P algorithm, a dramatic enhancement of the waterfall transformation

This document has been extended by "Towards a unification of waterfalls, standard and P algorithms", see http://hal-ensmp.archives-ouvertes.fr/hal-00835016.This document describes an efficient enhancement of the waterfall algorithm, a hierarchical segmentation algorithm defined from the watershed transformation. The first part of the document recalls the definition of the waterfall algorithm, its various avatars as well as its links with the geodesic reconstruction. The second part starts by analyzing the different shortcomings of the algorithm and introduces several strategies to palliate them. Two enhancements are presented, the first one named standard algorithm and the second one, P algorithm. The different properties of P algorithm are analyzed. This analysis is detailed in the last part of the document. The performances of the two algorithms, in particular, are addressed and their analogies with perception mechanisms linked to the brightness constancy phenomenon are discussed

Cross-Spectral Face Recognition Between Near-Infrared and Visible Light Modalities.

In this thesis, improvement of face recognition performance with the use of images from the visible (VIS) and near-infrared (NIR) spectrum is attempted. Face recognition systems can be adversely affected by scenarios which encounter a significant amount of illumination variation across images of the same subject. Cross-spectral face recognition systems using images collected across the VIS and NIR spectrum can counter the ill-effects of illumination variation by standardising both sets of images. A novel preprocessing technique is proposed, which attempts the transformation of faces across both modalities to a feature space with enhanced correlation. Direct matching across the modalities is not possible due to the inherent spectral differences between NIR and VIS face images. Compared to a VIS light source, NIR radiation has a greater penetrative depth when incident on human skin. This fact, in addition to the greater number of scattering interactions within the skin by rays from the NIR spectrum can alter the morphology of the human face enough to disable a direct match with the corresponding VIS face. Several ways to bridge the gap between NIR-VIS faces have been proposed previously. Mostly of a data-driven approach, these techniques include standardised photometric normalisation techniques and subspace projections. A generative approach driven by a true physical model has not been investigated till now. In this thesis, it is proposed that a large proportion of the scattering interactions present in the NIR spectrum can be accounted for using a model for subsurface scattering. A novel subsurface scattering inversion (SSI) algorithm is developed that implements an inversion approach based on translucent surface rendering by the computer graphics field, whereby the reversal of the first order effects of subsurface scattering is attempted. The SSI algorithm is then evaluated against several preprocessing techniques, and using various permutations of feature extraction and subspace projection algorithms. The results of this evaluation show an improvement in cross spectral face recognition performance using SSI over existing Retinex-based approaches. The top performing combination of an existing photometric normalisation technique, Sequential Chain, is seen to be the best performing with a Rank 1 recognition rate of 92. 5%. In addition, the improvement in performance using non-linear projection models shows an element of non-linearity exists in the relationship between NIR and VIS

Development of image processing and vision systems with industrial applications

Ph.DDOCTOR OF PHILOSOPH

Algorithms for multi-modal human movement and behaviour monitoring

This thesis describes investigations into improvements in the field of automated people tracking using multi-modal infrared (IR) and visible image information. The research question posed is; “To what extent can infrared image information be used to improve visible light based human tracking systems?” Automated passive tracking of human subjects is an active research area which has been approached in many ways. Typical approaches include the segmentation of the foreground, the location of humans, model initialisation and subject tracking. Sensor reliability evaluation and fusion methods are also key research areas in multi-modal systems. Shifting illumination and shadows can cause issues with visible images when attempting to extract foreground regions. Images from thermal IR cameras, which use long-wavelength infrared (LWIR) sensors, demonstrate high invariance to illumination. It is shown that thermal IR images often provide superior foreground masks using pixel level statistical extraction techniques in many scenarios. Experiments are performed to determine if cues are present at the data level that may indicate the quality of the sensor as an input. Modality specific measures are proposed as possible indicators of sensor quality (determined by foreground extraction capability). A sensor and application specific method for scene evaluation is proposed, whereby sensor quality is measured at the pixel level. A neuro-fuzzy inference system is trained using the scene quality measures to assess a series of scenes and make a modality decision

Yüksek uzaysal çözünürlüklü uydu görüntülerinin watershed kullanılarak çok ölçekli otomatik bölütlenmesi.

Useful information extraction from satellite images for the use of other higher level applications such as road network extraction and update, city planning etc. is a very important and active research area. It is seen that pixel-based techniques becomes insufficient for this task with increasing spatial resolution of satellite imaging sensors day by day. Therefore, the use of object-based techniques becomes indispensable and the segmentation method selection is very crucial for object-based techniques. In this thesis, various segmentation algorithms applied in remote sensing literature are presented and a segmentation process that is based on watersheds and multi-scale segmentation is proposed to use as the segmentation step of an object-based classifier. For every step of the proposed segmentation process, qualitative and quantitative comparisons with alternative approaches are done. The ones which provide best performance are incorporated into the proposed algorithm. Also, an unsupervised segmentation accuracy metric to determine all parameters of the algorithm is proposed. By this way, the proposed segmentation algorithm has become a fully automatic approach. Experiments that are done on a database formed with images taken from Google Earth® software provide promising results.M.S. - Master of Scienc

Image texture analysis for inferential sensing in the process industries

Thesis (MScEng)-- Stellenbosch University, 2013.ENGLISH ABSTRACT: The measurement of key process quality variables is important for the efficient and economical operation of many chemical and mineral processing systems, as these variables can be used in process monitoring and control systems to identify and maintain optimal process conditions. However, in many engineering processes the key quality variables cannot be measured directly with standard sensors. Inferential sensing is the real-time prediction of such variables from other, measurable process variables through some form of model. In vision-based inferential sensing, visual process data in the form of images or video frames are used as input variables to the inferential sensor. This is a suitable approach when the desired process quality variable is correlated with the visual appearance of the process. The inferential sensor model is then based on analysis of the image data. Texture feature extraction is an image analysis approach by which the texture or spatial organisation of pixels in an image can be described. Two texture feature extraction methods, namely the use of grey-level co-occurrence matrices (GLCMs) and wavelet analysis, have predominated in applications of texture analysis to engineering processes. While these two baseline methods are still widely considered to be the best available texture analysis methods, several newer and more advanced methods have since been developed, which have properties that should theoretically provide these methods with some advantages over the baseline methods. Specifically, three advanced texture analysis methods have received much attention in recent machine vision literature, but have not yet been applied extensively to process engineering applications: steerable pyramids, textons and local binary patterns (LBPs). The purpose of this study was to compare the use of advanced image texture analysis methods to baseline texture analysis methods for the prediction of key process quality variables in specific process engineering applications. Three case studies, in which texture is thought to play an important role, were considered: (i) the prediction of platinum grade classes from images of platinum flotation froths, (ii) the prediction of fines fraction classes from images of coal particles on a conveyor belt, and (iii) the prediction of mean particle size classes from images of hydrocyclone underflows. Each of the five texture feature sets were used as inputs to two different classifiers (K-nearest neighbours and discriminant analysis) to predict the output variable classes for each of the three case studies mentioned above. The quality of the features extracted with each method was assessed in a structured manner, based their classification performances after the optimisation of the hyperparameters associated with each method. In the platinum froth flotation case study, steerable pyramids and LBPs significantly outperformed the GLCM, wavelet and texton methods. In the case study of coal fines fractions, the GLCM method was significantly outperformed by all four other methods. Finally, in the hydrocyclone underflow case study, steerable pyramids and LBPs significantly outperformed GLCM and wavelet methods, while the result for textons was inconclusive. Considering all of these results together, the overall conclusion was drawn that two of the three advanced texture feature extraction methods, namely steerable pyramids and LBPs, can extract feature sets of superior quality, when compared to the baseline GLCM and wavelet methods in these three case studies. The application of steerable pyramids and LBPs to further image analysis data sets is therefore recommended as a viable alternative to the traditional GLCM and wavelet texture analysis methods.AFRIKAANSE OPSOMMING: Die meting van sleutelproseskwaliteitsveranderlikes is belangrik vir die doeltreffende en ekono-miese werking van baie chemiese– en mineraalprosesseringsisteme, aangesien hierdie verander-likes gebruik kan word in prosesmonitering– en beheerstelsels om die optimale prosestoestande te identifiseer en te handhaaf. In baie ingenieursprosesse kan die sleutelproseskwaliteits-veranderlikes egter nie direk met standaard sensors gemeet word nie. Inferensiële waarneming is die intydse voorspelling van sulke veranderlikes vanaf ander, meetbare prosesveranderlikes deur van ‘n model gebruik te maak. In beeldgebaseerde inferensiële waarneming word visuele prosesdata, in die vorm van beelde of videogrepe, gebruik as insetveranderlikes vir die inferensiële sensor. Hierdie is ‘n gepaste benadering wanneer die verlangde proseskwaliteitsveranderlike met die visuele voorkoms van die proses gekorreleer is. Die inferensiële sensormodel word dan gebaseer op die analise van die beelddata. Tekstuurkenmerkekstraksie is ‘n beeldanalisebenadering waarmee die tekstuur of ruimtelike organisering van die beeldelemente beskryf kan word. Twee tekstuurkenmerkekstraksiemetodes, naamlik die gebruik van grysskaalmede-aanwesigheidsmatrikse (GSMMs) en golfie-analise, is sterk verteenwoordig in ingenieursprosestoepassings van tekstuuranalise. Alhoewel hierdie twee grondlynmetodes steeds algemeen as die beste beskikbare tekstuuranalisemetodes beskou word, is daar sedertdien verskeie nuwer en meer gevorderde metodes ontwikkel, wat beskik oor eienskappe wat teoreties voordele vir hierdie metodes teenoor die grondlynmetodes behoort te verskaf. Meer spesifiek is daar drie gevorderde tekstuuranalisemetodes wat baie aandag in onlangse masjienvisieliteratuur geniet het, maar wat nog nie baie op ingenieursprosesse toegepas is nie: stuurbare piramiedes, tekstons en lokale binêre patrone (LBPs). Die doel van hierdie studie was om die gebruik van gevorderde tekstuuranalisemetodes te vergelyk met grondlyntekstuuranaliesemetodes vir die voorspelling van sleutelproseskwaliteits-veranderlikes in spesifieke prosesingenieurstoepassings. Drie gevallestudies, waarin tekstuur ‘n belangrike rol behoort te speel, is ondersoek: (i) die voorspelling van platinumgraadklasse vanaf beelde van platinumflottasieskuime, (ii) die voorspelling van fynfraksieklasse vanaf beelde van steenkoolpartikels op ‘n vervoerband, en (iii) die voorspelling van gemiddelde partikelgrootteklasse vanaf beelde van hidrosikloon ondervloeie. Elk van die vyf tekstuurkenmerkstelle is as insette vir twee verskillende klassifiseerders (K-naaste bure en diskriminantanalise) gebruik om die klasse van die uitsetveranderlikes te voorspeel, vir elk van die drie gevallestudies hierbo genoem. Die kwaliteit van die kenmerke wat deur elke metode ge-ekstraheer is, is op ‘n gestruktureerde manier bepaal, gebaseer op hul klassifikasieprestasie na die optimering van die hiperparameters wat verbonde is aan elke metode. In die platinumskuimflottasiegevallestudie het stuurbare piramiedes en LBPs betekenisvol beter as die GSMM–, golfie– en tekstonmetodes presteer. In die steenkoolfynfraksiegevallestudie het die GSMM-metode betekenisvol slegter as al vier ander metodes presteer. Laastens, in die hidrosikloon ondervloeigevallestudie het stuurbare piramiedes en LBPs betekenisvol beter as die GSMM– en golfiemetodes presteer, terwyl die resultaat vir tekstons nie beslissend was nie. Deur al hierdie resultate gesamentlik te beskou, is die oorkoepelende gevolgtrekking gemaak dat twee van die drie gevorderde tekstuurkenmerkekstraksiemetodes, naamlik stuurbare piramiedes en LBPs, hoër kwaliteit kenmerkstelle kan ekstraheer in vergelyking met die GSMM– en golfiemetodes, vir hierdie drie gevallestudies. Die toepassing van stuurbare piramiedes en LBPs op verdere beeldanalise-datastelle word dus aanbeveel as ‘n lewensvatbare alternatief tot die tradisionele GSMM– en golfietekstuuranalisemetodes