• Corpus ID: 207879431

Sanitation and of the staff of the Neighborhood Action Program , and the help of the residents of

  title={Sanitation and of the staff of the Neighborhood Action Program , and the help of the residents of},
  author={Robert M. Haralick and K. Sam Shanmugam},
Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. [] Key Method We use two kinds of decision rules: one for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and…



Computer Classification of Reservoir Sandstones

A procedure is developed to extract numerical features which characterize the pore structure of reservoir rocks. The procedure is based on a set of descriptors which give a statistical description of

Texture-Tone Study with Application to Digitized Imagery.

Abstract : The report describes initial research efforts undertaken to determine if texture - a significant but ill-defined property of virtually all substances can be used in a practicable automated

A Survey of Preprocessing and Feature Extraction Techniques for Radiographic Images

Several preprocessing techniques for enhancing selected features and removing irrelevant data are described and compared and a practical image pattern recognition problem is solved using some of the described techniques.

Visual texture analysis

Differentiation between the coarsenesses of samples of a given texture may be successfully effected using any of the following measures: (1) Amount of edge per unit area, (2) Self-match (as measured

Visual Texture Analysis IV.

Abstract : Several measures of texture coarseness, based on average 'best' edge size or spot size in one or two dimensions, were computed for texture samples having a known scale ratio. The results

Pattern Recognition from Satellite Altitudes

Several decision algorithms were used to classify complex patterns recorded by TV cameras aboard unmanned, scientific satellites, and these accuracies ranged from 53 percent to 99 percent on independent data.

Introduction to Statistical Pattern Recognition

Two approaches to dimensionality reduction, namely feature selection (FS) and feature extraction (FE) are specified, though FS is a special case of FE, they are very different from a practical viewpoint and thus must be considered separately.

Image processing by digital computer