Automatic thresholding of gray-level pictures using two-dimensional entropy
Computer Vision, Graphics, and Image Processing
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
The lifting scheme: a construction of second generation wavelets
SIAM Journal on Mathematical Analysis
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Point Processes for Unsupervised Line Network Extraction in Remote Sensing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Building Outline Extraction from Digital Elevation Models Using Marked Point Processes
International Journal of Computer Vision
Object Extraction Using a Stochastic Birth-and-Death Dynamics in Continuum
Journal of Mathematical Imaging and Vision
Edge-avoiding wavelets and their applications
ACM SIGGRAPH 2009 papers
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Automatically analyzing morphology of biological objects such as cells, nuclei, and vessels is important for medicine and biology. However, detecting individual biological objects is challenging because biomedical images tend to have a complex structure composed of many morphologically distinct objects and unclear object boundaries. In this paper, we present a novel approach to automatically detect individual objects in biomedical images using a multiple marked point process, in which points are the positions of the objects and marks are their geometric attributes. With this model, we can consider both prior knowledge of the structure of the objects and observed data of an image in object detection. Our proposed method also uses the second generation wavelets-based edge-preserving image smoothing technique to cope with unclear boundaries of biological objects. The experimental results show the effectiveness of our method.