Filters

2012 - 20154
Reset filters

Settings

Publication Index: ScopusSubject: image processing

Search Results

Now showing 1 - 4 of 4
  • Thumbnail Image
    Conference Object
    A Component-Based Object Detection Method Extended With a Fuzzy Inference Engine
    (Institute of Electrical and Electronics Engineers Inc., 2015) Koyuncu,M.; Cetinkaya,B.
    In this paper, we propose a component-based object detection method extended with the fuzzy inference technique. The proposed method detects constituent components of a complex object instead of a whole object in images. For component detection, multiple multi-class support vector machines (SVM) are used in parallel. Each SVM classifies the candidate component using a different low-level image feature. The obtained results are fused to reach a decision about the component. Then, a fuzzy object extractor determines the whole object considering the detected components and their geometric configurations. The fuzzy object extractor is a fuzzy inference engine which tests various combinations of detected components and their fuzzified directions and distances. The initial tests yield promising results and encourage further studies to extend proposed method. © 2015 IEEE.
  • Thumbnail Image
    Conference Object
    Part-Based Object Extraction for Complex Objects;
    (IEEE Computer Society, 2014) Koyuncu,M.; Cetinkaya,B.
    Indexing requirement for efficient accessing to visual data has been increased with the widespread use of multimedia applications. Satisfaction of this requirement mostly depends on the automatic extraction of objects in the visual data. In this study, component-based object extraction method is compared with object extraction in its entirety. Applied method, implemented system and conducted tests are presented in this paper. Test results show that, even in the case of a good segmentation is achieved for whole object, object components are classified more successfully compared to whole object. © 2014 IEEE.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 3
    Emotion classification using hidden layer outputs
    (2012) Günler,M.A.; Tora,H.
    Neural network (NN) with Multi-Layer Perceptron (MLP) is a supervised learning algorithm composed of artificial neurons. Multilayer NN is capable of solving nonlinear classification problems such as emotion identification by using facial expressions that is presented in this paper. Hidden layer outputs of NN provide useful information about facial appearance. This study addresses that without fully training NN hidden layer outputs can be used as feature. It is shown that an acceptable recognition rate is obtained by means of hidden layer outputs. © 2012 IEEE.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 2
    Recognition of Hand-Sketched Digital Logic Gates;
    (Institute of Electrical and Electronics Engineers Inc., 2015) Gül,N.; Tora,H.
    Hand-Sketched circuit recognition is a very useful tool in engineering area. Because most of the engineers prefer to design their circuits on the paper firstly. So, this can cause time wasting and some mistakes. In this study, which is based on the solving these kinds of problems, classification and recognition of the handwritten digital logic gates according to their complex and scalar FDs (Fourier Descriptors) is presented. Test results are obtained as 84.3 % accuracy rate for complex FDs, 98.6 % for scalar FDs. Then these results are compared and decided the optimum FDs type for this study. © 2015 IEEE.