Therefore, for each pixel of the image we use the following observation window:
where c is the current pixel.
We then compute the following products:
Finally the algorithm checks if up > down and down > 127.
The main drawback of this algorithm is for images with a low contrast where too many pixels
are deleted from the original image. Therefore, the Hough transform is not able to estimate
properly the skew angle.
1.3 The Hough transform
The Hough transform is an algorithm invented by Paul Hough in 1962. It has been designed
to detect particular features of common shapes like circles or lines in digitalized images. The
classical transform is restricted to features that can be described in a parametric form. There-
fore, the Generalized Hough transform was introduced for features with more complex analytic
form.
In this section, we will only describe the classical Hough transform for straight lines detection.
1.3.1 The Hough space
In a 2-dimensional space, a line can be represented through the two parameters x and y:
and can be plotted for each pair (x, y) image points.
The main idea of the Hough transform for straight line detection is to consider each line with
its slope parameter a and its intercept parameter b, instead of the coordinates x and y. However,
this representation has some weaknesses, especially when we need to represent a vertical line.
In this case, the slope parameter tends to infinity. Thus, for computational reasons, it is simplier
to represent a line with the common parameters ρ and θ, where ρ is the distance from the line
to the origin, and θ the line angle.
Thenceforth, by using this parametrization the line equation can be rewritten as follows:
An infinite...
... middle of paper ...
... histogram is taken as
the estimated skew angle.
1.6.2 Deskewing using grayscale images
This algorithm only uses the information of the grayscale image to estimate the skew angle. It
is based on the grayscale images filtering algorithm 1.2.2, the Sobel edge detection filter and the
classical Hough transform.
The input image is first filtered using the grayscale images filter. For each pixel satisfying the
filter conditions, the Sobel edge detection algorithm is applied and the gradient directory φ is
computed by using equation (1.4).
An estimate of the skew angle at the current point is:
Therefore, instead of voting in all directions, the vote can be performed for only a few values
of θ. In order to keep accuracy, votes are performed between θ − 2◦ and θ + 2◦ .
Peaks in the accumulator are located by using the method proposed in 1.5.2.
Retinal vessel segmentation is important for the diagnosis of numerous eye diseases and plays an important role in automatic retinal disease screening systems. Automatic segmentation of retinal vessels and characterization of morphological attributes such as width, length, tortuosity, branching pattern and angle are utilized for the diagnosis of different cardiovascular and ophthalmologic diseases. Manual segmentation of retinal blood vessels is a long and tedious task which also requires training and skill. It is commonly accepted by the medical community that automatic quantification of retinal vessels is the first step in the development of a computer-assisted diagnostic system for ophthalmic disorders. A large number of algorithms for retinal vasculature segmentation have been proposed. The algorithms can be classified as pattern recognition techniques, matched filtering, vessel tracking, mathematical morphology, multiscale approaches, and model based approaches. The first paper on retinal blood vessel segmentation appeared in 1989 by Chaudhuri et al. [21]...
The MATALBs ‘edge(… )’ function is used to detect edges in the input image with various options for an argument (e.g. ‘Sobel’, ‘Canny’, ‘Prewitt’, ‘zerocross’). An example of detected edges is shown in Fig 10.
A common characteristic of most of the images is that the neighboring pixels are highly correlated and therefore contain superfluous information. I...
...omated detection of lines and points in the images and the use of smart markers in reference video recordings.
Essentially, once an image exists in digital form, it can either be tweaked to adjust even its most indiscernible features or it can be entirely morphed into something altogether different. There ...
...l intensity (black or almost black). To find the pupil, a linear threshold (of value 70 in present work) is applied to the image as
The frequency enhancement routines are: low-pass filter, high pass filter, band pass and band stop filtering and homomorphic filtering etc. Homomorphic filtering result is non-uniform light. The picture in the dynamic range is not clear pictures. The high-pass filter system dependably overlooks picture part and highlighting points of interest. That can speak to high frequency components, enhancing the piece of the edge subtle element. This technique is suitable for edge detection of objects in the image. Because of the low frequency method, the visual effect of the prepared picture is not very good.
The Canny edge detection algorithm is commonly known as the optimal edge detector. During his research work, Canny's main intentions were to enhance the edge detectors which were already out at that time. Canny was successful in his objective and published a paper entitled "A Computational Approach to Edge Detection" in which he mentions a list of criteria which could improve current methods of edge detection. According to him, low error rate was one of the important criteria. Secondly, the edges in the image must not be missed and there must be no response to non-edges. Thirdly, the edge points must be well localized that is the distance between the edge pixels found by the detector and the actual edge must be minimum. And lastly, only one response
Image segmentation divides a digital image into multiple regions in order to analyze them. It is also used to distinguish different objects in the image. Several image segmentation techniques have been developed by the researchers in order to make images smooth and easy to evaluate. Famous techniques of image segmentation which are still being used by the researchers are Edge Detection, Threshold, Histogram, Region based methods, and Watershed Transformation.
It then applied circular hough transform to the template. It is assumed to be effective for images with high specular reflections, but since it hough transform uses brute force approach, hence computationally intensive.
The assumption behind method is that smaller image regions are more likely to have approximately uniform illumination, thus being more suitable for thresholding. Firstly, we develop a method based on the local row average to binarise the current line using that threshold. We then extend this technique to a moving window of different sizes.
This feature is used to measure the uniformity or energy from an image. Angular Second Moment is very large, if the pixels are very similar.
Following figure show the gray level image. An image is used in many fields for example entertainment, remote sensing, medical image, etc. which is very useful for human life. In this thesis gray scale image is use for remove impulse noise.
This paper describes the basic technological blee of Digital Image Processing . Image processing is basically classified in to three categories: The Rectification and Restoration, Enhancement and Information Extraction. The Rectification deals with incipient processing of raw image data to correct for geometric distortion, to calibrate the data radiometrically and to eliminate noise present in the data. The enhancement procedures are applied to image data in order to display the data for subsequent visual interpretation effectively. It involves various techniques for increasing the visual distinction between features in a scene. The objective of the information extraction operations is to replace visual analysis
In order to separate the finger vein sample from background, we have to trace each column of finger vein sample to find the outer bound of the finger using the method presented in [10] [<-v1]. This method will detect both upper and lower bounds of the finger vein. Next, we determine the center of finger vein by calculating the midpoint with upper and lower bounds. We then find the rotation and translation parameters based on the attained finger vein sample by matching it to the original image reference axis [3]