Signal Processing for Computational Intelligence Group

About the lab

Signal Processing for Computational Intelligence Group: At SP4CING, we aim at designing signal processing techniques for computational intelligence applications. Our research covers problems in diversified fields, ranging from environmental monitoring to multi-modal surveillance, from bio-image analysis to remote sensing. For more information, visit us at:

Featured projects (1)

Phase contrast time-lapse microscopy is a non-destructive technique that generates large volumes of image-based information to quantify the behavior of individual cells or cell populations.

Featured research (29)

One of the most efficient methods for model compression is hint distillation, where the student model is injected with information (hints) from several different layers of the teacher model. Although the selection of hint points can drastically alter the compression performance, conventional distillation approaches overlook this fact and use the same hint points as in the early studies. Therefore, we propose a clustering based hint selection methodology, where the layers of teacher model are clustered with respect to several metrics and the cluster centers are used as the hint points. Our method is applicable for any student network, once it is applied on a chosen teacher network. The proposed approach is validated in CIFAR-100 and ImageNet datasets, using various teacher–student pairs and numerous hint distillation methods. Our results show that hint points selected by our algorithm results in superior compression performance compared to state-of-the-art knowledge distillation algorithms on the same student models and datasets.
Cervical squamous intra-epithelial lesions (SIL) are precursor cancer lesions and their diagnosis is important because patients have a chance to be cured before cancer develops. In the diagnosis of the disease, pathologists decide by considering the cell distribution from the basal to the upper membrane. The idea, inspired by the pathologists' point of view, is based on the fact that cell amounts differ in the basal, central, and upper regions of tissue according to the level of Cervical Intraep- ithelial Neoplasia (CIN). Therefore, histogram information can be used for tissue classification so that the model can be explainable. In this study, two different classification schemes are proposed to show that the local histogram is a useful feature for the classification of cervical tissues. The first classifier is Kullback Leibler divergence-based, and the second one is the classification of the histogram by combining the embedding feature vector from morpho- metric features. These algorithms have been tested on a public dataset. <sup xmlns:mml="" xmlns:xlink="">1</sup> The method we propose in the study achieved an accuracy performance of 78 . 69% in a data set where morphology-based methods were 69 . 07% and Convo- lutional Neural Network (CNN) patch-based algorithms were 75 . 77%. The proposed statistical features are robust for tackling real-life problems as they operate independently of the lesions manifold.
Despite recent advances, object detection in aerial images is still a challenging task. Specific problems in aerial images makes the detection problem harder, such as small objects, densely packed objects, objects in different sizes and with different orientations. To address small object detection problem, we propose a two-stage object detection framework called "Focus-and-Detect". The first stage which consists of an object detector network supervised by a Gaussian Mixture Model, generates clusters of objects constituting the focused regions. The second stage, which is also an object detector network, predicts objects within the focal regions. Incomplete Box Suppression (IBS) method is also proposed to overcome the truncation effect of region search approach. Results indicate that the proposed two-stage framework achieves an AP score of 42.06 on VisDrone validation dataset, surpassing all other state-of-the-art small object detection methods reported in the literature, to the best of authors' knowledge.

Lab head

Behçet Uğur Töreyin
  • Informatics Institute
About Behçet Uğur Töreyin
  • Dr. Töreyin's research interests broadly lie in signal processing and pattern recognition with applications to image/video analysis and communication systems. His research is focused on developing algorithms to analyze the content of signals from a multitude of sensors such as visible/infra-red/hyperspectral cameras, microphones, passive infra-red sensors, vibration sensors, and spectrum sensors for wireless communications. For more information, visit

Members (10)

Abdulkerim Capar
  • Istanbul Technical University
Sibel Çimen
  • Yildiz Technical University
Reyhan Kevser Keser
  • Istanbul Technical University
Dursun Ali Ekinci
  • Istanbul Technical University
Kaan Aykut Kabakçı
  • Istanbul Technical University
Muhammet Beratoğlu
  • Istanbul Technical University
Onur Can Koyun
  • Istanbul Technical University
Indrit Nallbani
  • Istanbul Technical University
Hüseyin Onur Yağar
Hüseyin Onur Yağar
  • Not confirmed yet

Alumni (5)

İrem Ülkü
  • Ankara University
Gözde Ayşe Tataroğlu
Gözde Ayşe Tataroğlu
Hilal Turk
Hilal Turk
tuğba oğuz
  • Cankaya University