Image analysis using threshold reduction
TL;DR: A class of shift-variant reduction operations is introduced, that is useful for performing efficient and controllable shape and texture transformations between resolution levels, and some general properties of the cycle are derived.
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Abstract: A class of shift-variant reduction operations is introduce d, that is useful for performing efficient and controllable shape and texture transformations between resolution levels. In their most general form, the operations proceed in three steps: (a) convolve a binary image with a kernel of arbitrary size; (b) threshold the result; (c) subsample to produce the reduced image. Taking a binary structuring element for the kernel, the threshold convolution on a binary image is equivalent to a rank order filter, and the full reduction operation is a threshold reduction. Threshold reductions that use convolution filters and subs ample tiles of equal size are optimized by combining the three operations, using only logical raster operations and producing threshold convolution values only at the sampling points. For 2x reduction, the four possible threshold values (1, 2, 3, and 4) refer to the minimum number of ON pixels within each 2x2 tile for which a pixel in the reduced image will be ON. Algorithms for boolean raster operations are given for 2x, 3x, and 4x threshold reduction, and lookup tables that effici ently implement column raster operations are provided. Threshold reduction rates of 2.5x pixel/second can be achieved with a Sun SparcStation2 . A mask-forming image analysis cycle of threshold reduction, augmented by morphology and followed by replicative expansion to full resolution, is described, an d some general properties of the cycle are derived. A simple application of threshold reduction to document image analysis, the extraction of halftone regions from scanned images that also contain text and line graphics, is illustrated. A sequence of 2x reductions with first low and then high thresholds is used to create a redu ced image consisting of a mask over the halftone regions. In this way, the extraction occurs as a nat ural consequence of the reductions.
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Figures

Figure 4. (a) 4x reduction withm = 1 for each stage. Resolution: sampling (75/in), rendering (196/in). (b) Closing with 3x3 SE. Resolution: same as (a). (c) Further4x eduction withm = 4 for each stage. Resolution: sampling (19/in), rendering (49/in). (d) Opening with 3x3 SE. Resolution: same as (c). 
Figure 3. Scanned image containing halftone image area(s). Sampling resolution is 300/in; rendering resolution is 375/in. 
Figure 2. (a) initial image, (b) image after first cycle, (c) image in second cycle after closing with 3x3 SE in (e), (d) image after second cycle. 
Figure 1. (a) Dilation filter for threshold 1; (b) erosion filter for threshold 4 
Table 1. Implementation of 2x threshold reduction with boolean operations.
Citations
Measuring document image skew and orientation
Dan S. Bloomberg,Gary E. Kopec,Lakshmi Dasari +2 more
- 30 Mar 1995
TL;DR: This method does not indicate when text is upside-down, and it also requires sampling the function at 90 degrees of rotation to measure text skew in landscape mode, but such text orientation can be determined by noting that Roman characters in all languages have many more ascenders than descenders, and using morphological operations to identify such pixels.
Multiresolution Morphological Approach to Document Image Analysis
Dan S. Bloomberg
- 01 Jan 1991
TL;DR: An image-based approach to document image analysis is presented, motivated by a merged view of shape and textural image properties at multiple scales, and the computational costs of the basic operations are given, so that algorithm efficiencies can be estimated.
82
Patent
Methods and apparatus for selecting semantically significant images in a document image without decoding image content
M Margaret Withgott,Steven C. Bagley,Dan S. Bloomberg,Per-Kristian Halvorsen,Daniel P. Huttenlocher,Todd A. Cass,Ronald M. Kaplan,Ramana B. Rao,Douglass R. Cutting +8 more
- 01 Sep 1992
TL;DR: In this paper, a method and apparatus for processing a document image, using a programmed general or special purpose computer, includes forming the image into image units, and at least one image unit classifier of each image unit is determined, without decoding the content of the image units.
70
Multiresolution morphological analysis of document images
Dan S. Bloomberg
- 01 Nov 1992
TL;DR: An image-based approach to document image analysis is presented, that uses shape and textural properties interchangeably at multiple scales, and the importance of operating at the lowest feasable resolution is demonstrated.
36
Using mathematical morphology for document skew estimation
Laurent Najman
- 19 Dec 2003
TL;DR: This work proposes a concise definition of the skew angle of document, based on mathematical morphology, that has the advantages to be applicable both for binary and grey-scale images.
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