Browsing by Author "Samet, Nermin"

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Open Access
Ground-nesting insects could use visual tracking for monitoring nest position during learning flights
(Springer Verlag, 2014-07) Samet, Nermin; Zeil, J.; Mair, E.; Boeddeker, N.; Stürzl, W.
Ants, bees and wasps are central place foragers. They leave their nests to forage and routinely return to their home-base. Most are guided by memories of the visual panorama and the visual appearance of the local nest environment when pinpointing their nest. These memories are acquired during highly structured learning walks or flights that are performed when leaving the nest for the first time or whenever the insects had difficulties finding the nest during their previous return. Ground-nesting bees and wasps perform such learning flights daily when they depart for the first time. During these flights, the insects turn back to face the nest entrance and subsequently back away from the nest while flying along ever increasing arcs that are centred on the nest. Flying along these arcs, the insects counter-turn in such a way that the nest entrance is always seen in the frontal visual field at slightly lateral positions. Here we asked how the insects may achieve keeping track of the nest entrance location given that it is a small, inconspicuous hole in the ground, surrounded by complex natural structures that undergo unpredictable perspective transformations as the insect pivots around the area and gains distance from it. We reconstructed the natural visual scene experienced by wasps and bees during their learning flights and applied a number of template-based tracking methods to these image sequences. We find that tracking with a fixed template fails very quickly in the course of a learning flight, but that continuously updating the template allowed us to reliably estimate nest direction in reconstructed image sequences. This is true even for later sections of learning flights when the insects are so far away from the nest that they cannot resolve the nest entrance as a visual feature. We discuss why visual goal-anchoring is likely to be important during the acquisition of visual-spatial memories and describe experiments to test whether insects indeed update nest-related templates during their learning flights. © 2014 Springer International Publishing Switzerland.
Open Access
Identification of illustrators
(2012-10) Şener Fadime; Samet, Nermin; Duygulu-Şahin Pınar
This paper is motivated by a book in which artists and illustrators from all over the world offer their personal interpretations of the declaration of human rights in pictures [1]. It was enthusiastic for a young reader to see an illustration of an artist that he already knows from his books . The characters were different, the topic was irrelevant, but still it was easy to identify the illustrators based on the style of the illustration. Inspired by the human's ability to identify illustrators, in this study we propose a method that can automatically learn to distinguish illustrations of different illustrators using computer vision techniques. © 2012 Springer-Verlag.
Open Access
Recognizing human actions from noisy videos via multiple instance learning
(IEEE, 2013) şener, Fadime; Samet, Nermin; Duygulu, Pınar; Ikizler-Cinbis, N.
In this work, we study the task of recognizing human actions from noisy videos and effects of noise to recognition performance and propose a possible solution. Datasets available in computer vision literature are relatively small and could include noise due to labeling source. For new and relatively big datasets, noise amount would possible increase and the performance of traditional instance based learning methods is likely to decrease. In this work, we propose a multiple instance learning-based solution in case of an increase in noise. For this purpose, each video is represented with spatio-temporal features, then bag-of-words method is applied. Then, using support vector machines (SVM), both instance-based learning and multiple instance learning classifiers are constructed and compared. The classification results show that multiple instance learning classifiers has better performance than instance based learning counterparts on noisy videos. © 2013 IEEE.
Open Access
Unsupervised segmentation and ordering of cervical cells : Serviks hücrelerinin öğreticisiz olarak bölütlenmesi ve sıralanması
(2014) Samet, Nermin
Cervical cancer is the second most common cause of cancer death among women worldwide, and it can be prevented if it is detected and treated in the precancerous stages. Pap smear test is a common, efficient and easy manual screening examination technique which is used to detect dysplastic changes in cervical cells. However, manual analyses of thousands of cells in Pap smear test slides by cyto-technicians is difficult, time consuming and subjective. To overcome these problems, we aim to automate the screening process and provide an ordered nuclei list to help the cyto-experts. Automating the screening procedure has been a longstanding challenge because of complex cell structures where current methods in the literature mostly consider the problem as the segmentation of single isolated cells and leave real challenges of Pap smear images such as poor contrast, inconsistent staining, and unknown number of cells unaddressed. We propose an unsupervised method to accurately segment the nuclei and order them according to their abnormality degree in Pap smear images. The method first uses a multi-scale hierarchical segmentation algorithm for accurate identification of the nuclei. The Pap smear images captured at high level magni- fication have more detailed texture but worse contrast. Contrast is an important property for segmentation and detailed texture is an important property for feature extraction. Therefore, as a solution to the segmentation problem, we proceed in two steps. First, we segment the Pap smear images at low (20x) magnification and eliminate non-nucleus regions based on several features. Then, we switch to high (40x) magnification and obtain a more detailed segmentation of the remaining nuclei. Following segmentation, we extract features for each resulting nucleus. Unlike related works that require a learning phase for classification, our method performs an unsupervised ordering of the nuclei based on features extracted at 40x magnification. We compare different ordering algorithms for ranking the nucleus regions according to their abnormality degrees. We evaluate our segmentation and ordering methods using two data sets. Our results show that the proposed method provides promising results for both segmentation and ordering steps.