Methods for Region Based Paper Surface Roughness Measurement

Paperin pinnan karheus on yksi paperin laatukriteereistä. Sitä mitataan fyysisestipaperin pintaa mittaavien laitteiden ja optisten laitteiden avulla. Mittaukset vaativat laboratorioolosuhteita, mutta nopeammille, suoraan linjalla tapahtuville mittauksilla olisi tarvetta paperiteollisuudessa. Paperin pinnan karheus voidaan ilmaista yhtenä näytteelle kohdistuvana karheusarvona. Tässä työssä näyte on jaettu merkitseviin alueisiin, ja jokaiselle alueelle on laskettu erillinen karheusarvo. Karheuden mittaukseen on käytetty useita menetelmiä. Yleisesti hyväksyttyä tilastollista menetelmää on käytetty tässä työssä etäisyysmuunnoksen lisäksi. Paperin pinnan karheudenmittauksessa on ollut tarvetta jakaa analysoitava näyte karheuden perusteella alueisiin. Aluejaon avulla voidaan rajata näytteestä selvästi karheampana esiintyvät alueet. Etäisyysmuunnos tuottaa alueita, joita on analysoitu. Näistä alueista on muodostettu yhtenäisiä alueita erilaisilla segmentointimenetelmillä. PNN -menetelmään (Pairwise Nearest Neighbor) ja naapurialueiden yhdistämiseen perustuvia algoritmeja on käytetty.Alueiden jakamiseen ja yhdistämiseen perustuvaa lähestymistapaa on myös tarkasteltu. Segmentoitujen kuvien validointi on yleensä tapahtunut ihmisen tarkastelemana. Tämän työn lähestymistapa on verrata yleisesti hyväksyttyä tilastollista menetelmää segmentoinnin tuloksiin. Korkea korrelaatio näiden tulosten välillä osoittaa onnistunutta segmentointia. Eri kokeiden tuloksia on verrattu keskenään hypoteesin testauksella. Työssä on analysoitu kahta näytesarjaa, joidenmittaukset on suoritettu OptiTopolla ja profilometrillä. Etäisyysmuunnoksen aloitusparametrit, joita muutettiin kokeiden aikana, olivat aloituspisteiden määrä ja sijainti. Samat parametrimuutokset tehtiin kaikille algoritmeille, joita käytettiin alueiden yhdistämiseen. Etäisyysmuunnoksen jälkeen korrelaatio oli voimakkaampaa profilometrillä mitatuille näytteille kuin OptiTopolla mitatuille näytteille. Segmentoiduilla OptiTopo -näytteillä korrelaatio parantui voimakkaammin kuin profilometrinäytteillä. PNN -menetelmän tuottamilla tuloksilla korrelaatio oli paras.

Performance Study of an Improved Legendre Moment Descriptor as Region-Based Shape Descriptor

This paper reports a novel region-based shape descriptor based on orthogonal Legendre moments. The preprocessing steps for invariance improvement of the proposed Improved Legendre Moment Descriptor (ILMD) are discussed. The performance of the ILMD is compared to the MPEG-7 approved region shape descriptor, angular radial transformation descriptor (ARTD), and the widely used Zernike moment descriptor (ZMD). Set B of the MPEG-7 CE-1 contour database and all the datasets of the MPEG-7 CE-2 region database were used for experimental validation. The average normalized modified retrieval rate (ANMRR) and precision- recall pair were employed for benchmarking the performance of the candidate descriptors. The ILMD has lower ANMRR values than ARTD for most of the datasets, and ARTD has a lower value compared to ZMD. This indicates that overall performance of the ILMD is better than that of ARTD and ZMD. This result is confirmed by the precision-recall test where ILMD was found to have better precision rates for most of the datasets tested. Besides retrieval accuracy, ILMD is more compact than ARTD and ZMD. The descriptor proposed is useful as a generic shape descriptor for content-based image retrieval (CBIR) applications

Region-Based Feature Interpretation for Recognizing 3D Models in 2D Images

In model-based vision, there are a huge number of possible ways to match model features to image features. In addition to model shape constraints, there are important match-independent constraints that can efficiently reduce the search without the combinatorics of matching. I demonstrate two specific modules in the context of a complete recognition system, Reggie. The first is a region-based grouping mechanism to find groups of image features that are likely to come from a single object. The second is an interpretive matching scheme to make explicit hypotheses about occlusion and instabilities in the image features.

A novel shape feature for fast region-based pedestrian recognition

A new class of shape features for region classification and high-level recognition is introduced. The novel Randomised Region Ray (RRR) features can be used to train binary decision trees for object category classification using an abstract representation of the scene. In particular we address the problem of human detection using an over segmented input image. We therefore do not rely on pixel values for training, instead we design and train specialised classifiers on the sparse set of semantic regions which compose the image. Thanks to the abstract nature of the input, the trained classifier has the potential to be fast and applicable to extreme imagery conditions. We demonstrate and evaluate its performance in people detection using a pedestrian dataset.

Pulmonary lymphangioleiomyomatosis: analysis of disease manifestation by region-based quantification of lung parenchyma

PURPOSE Lymphangioleiomyomatosis (LAM) is characterized by proliferation of smooth muscle tissue that causes bronchial obstruction and secondary cystic destruction of lung parenchyma. The aim of this study was to evaluate the typical distribution of cystic defects in LAM with quantitative volumetric chest computed tomography (CT). MATERIALS AND METHODS CT examinations of 20 patients with confirmed LAM were evaluated with region-based quantification of lung parenchyma. Additionally, 10 consecutive patients were identified who had recently undergone CT imaging of the lung at our institution, in which no pathologies of the lung were found, to serve as a control group. Each lung was divided into three regions (upper, middle and lower thirds) with identical number of slices. In addition, we defined a "peel" and "core" of the lung comprising the 2 cm subpleural space and the remaining inner lung area. Computerized detection of lung volume and relative emphysema was performed with the PULMO 3D software (v3.42, Fraunhofer MEVIS, Bremen, Germany). This software package enables the quantification of emphysematous lung parenchyma by calculating the pixel index, which is defined as the ratio of lung voxels with a density <-950HU to the total number of voxels in the lung. RESULTS Cystic changes accounted for 0.1-39.1% of the total lung volume in patients with LAM. Disease manifestation in the central lung was significantly higher than in peripheral areas (peel median: 15.1%, core median: 20.5%; p=0.001). Lower thirds of lung parenchyma showed significantly less cystic changes than upper and middle lung areas combined (lower third: median 13.4, upper and middle thirds: median 19.0, p=0.001). CONCLUSION The distribution of cystic lesions in LAM is significantly more pronounced in the central lung compared to peripheral areas. There is a significant predominance of cystic changes in apical and intermediate lung zones compared to the lung bases.

A region based approach to background modeling in a wavelet multi-resolution framework

In the field of detection and monitoring of dynamic objects in quasi-static scenes, background subtraction techniques where background is modeled at pixel-level, although showing very significant limitations, are extensively used. In this work we propose a novel approach to background modeling that operates at region-level in a wavelet based multi-resolution framework. Based on a segmentation of the background, characterization is made for each region independently as a mixture of K Gaussian modes, considering the model of the approximation and detail coefficients at the different wavelet decomposition levels. Background region characterization is updated along time, and the detection of elements of interest is carried out computing the distance between background region models and those of each incoming image in the sequence. The inclusion of the context in the modeling scheme through each region characterization makes the model robust, being able to support not only gradual illumination and long-term changes, but also sudden illumination changes and the presence of strong shadows in the scene

Region-based moving object detection using spatially conditioned nonparametric models in a GPU

A novel GPU-based nonparametric moving object detection strategy for computer vision tools requiring real-time processing is proposed. An alternative and efficient Bayesian classifier to combine nonparametric background and foreground models allows increasing correct detections while avoiding false detections. Additionally, an efficient region of interest analysis significantly reduces the computational cost of the detections.

High-quality region-based foreground segmentation using a spatial grid of SVM classifiers

This paper presents a novel background modeling system that uses a spatial grid of Support Vector Machines classifiers for segmenting moving objects, which is a key step in many video-based consumer applications. The system is able to adapt to a large range of dynamic background situations since no parametric model or statistical distribution are assumed. This is achieved by using a different classifier per image region that learns the specific appearance of that scene region and its variations (illumination changes, dynamic backgrounds, etc.). The proposed system has been tested with a recent public database, outperforming other state-of-the-art algorithms.

Multi-sensor background subtraction by fusing multiple region-based probabilistic classifiers

In the recent years, the computer vision community has shown great interest on depth-based applications thanks to the performance and flexibility of the new generation of RGB-D imagery. In this paper, we present an efficient background subtraction algorithm based on the fusion of multiple region-based classifiers that processes depth and color data provided by RGB-D cameras. Foreground objects are detected by combining a region-based foreground prediction (based on depth data) with different background models (based on a Mixture of Gaussian algorithm) providing color and depth descriptions of the scene at pixel and region level. The information given by these modules is fused in a mixture of experts fashion to improve the foreground detection accuracy. The main contributions of the paper are the region-based models of both background and foreground, built from the depth and color data. The obtained results using different database sequences demonstrate that the proposed approach leads to a higher detection accuracy with respect to existing state-of-the-art techniques.

Psychophysical evidence for fast region-based segmentation processes in motion and color.

Theories of image segmentation suggest that the human visual system may use two distinct processes to segregate figure from background: a local process that uses local feature contrasts to mark borders of coherent regions and a global process that groups similar features over a larger spatial scale. We performed psychophysical experiments to determine whether and to what extent the global similarity process contributes to image segmentation by motion and color. Our results show that for color, as well as for motion, segmentation occurs first by an integrative process on a coarse spatial scale, demonstrating that for both modalities the global process is faster than one based on local feature contrasts. Segmentation by motion builds up over time, whereas segmentation by color does not, indicating a fundamental difference between the modalities. Our data suggest that segmentation by motion proceeds first via a cooperative linking over space of local motion signals, generating almost immediate perceptual coherence even of physically incoherent signals. This global segmentation process occurs faster than the detection of absolute motion, providing further evidence for the existence of two motion processes with distinct dynamic properties.

Power tapping function in near infra-red region based on 45° tilted fiber gratings

We report an efficient power tapping device working in near infra-red (800 nm) wavelength region based on UV-in- scribed 45° tilted fiber grating (45°-TFG) structure. Five 45°-TFGs were UV-inscribed in hydrogenated PS750 fiber using a custom-designed phase mask with different grating lengths of 3 mm, 5 mm, 9 mm, 12 mm and 15 mm, showing polarization dependent losses (PDLs) of 1 dB, 3 dB, 7 dB, 10 dB and 13 dB, respectively. The power side-tapping efficiency is clearly depending on the grating strength. It has been identified that the power tapping efficiency increases with the grating strength and deceases along the grating length. The side-tapped power profile has also been examined in azimuthal direction, showing a near-Gaussian distribution. These experimental results clearly demonstrated that 45°- TFGs may be used as in-fiber power tapping devices for applications requiring in-line signal monitoring.

Seutulogistisen palveluyrityksen palvelutarjooman kehittäminen

Tämän diplomityön aiheena on palvelutarjooman kehittäminen elintarvikeketjussa toimivalle seutulogistiikan palveluyritykselle. Työn tavoitteena on tuottaa tietoa uudenlaisen logistiikkapalvelun käynnistämisen tueksi. Työssä kartoitetaan kaakkoissuomalaisen elintarvikeketjun logistiikan toimintamalleja ja palvelutarpeita. Aineistona on käytetty 23 elintarvikeketjun osapuolen (tuottaja, jalostaja, myyjä) haastatteluja sekä olemassa olevia selvityksiä aiheesta. Työ sisältää katsauksen kaakkoissuomalaiseen ruokatuotantoon ja elintarvikeketjun eri vaiheiden nykytilaan. Tuloksena ovat jäsennetyt palvelutarpeet elintarvikkeiden toimitusketjun eri osapuolille. Lisäksi palveluyrityksen toimintaa selvennettiin rakentamalla sille liiketoimintamalli, joka sisältää arviot yrityksen tärkeimpien osa-alueiden sisällöstä ja toimintatavasta. Liiketoimintamallin ja eri osapuolien palvelutarpeiden pohjalta työssä rakennettiin kaksi palvelutarpeisiin perustuvaa vaihtoehtoista palvelutarjoomaa ja arvioitiin niiden ominaisuuksia ja toteutettavuutta Kaakkois-Suomessa. Työn sisältö ja lopputulokset ovat julkisesti hyödynnettävissä paikallisesti tuotetun ruoan tuottajien liiketoimintamahdollisuuksien ja palvelun parantamiseksi.

Plasmodium vivax resistance to chloroquine (R2) and mefloquine (R3) in Brazilian Amazon region

We report for the first time a patient with malaria due to Plasmodium vivax who showed R2 resistance to chloroquine and R3 resistance to mefloquine in the Brazilian Amazon region based on WHO clinical criteria for diagnosis of malaria resistance. Failure was observed with unsupervised oral chloroquine, chloroquine under rigorous supervision and mefloquine in the same scheme. Finally, the patient was cured with oral artesunate.

Fourier-based registration for robust forward-looking sonar mosaicing in low-visibility underwater environments

Vehicle operations in underwater environments are often compromised by poor visibility conditions. For instance, the perception range of optical devices is heavily constrained in turbid waters, thus complicating navigation and mapping tasks in environments such as harbors, bays, or rivers. A new generation of high-definition forward-looking sonars providing acoustic imagery at high frame rates has recently emerged as a promising alternative for working under these challenging conditions. However, the characteristics of the sonar data introduce difficulties in image registration, a key step in mosaicing and motion estimation applications. In this work, we propose the use of a Fourier-based registration technique capable of handling the low resolution, noise, and artifacts associated with sonar image formation. When compared to a state-of-the art region-based technique, our approach shows superior performance in the alignment of both consecutive and nonconsecutive views as well as higher robustness in featureless environments. The method is used to compute pose constraints between sonar frames that, integrated inside a global alignment framework, enable the rendering of consistent acoustic mosaics with high detail and increased resolution. An extensive experimental section is reported showing results in relevant field applications, such as ship hull inspection and harbor mapping