Binary descriptors are becoming increasingly popular as a means to compare feature points very fast and while requiring comparatively small amounts of memory. The typical approach to creating them is to first compute floating-point ones, using an algorithm such as SIFT, and then to binarize them.In this paper, we show that we can directly compute a binary descriptor we call BRIEF on the basis of simple intensity difference tests. As a result, BRIEF is very fast both to build and to match. We compare it against SURF and SIFT on standard benchmarks and show that it yields comparable recognition accuracy, while running in an almost vanishing fraction of the time required by either.

While feature point recognition is a key component of modern approaches to object detection, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. In this paper, we show that formulating the problem in a Naive Bayesian classification framework makes such preprocessing unnecessary and produces an algorithm that is simple, efficient, and robust.Furthermore, it scales well as number of classes grows.To recognize the patches surrounding keypoints, our classifier uses hundreds of simple binary features and models class posterior probabilities. We make the problem computationally tractable by assuming independence between arbitrary sets of features. Even though this is not strictly true, we demonstrate that our classifier nevertheless performs remarkably well on image datasets containing very significant perspective changes. Index TermsImage processing and computer vision, object recognition, tracking, image registration, feature matching, naive bayesian 1

Abstract

The machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely employed in bioinformatics. The difficulties and cost of biological analyses have led to the development of sophisticated machine learning approaches for this application area. In this chapter, we first review the fundamental concepts of machine learning such as feature assessment, unsupervised versus supervised learning and types of classification. Then, we point out the main issues of designing machine learning experiments and their performance evaluation. Finally, we introduce some supervised learning methods.

Most state-of-the-art approaches to action recognition rely on global representations either by concatenating local information in a long descriptor vector or by computing a single location independent histogram. This limits their performance in presence of occlusions and when running on multiple viewpoints. We propose a novel approach to providing robustness to both occlusions and viewpoint changes that yields significant improvements over existing techniques. At its heart is a local partitioning and hierarchical classification of the 3D Histogram of Oriented Gradients (HOG) descriptor to represent sequences of images that have been concatenated into a data volume. We achieve robustness to occlusions and viewpoint changes by combining training data from all viewpoints to train classifiers that estimate action labels independently over sets of HOG blocks. A top level classifier combines these local labels into a global action class decision.

We propose an approach to overcome the two main challenges of 3D multiview object detection and localization:

We propose an approach to overcome the two main challenges of 3D multiview object detection and localization:

Anadolu, tarihsel süreç içerisinde konumu itibariyle birçok medeniyete ve faklı kültürel kimlikteki topluluklara ev sahipliği yapmıştır. Bu toplulukların birçok kültürel üretimleri zamanla değişikliklere uğrasa da günümüze kadar ulaşmış ve çeşitli araştırmalara konu olmuştur. 6. Yüzyıldan bu yana Doğu Karadeniz bölgesinde yaşamlarını sürdüren Hemşinliler de çeşitli araştırmalara konu olmuş topluluklardan birisidir. Hemşinliler; bölgesel, teolojik ve fonolojik açıdan Batı (Rize), Doğu (Artvin) ve Kuzey (Gürcistan, Rusya) olmak üzere 3 farklı grup içerisinde incelenmektedir. Yüksek rakımlı yaylalarda tarım, hayvancılık ve günümüzde turizmle yaşantılarını sürdüren Hemşinlilerin müziksel üretimlerinin uğraşmış oldukları meslek kollarıyla bağlantılı olarak şekillendiği söylenebilir. Bu etnik yapının ve bölgenin müzik kültürü içerisinde görülen tek çalgı tulumdur. Çalgı hakkında yazılı kaynakların kısıtlı sayıda olması bu çalışmanın oluşmasında etkili rol oynamıştır. Hazırlanan bu çalışma 2017 yılında tarafımca hazırlanan "Rize Çamlıhemşin Bölgesinde Görülen Tulum Çalgısının Yapısal ve İcrasal Özellikleri" adlı yüksek lisans tezinden üretilmiştir. Çalışmada literatür taraması yapılarak çalgı hakkında elde edilen veriler kronolojik şekilde incelenmiş, alan çalışması yöntemiyle de bölgede çalgının yapımı ve icrası hakkında geniş bilgiye sahip olan kaynak kişiler Varol Taşer ve Murat Atacan'dan alınan veriler sunulmuştur.