Sulpun kuvantaminen painelajittelussa

Painelajittelussa sellusta poistetaan epäpuhtauksia. Painelajittimien suunnittelussa on tärkeää ymmärtää lajittimessa tapahtuvia ilmiöitä. Työn tavoitteena oli kehittää kuvaamiseen perustuva mittausjärjestelmä kuitujen liikkeiden mittaamista varten. Mittauksen kohteena ovat sellusulpun kuitujen ja epäpuhtauksien nopeudet. Kuvaamisessa käytetyllä kaksoisvalotuksella pystytään mittaamaan kuitujen ja roskien nopeuksia. Nopeuksien mittaamiseen kuvista kehitettiin järjestelmä ja tutkittiin mahdollisuutta automatisoida mittaaminen. Yksittäisten kuitujen havaitsemiseen sellumassasta käytettiin optisella kirkasteella kirkastettuja kuituja ja UV-valoa. Kuituja värjättiin myös mustiksi ja kuvattiin näkyvällä valolla. Kaksoisvalotukseen käytettiin kahta stroboskooppia. Prosessin kuvaamisessa käytettiin ulkoisella herätteellä ohjattavaa kameraa. Kuvan tuomiseen kameralle ja kohteen valaistukseen käytettiin boroskooppia. Saatujen kuvien käsittelyä ja nopeuksien mittausta varten tehtiin tietokoneohjelma. Käytetyn boroskoopin valovoima ei ollut riittävä kuvausten suorittamiseen, mutta muilta osin laitteisto havaittiin toimivaksi. Kuitujen ja roskien nopeuksia pystyttiin laskemaan ohjelmalla kuvista, joita otettiin ilman boroskooppia. Mittaustiedon hankinnan automatisointi näyttää mahdolliselta tekemällä muutoksia kuvauslaitteistoon.

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.

Enhancement of Calcifications in Mammograms using Volterra Series based Quadratic Filter

The paper summarizes the design and implementation of a quadratic edge detection filter, based on Volterra series, for enhancing calcifications in mammograms. The proposed filter can account for much of the polynomial nonlinearities inherent in the input mammogram image and can replace the conventional edge detectors like Laplacian, gaussian etc. The filter gives rise to improved visualization and early detection of microcalcifications, which if left undetected, can lead to breast cancer. The performance of the filter is analyzed and found superior to conventional spatial edge detectors

Design, Development and Realization of Quadratic Volterra Filters for Selected Applications

The basic concepts of digital signal processing are taught to the students in engineering and science. The focus of the course is on linear, time invariant systems. The question as to what happens when the system is governed by a quadratic or cubic equation remains unanswered in the vast majority of literature on signal processing. Light has been shed on this problem when John V Mathews and Giovanni L Sicuranza published the book Polynomial Signal Processing. This book opened up an unseen vista of polynomial systems for signal and image processing. The book presented the theory and implementations of both adaptive and non-adaptive FIR and IIR quadratic systems which offer improved performance than conventional linear systems. The theory of quadratic systems presents a pristine and virgin area of research that offers computationally intensive work. Once the area of research is selected, the next issue is the choice of the software tool to carry out the work. Conventional languages like C and C++ are easily eliminated as they are not interpreted and lack good quality plotting libraries. MATLAB is proved to be very slow and so do SCILAB and Octave. The search for a language for scientific computing that was as fast as C, but with a good quality plotting library, ended up in Python, a distant relative of LISP. It proved to be ideal for scientific computing. An account of the use of Python, its scientific computing package scipy and the plotting library pylab is given in the appendix Initially, work is focused on designing predictors that exploit the polynomial nonlinearities inherent in speech generation mechanisms. Soon, the work got diverted into medical image processing which offered more potential to exploit by the use of quadratic methods. The major focus in this area is on quadratic edge detection methods for retinal images and fingerprints as well as de-noising raw MRI signals

Finding Texture Boundaries in Images

Texture provides one cue for identifying the physical cause of an intensity edge, such as occlusion, shadow, surface orientation or reflectance change. Marr, Julesz, and others have proposed that texture is represented by small lines or blobs, called 'textons' by Julesz [1981a], together with their attributes, such as orientation, elongation, and intensity. Psychophysical studies suggest that texture boundaries are perceived where distributions of attributes over neighborhoods of textons differ significantly. However, these studies, which deal with synthetic images, neglect to consider two important questions: How can these textons be extracted from images of natural scenes? And how, exactly, are texture boundaries then found? This thesis proposes answers to these questions by presenting an algorithm for computing blobs from natural images and a statistic for measuring the difference between two sample distributions of blob attributes. As part of the blob detection algorithm, methods for estimating image noise are presented, which are applicable to edge detection as well.

Optimal Unsupervised Learning in Feedforward Neural Networks

We investigate the properties of feedforward neural networks trained with Hebbian learning algorithms. A new unsupervised algorithm is proposed which produces statistically uncorrelated outputs. The algorithm causes the weights of the network to converge to the eigenvectors of the input correlation with largest eigenvalues. The algorithm is closely related to the technique of Self-supervised Backpropagation, as well as other algorithms for unsupervised learning. Applications of the algorithm to texture processing, image coding, and stereo depth edge detection are given. We show that the algorithm can lead to the development of filters qualitatively similar to those found in primate visual cortex.

Extraction of tidal channel networks from airborne scanning laser altimetry and aerial photography

The study of the morphodynamics of tidal channel networks is important because of their role in tidal propagation and the evolution of salt-marshes and tidal flats. Channel dimensions range from tens of metres wide and metres deep near the low water mark to only 20-30cm wide and 20cm deep for the smallest channels on the marshes. The conventional method of measuring the networks is cumbersome, involving manual digitising of aerial photographs. This paper describes a semi-automatic knowledge-based network extraction method that is being implemented to work using airborne scanning laser altimetry (and later aerial photography). The channels exhibit a width variation of several orders of magnitude, making an approach based on multi-scale line detection difficult. The processing therefore uses multi-scale edge detection to detect channel edges, then associates adjacent anti-parallel edges together to form channels using a distance-with-destination transform. Breaks in the networks are repaired by extending channel ends in the direction of their ends to join with nearby channels, using domain knowledge that flow paths should proceed downhill and that any network fragment should be joined to a nearby fragment so as to connect eventually to the open sea.

Uses of airborne scanning laser altimetry for river flood modelling and coastal geomorphology

Two ongoing projects at ESSC that involve the development of new techniques for extracting information from airborne LiDAR data and combining this information with environmental models will be discussed. The first project in conjunction with Bristol University is aiming to improve 2-D river flood flow models by using remote sensing to provide distributed data for model calibration and validation. Airborne LiDAR can provide such models with a dense and accurate floodplain topography together with vegetation heights for parameterisation of model friction. The vegetation height data can be used to specify a friction factor at each node of a model’s finite element mesh. A LiDAR range image segmenter has been developed which converts a LiDAR image into separate raster maps of surface topography and vegetation height for use in the model. Satellite and airborne SAR data have been used to measure flood extent remotely in order to validate the modelled flood extent. Methods have also been developed for improving the models by decomposing the model’s finite element mesh to reflect floodplain features such as hedges and trees having different frictional properties to their surroundings. Originally developed for rural floodplains, the segmenter is currently being extended to provide DEMs and friction parameter maps for urban floods, by fusing the LiDAR data with digital map data. The second project is concerned with the extraction of tidal channel networks from LiDAR. These networks are important features of the inter-tidal zone, and play a key role in tidal propagation and in the evolution of salt-marshes and tidal flats. The study of their morphology is currently an active area of research, and a number of theories related to networks have been developed which require validation using dense and extensive observations of network forms and cross-sections. The conventional method of measuring networks is cumbersome and subjective, involving manual digitisation of aerial photographs in conjunction with field measurement of channel depths and widths for selected parts of the network. A semi-automatic technique has been developed to extract networks from LiDAR data of the inter-tidal zone. A multi-level knowledge-based approach has been implemented, whereby low level algorithms first extract channel fragments based mainly on image properties then a high level processing stage improves the network using domain knowledge. The approach adopted at low level uses multi-scale edge detection to detect channel edges, then associates adjacent anti-parallel edges together to form channels. The higher level processing includes a channel repair mechanism.

Dynamic programming approach for road centerline extraction from digital images

This paper presents a dynamic programming approach for semi-automated road extraction from medium-and high-resolution images. This method is a modified version of a pre-existing dynamic programming method for road extraction from low-resolution images. The basic assumption of this pre-existing method is that roads manifest as lines in low-resolution images (pixel footprint> 2 m) and as such can be modeled and extracted as linear features. On the other hand, roads manifest as ribbon features in medium- and high-resolution images (pixel footprint ≤ 2 m) and, as a result, the focus of road extraction becomes the road centerlines. The original method can not accurately extract road centerlines from medium- and high- resolution images. In view of this, we propose a modification of the merit function of the original approach, which is carried out by a constraint function embedding road edge properties. Experimental results demonstrated the modified algorithm's potential in extracting road centerlines from medium- and high-resolution images.

Algoritmos de detecção de bordas implementados em FPGA

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Extração semi-automática de edificações com análise do modelo numérico de elevações

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Implementação em FPGA de um sistema para processamento de imagens digitais para aplicações diversificadas

Pós-graduação em Ciência da Computação - IBILCE

Medidas de deslocamento e vibrações através de imagens de câmeras digitais

Pós-graduação em Engenharia Mecânica - FEIS

Segmentação de texturas em imagens digitais utilizando wavelets redundantes

Pós-graduação em Matematica Aplicada e Computacional - FCT

Tecniche per la separazione live di piani di profondita in un flusso video mediante Kinect e una applicazione nell'intrattenimento digitale

Keying e composizione sono da sempre tecniche ampiamente utilizzate in contesti multimediali, quali produzione cinematografica e televisiva; il chroma keying è in particolare la tecnica più popolare, ma presenta una serie di limiti e problematiche. In questo elaborato viene proposta una tecnica alternativa di estrazione, basata sull'uso della profondità, operante in tempo reale e che sfrutta il device Kinect di Microsoft. Sono proposti una serie di algoritmi, basati su tecniche di edge detection, utilizzati per il miglioramento della depth map lungo i bordi di estrazione; viene infine testato il risultato ottenuto dall'implementazione del sistema e proposta una possibile applicazione nell'ambito del teatro multimediale.