Image Processing
Image processing aims to improve the quality of the image to be easily interpreted by humans or machines. Image processing techniques to transform the image into another image. Input to this process is the image and the output is also the form of the image with the image quality better than the previous input.
Image Acquisition
Data acquisition system can be defined as a system that functions to retrieve, collect and prepare the data, to process it to produce the desired data. In the data type is still shaped analog image, the image has perpetually represented numerically by discrete values that can be processed by a digital computer. Image representation of continuous functions into discrete values is called digitization which produces digital ciitra. Digitizer or digitizing tools to perform can be either a scanner or a video camera. Digitizer consists of three basic components. First, the image sensor that works as a measure of light intensity. Second, the explorer who serves intensity measurements recorded in all parts of the image. Third, analog to digital converter that serves did penerokan and quantitation.
Filtering
An initial preprocessing filter process is used to remove the parts that are not needed (noise) on the image for further processing. Filter or filtering method is best depends on the situation and the type of noise or image degradation found in the image. Some types of filters are:
1. Linear Filter provides simplicity, speed, and very helpful in the process of noise reduction if the information tenta ng very limited frequency band.
2. Median filter is the most effective filter for removing salt and pepper noise.
3. The Wiener filter is a linear adaptive filter based on the characteristics of local variance of the image. Wiener filter smoothing the image area that changes the image of the most visible noise, but retain the area where the detail is very visible and less visible noise.
Black & White Image Conversion Image to Binary
Black and white image conversion into a binary image to identify the presence of objects represented as a region (region) in the image. In addition to identifying the presence of objects, the conversion can be done to focus on the analysis of morphological forms assuming the pixel intensities less important than its form. Once the object is separated from its background, geometrical properties and morphological / topological objects can be calculated from the binary image. In the case of pattern recognition, it is useful for decision making which belong to the classification of the image is located. Conversion of black-and-white image into a binary image is done by floating operations (thresholding).
Scaling
Scaling is the process of changing the dimensions of each character region and the thickness of the character. This process is used so that each character has the same dimensions of the region.
Image processing aims to improve the quality of the image to be easily interpreted by humans or machines. Image processing techniques to transform the image into another image. Input to this process is the image and the output is also the form of the image with the image quality better than the previous input.
Image Acquisition
Data acquisition system can be defined as a system that functions to retrieve, collect and prepare the data, to process it to produce the desired data. In the data type is still shaped analog image, the image has perpetually represented numerically by discrete values that can be processed by a digital computer. Image representation of continuous functions into discrete values is called digitization which produces digital ciitra. Digitizer or digitizing tools to perform can be either a scanner or a video camera. Digitizer consists of three basic components. First, the image sensor that works as a measure of light intensity. Second, the explorer who serves intensity measurements recorded in all parts of the image. Third, analog to digital converter that serves did penerokan and quantitation.
Filtering
An initial preprocessing filter process is used to remove the parts that are not needed (noise) on the image for further processing. Filter or filtering method is best depends on the situation and the type of noise or image degradation found in the image. Some types of filters are:
1. Linear Filter provides simplicity, speed, and very helpful in the process of noise reduction if the information tenta ng very limited frequency band.
2. Median filter is the most effective filter for removing salt and pepper noise.
3. The Wiener filter is a linear adaptive filter based on the characteristics of local variance of the image. Wiener filter smoothing the image area that changes the image of the most visible noise, but retain the area where the detail is very visible and less visible noise.
Black & White Image Conversion Image to Binary
Black and white image conversion into a binary image to identify the presence of objects represented as a region (region) in the image. In addition to identifying the presence of objects, the conversion can be done to focus on the analysis of morphological forms assuming the pixel intensities less important than its form. Once the object is separated from its background, geometrical properties and morphological / topological objects can be calculated from the binary image. In the case of pattern recognition, it is useful for decision making which belong to the classification of the image is located. Conversion of black-and-white image into a binary image is done by floating operations (thresholding).
Scaling
Scaling is the process of changing the dimensions of each character region and the thickness of the character. This process is used so that each character has the same dimensions of the region.
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