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Why Image Processing Projects ?
Image Processing Toolbox™ provides a comprehensive set of reference-standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing.
Image Processing Toolbox apps let you automate common image processing workflows. You can interactively segment image data, compare image registration techniques, and batch-process large data sets. Visualization functions and apps let you explore images, 3D volumes, and videos; adjust contrast; create histograms; and manipulate regions of interest (ROIs).
You can accelerate your algorithms by running them on multicore processors.
Digital image processing is the use of computer algorithms to create, process, communicate, and display digital images. Digital image processing algorithms can be used to:
* Convert signals from an image sensor into digital images
* Improve clarity, and remove noise and other artifacts
* Extract the size, scale, or number of objects in a scene
* Prepare images for display or printing
* Compress images for communication across a network
Image Processing Toolbox apps let you automate common image processing workflows. You can interactively segment image data, compare image registration techniques, and batch-process large data sets. Visualization functions and apps let you explore images, 3D volumes, and videos; adjust contrast; create histograms; and manipulate regions of interest (ROIs).
You can accelerate your algorithms by running them on multicore processors.
Digital image processing is the use of computer algorithms to create, process, communicate, and display digital images. Digital image processing algorithms can be used to:
* Convert signals from an image sensor into digital images
* Improve clarity, and remove noise and other artifacts
* Extract the size, scale, or number of objects in a scene
* Prepare images for display or printing
* Compress images for communication across a network
Latest 2018 Matlab Image Processing Projects Titles
| Project Code | Category | Project Title |
|---|---|---|
| 2018 AI1 | Agriculture | Analysis of morphology-based features for classification of crop and weeds in precision agriculture |
| 2018 AI6 | Agriculture | Failure Detection in Row Crops From UAV Images Using Morphological Operators |
| 2018 AI9 | Agriculture | Expert System for Diagnosis Mango Diseases Using Leaf Symptoms Analysis |
| 2018 AI10 | Agriculture | Recognition and Detection of Tea Leaf’s Diseases Using Support Vector Machine |
| 2018 AI11 | Agriculture | Semi-automatic leaf disease detection and classification system for soybean culture |
| 2018 AI12 | Agriculture | Multi-species fruit flower detection using a refined semantic segmentation network |
| 2018 AI13 | Agriculture | Classification on Passion Fruit’s Ripeness using K-Means Clustering and Artificial Neural Network |
| 2018 BM1 | Biometric | Texture Based Vein Biometrics for Human Identification: A Comparative Study |
| 2018 BM2 | Biometric | Biometrics Detection and Recognition Based-on Geometrical Features Extraction |
| 2018 BM5 | Biometric | A Novel Face Detection Approach using Local Binary Pattern Histogram and Support Vector Machine |
| 2018 BM6 | Biometric | An Iris Recognition Algorithm for identity Authentication |
| 2018 BM9 | Biometric | Incremental Sparse Linear Regression Algorithms for Face Recognition |
| 2018 BM10 | Biometric | A Robust and Secure Palm Vessel Biometric Sensing System based on Photoacoustics |
| 2018 CP1 | Compression | Entropy Based Adaptive Image Compression Procedure |
| 2018 CP2 | Compression | Enhancement of DCT-based Image Compression using Trigonometric Functions |
| 2018 CP4 | Compression | SPIHT Algorithm with Adaptive Selection of Compression Ratio Depending on DWT Coefficients |
| 2018 CP6 | Compression | Analysis on Image Compression & Encryption |
| 2018 DB4 | Deblurring | Image Deblurring of License Plate |
| 2018 DB5 | Deblurring | Analysis of Image Deblurring Techniques with Variations in Gaussian Noise and Regularization factor |
| 2018 DN1 | Denoising | Image Denoising Using Discrete Wavelet Transform and Edge Information |
| 2018 DN2 | Denoising | Hybrid image denoising method based on non-subsampled contourlet transform and bandelet transform |
| 2018 DN4 | Denoising | Single image dehazing and denoising combining dark channel prior and variational models |
| 2018 HS1 | Hyperspectral | Cascaded Random Forest for Hyperspectral Image Classification |
| 2018 HS2 | Hyperspectral | Learning Spatial–Spectral Features for Hyperspectral Image Classification |
| 2018 HS3 | Hyperspectral | Salient Features Extraction in Hyperspectral Images by Hierarchical Fusion |
| 2018 IA1 | Image Analysis | Fabric Defect Detection Based on Biological Vision Modeling |
| 2018 IA2 | Image Analysis | New Procedure for weld defect detection based Gabor filter |
| 2018 IE1 | Image Enhancement | Hue preserving-based approach for underwater colour image enhancement |
| 2018 IE2 | Image Enhancement | Effective image enhancement techniques for fog-affected indoor and outdoor images |
| 2018 IE3 | Image Enhancement | Automatic Contrast Enhancement of Complex Low-contrast Images |
| 2018 IE4 | Image Enhancement | Contrast Enhancement of Low-light Image Using Histogram Equalization and Illumination Adjustment |
| 2018 IE5 | Image Enhancement | A Framework for Outdoor RGB Image Enhancement and Dehazing |
| 2018 IF4 | Image Fusion | Medical Image Fusion With Parameter-Adaptive Pulse Coupled-Neural Network in Nonsubsampled Shearlet Transform Domain |
| 2018 IR1 | Image Retrieval | Content Based Image Retrieval: A Review |
| 2018 M1 | Medical Imaging | Segmentation of Optic Disc from Fundus images |
| 2018 M2 | Medical Imaging | Leukocyte Segmentation and Counting Based on Microscopic Blood Images Using HSV Saturation Component with Blob Analysis |
| 2018 M3 | Medical Imaging | Active Contour Segmentation of Polyps in Capsule Endoscopic Images |
| 2018 M4 | Medical Imaging | Segmentation of Optic Disc and Cup in Fundus Images using Maximally stable Extremal Regions |
| 2018 M5 | Medical Imaging | Semi-Automatic Lymph Node Segmentation and Classification using Cervical Cancer MR Imaging |
| 2018 M10 | Medical Imaging | Active Contours Based Segmentation and Lesion Periphery Analysis For Characterization of Skin Lesions in Dermoscopy Images |
| 2018 M11 | Medical Imaging | Segmentation of MRI Images for Brain Cancer Detection |
| 2018 ML1 | Machine Learning | Microaneurysm Detection Using Principal Component Analysis and Machine Learning Methods |
| 2018 ML2 | Machine Learning | A Novel Method to Predict Knee Osteoarthritis Progression on MRI Using Machine Learning Methods |
| 2018 ML8 | Machine Learning | Detection and Classification of HGG and LGG Brain Tumor Using Machine Learning |
| 2018 SG2 | Segmentation | A Novel Segmentation Algorithm for MRI Brain Tumor Images |
| 2018 SG8 | Segmentation | Adaptive K-Means Image Segmentation based on Meta Heuristic Algorithm |
Latest 2017 image processing Projects Titles
| Project Code | Project Title |
|---|---|
| ImgPrc2017001 | Going Deeper with Contextual CNN for Hyperspectral Image Classification |
| ImgPrc2017002 | Hierarchical Contour Closure-Based Holistic Salient Object Detection |
| ImgPrc2017003 | Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising |
| ImgPrc2017004 | Illumination Decomposition for Photograph With Multiple Light Sources |
| ImgPrc2017005 | An Efficient Fusion-Based Defogging |