A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Trainable method of parametric shape description
Image and Vision Computing - Special issue: BMVC 1991
The nature of statistical learning theory
The nature of statistical learning theory
BMVC '95 Proceedings of the 1995 British conference on Machine vision (Vol. 1)
Geodesic Saliency of Watershed Contours and Hierarchical Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Direct Least Square Fitting of Ellipses
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of Interest Point Detectors
International Journal of Computer Vision - Special issue on a special section on visual surveillance
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Comparing in situ mRNA expression patterns of drosophila embryos
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Generalized low rank approximations of matrices
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A review of vessel extraction techniques and algorithms
ACM Computing Surveys (CSUR)
Automatic mining of fruit fly embryo images
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Interest point detection using imbalance oriented selection
Pattern Recognition
Detecting image points of general imbalance
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A global-to-local scheme for imbalanced point matching
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Bioinformatics
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
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Contour extraction of Drosophila (fruit fly) embryos is an important step to build a computational system for matching expression pattern of embryonic images to assist the discovery of the nature of genes. Automatic contour extraction of embryos is challenging due to severe image variations, including 1) the size, orientation, shape, and appearance of an embryo of interest; 2) the neighboring context of an embryo of interest (such as nontouching and touching neighboring embryos); and 3) illumination circumstance. In this paper, we propose an automatic framework for contour extraction of the embryo of interest in an embryonic image. The proposed framework contains three components. Its first component applies a mixture model of quadratic curves, with statistical features, to initialize the contour of the embryo of interest. An efficient method based on imbalanced image points is proposed to compute model parameters. The second component applies active contour model to refine embryo contours. The third component applies eigen-shape modeling to smooth jaggy contours caused by blurred embryo boundaries. We test the proposed framework on a data set of 8,000 embryonic images, and achieve promising accuracy (88 percent), that is, substantially higher than the-state-of-the-art results.