Image processing Project Titles

By

It is shown below IEEE project titles of image processing on Image Enhancement, Image De-noising, Image Segmentation and Object Recognition.



Image Enhancement
A Dynamic Histogram Equalization for Image Contrast Enhancement.
Automatic Image Equalization and Contrast Enhancement Using Gaussian Mixture Modelling.
Contrast Enhancement Using Dominant Brightness Level Analysis and Adaptive Intensity Transformation for Remote Sensing Images.
Contrast-Preserved Chroma Enhancement Technique using YCbCr Colour Space.
Multi Segment Histogram Equalization for Brightness Preserving Contrast Enhancement.
Efficient Contrast Enhancement Using Adaptive Gamma Correction With Weighting Distribution.


Image Denoising
SUSAN controlled decay parameter adaption for non-local means image denoise.
Turbulent-PSO-Based Fuzzy Image Filter With No-Reference Measures for High-Density Impulse Noise.
Two-Direction Nonlocal Model for Image Denoising.
Direction based adaptive weighted switching median filter for removing high density impulse noise.
An Efficient Edge -based Bilateral Filter for Restoring Real noisy Image.
SAR Image Despeckling using edge detection and feature clustering in bandelet domain.


Image Segmentation
Fuzzy C Means Clustering With Local Information and Kernel Metric for Image Segmentation.
An Edge Weighted Centroidal Voronoi Tessellation Model for Image Segmentation.
A Robust Fuzzy Local Information.
Automated two-dimensional K-means clustering algorithm for unsupervised image segmentation.
Fuzzy PCA Guided Robust k Means Clustering.
A new Iterative Triclass Threshold technique in image segmentation.
Spectral grouping using Nystrom method.
A new image segmentation algorithm based on modified seeded region growing and particle swarm optimization.
Image segmentation using Association rule features.
Variant of seeded region growing.


Image Recognition
Silhouette Analysis-Based Action Recognition Via Exploiting Human Poses.
Improved Iris Recognition through Fusion of Hamming Distance and Fragile Bit Distance.
Illumination Invariant Face Recognition Using Near-Infrared Images.
Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions.
Face Description with Local Binary Patterns: Application to Face Recognition.
Local Derivative Pattern versus Local Binary Pattern Face Recognition With High Order Local Pattern Descriptor.
Offline Text-Independent writer Identification based on scale Invariant Feature Transform.
Discriminative common vectors for face recognition.


Image Texture Processing
A Texture based method for modelling the background and detecting moving object.
Multi-resolution Gray-scale and rotation invariant texture classification with local binary pattern.
Dominant local binary pattern for texture classification.
Statistical properties of Bit-Plane probability model and its Application in Supervised Texture Classification.
Wavelet- based Image Texture Classification using Local Energy Histogram.


Image Retrieval
Local Tetra patterns A new Feature descriptor for content based Image Retrieval.
A performance evaluation of gradient field HOG descriptor for sketch based image retrieval.
Texture features for browsing and retrieval of image data.


Remote Sensing Image processing
Adaptive Multi-objective memetic fuzzy clustering algorithm for remote sensing imagery.
A kernel clustering algorithm with fuzzy factor application to SAR image segmentation.


Image Fusion
Multimodal image fusion algorithm using Dual-Tree Complex wavelet Transform and particle swarm optimization.
Multi-focus image fusion for visual sensor networks in DCT domain.


Medical Image processing
Segmentation of Brain Tissues from Magnetic Resonance images using adaptively regularized kernel based fuzzy c-means clustering.
Recognizing common CT imagine signs of lung diseases through a new feature selection method based on fisher criterion and genetic optimization.


Image Compression
Medical ultrasound image compression using contextual vector quantization.
Fast vector quantization using a Bat algorithm for image compression.
Design of quantisation table for JPEG baseline algorithm using particle swarm optimization.
Designing an Efficient Image Encryption then Compression system via prediction Error Clustering and Random permutation.


Video Image Processing
Cross -Domain Multicue fusion for concepts based video indexing.
A Robust Object Segmentation system using a probability based background extraction algorithm.
Adaptive Human action recognition with an Evolving bag of key poses.
Silhouette Analysis -based Action Recognition via exploiting human poses.
Learning a scene background model via classification.
A self-Organizing approach to background subtraction for visual surveillance application.

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

Genetic Algorithm for Centroid Selection on Kmeans Image Segmentation

By
Image
The K-means clustering algorithm has wide applications for data and document-mining, digital image processing and different engineering fields. In addition to that, the clustering algorithm is composed of simple algorithm steps and has fast convergence, however it is suffered by initial centroid selection while clustering an image. The initial centroid selection determines the clustering algorithm consistent results and performance. The good initial cluster-centroids lead to good clustering results, otherwise the bad initial cluster-centroids selection lead to bad results. In order to achieve K-means clustering's consistent result and performance, we propose initial cluster centroids selection by Genetic optimization algorithm. The GA (genetics algorithm) has efficient search operations (selection, crossover and mutation) for determination of global minima on selection of cluster centroid problems GA - population A set of solutions or chromosomes are called...
Keep reading