abstract: Image segmentation is of great importance and value in many applications. In computer vision, image segmentation is the tool and process of locating objects and boundaries within images. The segmentation result may provide more meaningful image data. Generally, there are two fundamental image segmentation algorithms: discontinuity and similarity. The idea behind discontinuity is locating the abrupt changes in intensity of images, as are often seen in edges or boundaries. Similarity subdivides an image into regions that fit the pre-defined criteria. The algorithm utilized in this thesis is the second category. This study addresses the problem of particle image segmentation by measuring the similarity between a sampled region and an adjacent region, based on Bhattacharyya distance and an image feature extraction technique that uses distribution of local binary patterns and pattern contrasts. A boundary smoothing process is developed to improve the accuracy of the segmentation. The novel particle image segmentation algorithm is tested using four different cases of particle image velocimetry (PIV) images. The obtained experimental results of segmentations provide partitioning of the objects within 10 percent error rate. Ground-truth segmentation data, which are manually segmented image from each case, are used to calculate the error rate of the segmentations. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2015

Η ανάπτυξη του διαδικτύου τα τελευταία χρόνια έχει φέρει αλλαγές στο μέγεθος και την ποιότητα του διαθέσιμου περιεχομένου. Οι χρήστες κυριολεκτικά κατακλύζονται από πληροφορία η οποία μπορεί να έχει διάφορες μορφές όπως κείμενο, ήχο, εικόνα, βίντεο. Η μεγάλη εξάπλωση του διαδικτύου, η εύκολη αναζήτηση σε μεγάλο όγκο πληροφορίας καθώς και η παρουσίαση του περιεχομένου με φιλικό τρόπο προς το χρήστη συνέβαλε στην ολοένα αυξανόμενη ανάγκη για προμήθεια εικόνων, βίντεο και μουσικής. Η ψηφιοποίηση του μεγαλύτερου όγκου περιεχομένου που διαχειρίζονται οι χρήστες τόσο στην προσωπική όσο και στην επαγγελματική ζωή τους οδήγησε στην ανάπτυξη νέων τεχνικών στεγανογραφίας για την ανταλλαγή κρυφής πληροφορίας, έννοια η οποία είναι ευρέως γνωστή από την αρχαιότητα. Η παρούσα μεταπτυχιακή εργασία υλοποιεί δύο από τους πιο δημοφιλείς αλγορίθμους στεγανογράφησης τον (Least Significant Bit) και τον LBP (Local Binary Pattern). Το σύστημα που αναπτύχθηκε είναι διαθέσιμο στο διαδίκτυο και μπορεί να χρησιμοποιηθεί από οποιοδήποτε χρήστη επιθυμεί να αποκρύψει πληροφορία (κείμενο ή εικόνα) μέσα σε μια εικόνα. Το σύστημα υλοποιεί όλο τον κύκλο της στεγανογράφησης δίνοντας τη δυνατότητα στο χρήστη όχι μόνο να κάνει απόκρυψη της πληροφορίας που επιθυμεί αλλά και την αντίστροφη διαδικασία δηλαδή την ανάκτηση της κρυμμένης πληροφορίας. Η διαδικασία είναι απλή και απαιτεί από τον αποστολέα (αυτός που κρύβει το μήνυμα) το ανέβασμα της εικόνας στο σύστημα, την εισαγωγή ενός μυστικού κλειδιού το οποίο πρέπει να είναι γνωστό για την ανάκτηση του μηνύματος, και φυσικά το μήνυμα, δηλαδή η προς απόκρυψη πληροφορία. Στη συνέχεια ο παραλήπτης για να ανακτήσει το μήνυμα θα πρέπει να ανεβάσει στο σύστημα τη στεγανογραφημένη εικόνα καθώς και το μυστικό κλειδί που έχει συμφωνήσει με τον αποστολέα. Τέλος, με κάποια σενάρια χρήσης, πραγματοποιούνται μετρήσεις, οι οποίες δείχνουν την απόδοση κάθε αλγορίθμου και γίνονται οι αντίστοιχες συγκρίσεις. Το σύστημα που υλοποιήθηκε στην παρούσα εργασία μπορεί να συμπεριλάβει και άλλες μεθόδους στεγανογράφησης καθώς επίσης και με την επέκταση του αλγορίθμου LBP ώστε να χρησιμοποιεί και τις τρεις χρωματικές συνιστώσες για την απόκρυψη της πληροφορίας.. Επίσης, θα είχε ιδιαίτερο ενδιαφέρον η παροχή της συγκεκριμένης διαδικασίας σαν ηλεκτρονική υπηρεσία (web service) ώστε να είναι εφικτό να χρησιμοποιηθεί ανεξάρτητα και να μπορεί να εισαχθεί ως αυτόνομο κομμάτι λογισμικού σε κάθε πλατφόρμα που υποστηρίζει web services. / The development of the internet in recent years has brought changes in the size and quality of the available content. Users literally flooded with information which may have various forms like text, audio, image, and video. The wide spread of the internet, the ease of search in a large amount of information and the presentation of the available content in a friendly way resulted in the need for more images, videos and music. With the digitization of the available content new steganography techniques were necessary so that users can exchange secret information. In the current thesis two of the most popular steganography algorithms are implemented: the LSB (Least Significant Bit) and the LBP (Local Binary Pattern). The system is publicly available and can be used by any user who wishes to hide information (text or image) within an image. The system provides functionalities so that user can hide information within an image and recover the hidden information. The sender (the person who wishes to hide a message) has to provide the following information in the system: upload the image, provide the secret key needed to retrieve the message, and upload the message. The receiver has to upload the image containing the message and the secret key needed to recover the message. Anumber of usage scenarios are implemented to measure the performance of the algorithms and make comparisons. The implemented system can easily include more steganografy methods and also the extension of the LBP algorithm so that the three color components are used to hide the information. It would be interested to provide the current process as an e-service (web service) that it is feasible to be used independently and can be introduced as a standalone piece of software in any platform that supports web services.

<p>This Masters thesis studies the concept of local binary patterns, which describe the neighbourhood of a pixel in a digital image by binary derivatives. The operator is often used in texture analysis and has been successfully used in facial recognition.</p><p>This thesis suggests two methods based on some basic ideas of Björn Kruse and studies of literature on the subject. The first suggested method presented is an algorithm which reproduces images from their local binary patterns by a kind of integration of the binary derivatives. This method is a way to prove the preservation of information. The second suggested method is a technique of interpolating missing pixels in a single CCD camera based on local binary patterns and machine learning. The algorithm has shown some very promising results even though in its current form it does not keep up with the best algorithms of today.</p>

Identification of individuals has been solved with many different solutions around the world, either using biometric data or external means of verification such as id cards or RFID tags. The advantage of using biometric measurements is that they are directly tied to the individual and are usually unalterable. Acquiring dependable measurements is however challenging when the individuals are uncooperative. A dependable system should be able to deal with this and produce reliable identifications. The system proposed in this thesis can autonomously classify uncooperative specimens from depth data. The data is acquired from a depth camera mounted in an uncontrolled environment, where it was allowed to continuously record for two weeks. This requires stable data extraction and normalization algorithms to produce good representations of the specimens. Robust descriptors can therefore be extracted from each sample of a specimen and together with different classification algorithms, the system can be trained or validated. Even with as many as 138 different classes the system achieves high recognition rates. Inspired by the research field of face recognition, the best classification algorithm, the method of fisherfaces, was able to accurately recognize 99.6% of the validation samples. Followed by two variations of the method of eigenfaces, achieving recognition rates of 98.8% and 97.9%. These results affirm that the capabilities of the system are adequate for a commercial implementation.

The essential procedure of constructing 3-D building models in urban areas is to extract the building boundary footprint. In the past researches, the common procedures used in extracting the building footprint are applying edge detection, vectorization, and generalization. However, the derived boundary lines occasionally occur zigzag patterns, thus, it still needs further building footprint regularization. This study proposed a new approach in the point of view that the points, lines and polygons are the essential elements in reconstructing 3-D building models. The proposed new method is based on ¡§corner detection approach (CDA)¡¨ and ¡§Adjustment of building footprints and corner points (ABFCO)¡¨ algorithm on Light Detection And Ranging (LiDAR) or binary classification resultant imagery. This study implements Harris and Local Binary Pattern (LBP) corner detection, afterward, connects all detected points by using convex hull algorithm. However, ortho-non-rectangle buildings would compose poor outlines after convex hull. This study combines open and dilation morphology with the find ignored point algorithm to improve any incorrect connections. Finally, performs the ABFCO algorithm to those points which belong to the same boundary to generalize a line segment, and to figure out the intersections and boundary lines of the buildings. The experiment results have proved that the overall accuracy of LBP corner detection is about 3.5% higher than Harris corner detection, its overall accuracy is about 92% in rectangular buildings and about 91% in non-rectangular buildings, its standard deviation of boundary length is 0.29m and better than Harris¡¦s 0.55m. We also compared LBP corner detection with edge detection. The overall accuracy of corner detection is about 3% higher than edge detection, standard deviation of boundary length 0.37m is also better than edge detection 0.75m. This study not only proved the corner detection is better than edge detection from data, but also developed ABFCO algorithm is helpful for extracting more accurate building footprint lines.

With the rapid growth of video services on smartphones such as video conferencing, video telephone and WebTV, implementation of video compression on mobile terminal becomes extremely important. However, the low computation capability of mobile devices becomes a bottleneck which calls for low complexity techniques for video coding. This work presents two set of algorithms for reducing the complexity of motion estimation. Binary motion estimation techniques using one-bit and two-bit transforms reduce the computational complexity of matching error criterion, however sometimes generate inaccurate motion vectors. The first set includes two neighborhood matching based algorithms which attempt to reduce computations to only a fraction of other methods. Simulation results demonstrate that full search local binary pattern (FS-LBP) algorithm reconstruct visually more accurate frames compared to full search algorithm (FSA). Its reduced complexity LBP (RC-LBP) version decreases computations significantly to only a fraction of the other methods while maintaining acceptable performance. The second set introduces edge detection approach for partial distortion elimination based on binary patterns. Spiral partial distortion elimination (SpiralPDE) has been proposed in literature which matches the pixel-to-pixel distortion in a predefined manner. Since, the contribution of all the pixels to the distortion function is different, therefore, it is important to analyze and extract these cardinal pixels. The proposed algorithms are called lossless fast full search partial distortion elimination ME based on local binary patterns (PLBP) and lossy edge-detection pixel decimation technique based on local binary patterns (ELBP). PLBP reduces the matching complexity by matching more contributable pixels early by identifying the most diverse pixels in a local neighborhood. ELBP captures the most representative pixels in a block in order of contribution to the distortion function by evaluating whether the individual pixels belong to the edge or background. Experimental results demonstrate substantial reduction in computational complexity of ELBP with only a marginal loss in prediction quality.

With the rapid growth of video services on smartphones such as video conferencing, video telephone and WebTV, implementation of video compression on mobile terminal becomes extremely important. However, the low computation capability of mobile devices becomes a bottleneck which calls for low complexity techniques for video coding. This work presents two set of algorithms for reducing the complexity of motion estimation. Binary motion estimation techniques using one-bit and two-bit transforms reduce the computational complexity of matching error criterion, however sometimes generate inaccurate motion vectors. The first set includes two neighborhood matching based algorithms which attempt to reduce computations to only a fraction of other methods. Simulation results demonstrate that full search local binary pattern (FS-LBP) algorithm reconstruct visually more accurate frames compared to full search algorithm (FSA). Its reduced complexity LBP (RC-LBP) version decreases computations significantly to only a fraction of the other methods while maintaining acceptable performance. The second set introduces edge detection approach for partial distortion elimination based on binary patterns. Spiral partial distortion elimination (SpiralPDE) has been proposed in literature which matches the pixel-to-pixel distortion in a predefined manner. Since, the contribution of all the pixels to the distortion function is different, therefore, it is important to analyze and extract these cardinal pixels. The proposed algorithms are called lossless fast full search partial distortion elimination ME based on local binary patterns (PLBP) and lossy edge-detection pixel decimation technique based on local binary patterns (ELBP). PLBP reduces the matching complexity by matching more contributable pixels early by identifying the most diverse pixels in a local neighborhood. ELBP captures the most representative pixels in a block in order of contribution to the distortion function by evaluating whether the individual pixels belong to the edge or background. Experimental results demonstrate substantial reduction in computational complexity of ELBP with only a marginal loss in prediction quality.

The aim of this diploma thesis is to explore methods of face detection and recognition in the picture. The method for face detection and the method for face recognition will be chosen according to literature survey. Both methods will be implemented using the OpenCV library and a program language C/C++. The result of this project is creation of graphic interface which use programmed function for face detection and recognition from a picture and also a camcorder.

No description available.

Facial recognition is the process in which a sample face can be correctly identified by a machine amongst a group of different faces. With the never-ending need for improvement in the fields of security, surveillance, and identification, facial recognition is becoming increasingly important. Considering this importance, it is imperative that the correct faces are recognized and the error rate is as minimal as possible. Despite the wide variety of current methods for facial recognition, there is no clear cut best method. This project reviews and examines three different methods for facial recognition: Eigenfaces, Fisherfaces, and Local Binary Patterns to determine which method has the highest accuracy of prediction rate. The three methods are reviewed and then compared via experiments. OpenCV, CMake, and Visual Studios were used as tools to conduct experiments. Analysis were conducted to identify which method has the highest accuracy of prediction rate with various experimental factors. By feeding a number of sample images of different people which serve as experimental subjects. The machine is first trained to generate features for each person among the testing subjects. Then, a new image was tested against the “learned” data and be labeled as one of the subjects. With experimental data analysis, the Eigenfaces method was determined to have the highest prediction rate of the three algorithms tested. The Local Binary Pattern Histogram (LBP) was found to have the lowest prediction rate. Finally, LBP was selected for the algorithm improvement. In this project, LBP was improved by identifying the most significant regions of the histograms for each person in training time. The weights of each region are assigned depending on the gray scale contrast. At recognition time, given a new face, different weights are assigned to different regions to increase prediction rate and also speed up the real time recognition. The experimental results confirmed the performance improvement.