Statistical properties of thresholded images

Panda, Durga P.

doi:10.1016/0146-664x(78)90061-8

Cited by 11 publications

(3 citation statements)

References 8 publications

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“…No methods currently exist for predicting the number of connected components of thresholded spatially correlated signal (or noise). However, it has been found possible [6] to estimate the moments of regions, the density of border points, and lower bounds on the number of connected components in thresholded noise images. The input grayscale image is modeled as a two-dimensional random process (stationary random field) characterized by its mean and power spectrum.…”

Section: Operator Response Prediction A~ ~Redictin 9 Results Of Thresmentioning

confidence: 99%

Object detection in infrared images

Milgram¹,

Rosenfeld

1981

Digital Image Processing Systems

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This paper describes algorithms for detecting and classifying objects such as tanks and trucks in forward-looking infrared (FLIR) imagery.It summarizes research conducted in the course of a two-year project in the areas of image modeling, pre-and post-processing, segmentation, feature extraction, and classification. Image modelsThe work on image modeling conducted under this project was concentrated in three main areas:i) Modeling of the joint (gray level, edge value) statistics of FLIR scenes, as a basis for defining threshold selection techniques.2) Modeling of thresholding and edge detection responses to background regions, as a basis for predicting false alarm

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Section: Operator Response Prediction A~ ~Redictin 9 Results Of Thresmentioning

confidence: 99%

Object detection in infrared images

Milgram¹,

Rosenfeld

1981

Digital Image Processing Systems

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“…Panda [23] uses an analogous approach to analyze background regions selected from forward-looking infrared (FLIR) imagery. He derives expressions for 1) density of border points and 2) average number of connected components in a row of the thresholded picture.…”

Section: A Statistical Modelsmentioning

confidence: 99%

Fitting Mosaic Models to Textures

Ahuja¹,

Rosenfeld²

1979

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“…Panda [15] uses an analogous approach to analyze back- age gray levels) on some synthetic pictures with normally distributed gray levels , and having different correlation co-…”

Section: Random Field Modelsmentioning

confidence: 99%

Mosaic Models for Image Analysis and Synthesis.

Ahuja¹

1977

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Statistical properties of thresholded images

Cited by 11 publications

References 8 publications

Object detection in infrared images

Object detection in infrared images

Fitting Mosaic Models to Textures

Mosaic Models for Image Analysis and Synthesis.

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