The purpose of this study was the hardware implementation of a real time moving object contour extraction.Segmentation of image frames to isolate moving objects followed by contour extraction using digitalmorphology was carried out in this work. Segmentation using temporal difference with median thresholdingapproach was implemented, experimental methods were used to determine the suitable morphological operatorsalong with their structuring elements dimensions to provide the optimum contour extraction.The detector with image resolution of 1280 x1024 pixels and frame rate of 60 Hz was successfully implemented,the results indicate the effect of proper use of morphological operators for post processing and contourextraction on the overall efficiency of the system. An alternative segmentation method based on Stauffer & Grimson algorithm was investigated and proposed which promises better system performance at the expense ofimage resolution and frame rate

O movimento dos lábios é um recurso visual relevante para a detecção da atividade de voz do locutor e para o reconhecimento da fala. Quando os lábios estão se movendo eles transmitem a idéia de ocorrências de diálogos (conversas ou períodos de fala) para o observador, enquanto que os períodos de silêncio podem ser representados pela ausência de movimentações dos lábios (boca fechada). Baseado nesta idéia, este trabalho foca esforços para detectar a movimentação de lábios e usá-la para realizar a detecção de atividade de voz. Primeiramente, é realizada a detecção de pele e a detecção de face para reduzir a área de extração dos lábios, sendo que as regiões mais prováveis de serem lábios são computadas usando a abordagem Bayesiana dentro da área delimitada. Então, a pré-segmentação dos lábios é obtida pela limiarização da região das probabilidades calculadas. A seguir, é localizada a região da boca pelo resultado obtido na pré-segmentação dos lábios, ou seja, alguns pixels que não são de lábios e foram detectados são eliminados, e em seguida são aplicados algumas operações morfológicas para incluir alguns pixels labiais e não labiais em torno da boca. Então, uma nova segmentação de lábios é realizada sobre a região da boca depois de aplicada uma transformação de cores para realçar a região a ser segmentada. Após a segmentação, é aplicado o fechamento das lacunas internas dos lábios segmentados. Finalmente, o movimento temporal dos lábios é explorado usando o modelo das cadeias ocultas de Markov (HMMs) para detectar as prováveis ocorrências de atividades de fala dentro de uma janela temporal. / Lips motion are relevant visual feature for detecting the voice active of speaker and speech recognition. When the lips are moving, they carries an idea of occurrence of dialogues (talk) or periods of speeches to the watcher, whereas the periods of silences may be represented by the absence of lips motion (mouth closed). Based on this idea, this work focus efforts to obtain the lips motion as features and to perform visual voice activity detection. First, the algorithm performs skin segmentation and face detection to reduce the search area for lip extraction, and the most likely lip regions are computed using a Bayesian approach within the delimited area. Then, the pre-segmentation of the lips is obtained by thresholding the calculated probability region. After, it is localized the mouth region by resulted obtained in pre-segmentation of the lips, i.e., some nonlips pixels detected are eliminated, and it are applied a simple morphological operators to include some lips pixels and non-lips around the mouth. Thus, a new segmentation of lips is performed over mouth region after transformation of color to enhance the region to be segmented. And, is applied the closing of gaps internal of lips segmented. Finally, the temporal motion of the lips is explored using Hidden Markov Models (HMMs) to detect the likely occurrence of active speech within a temporal window.

Nowadays, there is growing interest in face detection applications for unconstrained environments. The increasing need for public security and national security motivated our research on the automatic face detection system. For public security surveillance applications, the face detection system must be able to cope with unconstrained environments, which includes cluttered background and complicated illuminations. Supervised approaches give very good results on constrained environments, but when it comes to unconstrained environments, even obtaining all the training samples needed is sometimes impractical. The limitation of supervised approaches impels us to turn to unsupervised approaches. In this thesis, we present an efficient and unsupervised face detection system, which is feature and configuration based. It combines geometric feature detection and local appearance feature extraction to increase stability and performance of the detection process. It also contains a novel adaptive lighting compensation approach to normalize the complicated illumination in real life environments. We aim to develop a system that has as few assumptions as possible from the very beginning, is robust and exploits accuracy/complexity trade-offs as much as possible. Although our attempt is ambitious for such an ill posed problem-we manage to tackle it in the end with very few assumptions.

O movimento dos lábios é um recurso visual relevante para a detecção da atividade de voz do locutor e para o reconhecimento da fala. Quando os lábios estão se movendo eles transmitem a idéia de ocorrências de diálogos (conversas ou períodos de fala) para o observador, enquanto que os períodos de silêncio podem ser representados pela ausência de movimentações dos lábios (boca fechada). Baseado nesta idéia, este trabalho foca esforços para detectar a movimentação de lábios e usá-la para realizar a detecção de atividade de voz. Primeiramente, é realizada a detecção de pele e a detecção de face para reduzir a área de extração dos lábios, sendo que as regiões mais prováveis de serem lábios são computadas usando a abordagem Bayesiana dentro da área delimitada. Então, a pré-segmentação dos lábios é obtida pela limiarização da região das probabilidades calculadas. A seguir, é localizada a região da boca pelo resultado obtido na pré-segmentação dos lábios, ou seja, alguns pixels que não são de lábios e foram detectados são eliminados, e em seguida são aplicados algumas operações morfológicas para incluir alguns pixels labiais e não labiais em torno da boca. Então, uma nova segmentação de lábios é realizada sobre a região da boca depois de aplicada uma transformação de cores para realçar a região a ser segmentada. Após a segmentação, é aplicado o fechamento das lacunas internas dos lábios segmentados. Finalmente, o movimento temporal dos lábios é explorado usando o modelo das cadeias ocultas de Markov (HMMs) para detectar as prováveis ocorrências de atividades de fala dentro de uma janela temporal. / Lips motion are relevant visual feature for detecting the voice active of speaker and speech recognition. When the lips are moving, they carries an idea of occurrence of dialogues (talk) or periods of speeches to the watcher, whereas the periods of silences may be represented by the absence of lips motion (mouth closed). Based on this idea, this work focus efforts to obtain the lips motion as features and to perform visual voice activity detection. First, the algorithm performs skin segmentation and face detection to reduce the search area for lip extraction, and the most likely lip regions are computed using a Bayesian approach within the delimited area. Then, the pre-segmentation of the lips is obtained by thresholding the calculated probability region. After, it is localized the mouth region by resulted obtained in pre-segmentation of the lips, i.e., some nonlips pixels detected are eliminated, and it are applied a simple morphological operators to include some lips pixels and non-lips around the mouth. Thus, a new segmentation of lips is performed over mouth region after transformation of color to enhance the region to be segmented. And, is applied the closing of gaps internal of lips segmented. Finally, the temporal motion of the lips is explored using Hidden Markov Models (HMMs) to detect the likely occurrence of active speech within a temporal window.

O movimento dos lábios é um recurso visual relevante para a detecção da atividade de voz do locutor e para o reconhecimento da fala. Quando os lábios estão se movendo eles transmitem a idéia de ocorrências de diálogos (conversas ou períodos de fala) para o observador, enquanto que os períodos de silêncio podem ser representados pela ausência de movimentações dos lábios (boca fechada). Baseado nesta idéia, este trabalho foca esforços para detectar a movimentação de lábios e usá-la para realizar a detecção de atividade de voz. Primeiramente, é realizada a detecção de pele e a detecção de face para reduzir a área de extração dos lábios, sendo que as regiões mais prováveis de serem lábios são computadas usando a abordagem Bayesiana dentro da área delimitada. Então, a pré-segmentação dos lábios é obtida pela limiarização da região das probabilidades calculadas. A seguir, é localizada a região da boca pelo resultado obtido na pré-segmentação dos lábios, ou seja, alguns pixels que não são de lábios e foram detectados são eliminados, e em seguida são aplicados algumas operações morfológicas para incluir alguns pixels labiais e não labiais em torno da boca. Então, uma nova segmentação de lábios é realizada sobre a região da boca depois de aplicada uma transformação de cores para realçar a região a ser segmentada. Após a segmentação, é aplicado o fechamento das lacunas internas dos lábios segmentados. Finalmente, o movimento temporal dos lábios é explorado usando o modelo das cadeias ocultas de Markov (HMMs) para detectar as prováveis ocorrências de atividades de fala dentro de uma janela temporal. / Lips motion are relevant visual feature for detecting the voice active of speaker and speech recognition. When the lips are moving, they carries an idea of occurrence of dialogues (talk) or periods of speeches to the watcher, whereas the periods of silences may be represented by the absence of lips motion (mouth closed). Based on this idea, this work focus efforts to obtain the lips motion as features and to perform visual voice activity detection. First, the algorithm performs skin segmentation and face detection to reduce the search area for lip extraction, and the most likely lip regions are computed using a Bayesian approach within the delimited area. Then, the pre-segmentation of the lips is obtained by thresholding the calculated probability region. After, it is localized the mouth region by resulted obtained in pre-segmentation of the lips, i.e., some nonlips pixels detected are eliminated, and it are applied a simple morphological operators to include some lips pixels and non-lips around the mouth. Thus, a new segmentation of lips is performed over mouth region after transformation of color to enhance the region to be segmented. And, is applied the closing of gaps internal of lips segmented. Finally, the temporal motion of the lips is explored using Hidden Markov Models (HMMs) to detect the likely occurrence of active speech within a temporal window.

Purpose of this thesis is the quality check of correct button text display on photographed monitors. These photographs contain a variety of image distortions which complicates the following image graphic element recognition. This paper outlines several possibilities to detect buttons on forms and further elaborates on the implemented detection based on contour shapes description. After buttons are found, their defects are detected subsequently. Additionally, this thesis describes an automatic identification of picture with the highest quality for documentation purposes.

Σκοπός της παρούσας Διπλωματικής Εργασίας είναι η ανάπτυξη συγκεκριμένης μεθοδολογίας και αλγορίθμων ψηφιακής επεξεργασίας εικόνων για την αυτόματη εκτίμηση των διαμέτρων αγγείων σε οφθαλμολογικές εικόνες. Η συγκεκριμένη μέτρηση της διαμέτρου των αγγείων διαδραματίζει σημαντικό ρόλο στην έγκαιρη διάγνωση παθήσεων καθώς έχει αποδειχθεί ότι υπάρχει συσχετισμός μεταξύ των μεταβολών των τιμών των εν λόγω διαμέτρων και της εμφάνισης αλλοιώσεων στον αμφιβληστροειδή. Στα πλαίσια της εργασίας, υλοποιήθηκαν δύο μεθοδολογίες για τον υπολογισμό των διαμέτρων αγγείων οφθαλμολογικών εικόνων, οι οποίες συλλέγονται με χρήση κάμερας πυθμένα (fundus camera). Η πρώτη μεθοδολογία στηρίζεται στην εύρεση των σημείων που αποτελούν τους κεντρικούς άξονες των υπό εξέταση αγγείων με χρήση διαφορικού λογισμού. Ακολούθως, σε κάθε σημείο που ανήκει σε κεντρικό άξονα αγγείου, υπολογίζονται οι παράμετροι μιας συνάρτησης. Η εν λόγω συνάρτηση περιγράφει βέλτιστα τα επίπεδα φωτεινότητας της εικόνας κατά μήκος του ευθύγραμμου τμήματος που διέρχεται από το σημείο και είναι κάθετο στο αγγείο. H εύρεση των παραμέτρων της συνάρτησης πραγματοποιείται με χρήση τεχνικών βελτιστοποίησης. Το τελικό βήμα της μεθοδολογίας είναι η εκτίμηση της διαμέτρου των αγγείων από τις τιμές των παραμέτρων που έχουν υπολογιστεί. Η δεύτερη μεθοδολογία στηρίζεται στον αλγόριθμο που προτείνει ο P.H. Gregson. Αρχικά, πραγματοποιείται κατάτμηση της εικόνας με κατωφλίωση και εφαρμόζονται μορφολογικοί τελεστές συστολής και διαστολής στην εικόνα. Στη συνέχεια, εφαρμόζεται ο αλγόριθμος λέπτυνσης (thinning algorithm) με σκοπό την εύρεση των κεντρικών αξόνων των αγγείων και τέλος εκτιμάται η διάμετρος σε κάθε σημείο του κεντρικού άξονα με χρήση των επιπέδων του γκρίζου των εικονοστοιχείων που κείνται στην ευθεία που είναι κάθετη στο αγγείο σε κάθε σημείο του. / The scope of this Thesis is the development of a methodology and advance image processing techniques in order to automatically estimate vessel diameters in ophthalmological images. Motivation for the thesis is the fact that the measurement of vessel diameter plays significant role in the seasonable diagnosis of vascular disorders, as it is believed to be a relation between the variation in diameters and the detection of retinal disorders. In this thesis, two methodologies are developed in order to be applied in ophthalmological images that are collected by using a fundus camera. The first methodology is based on the detection of the pixels that constitute the centerlines of vessels, by using differential calculus. Specifically, at each pixel that belongs to a centerline of vessel, the parameters of a specific function are calculated. This function describes as accurately as possible the intensity levels along the segment that passes through the specific pixel and is perpendicular to the vessel. The parameters of this function are estimated using optimization techniques. The final step of the methodology is the assessment of the diameters of vessels using the values of the parameters. The second methodology is based on the algorithm that P.H.Gregson has proposed. At first, the vessels are detected by tresholding and a morphological closing algorithm is applied. Then, a thinning algorithm is used in order to detect the pixels that constitute the centerlines of the vessels and ultimately the diameter at each pixel of the centerlines is assessed using the gray levels of the pixels that constitute the segment that is perpendicular to the vessel at each specific pixel.