In this paper,we present an interactive static image composition approach,namely color retargeting,to flexibly represent time-varying color editing effect based on time-lapse video sequences.Instead of performing prec...In this paper,we present an interactive static image composition approach,namely color retargeting,to flexibly represent time-varying color editing effect based on time-lapse video sequences.Instead of performing precise image matting or blending techniques,our approach treats the color composition as a pixel-level resampling problem. In order to both satisfy the user's editing requirements and avoid visual artifacts,we construct a globally optimized interpolation field. This field defines from which input video frames the output pixels should be resampled.Our proposed resampling solution ensures that(i) the global color transition in the output image is as smooth as possible,(ii) the desired colors/objects specified by the user from different video frames are well preserved,and(iii) additional local color transition directions in the image space assigned by the user are also satisfied.Various examples have been shown to demonstrate that our efficient solution enables the user to easily create time-varying color image composition results.展开更多
In order to understand the development of stem cells into specialized mature cells it is necessary to study the growth of cells in culture. For this purpose it is very useful to have an efficient computerized cell tra...In order to understand the development of stem cells into specialized mature cells it is necessary to study the growth of cells in culture. For this purpose it is very useful to have an efficient computerized cell tracking system. In this paper a prototype system for tracking neural stem cells in a sequence of images is described. In order to get reliable tracking results it is important to have good and robust segmentation of the cells. To achieve this we have implemented three levels of segmentation. The primary level, applied to all frames, is based on fuzzy threshold and watershed segmentation of a fuzzy gray weighted distance transformed image. The second level, applied to difficult frames where the first algorithm seems to have failed, is based on a fast geometric active contour model based on the level set algorithm. Finally, the automatic segmentation result on the crucial first frame can be interactively inspected and corrected. Visual inspection and correction can also be applied to other frames but this is generally not needed. For the tracking all cells are classified into inactive, active, dividing and clustered cells. Different algorithms are used to deal with the different cell categories. A special backtracking step is used to automatically correct for some common errors that appear in the initial forward tracking process.展开更多
基金supported by the iMinds visualization research program(HIVIZ)
文摘In this paper,we present an interactive static image composition approach,namely color retargeting,to flexibly represent time-varying color editing effect based on time-lapse video sequences.Instead of performing precise image matting or blending techniques,our approach treats the color composition as a pixel-level resampling problem. In order to both satisfy the user's editing requirements and avoid visual artifacts,we construct a globally optimized interpolation field. This field defines from which input video frames the output pixels should be resampled.Our proposed resampling solution ensures that(i) the global color transition in the output image is as smooth as possible,(ii) the desired colors/objects specified by the user from different video frames are well preserved,and(iii) additional local color transition directions in the image space assigned by the user are also satisfied.Various examples have been shown to demonstrate that our efficient solution enables the user to easily create time-varying color image composition results.
文摘In order to understand the development of stem cells into specialized mature cells it is necessary to study the growth of cells in culture. For this purpose it is very useful to have an efficient computerized cell tracking system. In this paper a prototype system for tracking neural stem cells in a sequence of images is described. In order to get reliable tracking results it is important to have good and robust segmentation of the cells. To achieve this we have implemented three levels of segmentation. The primary level, applied to all frames, is based on fuzzy threshold and watershed segmentation of a fuzzy gray weighted distance transformed image. The second level, applied to difficult frames where the first algorithm seems to have failed, is based on a fast geometric active contour model based on the level set algorithm. Finally, the automatic segmentation result on the crucial first frame can be interactively inspected and corrected. Visual inspection and correction can also be applied to other frames but this is generally not needed. For the tracking all cells are classified into inactive, active, dividing and clustered cells. Different algorithms are used to deal with the different cell categories. A special backtracking step is used to automatically correct for some common errors that appear in the initial forward tracking process.
