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
Biometrics are safer and more convenient than conventional authentication methods, including vulnerable passwords and ID cards. A unimodal biometric system has also been rapidly evolving in its precision, as part of biometric systems. However, every unimodal biometric feature has a disadvantage, such as loud sensed information, variations intraclasses, absence of individuality, non-universality, and spoof attack. These constraints have established the maximum efficiency of unimodal systems. This means that the research community is developing solid and guaranteed biometric systems that are harder to delude than systems based on a single biometrics. During the classification phase, the neural network (MLP) is explored for robust decision in the presence of slight variations and noise. The feasibility of all these algorithms has been successfully tested. It has been shown that biomodal biometric systems are more accurate and noise robust than unimodal system. Compared with unimodal structures, the suggested bimodal biometric technologies generate promising and improved outcomes.
Target search in content based image retrieval systems refer to finding a needed image based on the users input query such as a particular registered logo or a specific historical photograph. Current method based on the user input system extract relevant images, and based on this extraction user need to specify the search related to the query. This process takes more time and it never brings more picture as per the query submitted by the user input. The proposed technique retrives more number of images and at the same time image directory is also created so that user judgment process has been done in an efficient manner. The outcomes of the investigation confirmed that this approach produces more result than the current method.
The disintegration of different source images utilizing Bi-Dimensional Empirical Mode Decomposition (BEMD) frequently delivers crisscrossed Bi-dimensional natural mode work, either by their number, or their recurrence, making image fusion troublesome. The image fusion measure is characterized as an interaction with all the significant data from various images, and their incorporation into single image, normally a solitary one. This single image is more useful and precise than any single source image, and it comprises all the fundamental data. This strategy is dependent on improved Bi- Dimensional Intrinsic Mode Function (BIMF). The greater part of the surface highlight is separated from its edges. BIMF is a novel decay method which is based on assortment of oscillatory mode signal. BIMF technique is for breaking down the indirect and non-fixed signs. The sign is decayed adaptively into natural oscillatory segments called inherent mode capacities. BEMD is a versatile deterioration measure, so the quantity of BIMF is controlled by the image information itself. The last perspective on the technique is to diminish the fogginess in the image and the fused image is acquired.
It is very important to detect brain tumor at right time in brain tumor diagnosis. Image fusion for CT and MRI images have been used in this paper. Image fusion is used to get more information for brain tumor diagnosis. Resultant fused image of CT and MRI images will improve accuracy of tumor detection. MRI and CT scan images are very useful in tumor disease diagnosis. Brain image is easily degraded by noises. For noise minimizing and enhancing the image quality we use discrete wavelet transform method. It provides enhanced image quality. Here we are using image fusion method and morphological image processing for increasing image intensity. Image segmentation has been used to detect tumor portion accurately and indicate about growing area of tumor.
The digitization of documents are popular with its enhanced portability, efficient storage, and easy retrieval. Digital acquisition of documents gets carried over through transmission with added noise. The process of removing the noises from digital images using the image processing techniques or image analysis methods or filters is referred to as noise removal methods, or techniques, or algorithms. This paper presents the review of literatures of noise removal methods efficient enough in removing unwanted noise from the document images. This paper discusses the methodology, contributions, advantages, and disadvantages of the reviewed methods. Further, it also highlights the problems in document image noise removal with the future scope.