Fingerprint Recognition using Gray Level Co-Occurrence Matrices (GLCM) and Discrete Wavelet Transform (DWT)

This paper is thoroughly investigated regarding the fingerprint recognition techniques. This is because the world of security had become more essential. Thus, fingerprint recognition is one of the security enforcement and needed to be developed essentially. This project is focused on the effectiveness of the Gray-Level Co-occurrence Matrices (GLCM) and Discrete Wavelet Transform (DWT) techniques for fingerprint recognition. As in the chapter one, this paper discusses regarding the background of the GLCM and the DWT as well as the reason of this project was initiated. Other than that, this paper also discuss regarding the problem that had been faced previously in order to recognise fingerprint optimally. This paper also discusses the objectives and the limitation of this project in this chapter. On the next chapter, history regarding the GLCM as well as DWT had been widely discuss that made the fingerprint recognition system becomes more popular nowadays. The definition of term, equation and equation related to the GLCM and DWT also had been explained. Moreover, some previous related study will also be discussed. On the third chapter, this paper reviews the method that will be approached for the project for the entire eight months’ timeframe. As for the last chapter, several initial conclusions had been made regarding the fingerprint recognition techniques

MANÜEL ÖZNİTELİK ÇIKARIMI VE DERİN ÖĞRENME KULLANILARAK KUMAŞ YUMUŞAKLIĞI VE BONCUKLANMA DEĞERLERİNİN OBJEKTİF BİR ŞEKİLDE ÖLÇÜLMESİ VE SINIFLANDIRILMASI

Fabric softness is a complex tactile sensation perceived by the user even before the fabrics are worn. Softness is usually the property of surface perceived by touching or pressing a finger on the fabric surface. Fabric friction properties significantly affect the tactile sensation of the garments. The yarn used, the finishing works, and the fabric structure (weaving, knitting, etc.) affect the softness. In addition, the hardness of the water used during washing, washing movements, the amount and content of the detergent and softener used also have permanent effects on the fabric softness. Softness can be evaluated by the jury members with proven effectiveness according to the predetermined scale. Our achievement within the scope of the thesis is to eliminate the differences that may occur as a result of the subjective evaluation, which may arise from qualitative observations by basing the degree of softness evaluated qualitatively on numerical data and to obtain clearer and more precise results by adding quantitative features to the evaluation process. The methodology developed for softness assessment is also applied for another textile deterioration parameter, namely pilling, and its results are also reported.Kumaş yumuşaklığı kumaşların giyilmesinden bile önce kullanıcı tarafından algılanan karmaşık bir dokunma hissidir. Yumuşaklık genellikle kumaşın parmaklarla sıkılması veya preslenmesi ile algılanan yüzey özelliğidir. Kumaş sürtünme özellikleri, giysilerin dokunma duyumlarını büyük ölçüde etkiler. Kullanılan iplik, bitim işleri ve kumaş yapısı (dokuma, örme vb.) yumuşaklığı etkilemektedir. Bunun yanında yıkama sırasında işlem gördüğü su sertliği, yıkama hareketleri, kullanılan deterjan ve yumuşatıcının miktarı ve içeriğinden de etkilenmektedir. Görsel olarak test edilen bir diğer tekstil özelliklerinden olan yumuşaklık, etkinliği kanıtlanmış jüri üyeleri tarafından aşağıdaki skalaya göre değerlendirilebilmektedir. Tez kapsamındaki kazanımımız nitel olarak değerlendirilen yumuşaklık derecesinin, sayısal verilere dayandırılarak, nitel gözlemlerden doğabilecek görsel değerlendirme sonucu oluşacak farklılıkların giderilmesi ve değerlendirme prosesine nicel özellik kazandırarak daha net ve kesin sonuçların elde edilmesidir. Yumuşaklık için geliştirilen metodoloji değerlendirme aynı zamanda başka bir tekstil bozulma parametresi, boncuklanma için de uygulanmış ve sonuçları raporlanmıştır.M.S. - Master of Scienc

Ensemble of texture descriptors and classifiers for face recognition

Abstract Presented in this paper is a novel system for face recognition that works well in the wild and that is based on ensembles of descriptors that utilize different preprocessing techniques. The power of our proposed approach is demonstrated on two datasets: the FERET dataset and the Labeled Faces in the Wild (LFW) dataset. In the FERET datasets, where the aim is identification, we use the angle distance. In the LFW dataset, where the aim is to verify a given match, we use the Support Vector Machine and Similarity Metric Learning. Our proposed system performs well on both datasets, obtaining, to the best of our knowledge, one of the highest performance rates published in the literature on the FERET datasets. Particularly noteworthy is the fact that these good results on both datasets are obtained without using additional training patterns. The MATLAB source of our best ensemble approach will be freely available at https://www.dei.unipd.it/node/2357

Perceptual texture similarity estimation

This thesis evaluates the ability of computational features to estimate perceptual texture similarity. In the first part of this thesis, we conducted two evaluation experiments on the ability of 51 computational feature sets to estimate perceptual texture similarity using two differ-ent evaluation methods, namely, pair-of-pairs based and retrieval based evaluations. These experiments compared the computational features to two sets of human derived ground-truth data, both of which are higher resolution than those commonly used. The first was obtained by free-grouping and the second by pair-of-pairs experiments. Using these higher resolution data, we found that the feature sets do not perform well when compared to human judgements. Our analysis shows that these computational feature sets either (1) only exploit power spectrum information or (2) only compute higher order statistics (HoS) on, at most, small local neighbourhoods. In other words, they cannot capture aperiodic, long-range spatial relationships. As we hypothesise that these long-range interactions are important for the human perception of texture similarity we carried out two more pair-of-pairs ex-periments, the results of which indicate that long-range interactions do provide humans with important cues for the perception of texture similarity. In the second part of this thesis we develop new texture features that can encode such data. We first examine the importance of three different types of visual information for human perception of texture. Our results show that contours are the most critical type of information for human discrimination of textures. Finally, we report the development of a new set of contour-based features which performed well on the free-grouping data and outperformed the 51 feature sets and another contour type feature set with the pair-of-pairs data