1.6.3 Deskewing using binary and grayscale images
Method 1
This first algorithm uses the information of the binary and grayscale images to estimate the
skew angle. It is based on the binary images filtering algorithm 1.2.1, the Sobel edge detection
filter and the classical Hough transform.
Because we are looking for angles between -25 and 25 degrees, the length of the window is
set to 3 and the threshold to 2 for the filtering algorithm.
If a white pixel satisfies the conditions of the filtering algorithm, we then apply the Sobel edge
detection filter at the considered point on the grayscale image.
If the gradient magnitude is greater than 255, votes are performed in all directions in the ac-
cumulator.
Peaks in the accumulator are located by using the method proposed in 1.5.2.
Method 2
This method only differs from the previous in the voting scheme. As a matter of fact, instead of
voting in all directions, the gradient directory is used to compute the estimated skew angle at
the considered point by using (1.12).
In order to keep accuracy, votes are performed between θ − 2◦ and θ + 2◦ .
In order to increase the algorithm accuracy for little cropped image, it might be interesting
to vote in more directions than -2 to 2 degrees. However, this implies a greater computational
time for maybe not a greater accuracy.
1.7 Results
For the experimentation, 25 documents from magazines, business letters, annual reports were
considered. The documents are tested by prespecified angles between 0 and 25 degrees.
The following table gives the mean (M), the standard deviation (SD) and the computational
time (T) in seconds for the different proposed methods.
These tests were performed on a Pentium 4 ...
... middle of paper ...
...s were made on 12 randomly
chosen words or groups of words.
Finally, the boldness could be estimated by looking to the estimated boldness variation be-
tween words on the same line. It also mandatory to notice that the estimated boldness varies
depending on the words fonts.
2.4 Scaling algorithms
2.4.1 Scale2x
Scale2x is real-time graphics effect able to increase the size of small bitmaps guessing the miss-
ing pixels without interpolating pixels and blurring the images.
It was originally developed for the AdvanceMAME project in the year 2001 to improve the
quality of old games with a low video resolution. Derivative Scale3x and Scale4x effects which
scale the image of 3x and 4x are also available (8).
The image upsampling is computed by applying some rules to each pixel of the input image.
First we consider the following 3 × 3 matrix:
Voting is at the center of every democratic system. In america, it is the system in which a president is elected into office, and people express their opinion. Many people walk into the voting booth with the thought that every vote counts, and that their vote might be the one that matters above all else. But in reality, America’s voting system is old and flawed in many ways. Electoral College is a commonly used term on the topic of elections but few people actually know how it works.
Many people feel that this system is outdated, unfair and/or biased; that it should be replaced with the popular voting system. Unfortunately it is not as simple as...
sin θ → sin θ = 16.99° 16.99° is the best angle on the ground si n(θ)=7/√((〖37.64〗^2+7^2)) → sin θ =
Computer generated imagery has evolved and spread throughout cinematography and the film world like wildfire. Although computer generated imagery offers countless creative opportunities, the art form of special effects makeup should be practiced and preserved, as just that- an art form. Most people have begun to describe special effects makeup as anachronistic. Considering how long special effects makeup has been around, people are convinced that its existence is coming to an end.
After the initial pre processing steps of smoothening and removal of noise, the edge strength is calculated by taking the gradient of the image. For the purpose of edge detection in an image, the Sobel operator first performs a 2-D spatial gradient measurement with the help of convolution masks. The convolution masks used is of the size 3X3, where one is used to calculate the horizontal gradient(Gx) while the other is used to calculate the vertical gradient(Gy). Then, the approximate absolute edge strength can be calculated at each point. The masks used for the convolution process is as shown
...ing Gradient and Curvature of Gray Scale Image', Patter Recognition vol. 35, no. 10, pp. 2051-9.
For the pixels towards the edges of the image, we check for the number of pixels preceding the centre pixel. If this number is less than half the window size, we modify our code accordingly to take care so that we calculate the average value for that centre pixel.
An image are two dimensional function T (p, q), where x and y are spatial (plane) coordinates (p, q). This coordinates are called gray level of the image. T, p and q are all finite and discrete quantities, the image is called a digital image [1].
An image is described as a two-dimensional function, f(x, y), in which x and y are plane (spatial) coordinate points, and the amplitude of at any two similar pair of coordinates (x, y) is called the intensity or gray level of the image at the particular point. When x and y the amplitude values of f are all discrete entities or finite the image is known as digital image. The domain of digital image processing directs to processing digital images by the help of a digital computer. Note that a digital image is made up of a finite number of parts, each of which has a certain place and amount. These parts are directed to as picture property, image property, pixels and peels. Pixel is the word most widely used to represent the individual elements of a
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
The ability to alter images can open creative outlets for photographers and In turn, produce better quality work. Any photog...
It could detect the variation of grey levels, but it is sensitive to noise. Edge detection is an important task in image processing. It is main tool in pattern recognition, image segmentation, and scene analysis. Edge are local changes in the image intensity edge typically occur on the boundary between two regions. The main features are extracted from the edges of an image. Edge detection has major features for image analysis. These features are used by advanced computer vision algorithm. Edge detection is used for object detection which serves various applications like medical image processing, biometrics
An image may be defined as a two dimensional function, f (x, y), where x and y are spatial (plane) coordinates and the amplitude of f at any pair of coordinates (x, y) is called as the intensity or gray level of the image at that point. When (x, y) and the amplitude values of f are all finite, discrete quantities, then the image is a digital image. The field of digital image processing refers to processing digital images by means of a digital computer. It also includes classification and segmentation of objects in the image using boundary information, texture analysis, analysis of sequence of images with an interest in estimating the motion of objects and scene analysis.
John Canny, “A Computational Approach to Edge Detection.” in IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 8, No. 6, November
First, the sampling period should be determined - the distance between two neighboring sampling points in the image.