Palm Print Edge Extraction Using Fractional Differential Algorithm

Algorithm based on fractional difference was used for the edge extraction of thenar palm print image. Based on fractional order difference function which was deduced from classical fractional differential G-L definition, three filter templates were constructed to extract thenar palm print edge. The experiment results showed that this algorithm can reduce noise and detect rich edge details and has higher SNR than traditional methods

Partitionnement des images hyperspectrales de grande dimension spatiale par propagation d'affinité

The interest in hyperspectral image data has been constantly increasing during the last years. Indeed, hyperspectral images provide more detailed information about the spectral properties of a scene and allow a more precise discrimination of objects than traditional color images or even multispectral images. High spatial and spectral resolutions of hyperspectral images enable to precisely characterize the information pixel content. Though the potentialities of hyperspectral technology appear to be relatively wide, the analysis and the treatment of these data remain complex. In fact, exploiting such large data sets presents a great challenge. In this thesis, we are mainly interested in the reduction and partitioning of hyperspectral images of high spatial dimension. The proposed approach consists essentially of two steps: features extraction and classification of pixels of an image. A new approach for features extraction based on spatial and spectral tri-occurrences matrices defined on cubic neighborhoods is proposed. A comparative study shows the discrimination power of these new features over conventional ones as well as spectral signatures. Concerning the classification step, we are mainly interested in this thesis to the unsupervised and non-parametric classification approach because it has several advantages: no a priori knowledge, image partitioning for any application domain, and adaptability to the image information content. A comparative study of the most well-known semi-supervised (knowledge of number of classes) and unsupervised non-parametric methods (K-means, FCM, ISODATA, AP) showed the superiority of affinity propagation (AP). Despite its high correct classification rate, affinity propagation has two major drawbacks. Firstly, the number of classes is over-estimated when the preference parameter p value is initialized as the median value of the similarity matrix. Secondly, the partitioning of large size hyperspectral images is hampered by its quadratic computational complexity. Therefore, its application to this data type remains impossible. To overcome these two drawbacks, we propose an approach which consists of reducing the number of pixels to be classified before the application of AP by automatically grouping data points with high similarity. We also introduce a step to optimize the preference parameter value by maximizing a criterion related to the interclass variance, in order to correctly estimate the number of classes. The proposed approach was successfully applied on synthetic images, mono-component and multi-component and showed a consistent discrimination of obtained classes. It was also successfully applied and compared on hyperspectral images of high spatial dimension (1000 × 1000 pixels × 62 bands) in the context of a real application for the detection of invasive and non-invasive vegetation species.Les images hyperspectrales suscitent un intérêt croissant depuis une quinzaine d'années. Elles fournissent une information plus détaillée d'une scène et permettent une discrimination plus précise des objets que les images couleur RVB ou multi-spectrales. Bien que les potentialités de la technologie hyperspectrale apparaissent relativement grandes, l'analyse et l'exploitation de ces données restent une tâche difficile et présentent aujourd'hui un défi. Les travaux de cette thèse s'inscrivent dans le cadre de la réduction et de partitionnement des images hyperspectrales de grande dimension spatiale. L'approche proposée se compose de deux étapes : calcul d'attributs et classification des pixels. Une nouvelle approche d'extraction d'attributs à partir des matrices de tri-occurrences définies sur des voisinages cubiques est proposée en tenant compte de l'information spatiale et spectrale. Une étude comparative a été menée afin de tester le pouvoir discriminant de ces nouveaux attributs par rapport aux attributs classiques. Les attributs proposés montrent un large écart discriminant par rapport à ces derniers et par rapport aux signatures spectrales. Concernant la classification, nous nous intéressons ici au partitionnement des images par une approche de classification non supervisée et non paramétrique car elle présente plusieurs avantages: aucune connaissance a priori, partitionnement des images quel que soit le domaine applicatif, adaptabilité au contenu informationnel des images. Une étude comparative des principaux classifieurs semi-supervisés (connaissance du nombre de classes) et non supervisés (C-moyennes, FCM, ISODATA, AP) a montré la supériorité de la méthode de propagation d'affinité (AP). Mais malgré un meilleur taux de classification, cette méthode présente deux inconvénients majeurs: une surestimation du nombre de classes dans sa version non supervisée, et l'impossibilité de l'appliquer sur des images de grande taille (complexité de calcul quadratique). Nous avons proposé une approche qui apporte des solutions à ces deux problèmes. Elle consiste tout d'abord à réduire le nombre d'individus à classer avant l'application de l'AP en agrégeant les pixels à très forte similarité. Pour estimer le nombre de classes, la méthode AP utilise de manière implicite un paramètre de préférence p dont la valeur initiale correspond à la médiane des valeurs de la matrice de similarité. Cette valeur conduisant souvent à une sur-segmentation des images, nous avons introduit une étape permettant d'optimiser ce paramètre en maximisant un critère lié à la variance interclasse. L'approche proposée a été testée avec succès sur des images synthétiques, mono et multi-composantes. Elle a été également appliquée et comparée sur des images hyperspectrales de grande taille spatiale (1000 × 1000 pixels × 62 bandes) avec succès dans le cadre d'une application réelle pour la détection des plantes invasives

Fabrication and nanoroughness characterization of specific nanostructures and nanodevice

Nanoroughness is becoming a very important specification for many nanostructures and nanodevices, and its metrology impacts not only the nanodevice properties of interest, but also its material selection and process development. This Ph.D. thesis presents an investigation into fabrication and nanoroughness characterization of nanoscale specimens and MIS (metal-insulator-semiconductor) capacitors with 2 HfO as a high k dielectric. Self-affine curves and Gaussian, non-Gaussian, self-affine as well as complicated rough surfaces were characterized and simulated. The effects of characteristic parameters on the CD (critical dimension) variation and the properties of these rough surfaces were visualized. Compared with experimental investigations, these simulations are flexible, low cost and highly efficient. Relevant conclusions were frequently employed in subsequent investigations. A proposal regarding the thicknesses of the deposited films represented by nominal linewidths and pitch was put forward. The MBE (Molecular Beam Epitaxy) process was introduced and AlGaAs and GaAs were selected to fabricate nanolinewidth and nanopitch specimens on GaAs substrate with nominal linewidths of 2nm, 4nm, 6nm and 8nm, and a nominal pitch of 5nm. HRTEM (High Resolution Transmission Electron Microscopy) image-based characterization of LER/LWR (Line Edge Roughness/Line Width Roughness) in real space and frequency domains demonstrated that the MBE-based process was capable of fabricating the desired nanolinewidth and nanopitch specimens and could be regulated accordingly. MIS capacitors with 2 HfO film as high k dielectric were fabricated, and SEM (Scanning Electron Microscope) image-based nanoroughness characterization, along with measurement of the MIS capacitor electrical properties were performed. It was concluded that the annealing temperature of the deposited 2 HfO film was an important process parameter and 700℃ was an optimal temperature to improve the properties of the MIS capacitor. Also, by quantitative characterization of the relevant nanoroughness, the fabrication process can be further regulated. The uncertainty propagation model of SEM based nanoroughness measurement was presented according to specific requirements of the relevant standards, ISO GPS (Geometric Product Specifications and Verification) and GUM (Guide to the Expression of Uncertainty in Measurement), and the method for implementating uncertainties was evaluated. The case study demonstrated that the total standard uncertainty of the nanoroughness measurement was 0.13nm, while its expanded uncertainty with the coverage factor k as 3 was 0.39nm. They are indispensable parts of LER/LWR measurement results

Wavelet based method for edge detection of defect on coated board during production proccess

Disertacija razmatra problem detekcije ivica defekata u slikama kartona upotrebom wavelet transformacije. Osobine wavelet transformacije, pre svega predstavljanje singulariteta u signalu malim brojem koeficijenata omogućava realizaciju efikasnog detektora ivica. U okviru ove doktorske disertacije dat je detaljan pregled postojećih detektora ivica baziran na diferenciranju i upotrebi Gausovog filtra. Veoma iscrpno su opisani i postupci zasnovani na wavelet transformaciju. Analizirani su načini poređenja predloženih sa drugim detektorima ivica i veoma su retki slučajevi u kojima autori primenjuju objektivne metode poređenja detektora, a i tada su rezultati komparacije zasnovani na sintetičkoj slici, zbog čega se izvedeni zaključci ne mogu generalizovati na realne slike. Ilustrovani su nedostaci klasičnog detektora ivica kada se primene na slike defekata. Predstavljene su najvažnije karakteristike wavelet transformacije i data je teorijska osnova detekcije singulariteta u signalu upotrebom wavelet transformacije. Kako karakteristike signala imaju uticaj na performanse detektora ivica, ispitivanjem slika defekata na kartonu izveden je matematički model ivice defekata. Na osnovu matematičkog modela i karakteristika slika defekata na kartonu ustanovljeno je da se bolji rezultat ostvaruje kada se u proizvod uključe tri skale wavelet transformacije, a ne samo dve kako je prvobitno predložno, jer je, pre svega, izraženije potiskivanje šuma. Zatim, pokazano je da se predloženi algoritam može primeniti na proizvoljan skup slika, pri čemu se na osnovu karakteristika slika može utvrditi početna skala za formiranje proizvoda koeficijenata wavelet transformacije. U nastavku, prikazane su i analizirane dostupne metodologije komparacije detektora ivica. Ustanovljeno je da se mora primeniti objektivna metoda zasnovana na korišćenju istinite mape ivica. Da bi se ostvarilo adekvatno poređenje, realizovana je baza od 50 slika defekata na kartonu sa odgovarajućim istinitim mapama ivica. Komparacija detektora ivica je izvršena i na osnovu postojeće baze svakodnevnih slika i slika iz vazduha (slike iz ptičje perspektive), kao i odgovarajućih mapa ivica. Za poređenje su izabrani klasični i najčešće korišćeni detektori ivica: Sobel, Canny i Marr-Hildreth, zatim dva detektora bazirana na wavelet transformaciji i noviji, često korišćen detektor ivica – SUSAN (eng. Smallest Univalue Segment Assimilating Nucleus) detektor...This thesis considers edge detection of defects on a coated board based on the wavelet transform. Properties of the wavelet transform, above all the possibility to represent singularity in the signal with a few coefficients, gives opportunity to realize the efficient edge detector. This thesis gives a detailed description of existing edge detection methods based on differentiation and Gaussian filtering with in-depth review of the wavelet transform techniques. It is analyzed how authors compare suggested methods with other edge detectors. If was found that only few authors use objective evaluation and those comparisons are based on a synthetic image and cannot be applied to real images. Shortcomings of classical edge detector when it is used with coated board images are shown. The most important properties of the wavelet transform are presented with theoretical background of singularity detection. Characteristics of the signal have influence on the edge detector performances and the model of an edge on coated board is developed. Using this model it is shown that better result are obtain when three, instead of originally suggested two scale of wavelet transform are multiplied. Additionally, it is shown that proposed algorithm can be applied on arbitrary image set and only initially scale for multiplication must be determined. Subsequently, the edge detector evaluation methods are shown and analyzed. It is concluded that objective methodology based on the ground truth images must be used. For the evaluation set of 50 defect images on coated board and 50 corresponding ground truth images are created. The comparison is also performed with on available sets of 50 object and 10 aerial images. Testing of proposed algorithm is done by comparing it with the classical and frequently used edge detector: Sobel, Canny and Marr-Hildreth; with two edge detectors based on the wavelet transform and one newly and commonly used edge detector – SUSAN (Smallest Univalue Segment Assimilating Nucleus) detector..