kolmiulotteisten kappaleiden visualisointiin, robotin navigointiin tai esineentunnistukseen.

Wood Volume Measurement Using Color Images

Tämä työ käsittelee puutukkien tilavuuden mittaamista värikonenäön avulla. Värikuvat on saatu Simpeleellä olevan metsäteollisuusyrityksen hiomosta. Työssä esitetään perusteellisesti matemaattinen teoria, joka liittyy käytettyihin kuvankäsittelymenetelmiin, kuten luokitteluun, kohinan poistoon ja tukkien segmentointiin. Esitetyt menetelmät implementointiin käytännössä ja eri menetelmillä saatuja tuloksia vertailtiin keskenään. Kuvankäsittelyalgoritmit on implementoitu Matlab 6.0:n avulla. Pääasiassa käytettiin uusinta Image Processing Toolboxia, joka on versio 3.0. Tämä työn näkökulma on pääasiassa käytäntöön soveltava, koska metsäteollsuus on korkealla tasolla Suomessa ja siellä on paljon alan yrityksiä, joissa tässä työssä kehitettyä menetelmää voidaan hyödyntää.

Computational modeling of resting-state activity demonstrates markers of normalcy in children with prenatal or perinatal stroke.

Children who sustain a prenatal or perinatal brain injury in the form of a stroke develop remarkably normal cognitive functions in certain areas, with a particular strength in language skills. A dominant explanation for this is that brain regions from the contralesional hemisphere "take over" their functions, whereas the damaged areas and other ipsilesional regions play much less of a role. However, it is difficult to tease apart whether changes in neural activity after early brain injury are due to damage caused by the lesion or by processes related to postinjury reorganization. We sought to differentiate between these two causes by investigating the functional connectivity (FC) of brain areas during the resting state in human children with early brain injury using a computational model. We simulated a large-scale network consisting of realistic models of local brain areas coupled through anatomical connectivity information of healthy and injured participants. We then compared the resulting simulated FC values of healthy and injured participants with the empirical ones. We found that the empirical connectivity values, especially of the damaged areas, correlated better with simulated values of a healthy brain than those of an injured brain. This result indicates that the structural damage caused by an early brain injury is unlikely to have an adverse and sustained impact on the functional connections, albeit during the resting state, of damaged areas. Therefore, these areas could continue to play a role in the development of near-normal function in certain domains such as language in these children.

Cooperative and Competitive Spreading Dynamics on the Human Connectome.

Increasingly detailed data on the network topology of neural circuits create a need for theoretical principles that explain how these networks shape neural communication. Here we use a model of cascade spreading to reveal architectural features of human brain networks that facilitate spreading. Using an anatomical brain network derived from high-resolution diffusion spectrum imaging (DSI), we investigate scenarios where perturbations initiated at seed nodes result in global cascades that interact either cooperatively or competitively. We find that hub regions and a backbone of pathways facilitate early spreading, while the shortest path structure of the connectome enables cooperative effects, accelerating the spread of cascades. Finally, competing cascades become integrated by converging on polysensory associative areas. These findings show that the organizational principles of brain networks shape global communication and facilitate integrative function.

Huulisuihkun homogeenisuuden vaikutus paperin rakenneominaisuuksiin

Työssä verrattiin perälaatikoilla 1 ja 2 valmistettujen papereiden rakenteellisia ominaisuuksia. Paperin rakenteessa formaatio oli tärkein ominaisuus ja sen jälkeen kuituorientaatio. Näytteiden valintaperusteena pidettiin sitä, että vertailtavilla näytteillä formaatio (pohja) oli paras kyseisellä perälaatikolla ja vetolujuussuhteet olivat samoja. Toinen valintatapa oli verrata samoissa virtausolosuhteissa valmistettuja papereita keskenään. Näytteiden formaatio mitattiin betaradiografialla. Fosforikuvalevystä skannattu kuva analysoitiin kuva-analyysillä. Mittauksen etuna oli suuri erottelukyky, joka mahdollisti monipuolisen tunnuslukujen laskennan. Näistä esimerkkeinä olivat keskihajonta, vinous ja huipukkuus. Lisäksi määritettiin flokkikokojakaumat sekä kone- että poikkisuuntaan. Kuituorientaation määrityksessä paperinäyte revittiin kerroksiin, kerrokset skannattiin ja kuvat analysoitiin kuvankäsittelyohjelmilla. Juova- ja kuituorientaatioanalyysissä määritettiin orientaatiokulma, max/min-arvo ja anisotropia. Virtaviiva-analyysin tunnusluku oli pyörrekoko. Käytettäessä tunnuslukuna variaatiokerrointa formaatio oli parempi perälaatikolla 1 ali- ja yliperällä. Tasaperän läheisyydessä formaatio oli huonompi. Keskihajonta oli pienempi perälaatikolla 1, mutta erot perälaatikoiden välillä tasaantuivat lähellä tasaperää. Flokkikoko oli koko s/v-alueella hieman suurempi perälaatikolla 1. Virtaviiva-analyysin avulla saatiin selville, että perälaatikolla 1 valmistettujen papereiden paikallinen orientaatiovaihtelu ja pintojen toispuoleisuus oli lievempää kuin perälaatikolla 2.

Improving Pitch Tracking Performance in Hard Noise Conditions by a Preprocessing Based on Mathematical Morphology

In this paper we show how a nonlinear preprocessing of speech signal -with high noise- based on morphological filters improves the performance of robust algorithms for pitch tracking (RAPT). This result happens for a very simple morphological filter. More sophisticated ones could even improve such results. Mathematical morphology is widely used in image processing and has a great amount of applications. Almost all its formulations derived in the two-dimensional framework are easily reformulated to be adapted to one-dimensional context

Phase mask selection in wavefront coding systems: an adaptive approach

A method for optimizing the strength of a parametric phase mask for a wavefront coding imaging system is presented. The method is based on an optimization process that minimizes a proposed merit function. The goal is to achieve modulation transfer function invariance while quantitatively maintaining nal image delity. A parametric lter that copes with the noise present in the captured images is used to obtain the nal images, and this lter is optimized. The whole process results in optimum phase mask strength and optimal parameters for the restoration lter. The results for a particular optical system are presented and tested experimentally in the labo- ratory. The experimental results show good agreement with the simulations, indicating that the procedure is useful.

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.

An efficient total variation algorithm for super-resolution in fetal brain MRI with adaptive regularization.

Although fetal anatomy can be adequately viewed in new multi-slice MR images, many critical limitations remain for quantitative data analysis. To this end, several research groups have recently developed advanced image processing methods, often denoted by super-resolution (SR) techniques, to reconstruct from a set of clinical low-resolution (LR) images, a high-resolution (HR) motion-free volume. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has been quite attracted by Total Variation energies because of their ability in edge preserving but only standard explicit steepest gradient techniques have been applied for optimization. In a preliminary work, it has been shown that novel fast convex optimization techniques could be successfully applied to design an efficient Total Variation optimization algorithm for the super-resolution problem. In this work, two major contributions are presented. Firstly, we will briefly review the Bayesian and Variational dual formulations of current state-of-the-art methods dedicated to fetal MRI reconstruction. Secondly, we present an extensive quantitative evaluation of our SR algorithm previously introduced on both simulated fetal and real clinical data (with both normal and pathological subjects). Specifically, we study the robustness of regularization terms in front of residual registration errors and we also present a novel strategy for automatically select the weight of the regularization as regards the data fidelity term. Our results show that our TV implementation is highly robust in front of motion artifacts and that it offers the best trade-off between speed and accuracy for fetal MRI recovery as in comparison with state-of-the art methods.

Comparison of Inoculation with the InoqulA and WASP Automated Systems with Manual Inoculation.

The quality of sample inoculation is critical for achieving an optimal yield of discrete colonies in both monomicrobial and polymicrobial samples to perform identification and antibiotic susceptibility testing. Consequently, we compared the performance between the InoqulA (BD Kiestra), the WASP (Copan), and manual inoculation methods. Defined mono- and polymicrobial samples of 4 bacterial species and cloudy urine specimens were inoculated on chromogenic agar by the InoqulA, the WASP, and manual methods. Images taken with ImagA (BD Kiestra) were analyzed with the VisionLab version 3.43 image analysis software to assess the quality of growth and to prevent subjective interpretation of the data. A 3- to 10-fold higher yield of discrete colonies was observed following automated inoculation with both the InoqulA and WASP systems than that with manual inoculation. The difference in performance between automated and manual inoculation was mainly observed at concentrations of >10(6) bacteria/ml. Inoculation with the InoqulA system allowed us to obtain significantly more discrete colonies than the WASP system at concentrations of >10(7) bacteria/ml. However, the level of difference observed was bacterial species dependent. Discrete colonies of bacteria present in 100- to 1,000-fold lower concentrations than the most concentrated populations in defined polymicrobial samples were not reproducibly recovered, even with the automated systems. The analysis of cloudy urine specimens showed that InoqulA inoculation provided a statistically significantly higher number of discrete colonies than that with WASP and manual inoculation. Consequently, the automated InoqulA inoculation greatly decreased the requirement for bacterial subculture and thus resulted in a significant reduction in the time to results, laboratory workload, and laboratory costs.