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We can represent images in entirely different ways, in order to fulfill different purposes. For object recognition, power of a representation comes from its discriminative ability. In this thesis work, handcrafted representations that dominated the last decade of computer vision are evaluated against the current paradigm of Deep Learning, to try and pinpoint the reasons behind why and how the fairly old Artificial Neural Network (ANN) framework suddenly emerged as the state of the art in discriminative representations. We observe, through our experiments, that true capabilities of Deep ANN's can only be achieved by having very large amounts of labeled data that have been made available only recently. This thesis work also deals with ensembles of both handcrafted and ANN based approaches to reinforce the new technology with years of established knowledge behind handcrafted feature based approaches. For this purpose, we propose a novel extension, based on Fisher Vectors, to the well known Selective Search algorithm, called the Fisher-Selective Search algorithm, and obtain a 10% relative increase in Average Precision at virtually no additional computation cost.M.S. - Master of Scienc

Superpixel Based Unsupervised Change Detection of Manmade Targets on Satellite Images

In this paper, a novel solution to the problem of change detection in bitemporal satellite images is presented. The approach can be described as 1) Preprocessing, in which both images' luminance levels are approximated to the same distribution via the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm and noise is filtered out by the use of a Bilateral Noise Reduction (BNR) filter; 2) Extraction of a joint superpixel map of both images using a novel method; 3) Calculation of a variety of metrics at the superpixel level that are directed towards finding man-made changes; 4) Calculating a final change mask from a final difference image via thresholding. The proposed method is tested on bitemporal images of a number of urban regions

Superpixel based unsupervised change detection of manmade targets on satellite images

In this paper, a novel solution to the problem of change detection in bitemporal satellite images is presented. The approach can be described as 1) Preprocessing, in which both images' luminance levels are approximated to the same distribution via the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm and noise is filtered out by the use of a Bilateral Noise Reduction (BNR) filter; 2) Extraction of a joint superpixel map of both images using a novel method; 3) Calculation of a variety of metrics at the superpixel level that are directed towards finding man-made changes; 4) Calculating a final change mask from a final difference image via thresholding. The proposed method is tested on bitemporal images of a number of urban regions

FISHER SELECTIVE SEARCH FOR OBJECT DETECTION

An enhancement to one of the existing visual object detection approaches is proposed for generating candidate windows that improves detection accuracy at no additional computational cost. Hypothesis windows for object detection are obtained based on Fisher Vector representations over initially obtained superpixels. In order to obtain new window hypotheses, hierarchical merging of superpixel regions are applied, depending upon improvements on some objectiveness measures with no additional cost due to additivity of Fisher Vectors. The proposed technique is further improved by concatenating these representations with that of deep networks. Based on the results of the simulations on typical data sets, it can be argued that the approach is quite promising for its use of handcrafted features left to dust due to the rise of deep learning

Uydu Görüntülerinin Otomatik Analizi ile Afet Hasar Tespiti ve Kanunsuz Sınır Geçişlerinin Önlenmesi

Proje önerisinin temel amacı ODTÜ Görüntü Analizi Uygulama ve Araştırma Merkezi (OGAM) bünyesinde yer alan araştırıcıların geçmişte HAVELSAN altyükleniciliğinde gerçekleştirdikleri savunma ile ilgili SSM destekli HASAT projesi deneyim ve sonuçlarının, Avrupa Birliği H2020 Programı Uzay Alanı ana başlıkları ve çağrı alanlarına uygulanacak şekilde kurgulanmasıdır. Bu kapsamda, HASAT projesi kapsamında uydu görüntüleri içinde yer alan ve otomatik olarak tanınması için ayrı ayrı çalışmalar yürütülmüş istihbarat hedeflerinin bir kısmı kullanılarak, H2020 Programı Uzay Alanı amaç ve hedefleri doğrultusunda bu hedefler bir arada değerlendirilip, bir yazılım arayüzü altında biraraya getirilip, hedeflenen uygulamalar için otomatik tanıma çözümleri yaratılacaktır.Bu amaçla yapılacak çalışmalar, ilgili deneyim ve birikimin OGAM bünyesinde kalıcı olması, sivil uygulamalara yönelik yeni uluslararası proje imkanları yaratması ve HASAT projesindeki otomatik tanıma çalışma sonuçlarının, hedeflenen yeni uygulamalara ait kıstasları da dikkate alarak ve farklı tanıma sonuçlarını birarada kullanarak daha ileriye götürülebilecek olması açılarından önemlidir