Browsing by Subject "Covariance features"

Now showing 1 - 3 of 3

Open Access
Co-difference based object tracking algorithm for infrared videos
(IEEE, 2016) Demir, H. Seçkin; Çetin, A. Enis
This paper presents a novel infrared (IR) object tracking algorithm based on the co-difference matrix. Extraction of co-difference features is similar to the well known covariance method except that the vector product operator is redefined in a multiplication-free manner. The new operator yields a computationally efficient implementation for real time object tracking applications. Experiments on an extensive set of IR image sequences indicate that the new method performs better than covariance tracking and other tracking algorithms without requiring any multiplication operations.
Open Access
Detection of fungal damaged popcorn using image property covariance features
(Elsevier, 2012) Yorulmaz, O.; Pearson, T. C.; Çetin, A.
Covariance-matrix-based features were applied to the detection of popcorn infected by a fungus that causes a symptom called " blue-eye" . This infection of popcorn kernels causes economic losses due to the kernels' poor appearance and the frequently disagreeable flavor of the popped kernels. Images of kernels were obtained to distinguish damaged from undamaged kernels using image-processing techniques. Features for distinguishing blue-eye-damaged from undamaged popcorn kernel images were extracted from covariance matrices computed using various image pixel properties. The covariance matrices were formed using different property vectors that consisted of the image coordinate values, their intensity values and the first and second derivatives of the vertical and horizontal directions of different color channels. Support Vector Machines (SVM) were used for classification purposes. An overall recognition rate of 96.5% was achieved using these covariance based features. Relatively low false positive values of 2.4% were obtained which is important to reduce economic loss due to healthy kernels being discarded as fungal damaged. The image processing method is not computationally expensive so that it could be implemented in real-time sorting systems to separate damaged popcorn or other grains that have textural differences.
Open Access
Flame detection method in video using covariance descriptors
(IEEE, 2011) Habiboǧlu, Y.H.; Günay, Osman; Çetin, A. Enis
Video fire detection system which uses a spatio-temporal covariance matrix of video data is proposed. This system divides the video into spatio-temporal blocks and computes covariance features extracted from these blocks to detect fire. Feature vectors taking advantage of both the spatial and the temporal characteristics of flame colored regions are classified using an SVM classifier which is trained and tested using video data containing flames and flame colored objects. Experimental results are presented. © 2011 IEEE.