What question did this study set out to answer?

Review the core principles and operations of mathematical morphology for shape-based object and defect recognition in industrial machine vision.

July 1, 1987IEEE Transactions on Pattern Analysis and Machine Intelligence

Image Analysis Using Mathematical Morphology

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Authors

RHRobert M. HaralickThe Graduate Center, CUNY SSS. SternbergUniversität Hamburg XZXinhua ZhuangQingdao University

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Overview

Methodological review demonstrates mathematical morphology operations for defect detection in industrial vision, highlighting their shape-based advantages over linear convolution.

Key Points

Review the core principles and operations of mathematical morphology for shape-based object and defect recognition in industrial machine vision.
Evaluated the mathematical formulations of binary morphology and grayscale morphology against traditional linear convolution techniques.
Systematically analyzed four primary morphological operators: dilation, erosion, opening, and closing, along with their algebraic interrelationships.
Established that morphological operators relate directly to geometric structure, providing superior shape-based feature extraction compared to signal processing convolution.
Characterized the algebraic dualities and complementary properties linking dilation, erosion, opening, and closing across both binary and grayscale image domains.

Cite This Study

Haralick et al. (1987) studied this question.

synapsesocial.com/papers/69f88d2ebcfc91ffbfa63854 https://doi.org/10.1109/tpami.1987.4767941

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July 1, 1987IEEE Transactions on Pattern Analysis and Machine Intelligence

Image Analysis Using Mathematical Morphology

View Full Paper

Ask AI

Bookmark

Authors

RHRobert M. HaralickThe Graduate Center, CUNY SSS. SternbergUniversität Hamburg XZXinhua ZhuangQingdao University

Discussion

Member takes

Overview

Methodological review demonstrates mathematical morphology operations for defect detection in industrial vision, highlighting their shape-based advantages over linear convolution.

Key Points

Review the core principles and operations of mathematical morphology for shape-based object and defect recognition in industrial machine vision.
Evaluated the mathematical formulations of binary morphology and grayscale morphology against traditional linear convolution techniques.
Systematically analyzed four primary morphological operators: dilation, erosion, opening, and closing, along with their algebraic interrelationships.
Established that morphological operators relate directly to geometric structure, providing superior shape-based feature extraction compared to signal processing convolution.
Characterized the algebraic dualities and complementary properties linking dilation, erosion, opening, and closing across both binary and grayscale image domains.

Cite This Study

Haralick et al. (1987) studied this question.

synapsesocial.com/papers/69f88d2ebcfc91ffbfa63854 https://doi.org/10.1109/tpami.1987.4767941

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