RESUMO
Cameras with rolling shutters (RSs) dominate consumer markets but are subject to distortions when capturing motion. Many methods have been proposed to mitigate RS distortions for applications such as vision-aided odometry and three-dimensional (3D) reconstruction. They usually need known line delay d between successive image rows. To calibrate d, several methods have been proposed that often involve complex procedures. This Letter proposes an easy RS calibration method by using an off-the-shelf light-emitting diode (LED) panel, using the fact that the RS causes the blinking LED columns to appear slanted in images by a static camera. The calibration starts with extracting the LED lights and then rectifies the images to remove the lens distortion and misalignment between the camera and the LED panel. Next, blocks of slanted bright LEDs are recognized and their inclination leads to the line delay estimate. Our method needs not to move the camera, adjust the ambient light, or calibrate camera intrinsic parameters beforehand, and it can usually estimate the line delay given two LED panel images in one second. Extensive tests with industrial cameras and consumer cameras of wide-angle and fish-eye lenses validate its competitive accuracy relative to the established methods.
RESUMO
Satellite sensor technology endorsed better discrimination of various landscape objects. Image segmentation approaches to extracting conceptual objects and patterns hence have been explored and a wide variety of such algorithms abound. To this end, in order to effectively utilize edge and topological information in high resolution remote sensing imagery, an object-oriented algorithm combining edge detection and region merging is proposed. Susan edge filter is firstly applied to the panchromatic band of Quickbird imagery with spatial resolution of 0.61 m to obtain the edge map. Thanks to the resulting edge map, a two-phrase region-based segmentation method operates on the fusion image from panchromatic and multispectral Quickbird images to get the final partition result. In the first phase, a quad tree grid consisting of squares with sides parallel to the image left and top borders agglomerates the square subsets recursively where the uniform measure is satisfied to derive image object primitives. Before the merger of the second phrase, the contextual and spatial information, (e. g., neighbor relationship, boundary coding) of the resulting squares are retrieved efficiently by means of the quad tree structure. Then a region merging operation is performed with those primitives, during which the criterion for region merging integrates edge map and region-based features. This approach has been tested on the QuickBird images of some site in Sanxia area and the result is compared with those of ENVI Zoom Definiens. In addition, quantitative evaluation of the quality of segmentation results is also presented. Experiment results demonstrate stable convergence and efficiency.
