Effects of Unequal Bit Costs on Chain Code
Gabor Based Band Selection for Multispectral Palmprint Recognition System using Feature Fusion
Primary Screening Technique for Detecting Breast Cancer
Defogging of Image Using Spatial MRF with Boundary Constraints
Change Detection Analysis using Landsat Multi-Temporal Imagery and GIS Techniques: A Case Study for Tirupati, South India
A Blind approach of QR Code based color Image watermarking using DWT
Improved Video Watermarking using Discrete Cosine Transform
Data Hiding in Encrypted Compressed Videos for Privacy Information Protection
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
Identification of Volcano Hotspots by using Resilient Back Propagation (RBP) Algorithm Via Satellite Images
Transformation in mobile networks and multimedia communications make image and video compression important aspects of digital image processing. The main aim of image or video compression is to reduce the size of the image or video (redundancy) with little or no degradation of quality for an effective transmission and storage. This paper presents an optimized video compression using modified intelligent behavior of firefly algorithm. A total of six (four acquired and two benchmark) sample video data were used to implement the achieved technique. Frames were extracted from the video data and stored in the form of images in a buffer. Compression of the video frames was achieved by reducing the effect of pixel intensity with larger distance part. This was identified as one of the shortcomings with the Firefly Optimization Algorithm (FOA) method of image compression. In this paper, the impact of the modification was clearly shown using the Peak Signal to Noise Ratio (PSNR). The modification was achieved by including the root mean square in the standard equations of the FOA. In order to reduce the effect of pixel intensity with larger distance part, this was identified as one of the shortcomings. When the image samples were subjected to the (mFOA) compression technique, a same amount of improvement was achieved. Simulation results indicated that the mFOA technique outperformed the FOA method. The PSNR evaluation showed an improved reduction of frame size by 7.34%, 3.30%, 4.90%, and 5.75% for respective NAERLS1.avi, NAERLS2.avi, NTA1.avi, and NTA2.avi captured benchmark video frames and also 3.56% and 3.86% for respective video frames of Akiyo.avi and Forman.avi
This paper deals with the Brain disorder caused due to dementia, where the brain size gets effected and reduces its volume. Estimating the grade of Alzheimer's is a challenging task. Spatial texture information collected from T2 Weighted MRI images are used to perform classification and validation. The authors use 54 Brain MRI Slices, Gray Level Cooccurrence matrix is used to extract the attributes, and those are used to train the classifiers. On comparing Support Vector Machine (SVM) with Naïve Bayes classifier and KNN, SVM gives good classification accuracy of 98.1%. The classifiers classify the MRI into AD, MCI, and CN. Five independent images are collected from the Internet sources, by testing those images using SVM and correlating with clinical data of those images, it achieves 100% accuracy.
Image processing is done to get a better version of an image or an enhanced or filtered image. It is used to extract important information from the image. A raw image is converted into a digital image by undergoing various processes for which proper algorithms and mechanisms are defined. To further investigate on an image, we can perform texture analysis on it. Texture recognition is also an important characteristic of image processing. Both texture recognition and image processing have a variety of uses in the medicine field. In this paper, the authors have discussed about how we can detect the texture of our skin and use it further to test the suitability of skin products that we use daily.
This paper proposes a novel feature descriptor using Optimized Orthogonal Wavelets and Local Tetra Patterns OOWLTrP. Texture features are extracted by using Local Tetra Pattern (LTrP) and wavelet features are extracted through optimized orthogonal wavelet. Eye image from various databases, such as CASIA, MMUI, and UBRIS are first preprocessed to remove salt and pepper noise. Later, the iris is segmented from the rest of the eye image. Features are then extracted by using the proposed method, which combines the goodness of local patterns and orthogonal wavelets. Feed Forward Back Propagation Neural Network (FFBNN) is used for classification of images. During training, the weights of FFBNN are optimized using the Adaptive Central Force Optimization (ACFO). The well trained FFBNN-ACFO is further used for classification of iris images. It has been observed that, there is considerable improvement in accuracy and validation time of the system. The increase in accuracy is due to the fact that, LTrP extracts information from four directions as compared to LBP (Local Binary Pattern), LDP (Local Derivative Pattern), and LTP (Local Ternary Pattern). Coefficients of the orthogonal wavelet are optimization by genetic operators, this adds to the improvement in accuracy and reduction in validation time.
Copy Move Forgery (CMF) is a manipulation process, where a part of the image is copied and moved to another region in the same image. The advanced growth in technology and photo-editing software lead to the malicious manipulation of images. Distribution of such tampered images through high speed digital networks and social media is also increased, which leads to the incredibility of the images and the underlying information. Hence, it is much demanded to develop, evaluate, and propose CMF detection techniques. CMF detection can be achieved either by keypoint approach or block-based approach. In this paper, performance of block-based and keypoint based CMF detection and localization techniques are analyzed.