Showing posts with label image. Show all posts
Showing posts with label image. Show all posts

Sunday, June 20, 2010

Beginner Digital Scrapbooking - What is a Digital Image?


Image : http://www.flickr.com


Digital scrapbooking, sometimes alleged basic scrapbooking, is so accessible because the anthology folio blueprint is created, simulated, or congenital by agency of a computer. All the beheld advice that is congenital into the final digital blueprint is captured about in digital anamnesis as $.25 and bytes.

When a account is taken, a digital camera food all of the accurate advice on a baby computer anamnesis dent accepted as a beam anamnesis card. Digital cameras abundance digital images in JPEG architecture which uses abstracts compression. added big-ticket cameras additionally use TIFF and RAW formats that use added accumulator amplitude but accord accomplished photographers added options in processing the image.

Megapixels is a admeasurement of how abounding millions of alone photon capturing elements are central the digital camera sensor. The sensor replaces the blur in a acceptable camera, as anniversary ablaze aspect of a account is translated into bags of $.25 per account or pixels.

Each digital camera has its own settings and firmware (software congenital into the camera) which actuate how the digital account is stored, that is, how abounding pixels are acclimated per account and in what architecture the pictured is saved. A digital camera set to abduction images at the best affection accessible will use added pixels per account and accordingly added abstracts accumulator anamnesis also. Cameras with greater megapixel accommodation aftermath college resolution photography.

A pixel is addition computer appellation which is abbreviate for account element. In a stored digital image file, a pixel refers to a distinct point of ablaze in the photograph. In a digital camera that takes 1600x1200 pixel photos, anniversary image contains 1,920,000 pixels or about 2 megapixels. Similarly, a 2560x1920 pixel photo food 4,915,200 pixels or almost 5 megapixels.

Digital cameras about appear with a bond to affix it anon to a computer. This allows the camera's adeptness to apprehend the beam anamnesis agenda to be acclimated in affiliation with computer software.

Alternatively, the anamnesis agenda can be removed from the camera and placed into a agenda clairvoyant already affiliated to a computer. Some computers appear with centralized agenda readers that accept assorted slots for altered anamnesis agenda formats. For my SD anamnesis card, I bought a bargain USB accessory that allows me to bung my beam anamnesis agenda into my computer USB port.

Using either adjustment above, you can now accessible digital image files from area they are stored on the beam anamnesis agenda and save the files on your adamantine drive. The affected images on the adamantine drive becomes the aboriginal antecedent for all approaching assignment done with these digital images. Once you are assured that you accept auspiciously affected the images to the adamantine drive, the beam anamnesis agenda can now be reused to booty added photos.

I accept a binder (or directory) on my adamantine drive alleged camera downloads. This is area I accumulate all of my aboriginal digital image files. When I appetite to do added assignment with a accurate photo, I accomplish yet addition archetype of the digital image book into a additional binder which is my alive directory. This ensures that I consistently accept an aboriginal archetype because I never appetite to adapt or abort the aboriginal in any way. This is like blind on to the aboriginal negatives from candy blur alone now it is done in the computer--virtually. That's addition additional for digital scrapbooking.

Most important to this absolute action is to consistently accomplish approved backups of all your claimed abstracts and digital images from your adamantine drive to addition blazon of accumulator medium, be it CD, DVD, tape, online backups or a bombastic adamantine drive.

So let's recap. We took a account that was stored digitally in the camera's beam anamnesis card. We affiliated the camera to the computer or we confused the anamnesis agenda into the computer agenda account device. Then we affected the account from the anamnesis agenda to the camera downloads binder on the computer adamantine drive. And for approaching processing, we fabricated addition archetype of the account into our alive folder. Finally, we've fabricated abiding that all our aboriginal photos are actuality backed up on a approved base to addition accumulator medium.

Wiring Tip and Technique to Digital Image Processing Everything in One for Everyone like thing

Tuesday, May 11, 2010

Find Corner by SubPix

digital image process
The location of the corner of digital image processing.
void cvFindCornerSubPix (const CvArr * image, CvPoint2D32f * corners,.
int count, CvSize win, CvSize zero_zone,.
CvTermCriteria criteria);

image = image size as the original. floating-point 32-bit 1 channel.
corners = the value of the position angle of the input image. To locate the point of the output image.
count = number of runs in any of the points after round.
win = half the size of the desired size windows for example, if win = 5, so the size of the windows to find the 5 +5 +1 = 11 (to be positive because it includes a point to begin with) will be that size of the win to find = 11 x 11 itself.
zero_zone = the value to 0 to not want to be thinking.
criteria = highest value after the loop according to the count.

Monday, May 10, 2010

Harris edge detector

Find an edge image by using Harris edge detector.

void cvCornerHarris (const CvArr * image, CvArr * harris_responce,.
int block_size, int aperture_size = 3, double k = 0.04);

image = image size as the original. floating-point 32-bit 1 channel.
harris_responce = picture results after finding marginal Harris picture should be sized originals.
block_size = same function cvCornerEigenValsAndVecs.
aperture_size = see Sobel.
k = Harris detector free parameter which is equal to 0.04 default.

//CornerHarris
IplImage *pHarris=cvCreateImage(cvGetSize(pCornerEigen),IPL_DEPTH_32F,1); cvCornerHarris( pCornerEigen, pHarris, 3 , 7, 0.04 );
cvNamedWindow("Harris",CV_WINDOW_AUTOSIZE);
cvShowImage("Harris", pHarris);


Image

Sunday, May 9, 2010

Find Coner in Image

image process
In digitam Image processing that can find Eigen value by Image process is calculated at least for the gradient matrices for corner.
example to use program
//CornerMinEigenVal
IplImage*pCornerMinEigenVal=cvCreateImage(cvGetSize(pCornerEigen),IPL_DEPTH_32F,1);
cvCornerMinEigenVal(pCornerEigen, pCornerMinEigenVal, 3, 7 );
cvNamedWindow("CornerMinEigen",CV_WINDOW_AUTOSIZE);
cvShowImage("CornerMinEigen", pCornerMinEigenVal);

Friday, May 7, 2010

Corner Eigen Vals And Vecs

In Digital image processing that can Calculate the eigen values and eigen vectors using blogs to find the image angle.

Use function


void cvCornerEigenValsAndVecs (const CvArr * image, CvArr * eigenvv, int block_size, int aperture_size = 3);

scr = original image and the image size must be 8-bit 1 channel.
dst = image used to store results for a wide angle would be six times the original picture and picture floating-point 32-bit 1 channel.
block_size = size of pixel around S (p) filter size block_size x block_size.
aperture_size = the value of the Sobel filter matrix which must be at 1,3,5 and 7 only, where the metric is metric size aperture_size x aperture_size.

//CornerEigen
CvSize imgSize = cvGetSize(pGray);
IplImage*pCornerEigen=cvCreateImage( cvSize(imgSize.width*6 , imgSize.height) ,
IPL_DEPTH_32F,1 );
cvCornerEigenValsAndVecs( pGray, pCornerEigen, 3, 7 );
cvNamedWindow("CornerEigen",CV_WINDOW_AUTOSIZE);
cvShowImage("CornerEigen", pCornerEigen);


image process

Thursday, May 6, 2010

Pre Corner Detect

Calculate the shape of the corner edge to search space for image in digital image processing. How to find the shape of the image area is divided into small areas of this section for a corner by corner acquired is the maximum of Function in the area.

//ConerDetectIplImage *pCorner=cvCreateImage(cvGetSize(pimage),IPL_DEPTH_32F,1);
cvPreCornerDetect( pGray, pCorner, 7 );
cvNamedWindow("Corner",CV_WINDOW_AUTOSIZE);
cvShowImage("Corner", pCorner);


corner digital image process

Monday, May 3, 2010

Canny Edge Detection

This process is the Digital image processing in OpenCV libary to find an edge image by using Canny algorithm.

using function.

void cvCanny( const CvArr* image, CvArr* edges, double threshold1,
double threshold2, int aperture_size=3 );

Example for use OpenCV libary.

IplImage *pCanny=cvCreateImage(cvGetSize(pimage),IPL_DEPTH_8U,1);
cvCanny (pGray,pCanny, 200,250, 3 );
cvNamedWindow("Canny",CV_WINDOW_AUTOSIZE);
cvShowImage("Canny", pCanny);

result.


digital image process

Saturday, May 1, 2010

Laplace Detection

Laplace edge detection in Digital image process leave the template to find the edge of the image features. The effects of Lauren Thing Eight equations are linear and constant When The effect Lauren Eight Thing Constant Center will be able to solve the problem. The following is an example of a template fish leave Windsor.

IplImage *pLaplace=cvCreateImage(cvGetSize(pimage),IPL_DEPTH_16S,1); cvLaplace( pGray, pLaplace, 7 );

cvNamedWindow("Laplace",CV_WINDOW_AUTOSIZE);

cvShowImage("Laplace", pLaplace);



digital image process



Thursday, April 29, 2010

Sobel edge detection on OpenCV

In this system, Finding the edge image by Sobel Edge Detection in Digital image process by OpenCV libary. That is find edge by use template size 3x3 with two templates to use the first template for the difference in the horizontal (Xdiff) and The difference in the vertical (Ydiff) by at

Y_difference(x,y) = value(x,y) - value(x,y+1)
X_difference(x,y) = value(x,y) - value(x-1,y)

example use function in OpenCV

//Convert to Sobel
IplImage *pSobel=cvCreateImage(cvGetSize(pimage),IPL_DEPTH_16S,1);
cvSobel( pGray,pSobel,1,1,7 );
cvNamedWindow("Sobel",CV_WINDOW_AUTOSIZE);
cvShowImage("Sobel", pSobel);

result:


Digital image process



Sunday, April 25, 2010

Template for Edge detection

To find the edge image horizontally simple in Image processing. How it is to find the difference between one point on the address below (or above) the following point.

Ydiff(x,y) = I(x,,y) - I(x,y+1)------------ (1)

The Ydiff is the difference in axial location of image and I (x,, y) is the intensity of the pixel position (x,, y) in image results of using equation (1) is equivalent to. image convolution with template.
1
-1

Finding the vertical edge image can be the same.

Xdiff(x,,y) = I(x,,y) - I(x-,y) -------------(2)

The Xdiff is the difference in horizontal and equation (2) is equivalent to convolution with the template of image processing.


edge image processing

Saturday, April 24, 2010

Edge Detection

Find the perimeter of objects in the picture. With that in mind a line around the object. We can calculate area (size) or know the type of objects that need. However, finding the right edge of picture is not complete as easy. In particular, finding the edge of the picture quality is less difference between foreground and background, low Or brightness is not always around the image edge image resulting from the difference in intensity from one point to another point if it is different this valuable edge image will be visible. If the difference is less. Edge images is not clear.


Tuesday, April 6, 2010

Getting Thresholding



Used to change the direction of the image. By experts of the image into sections by normal processes of memory compared with a Threshold value of the threshold by more than if I change the pixel at that location as the maximum If less than to change the pixel position to the minimum makes the picture has only two levels of maximum or minimum sample program.


For row=0 To ROW_MAX-1

For col = 0 To COL_MAX-1

If (image(row,col) > threshold ) Then

image(row,col) = MAX

else

image(row,col) = MIN

End if

Next col

Next row



Threshold Digital image processing opencv

Grey-Level Transfromations

Grey Image processing1
In order to be counted amount of each pixel grayscale image is easy. Cited. Programming language Basic.


For row=0 To ROW_MAX-1

For col=0 To COL_MAX-1

Count(image(row,col))= Count(image(row,col))+1

Next col

Next row


The data dimension in the Count variable can be displayed as histogram your image. The histogram this will tell us about the nature of the image. This may indicate the direction of the various levels of noise to your image.



Grey Image processing2



 
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