Now showing items 1-4 of 4

    • Boosted LMS-Based Piecewise Linear Adaptive Filters 

      Kari, D.; Marivani, I.; Delibalta, I.; Kozat, S.S. (European Signal Processing Conference, EUSIPCO, 2016)
      We introduce the boosting notion extensively used in different machine learning applications to adaptive signal processing literature and implement several different adaptive filtering algorithms. In this framework, we ...
    • Boosting fully convolutional networks for gland instance segmentation in histopathological images 

      Güneşli, Gözde Nur (Bilkent University, 2019-08)
      In the current literature, fully convolutional neural networks (FCNs) are the most preferred architectures for dense prediction tasks, including gland segmentation. However, a signi cant challenge is to adequately train ...
    • Compressive sensing based flame detection in infrared videos 

      Günay, Osman; Çetin, A. Enis (IEEE, 2013)
      In this paper, a Compressive Sensing based feature extraction algorithm is proposed for flame detection using infrared cameras. First, bright and moving regions in videos are detected. Then the videos are divided into ...
    • A robust system for counting people using an infrared sensor and a camera 

      Erden, F.; Alkar, A. Z.; Cetin, A. E. (Elsevier BV, 2015)
      In this paper, a multi-modal solution to the people counting problem in a given area is described. The multi-modal system consists of a differential pyro-electric infrared (PIR) sensor and a camera. Faces in the surveillance ...