G06V 10/36
Definition
Diese Klassifikationsstelle umfasst:(Für diese Definition ist die deutsche Übersetzung noch nicht abgeschlossen)
Image or video pre-processing techniques which examine a local neighbourhood around a pixel and assign a value to the pixel, which is a function of the values (e.g. colour values or luminance values) of the pixels in this local neighbourhood.
The application of local operators in the spatial domain (e.g. by convolving the image with a predefined kernel matrix) or in the frequency domain (e.g. by calculating the Fourier transform and performing a point-wise multiplication in the frequency domain).
Notes – technical background
These notes provide more information about the technical subject matter that is classified in this place:
1. Usually the local neighbourhood is defined as a rectangular region of pixels with the pixel of interest placed at its centre, e.g. a 3*3 pixels neighbourhood or a 5*5 pixels neighbourhood; neighbourhoods having other shapes are also possible, but they are less common.
2. Local operators include:
- Linear operators, e.g. convolutions with low-pass filter matrices (such as a Gaussian kernel or a boxcar function; and convolutions with high-pass filter matrices for sharpening the image, or convolutions with spatial band-pass filters (such as the difference of Gaussians filter);
- Non-linear operators, e.g. median filters and more complex operators such as those for evaluating local luminance differences in order to detect sparkle points which are significantly brighter than their immediate surroundings;
- Non-linear operators, e.g. the Sobel operator and the Marr Hildreth operator are also frequently used for emphasising object boundaries or elongated structures in images;
- Differential operators such as the Laplace operator and filter matrices for calculating image gradients.
Notes – other classification places
Use of low-pass filter matrices for noise removal – group G06V 10/30.
Use of median filters for noise removal - group G06V 10/30.
Use of the Sobel operator and the Marr Hildreth operator for edge detection - group G06V 10/44.
Examples
Analysis of local image patches of a face image using a local operator and encoding the representation for subsequent face recognition
Querverweise
Informative Querverweise
| Noise removal for image or video recognition or understanding | G06V 10/30 |
| Detecting edges or corners for image or video recognition or understanding | G06V 10/44 |
| Extracting features from image blocks | G06V 10/50 |
| Local operators for general image enhancement | G06T 5/20 |
Glossar
G06V 10/36
Definition Statement
This place covers:Image or video pre-processing techniques which examine a local neighbourhood around a pixel and assign a value to the pixel, which is a function of the values (e.g. colour values or luminance values) of the pixels in this local neighbourhood.
The application of local operators in the spatial domain (e.g. by convolving the image with a predefined kernel matrix) or in the frequency domain (e.g. by calculating the Fourier transform and performing a point-wise multiplication in the frequency domain).
Notes – technical background
These notes provide more information about the technical subject matter that is classified in this place:
1. Usually the local neighbourhood is defined as a rectangular region of pixels with the pixel of interest placed at its centre, e.g. a 3*3 pixels neighbourhood or a 5*5 pixels neighbourhood; neighbourhoods having other shapes are also possible, but they are less common.
2. Local operators include:
- Linear operators, e.g. convolutions with low-pass filter matrices (such as a Gaussian kernel or a boxcar function; and convolutions with high-pass filter matrices for sharpening the image, or convolutions with spatial band-pass filters (such as the difference of Gaussians filter);
- Non-linear operators, e.g. median filters and more complex operators such as those for evaluating local luminance differences in order to detect sparkle points which are significantly brighter than their immediate surroundings;
- Non-linear operators, e.g. the Sobel operator and the Marr Hildreth operator are also frequently used for emphasising object boundaries or elongated structures in images;
- Differential operators such as the Laplace operator and filter matrices for calculating image gradients.
Notes – other classification places
Use of low-pass filter matrices for noise removal – group G06V 10/30.
Use of median filters for noise removal - group G06V 10/30.
Use of the Sobel operator and the Marr Hildreth operator for edge detection - group G06V 10/44.
Examples
Analysis of local image patches of a face image using a local operator and encoding the representation for subsequent face recognition
References
Informative references
| Noise removal for image or video recognition or understanding | G06V 10/30 |
| Detecting edges or corners for image or video recognition or understanding | G06V 10/44 |
| Extracting features from image blocks | G06V 10/50 |
| Local operators for general image enhancement | G06T 5/20 |