Contact Us

By


Pixelsciences Engineering project Consultancy
D.No. 23, 1st Floor,
Nanmai tharuvar kovil street,
madurai-625001.
Tamilnadu
India.
Email - pixelsciences@gmail.com
Mobile - 9159340375


Comments

Post a Comment

Popular posts from this blog

Image Segmentation - K-means Clustering

By
Image
Introduction to K-means clustering-algorithm The K-means clustering algorithm comprised of three steps, they distance, minimum-distance cluster assignment and cluster centroid update. These three steps are repeatedly executed until convergence meet or number of iteration end. The K-means algorithm splits the given dataset (image) into K number of clusters or groups It assigns a member(pixel) into a cluster (group) based on minimum distance between the pixel and all cluster centroids The algorithm is not complex and iterative procedure steps The high speed convergence but stayed on local minimum at most of times Unsupervised Clustering The K-means algorithm has no training phase. The dataset (image pixels) to be clustered is not attached with class or target variables. Assumed K number of C...
Keep reading

Adaptive Median Filter for Image Corrupted by Salt and Pepper Noise

By
Image
The image denoising is a preprocessing step in image processing and is used to recover the image which is corrupted by noise. It shown below the image corrupted by impulsive noise or salt and pepper noise is denoised by Adaptive Median filter. Impulsive noise is added to an image when the image is transmitted over a noisy channel and decoding error on the receiver side. It is modelled by imnoise function in Matlab programming. Median filter with small fixed window size is a preferred technique for denoising an image corrupted by salt & pepper noise because of simple and efficient. However, it is performance decreased the image corrupted by high density noise pixels. Adaptive Median filter changing it's window size depends on density of noise is a preferred technique for denoising an image corrupted by high density salt & pepper noise. Original Image and 70% salt & pepper noised image Denoised image by Adaptive median...
Keep reading

Image Contrast Enhancement by Histogram Equalization

By
Image
A contrast enhancement of images by histogram equalization (HE) is very popular image processing technique widely used as preprocessing steps for medical and many other image and video processing applications. HE performs its operation by remapping the gray levels of the image based on the cumulative probability distribution of the input gray levels Test image 1 : butterfly Algorithm steps Histogram Equalization set maxI=255, minI=0 set nbins =255 Read Input-Image, im hc <- histogram-count (im,nbins) prob <- (hc) / sum(hc) cprob <- cumlative-sum(prob) Tf <- minI + (maxI -minI) x cprob Eim = Tf(im) maxI - maximum intensity level , minI - maximum intensity level nbins - number of bins(level) for histograms count Tf - Intensity mapping transfer function im - Input Image Eim - Enhanced output Image Matlab code for...
Keep reading