Hatice Çinar Akakin

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In this paper, we describe the design and development of a multitiered content-based image retrieval (CBIR) system for microscopic images utilizing a reference database that contains images of more than one disease. The proposed CBIR system uses a multitiered approach to classify and retrieve microscopic images involving their specific subtypes, which are(More)
In this paper, we propose a generalized Laplacian of Gaussian (LoG) (gLoG) filter for detecting general elliptical blob structures in images. The gLoG filter can not only accurately locate the blob centers but also estimate the scales, shapes, and orientations of the detected blobs. These functions can be realized by generalizing the common 3-D LoG(More)
In this paper, we focus on the reliable detection of facial fiducial points, such as eye, eyebrow and mouth corners. The proposed algorithm aims to improve automatic land-marking performance in challenging realistic face scenarios subject to pose variations, high-valence facial expressions and occlusions. We explore the potential of several feature(More)
We consider two novel representations and feature extraction schemes for automatic recognition of emotion related facial expressions. In one scheme facial landmark points are tracked over successive video frames using an effective detector and tracker to extract landmark trajectories. Features are extracted from landmark trajectories using Independent(More)
In this study content-based image retrieval is applied to hematoxylin and eosin H&E stained Follicular lymphoma centroblast cell images and K-nearest neighbour classifier is used with multi-texton histogram features. With developed method, it is aimed to assist pathologists in their diagnosis of follicular lymphoma disease. The purpose of this project(More)
Automatic analysis of head gestures and facial expressions is a challenging research area and it has significant applications in humancomputer interfaces. In this study, facial landmark points are detected and tracked over successive video frames using a robust method based on subspace regularization, Kalman prediction and refinement. The trajectories (time(More)
Deep convolutional neural networks is a recently developed method that yields very successful results in image classification. Deep neural networks, which have a high number of parameters, require a large amount of data to avoid overfitting during training. For applications in which the available data is not adequate to train a deep neural network from the(More)
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