matlab source code

Multiple face detection Face is the primary index for imparting the identity. Automated face detection is one of the interesting field of research. Face detection of digital image has acquired much importance and interest in last two decades, which has applications in different fields. Computerizing the process needs many image processing methods. In this paper, a new face detection approach using color base segmentation and morphological operations is presented. The algorithm uses color plane extraction, background subtraction, thresholding, morphological operations (such as erosion and dilation), filtering (to avoid false detection). Then particle analysis is done to detect only the face area in the image and not the other parts of the body. The color planes are extracted using vision module the RGB color space is converted into suitable color space such as HSV and YCbCr. The algorithm can be used to detect both single as well as multiple persons in a image. Experimental results...

Eyes detection what is eye detection: To detect and track eye images with complex background, distinctive features of user eye are used. Generally, an eye-tracking and detection system can be divided into four steps: Face detection, eye region detection, pupil detection and eye tracking. To find the position of pupil, first, face region must be separated from the rest of the image using mixture of Gaussian, this will cause the images background to be non effective in our next steps. We used the horizontal projection obtained from face region, to separate a region containing eyes and eyebrow. This will result in decreasing the computational complexity and ignoring some factors such as bread. Finally, in proposed method points with the highest values * of are selected as the eye candidate's. The eye region is well detected among these points. Color entropy in the eye region is used to eliminate the irrelevant candidates. In the next step, we perform eye tracking. In the propo...

FACE BLURR Blurr an Image: A blur is a shape or area which you cannot see clearly because it has no distinct outline or because it is moving very fast.    When a thing blurs or when something blurs it, you cannot see it clearly because its edges are no longer distinct.    Steps to blurr the image: Step 1: Import the image to the working directory Step 2. Read the image by using imread(); command Step 3: Initialize variable as much you want the blurr level of image e.g 13,50… Step 4: Initialize variable by using any name than assign fspecial function which will be like  h  = fspecial('gaussian', hsize , sigma )  returns a rotationally symmetric Gaussian lowpass filter of size  hsize  with standard deviation  sigma  (positive). Step 5. blurred = imfilter(subImage,H); filters the multidimensional array  A  with the multidimensional filter conv. The array   ...

Image format conversion Step1. Just read the image Step2. Fullfile() function Build full file name from parts fullfile  replaces all forward slashes ( / ) with backslashes ( \ ) on Windows. On UNIX platforms, the backlash ( \ ) character is a valid character in file names and is not replaced. fullfile  does not trim leading or trailing separators.  fullfile  collapses inner repeated file separators unless they appear at the beginning of the full file specification.  fullfile  also collapses relative directories indicated by the dot symbol, unless they appear at the end of the full file specification. Relative directories indicated by the double-dot symbol are not collapsed. Step 3. Just write the image of any extension.

Generating RGB image RGB concept: An RGB image, sometimes referred to as a  truecolor  image, is stored as an  m -by- n -by-3 data array that defines red, green, and blue color components for each individual pixel. RGB images do not use a palette. The color of each pixel is determined by the combination of the red, green, and blue intensities stored in each color plane at the pixel's location. Graphics file formats store RGB images as 24-bit images, where the red, green, and blue components are 8 bits each. This yields a potential of 16 million colors. The precision with which a real-life image can be replicated has led to the nickname “truecolor image.” Code description: Zeros(); X = zeros( n )  returns an  n -by- n  matrix of zeros Create array of all zeros Cat concept: Concatenate arrays along specified dimension Here is source code output 

Image compression Wcompress command: The  wcompress  command performs either compression or uncompression of grayscale or truecolor images. Spiht parameter of wcompress: spiht' Set Partitioning In Hierarchical Trees Xc = wcompress( 'u' , 'wpeppers.wtc' ); uncompresses the file  SAV_FILENAME , which contains the compressed image, and returns the image  XC . Depending on the initial compressed image,  XC  can be a 2-D array containing either an indexed image or a 3-D array of  uint8  containing a truecolor image. [CR,BPP] = wcompress( 'c' ,X, 'wpeppers.wtc' , 'spiht' , 'maxloop' ,12); [COMPRAT,BPP] = wcompress('c',...)  returns the compression ratio  COMPRAT  and the bit_per_pixel ratio  BPP .` Comprat  states the compression ratio ‘ BPP'  =  'bpp'  or  'BPP'  sets the bit-per-pixel ratio. 'c'  : color transformation type (RGB  uint8 ). axis...

Dissolve the matrix Source 1 2 4 4 7 9  11 12 1 Destination 11 12 13 2 7 9 8 9 1 source code for given task to reach from source to destination:

Concatenating gray scale and rgb images in Mat-lab To concatenate images following steps will be required: Step1: initialize or load image by using imread command Step2: plot the image at any axis Step3: initialize or load another image by using imread command Step4: plot the image at any axis Step5: initialize a variable third variable where RGB can be converted into gray scale Step6: change the values of dimension by different ratio Be the innovator of your own thought if follow the other thought will be domed:) and get a tag of crammer :) source code: output

Concatenating horizontal images in Mat-lab To concatenate images following steps will be required: Step1: initialize or load image by using imread command Step2: plot the image at any axis Step3: initialize or load another image by using imread command Step4: plot the image at any axis Step5: initialize a variable than load both image in array[img img2]   here is the source code: view the output

Concatenating vertical images in Mat-lab To concatenate images following steps will be required: Step1: initialize or load image by using imread command Step2: plot the image at any axis Step3: initialize or load another image by using imread command Step4: plot the image at any axis Step5: initialize a variable than  load both image in array[img;img2]   here is the source code:) be pioneer of your own thoughts here is the output

Adding block in picture is as easy as adding block with pen or pencil steps: 1. just load the picture first 2. setting up the matrix dimension for the block to be set where ever you want use any color of choice color code id: 3. just show the image i am sharing my code as well with output end result

Image recognition Image recognition is the process of identifying and detecting an object or a feature in a digital image or video. This concept is used in many applications like systems for factory automation, toll booth monitoring, and security surveillance. Typical image recognition algorithms . Blurr an Image: A blur is a shape or area which you cannot see clearly because it has no distinct outline or because it is moving very fast.    When a thing blurs or when something blurs it, you cannot see it clearly because its edges are no longer distinct.    Steps to blurr the image: Step 1: Import the image to the working directory Step 2. Read the image by using imread(); command Step 3: Initialize variable as much you want the blurr level of image e.g 13,50… Step 4: Initialize variable by using any name than assign fspecial function which will be like  h  = fspecial('gaussian', hsize , sigma )  returns a rotationally symmetr...

Compression an Image in matlab Compressing an image refers to  Image compression is minimizing the size in bytes of a graphics file without degrading the quality of the image to an unacceptable level. The reduction in file size allows more images to be stored in a given amount of disk or memory space. It also reduces the time required for images to be sent over the Internet or downloaded from Web pages.  There are several different ways in which image files can be compressed. For Internet use, the two most common compressed graphic image formats are the  JPEG  format and the  GIF  format. The JPEG method is more often used for photographs, while the GIF method is commonly used for line art and other images in which geometric shapes are relatively simple. I have compressed the complete folder images. Following is the complete guideline to compress. Step 1: Initialize the folder where all the images are store providing the path in the direct...