Enhanced Disease Detection through Image Fusion in Solanum Tuberosum L.
An Improved Technique for Enhancement of Satellite Image
Magnetic Resonance and Computer Tomography Image Fusion using Novel Weight Maps Obtained by using Median and Guided Filters
Thresholding Techniques in Computer Vision Applications
Advancement in Brain Tumour Detection using Deep Learning Technique
Identification of Volcano Hotspots by using Resilient Back Propagation (RBP) Algorithm Via Satellite Images
Data Hiding in Encrypted Compressed Videos for Privacy Information Protection
Improved Video Watermarking using Discrete Cosine Transform
Contrast Enhancement based Brain Tumour MRI Image Segmentation and Detection with Low Power Consumption
Denoising of Images by Wavelets and Contourlets using Bi-Shrink Filter
Information extraction from high resolution satellite images is very important for various purposes. The extracted information depicts the factual data about the identified objects, their positions, sizes, and the inter relationship between the objects. Here, the information extraction highlights extracting the general segmentation of Open Area, Water, Soil, Cloud and Snow, Buildings, Vegetation, Water Bodies, Road Center-lines, and so on, without any human intervention or interpretation. This paper presents a heterogeneous approach to extract information in a fully automatic manner using an algorithm, which employs satellite image processing of higher resolution satellite images taken from IRS (Indian Remote Sensing). Thus working on this approach has brought a futuristic research, which reveals how extracted information can be maneuvered.
Compressive technique is also known as compressive sampling in signal processing domain and its recent algorithm can be integrated into the MRI reconstruction pipeline for acquiring the complete MR data. The proposed work is based on the recursive iterative adaptive filtering technique based on every injection of random noise in the unobserved portion of the spectrum. The proposed volumetric filter attenuates the noise and relevant features from the incomplete k-space measurements which is required for the clinical practice. The proposed algorithm is based on stochastic approximation method with regularization parameter enabled by a spatial adaptive block-wise volumetric filter. The effectiveness of the proposed reconstruction algorithm is compared with CT reconstruction algorithm radon inversion from sparse projections, spiral, radial, and limited angle tomography. From the reconstruction algorithm it is observed that it achieves exact reconstruction from phantom data even at small projections. The accuracy of this method is to compete with the compressed sensing field. The proposed algorithm is tested on different sampling trajectories, especially on non- Cartesian data and reconstruction of volumetric phantom data with non-zero phase from noisy and incomplete Fourierdomain (k-space) measurements. Experimental results demonstrate that its performance is evaluated by PSNR, SSIM, and execution time.
Age estimation plays a vital role in human computer interaction where the image is given as input to the system after which, with the applied techniques the system provides result. An age group prediction system is estimated through AAM (Active Appearance Model) which calculates the texture and shape. A wrinkle is identified as a part of the shape information and the features, such as eye, nose, chin, lip, cheeks are extracted using PCA (Principle Component Analysis) of AAM, which then calculates the distance between the features and are stored as facial landmark points. The points are fed as input to the Mean Classification Algorithm which classifies based on two age groups, adult and old. Finally the Mean Absolute Error (MAE) value is estimated to determine the accuracy.
News video concepts based event classification is a system where the classification of videos will be done on the basis of video contents. For such classification firstly key frames will be extracted from the video and these key frames will be processed for Feature Extraction. In this system, basically three features will be extracted- Audio, Visual, and Motion. Using these extracted features, the system will be trained. And finally, using knowledge base, the videos will be classified according to their contents using Support Vector Machine.
One of the major problems in the field of photography is a blur. A blur in the image is obtained by the disturbance in the setting of the camera or due to the motion of the things to be captured and noise added to the image. This artifact becomes very crucial nowadays in the field of photography. There are various works already been done by the researchers and a lot of work is still in progress. But, the restoring of the image in its original state are still a big problem. In this paper, the authors propose a method, in which the blur can be removed by using whiteness measurement of the image captured or stored.
The research focuses on capturing the picture of Mozart of any music or instrument and then processing the captured image. All these information are then passed to the Matlab software for image processing. The algorithm separates each line of Mozart one by one. After separating the lines, the next step is to separate the beats one by one from the separated lines from the picture of Mozart. In this way, all the lines and beats of Mozart are separated using the Matlab software. When all the beats and lines are seperate, meaning according to their symbol is formed out and all the tune related to the whole music or instrument is combined. Then the whole music which is the combination of the image of Mozart (musical notes) is played through the Matlab software.
