Software Listing: Histogram

It is very fast and simple file to display histogram of a 3D image imhist3(I,n) n is the number of bins in the histogram imhist3(X,map) X isindexed 3D image and map is colormap map.

  • Platform: Matlab, Scripts

in this test program, we calculate the cumulative histogram in a local window centered at each pixel,this local cumulative histogram can be used to drive the level set for image and texture segmentation. Ref. Tony Chan, Selim Esedoglu, and Kangyu Ni, Histogram Based Segmentation Using Wasserstein Distances, SSVM 2007, LNCS 4485, pp. 697 708, 2007.

  • Platform: Matlab, Scripts

Following features are calculated: % Mean % Variance % Skewness % Kurtosis % Energy % Entropy Any Other histogram based features can be easily incorporated.Enjoy it.

  • Publisher: Xunkai Wei
  • Date: 26-01-2013
  • Size: 10 KB
  • Platform: Matlab, Scripts

Histogram Manager is a lightweight, yet handy application designed to help you generate and compare histograms.

Histogram Manager can process time data series and generate the corresponding histograms, build the distribution of similar occurrences and perform H1 or H2 comparisons in order to decide whether two histograms are similar or not.


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  • Platform: Windows 7, Windows 8, WinOther, WinVista

"Histogram for Lightroom" makes it easier for photographer to check the sRGB histogram in the Adobe Photoshop Lightroom.
It's developed in C#..

  • Platform: Windows 7, Windows 8, WinOther, WinVista

Free Histogram Equilization plug-in for Adobe PhotoShop compatible plug-in hosts. It increases the contrast of images..

  • Platform: Win2000, Windows Server, WinOther, WinVista

Compute simple arithmetic to high-precision calculations (up to 60 decimal places), render beautiful 2D/3D graphics and histogram/scatter plot quickly with smart formatting, trace your graphic coordinates and send professional reports in seconds. Enter your data as a vector, mixed/improper fraction, any degree/radian/grad angle, sexagesimal and hexadecimal/decimal/octal/binary number to compute any scientific, logical, vectorial and statistical operations including standard deviations and nonlinear regressions. The program comes with a complete documentation, recognizes expressions having polynomials with implicit multiplications, affects entire expression to any variable upon request (thus programmable), and spaces your operators intelligently.

  • Platform: Win2000, Windows Server, WinOther

This applet you shows a simple histogram. It is possible to set colors for each value and select type of histogram - solid or stripped..

  • Platform: JavaScript, Scripts

PCSA is a frequency domain analysis technique that can be used to transform PSDs (as those in a spectrogram) to the form of a two-dimensional histogram with frequency-magnitude bins. PCSA can be used to examine the spectral characteristics of a long period of data. It summarizes magnitudes and frequencies in the whole data sample into one 2D plot. See http://www.grc.nasa.gov/WWW/MMAP/PIMS/MEIT/MEIT_pdfs/meit2002/Section7.pdf ://http://www.grc.nasa.gov/WWW/MMAP/PI...2/Section7.pdf.

  • Platform: Matlab, Scripts

While working with a histogram we may need to detect a peak or crest of the histogram.But the histogram contains a number of local minima & maxima which makes the histogram extremely uneven.While detecting a peak or crest we are intended to detect the global peak or crest.This code replaces every local minima-maxima pair by a plate height of which is equal to the average height of the local minima-maxima pair and width is equal to the width of the local minima-maxima pair.Further smoothness is added as floor of the average value is taken as the crest value.This means if there are 2 or more consecutive plates of height say 5.

  • Platform: Matlab, Scripts

function [optN, C, N] = sshist(x,N) [optN, C, N] = sshist(x,N) Function `sshist' returns the optimal number of bins in a histogram used for density estimation. Optimization principle is to minimize expected L2 loss function between the histogram and an unknown underlying density function. An assumption made is merely that samples are drawn from the density independently each other. The optimal binwidth D* is obtained as a minimizer of the formula, (2K-V) / D^2, where K and V are mean and variance of sample counts across bins with width D. Optimal number of bins is given as (max(x) - min(x)) / D*.

  • Platform: Matlab, Scripts

This is an image contrast enhancement algorithm that overcomes limitations in standard histogram equalization (HE). The two primary features is adaptive HE (AHE), which divides the images into regions and performs local HE, and the contrast limited AHE (CLAHE), which reduces noise by partially reducing the local HE. Bilinear interpolation is used to avoid visibility of region boundaries. "Contrast Limited Adaptive Histogram Equalization" by Karel Zuiderveld, karel@cv.ruu.nl in "Graphics Gems IV", Academic Press, 1994 Ported by Leslie Smith.

  • Platform: Matlab, Scripts

This function finds dominant peaks and valleys of an image histogram. Minimum peak distance and averaging filter size can be changed to suit your need. Although, the function is originally written for mages, there is no reason why this can't be used on one dimensional data. USAGE: [peaks minima_low minima_high] = findlocalminima(I,10,5,0); OR [peaks minima_low minima_high] = findlocalminima(I); Example: I = imread('cameraman.tif'); [peaks minima_low minima_high] = findlocalminima(I); 'minima_low' and 'minima_high' correspond to the local minimums beside each of the peak identified in 'peaks' This is the function that is used for peak detection in my paper, "De Silva, D.

  • Platform: Matlab, Scripts

K-means image segmentation based on histogram to reduce memory usage which is constant for any image size..

  • Platform: Matlab, Scripts

HISTCND Histogram count for n dimensional data. N = HISTCND(X,EDGES), for row vectors X, counts the number of values in X that fall between the grid defined by the cell array of EDGES, each of whose element is a vector that contain monotonically non-decreasing values. N is an N-D array each of whose dimension corresponds to LENGTH(EDGES{j}) and each element contains a count of data that falls into the edge. X is N-by-D matrix representing N data points in D dimensional space. EDGES must have the same length to the number of columns of X. Alternatively, EDGES can be a numeric vector which gives a uniform grid for all dimensions of X.

  • Platform: Matlab, Scripts

Brightness Preserving Dynamic Fuzzy Histogram Equalization(BPDFHE) proposes a novel modification of the brightness preserving dynamic histogram equalization technique to improve its brightness preserving and contrast enhancement abilities while reducing its computational complexity. This technique, called uses fuzzy statistics of digital images for their representation and processing. Representation and processing of images in the fuzzy domain enables the technique to handle the inexactness of gray level values in a better way, resulting in improved performance. Besides, the imprecision in gray levels is handled well by fuzzy statistics, fuzzy histogram, when computed with appropriate fuzzy membership function, does not have random fluctuations or missing intensity levels and is essentially smooth.

  • Platform: Matlab, Scripts

This script transforms an image such that the object in the "foreground" of the image now becomes the histogram of the transformed image. I provide three examples with 3 demo images: I change the image of a city skyline so that the histogram of the new image looks like the skyline. I change the image of a car so that the histogram of the new image looks like the car shape. I change the image of a woman so that the histogram of the new image looks like the shape of the woman. (See screenshot) The script can also produce a perfectly flat histogram - the ultimate histogram equalization, far better than any standard histogram equalization method because the final histogram is TRULY FLAT.

  • Platform: Matlab, Scripts

Here we find out the histogram of any gray level image by not using the command imhist(), which is an inbuilt one rather by without using the inbuilt command. Reader may verify the plot by comparing with that generated from using the inbuilt command..

  • Platform: Matlab, Scripts

The provided functions demonstrate a histogram-based measure for class separability, given the samples from two classes (binary classification problem). The proposed error classification estimation method is described in (B) and it is based on estimating the pdf of each class using histograms. The function that estimates the class seperability method is computeHistError(). Function theoreticalError() computes the theoretical error for two Gaussian distributed classes. Function testClassSeperability() calls the other two functions and displays the results for two Gaussian distributed functions. It has to be noted that computeHistError() can be used for any kind of class distribution, since it estimates the pdf of each class using the histogram method.

  • Platform: Matlab, Scripts

Choose automatically the number of bin of a regular histogram using penalized likelihood procedure following paper of BirgdoTe-Rozenholc in ESAIM P&S (2006), 10, pp.24-45 "How many bins should be put in a regular histogram?".

  • Platform: Matlab, Scripts

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