Combined local color and texture analysis of stained cells
Computer Vision, Graphics, and Image Processing
Logical foundations of artificial intelligence
Logical foundations of artificial intelligence
A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Applied regression analysis and other multivariable methods
Applied regression analysis and other multivariable methods
Random number generators: good ones are hard to find
Communications of the ACM
Operations Useful for Similarity-Invariant Pattern Recognition
Journal of the ACM (JACM)
A Local Visual Operator Which Recognizes Edges and Lines
Journal of the ACM (JACM)
Computer Processing of Line-Drawing Images
ACM Computing Surveys (CSUR)
Algorithms for Graphics and Imag
Algorithms for Graphics and Imag
Computer Vision
Scene segmentation by cluster detection in color spaces
ACM SIGART Bulletin
Fourier Preprocessing for Hand Print Character Recognition
IEEE Transactions on Computers
Fourier Descriptors for Plane Closed Curves
IEEE Transactions on Computers
Morphological multiscale decomposition of connected regions with emphasis on cell clusters
Computer Vision and Image Understanding
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Advances in computer graphics and electronics have contributed significantly to the increased utilization of digital imaging throughout the scientific community. Recently, as the volume of data being gathered for biomedical applications has begun to approach the human capacity for processing, emphasis has been placed on developing an automated approach to assist health scientists in assessing images. Methods that are currently used for analysis often lack sufficient sensitivity for discriminating among elements that exhibit subtle differences in feature measurements. In addition, most approaches are highly interactive. This paper presents an automated approach to segmentation and object recognition in which the spectral and spatial content of images is statistically exploited. Using this approach to assess noisy images resulted in correct classification of more than 97% of the pixels evaluated during segmentation and in recognition of geometric shapes irrespective of variations in size, orientation, and translation. The software was subsequently used to evaluate digitized stained blood smears.