By Eyke Hüllermeier, Rudolf Kruse, Frank Hoffmann
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Extra info for Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part II
The omission rate is 0 in 5 of the 6 proofs, and only 1 in the other case. The commission rate is 0 in all tests. The K-Means method is a fast method to detect olive trees since the number of clusters is small (2 or 3) and because it can avoid the diﬀerent ground tones through the number of clusters. The K-Means algorithm is an automatic method, meanwhile the reference work in this subject (OLICOUNT) needs an operator for tunning its four parameters . In addition, the presence of parameters in image segmentation has a negative impact in the behavior of the method.
Instead of using window of consecutive frames to build background and keep them in the memory for oﬀ-line processing [4, 5], we propose a fully autonomous analysis on a per frame basis which is using recursive calculations and removes the need of computer storage to archive video frames. Additionally, the introduced approach is threshold-independent and minimises the processing time by discarding the unnecessary data. The main idea of the proposed approach is to approximate the probability density function (pdf) using a Cauchy type of kernel (as opposed to Gaussian one used in KDE technique), and then in order to update this estimation we apply a recursive expression using the colour intensity of each pixel.
3). The spatial density can be calculated recursively in a vector form similarly to (6)-(9): l −1 ; l=[1,F] (l − 1)( f T f + 1) − 2γ + β (13) γ = f Tδ (14) β (1) = 0 (15) δ (l ) = δ (l − 1) + f (l − 1) ; δ (1) = 0 (16) D(Ot* ) = 2 β (l ) = β (l − 1) + f (l − 1) ; where f∈ RF denotes the vector of the foreground pixels in a frame This method can be extended for image segmentation  , and landmark detection  used in self-localisation in robotics  . As result it is more robust to locate the position of the object in the current image frame compare to the standard mean value technique (Fig.
Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part II by Eyke Hüllermeier, Rudolf Kruse, Frank Hoffmann