Metal surface inspection using image processing techniques

Don, Hon-Son; Fu, King-Sun; Liu, C. R.; Wang, Lin

doi:10.1109/tsmc.1984.6313276

Cited by 39 publications

(19 citation statements)

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“…It has been used in various ways. Don et al (1984) used it to measure surface roughness in metals and Weszka et al (1976) classified terrain from aerial and satellite images.…”

Section: Pre-processingmentioning

confidence: 99%

Towards automation of palynology 2: the use of texture measures and neural network analysis for automated identification of optical images of pollen grains

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Flenley

et al. 2004

J Quaternary Science

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L. 2004. Towards automation of palynology 2: the use of texture measures and neural network analysis for automated identification of optical images of pollen grains.ABSTRACT: The automation of palynology (the identification and counting of pollen grains and spores) will be a small step for image recognition, but a giant stride for palynology. Here we show the first successful automated identification, with 100% accuracy, of a realistic number of taxa. The technique used involves a neural network classifier applied to surface texture data from light microscope images. A further significance of the technique is that it could be adapted for the identification of a wide range of biological objects, both microscopic and macroscopic.

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“…It has been used in various ways. Don et al (1984) used it to measure surface roughness in metals and Weszka et al (1976) classified terrain from aerial and satellite images.…”

Section: Pre-processingmentioning

confidence: 99%

Towards automation of palynology 2: the use of texture measures and neural network analysis for automated identification of optical images of pollen grains

Treloar

Flenley

et al. 2004

J Quaternary Science

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“…Due to the subjectivity of the decision, the cutoff point can move out over time or among people. Research has shown that training must be ongoing to keep personnel "calibrated" [9].…”

Section: Labor Expectationsmentioning

confidence: 99%

“…Due to the repetitive nature of the roughness on machined surfaces, stylus profilometers are typically employed to measure a two-dimensional data profile on the surface [5]. Alternative research methods use non-contact methods such as optoelectric profilometers [6], angular specklecorrelation [7], reflectivity [8,9], or image pattern recognition [9]. A non-contact method was also explored by Nwaogu et al [10] to evaluate the surface roughness of castings.…”

Section: Introductionmentioning

confidence: 99%

Development of a Digital Standard to Specify Surface Requirements of Cast Metal Surfaces

Voelker

Peters

2017

Matls. Perf. Charact.

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Communication of specifications between a customer and a manufacturer is important for meeting form, fit, and functional requirements of any part.Current standards for the requirements of cast metal surfaces use qualitative methods, including comparator plates and images of surfaces, to specify the surface quality allowing ample room for variation in interpretation of the standard. The length scale of existing contact surface measurements is too small for most casting surfaces. This paper covered a proposed digital standard for specifying cast metal surfaces. The proposed digital standard used point cloud data of a cast surface, likely attained using a non-contact capture method, in order to identify roughness properties and anomalies caused by the casting process. Unlike current qualitative methods, this standard does not specify the potential causes of surface issues, such as porosity or inclusions. This standard was developed in order to reduce measurement variation and eliminate confusion between the customer and manufacturer. Assigning quantitative criterion to the surface allows the customer to specify exactly what is needed as opposed to limiting them to a subjective comparator or image to base their requirements. Additionally, this quantitative method could be used to verify visual inspection results among the inspectors within a production facility to reduce their measurement error and improve productivity.

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“…Batchelor and Cotter [1] applied some general image processes like dilation, erosion, and thinning to detect spots and streaks of pollution. Don et al [8] applied image processing techniques to metal surface inspection, in which both feature extraction and pattern classification were proposed. Xian et al [31] presented three methods: (1) averaged based shading correction, (2) background model for pollution erasing, and (3) a local iterative operator for defect and background clustering on the surface of bearing rollers.…”

Section: Introductionmentioning

confidence: 99%