Advantages And Disadvantages Of Adaptive Histogram Equalization

in our multi­feature camera so that it would be at a 3½ star quality. In order to compensate for

Shi Et al [13] used a local projection profile at each pixel of the image, and transform the original image into an adaptive local connectivity map (ALCM). For this process, first the gray scale image is reversed so the foreground pixels (text) have idensity values of up to 255. Then the image is downsample to ¼ of each size, ½ in each direction. Next a sliding window of size 2c scans the image from left to right, and right to left in order to compute the cumulative idensity of every neighborhood. This technique is identical to computing the projection profile of every sliding window, (i.e. counting the foreground pixels), but instead of outpiting a projection profile histogram, the entire sliding window sum is saved on the ALCM image. Finaly

One frame from each camera is pulled into the program and then rectified based on calibration of each camera. The calibration process takes in the camera calibration parameters of the cameras, the rotation, and translation of the second camera in relation to the first camera. After being rectified based on the calibration, a disparity map is created. A disparity map is an intensity map that shows the measurements of

To introduce color composites, histograms, and scatterplots as tools for exploring image data stored in database channels

Has complementary metal–oxide–semiconductor (CMOS) sensor to provide crystal-clear images with 1920 x 1080 resolution. [1]

Another step in pre-processing is to make the image width and height divisible by 8. Let w and h represent the width and height of the image respectively. w and h are converted into w* and h* such that 8|w* and 8|h* are as follows in (1) and (2):

For detecting various types of textures it uses local masks. To compute the energy of texture it uses convolution masks of 5×5 which is represented by a nine element vector for each pixel.

b represents the bias field that indicates the intensity inhomogeneity. The bias field is slowly varying, which implies that b can be will approximated by a constant in a neighbourhood of each point in the image domain. Energy function has to be minimized within a boundary where the level set evolves. For this a Neumann Boundary condition is defined and is applied to the level set function to get object boundary. Within this specific boundary, the Level Set Evolution process will take place. The level set function is obtained by taking the signed function of randomized image and has values 0, 1, and -1. Local intensity clustering property indicates that the image can be segmented into three regions based on the values of level set function. Standard K-means Criterion is used to classify the local intensity which can be defined as

Image segmentation attempts to separate an image into its object classes. Clustering methods, edge based methods, histogram-based methods, and region growing methods offer different advantages and disadvantages. The use of a Gaussian mixture expectation maximization (EM) method has been investigated to realize segmentation specifically for x-ray luggage scans [131]. Namely, k Gaussian distributions are added to best fit the image histogram, with each Gaussian distribution corresponding to its own object class. In an x-ray image, high density objects absorb more x-ray photons and appear more intensely than low density objects. In a typical x-ray luggage scan image, there will generally be a mix of low density, medium density, and high density objects. Because of this characteristic, an image segmentation algorithm which requires knowledge of the number of partitions in the segmentation, such as in EM segmentation, is still a viable and perhaps even favorable method. By segmenting an x-ray image

This can now be enhanced using functions such as contrast, brightness, zoom and filtration to amplify images quality.

The method is useful in images with backgrounds and foregrounds that are both bright or both dark. In particular, the method can lead to better views of bone structure in x-ray images, and to better detail in photographs that are over or under-exposed. A key advantage of the method is that it is a fairly straightforward technique and an invertible operator. So in theory, if the histogram equalization function is known, then the original histogram can be recovered. The calculation is not computationally intensive. A disadvantage of the method is that it is indiscriminate. It may increase the contrast of background noise, while decreasing the usable signal.

each related to a different segment of the image, and uses them to sort out the lightness values of the image. It is therefore proper for improving the local contrast of an image and bringing out more detail.A variant of adaptive histogram equalization called Contrast Limited Adaptive Histogram Equalization (CLAHE) prevents this by restricting the amplification.The form of a picture is rectangular. However, the shape of an eye is round because the rectangular representation contains the round eye; in the fundus image, the dark outside surface part which surroundings an eye appears. In general-purpose histogram equalization, the picture element of dark outside region is added to the histogram, so values of element close this dark outside region were less than the expected ones. CLAHE work on tiny regions, called tiles, while the generic algorithmic program works on the whole image. As the result of extremely dark and bright regions is limited to the local tile, a uniform image can be occur.[1]

Firstly I will explain what is signal ,signal processing ,analogue viruses digital signal types of signal processing their advantages and disadvantages and their comparison .I-e which one is better …….why analog signal processing (ASP) is replaced with digital signal processing (DSP).

High dynamic range is a digital photography technique that collect multiple exposures of the same scene where captured with different exposure settings and merged using image editing camera software to create a more realistic image called HDR images. The dynamic range of a scene, image or imaging device is defined as the ratio of the highest to the lowest luminance or signal level. High dynamic range image can capture great dynamic rage that represents the whole tonal range of real-world scenes. HDR image is encoded in a format that allows the largest range of values using floating-point values stored with 32 bits per color channel, but the modern display devices have limited dynamic rage for displaying HDR image, so tone mapping technique have been used to solve this problem.

DLP is the future of Home Cinema Quality projection. However, due to the high cost of manufacturing the chips, LCD and CRt are more viable options on the cost front. The potential drawback of this single-chip DLP technology is that in any given instant, the picture on the screen is not the total image, but is instead rapidly alternating between images consisting of the individual red, green, and blue colors. Thus the eye and the brain play the last critical role in making single chip DLP projectors work, by combining or averaging or integrating the picture, so that the viewer perceives the desired image and not the rapidly flashing momentary components of the image.