isprsarchives XXXIX B3 409 2012
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In this improved algorithm, the original image is divided into blocks, and then the image blocks with different definitions are smoothed using the low-pass
To overcome the data association problem, we apply a Tracking- by-Detection strategy that uses Randomized Forests as a classifier together with a Kalman filter.. Randomized
Bruzzone (L.Bruzzone, and L.Carlin, 2006)proposes a supervised method to classify high resolution image. Adaptive multilevel spatial context driven feature extraction is first
According to the image retreival results of remote sensing images at differ- ent reslutions, the combination of the radiometric features, the GLCM features and the Gaussian
does not apply to ground image, because there are some noise by foreground and background. Vegetation extraction based on SVM is utilized under human supervision,
Hence, the image is only used to detect an approximate set of boundary points (real boundary points and outliers) from the original data. Such initially extracted boundaries
Therefore, an external bundle adjustment, which imports the results of Bundler (3D points, 2D points, image ori- entation) has been used to compute the results with accuracy de-
Partial occlusions in the image due to objects between the focussed person and one of the cameras can be handled by the presented approach based on the use of 3D information
