Tire visual inspection by active image sensing and smart feature extraction

Feature extraction from physiological signals of EEG (electroencephalogram) is an essential part for sleep staging. In this study, multidomain feature extraction was investigated based on time domain analysis, nonlinear analysis, and frequency domain analysis. Unlike the traditional feature calculation in time domain, a sequence merging method was developed as a preprocessing procedure. The objective is to eliminate the clutter waveform and highlight the characteristic waveform for further analysis. The numbers of the characteristic activities were extracted as the features from time domain. The contributions of features from different domains to the sleep stages were compared. The effectiveness was further analyzed by automatic sleep stage classification and compared with the visual inspection. The overnight clinical sleep EEG recordings of 3 patients after the treatment of Continuous Positive Airway Pressure (CPAP) were tested. The obtained results showed that the developed method can highlight the characteristic activity which is useful for both automatic sleep staging and visual inspection. Furthermore, it can be a training tool for better understanding the appearance of characteristic waveforms from raw sleep EEG which is mixed and complex in time domain.

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P5-15 Feature extraction for hand movement of PD patients under visual target tracking and its relation to visual inspection

Clinical Neurophysiology ◽

10.1016/s1388-2457(10)60537-8 ◽

2010 ◽

Vol 121 ◽

pp. S130-S131

Author(s):

J. Ide ◽

T. Sugi ◽

F. Shima ◽

H. Shibasaki ◽

M. Nakamura

Keyword(s):

Feature Extraction ◽

Target Tracking ◽

Visual Inspection ◽

Visual Target ◽

Hand Movement ◽

Visual Target Tracking

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Texture feature extraction for a visual inspection of ceramic tiles

Proceedings of the IEEE International Symposium on Industrial Electronics, 2005. ISIE 2005. ◽

10.1109/isie.2005.1529109 ◽

2005 ◽

Cited By ~ 5

Author(s):

I. Novak ◽

Z. Hocenski

Keyword(s):

Feature Extraction ◽

Visual Inspection ◽

Texture Feature ◽

Ceramic Tiles ◽

Texture Feature Extraction

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An Improved Quantitative Image Analyser

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100078663 ◽

1977 ◽

Vol 35 ◽

pp. 226-227

Author(s):

J.P. Fallon ◽

P.J. Gregory ◽

C.J. Taylor

Keyword(s):

Feature Extraction ◽

Quantitative Analysis ◽

Frequency Content ◽

General Sense ◽

Physical Measurements ◽

Quantitative Image ◽

Image Analyser ◽

Automatic Methods ◽

Quantitative Results

Quantitative image analysis systems have been used for several years in research and quality control applications in various fields including metallurgy and medicine. The technique has been applied as an extension of subjective microscopy to problems requiring quantitative results and which are amenable to automatic methods of interpretation.Feature extraction. In the most general sense, a feature can be defined as a portion of the image which differs in some consistent way from the background. A feature may be characterized by the density difference between itself and the background, by an edge gradient, or by the spatial frequency content (texture) within its boundaries. The task of feature extraction includes recognition of features and encoding of the associated information for quantitative analysis.Quantitative Analysis. Quantitative analysis is the determination of one or more physical measurements of each feature. These measurements may be straightforward ones such as area, length, or perimeter, or more complex stereological measurements such as convex perimeter or Feret's diameter.

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