Advisors
Now showing items 1-20 of 25
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Anomaly detection with sparse unmixing and gaussian mixture modeling of hyperspectral images
(Bilkent University, 2015-07)One of the main applications of hyperspectral image analysis is anomaly detection where the problem of interest is the detection of small rare objects that stand out from their surroundings. A common approach to anomaly ... -
Detection and classification of breast cancer in whole slide histopathology images using deep convolutional networks
(Bilkent University, 2016-07)The most frequent non-skin cancer type is breast cancer which is also named one of the most deadliest diseases where early and accurate diagnosis is critical for recovery. Recent medical image processing researches have ... -
Fine-grained object recognition in remote sensing imagery
(Bilkent University, 2018-06)Fine-grained object recognition aims to determine the type of an object in domains with a large number of sub-categories. The steadily increase in spatial and spectral resolution entailing new details in remote sensing ... -
Generalized texture models for detecting high-level structures in remotely sensed images
(Bilkent University, 2007)With the rapid increase in the amount and resolution of remotely sensed image data, automatic extraction and classification of information obtained from such images have been an important problem in the field of pattern ... -
Hierarchical segmentation, object detection and classification in remotely sensed images
(Bilkent University, 2007)Automatic content extraction and classification of remotely sensed images have become highly desired goals by the advances in satellite technology and computing power. The usual choice for the level of processing image ... -
Image information mining using spatial relationship constraints
(Bilkent University, 2012)There is a huge amount of data which is collected from the Earth observation satellites and they are continuously sending data to Earth receiving stations day by day. Therefore, mining of those data becomes more important ... -
Image super-resolution using deep feedforward neural networks in spectral domain
(Bilkent University, 2018-03)With recent advances in deep learning area, learning machinery and mainstream approaches in computer vision research have changed dramatically from hardcoded features combined with classi ers to end-to-end trained deep ... -
Iterative estimation of Robust Gaussian mixture models in heterogeneous data sets
(Bilkent University, 2014-07)Density estimation is the process of estimating the parameters of a probability density function from data. The Gaussian mixture model (GMM) is one of the most preferred density families. We study the estimation of a ... -
Learning efficient visual embedding models under data constraints
(Bilkent University, 2019-09)Deep learning models require large-scale datasets to learn rich sets of low and mid-level patterns and high-level semantics. Therefore, given a high-capacity neural network, one way to improve the performance of a model ... -
Modeling of flexible needle insertion in moving tissue
(Bilkent University, 2012)Steerable needles can be used for minimally invasive surgeries to reach clinical targets which were previously inaccessible by rigid needles. Using such flexible needles to plan an insertion for these procedures is ... -
Object detection using optical and lidar data fusion with graph-cuts
(Bilkent University, 2017-03)Object detection in remotely sensed data has been a popular problem and is commonly used in a wide range of applications in domains such as agriculture, navigation, environmental management, urban monitoring and mapping. ... -
An object recognition framework using contextual interactions among objects
(Bilkent University, 2009)Object recognition is one of the fundamental tasks in computer vision. The main endeavor in object recognition research is to devise techniques that make computers understand what they see as precise as human beings. The ... -
One-stage oriented object detection in remote sensing images
(Bilkent University, 2022-03)Advances in technology resulted in an enormous amount of information collected from high technology satellites and aircraft sensors. These high-resolution images obtained by the said platforms enabled humans to understand ... -
Prototypes : exemplar based video representation
(Bilkent University, 2016-06)Recognition of actions from videos is a widely studied problem and there have been many solutions introduced over the years. Labeling of the training data that is required for classification has been an important bottleneck ... -
Scene classification using bag-of-regions representation
(Bilkent University, 2007)Significant growth of multimedia data creates the need for more complicated approaches in image understanding, classification and retrieval. Semantic scene classification is a popular research area which categorizes ... -
Segmentation and classification of cervical cell images
(Bilkent University, 2010)Cervical cancer can be prevented if it is detected and treated early. Pap smear test is a manual screening procedure used to detect cervical cancer and precancerous changes in an uterine cervix. However, this procedure ... -
Self-supervised representation learning with graph neural networks for region of interest analysis in breast histopathology
(Bilkent University, 2020-12)Deep learning has made a major contribution to histopathology image analysis with representation learning outperforming hand-crafted features. However, two notable challenges remain. The first is the lack of large ... -
Semantic scene classification for content-based image retrieval
(Bilkent University, 2008)Content-based image indexing and retrieval have become important research problems with the use of large databases in a wide range of areas. Because of the constantly increasing complexity of the image content, low-level ... -
Structural scene analysis of remotely sensed images using graph mining
(Bilkent University, 2010)The need for intelligent systems capable of automatic content extraction and classi cation in remote sensing image datasets, has been constantly increasing due to the advances in the satellite technology and the availability ... -
Style synthesizing conditional generative adversarial networks
(Bilkent University, 2020-01)Neural style transfer (NST) models aim to transfer a particular visual style to a image while preserving its content using neural networks. Style transfer models that can apply arbitrary styles without requiring style-specific ...
