"4"^^ . "[4ABFB60ECDF8]" . "London" . . . "4"^^ . "Fusek, Radovan" . "Cherbourg" . "978-3-319-07997-4" . "An improvement of energy-transfer features using DCT for face detection"@en . "An improvement of energy-transfer features using DCT for face detection" . . . "Springer-Verlag" . "0302-9743" . . "face detection, energy transfer; DCT; image features"@en . "2014-06-30+02:00"^^ . . "27240" . . . "Lecture Notes in Computer Science. Volume 8509" . "RIV/61989100:27240/14:86092994!RIV15-MSM-27240___" . "S" . "http://link.springer.com/chapter/10.1007%2F978-3-319-07998-1_59" . . . . "RIV/61989100:27240/14:86092994" . "The basic idea behind the energy-transfer features (ETF) is that the appearance of objects can be successfully described using the function of energy distribution in the image. This function has to be reduced into a reasonable number of values. These values are then considered as the vector that is used as an input for the SVM classifier. The process of reducing can be simply solved by sampling; the input image is divided into the regular cells and inside each cell, the mean of the values is calculated. In this paper, we propose an improvement of this process; the Discrete Cosine Transform (DCT) coefficients are calculated inside the cells (instead of the mean values) to construct the feature vector. In addition, the DCT coefficients are reduced using the Principal Component Analysis (PCA) to create the feature vector with a relatively small dimensionally. The results show that using this approach, the objects can be efficiently encoded with the relatively small set of numbers with promising results that outperform the results of state-of-the-art detectors."@en . . "Sojka, Eduard" . . "2503" . . "10.1007/978-3-319-07998-1_59" . . . "The basic idea behind the energy-transfer features (ETF) is that the appearance of objects can be successfully described using the function of energy distribution in the image. This function has to be reduced into a reasonable number of values. These values are then considered as the vector that is used as an input for the SVM classifier. The process of reducing can be simply solved by sampling; the input image is divided into the regular cells and inside each cell, the mean of the values is calculated. In this paper, we propose an improvement of this process; the Discrete Cosine Transform (DCT) coefficients are calculated inside the cells (instead of the mean values) to construct the feature vector. In addition, the DCT coefficients are reduced using the Principal Component Analysis (PCA) to create the feature vector with a relatively small dimensionally. The results show that using this approach, the objects can be efficiently encoded with the relatively small set of numbers with promising results that outperform the results of state-of-the-art detectors." . "An improvement of energy-transfer features using DCT for face detection"@en . . "9"^^ . . . "Mozd\u0159e\u0148, Karel" . "\u0160urkala, Milan" . "An improvement of energy-transfer features using DCT for face detection" .