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In medical science, the role of medical images is very crucial to identify the diagnosis. Variety of medical images such as CT (Computed Tomography) images, MRI (magnetic resonance imaging) images are available but many times these different modalities need more informatics images. Therefore, multi-modality medical image fusion technique is one of the solutions which can provide more informatics details by fusing multi-modality medical images. In this paper, a new approach of multi-modality medical image fusion approach is proposed where fuzzy local information based C-means clustering and local entropy fusion approach is performed in SWT (stationary wavelet transform) Domain. To measure the performance of proposed scheme, a comparative study is also performed over the existing methods. The result analysis is performed in both visually and also by performance metrics such as mutual information, edge based similarity and blind image quality. From experimental evaluation, it can be analyzed that proposed scheme gives better outcomes in compare to existing methods.

In today’s world, face recognition has turned out to be one of the key aspects of Computer Vision. People are truly adept at perceiving faces and computer complex figures. Indeed, even an entry of time doesn’t influence this ability and along these lines, it would help become as hearty as people in face acknowledgment. Machine acknowledgment of human countenances from still or video pictures has pulled in a lot of consideration in the brain research, picture handling, design acknowledgment, neural science, computer security, and computer vision networks. Face recognition is presumably a standout amongst the most non-meddlesome and easy to use biometric validation techniques right now accessible; a screensaver furnished with face recognition innovation can naturally open the screen at whatever point the approved client approaches the machine. Tech organizations are utilizing these uncommon advances in their items nowadays in all respects now and again. The face is a significant piece of our identity and how individuals recognize us. Face recognition has been one of the fast-growing, exacting and very keen areas in real-time applications. It is seemingly an individual’s most extraordinary physical trademark. While people have had the intrinsic capacity to perceive and recognize various faces for many years, computers are a little difficult to perform so while it’s getting up to speed. Facial recognition programming is intended to pinpoint a face and measure its highlights or various components. Each face has a certain breakthrough, which makes up the distinctive facial highlights. These milestones are implied as nodal focuses. There are around 80 nodal focuses on a human face.

This paper presents a new hybrid and parallel processing image fusion technique for multi-focus images. Here, two different methods are used i.e. Stationary Wavelet Transform (SWT) and Principal Component Analysis (PCA) that are implemented on the input images in parallel. These two methods are applied on same input dataset. This method is although computationally bit slower than the compared method but still it shows better results. The fused images obtained from the SWT and PCA are later again fused using PCA method. This is a parallel processing technique. The result of proposed method is compared with other traditional and conventional methods like DWT, SWT and PCA. It is observed that the result of proposed method is better than the compared methods. The result of the proposed method is analyzed qualitatively (visual appearance) and quantitatively using CC (Correlation Coefficient), UIQI (Universal Image Quality Index), and PSNR (Peak Signal-to-Noise Ratio). The proposed technique will have the capability to be implemented in real time applications of Visual Sensor Network (VSN).

Multifocus Image fusion is a technique to combine two or more different multi-focused images to generate an all-in-one focus image. In this paper, a new wavelet-based multi focus hybrid technique using method noise is proposed. Firstly SWT is applied to both the images to decompose the images in respective four subbands (LL, LH, HL, and HH). LL (low) is the approximation coefficient and others are LH, HL and HH are detailed coefficients for the source image. Then after that with the help of Inverse SWT we reconstruct the enhanced decomposed coefficients. When we compare the fused image with source image we get some residual part known as method noise. After processing i.e. filtering the method noise part, the enhance fused image is produced which is better than previous without method noise and other previous mentioned available literature. The proposed hybrid approach is used to generate a better enhanced and less distorted image that contains the full information of both degraded input images. The experimental results show that the proposed approach is better than the previous literature related to wavelet in terms of qualitative and quantitative results.

The digital image carries a lot of pictorial information which has now become one of the chief ways of communication in this generation. The transmission of media especially image is often corrupted by the Gaussian noise due to various issues. This noise is needed to be removed. Image denoising technique is applied to remove such noise and make it of high quality. This additive Gaussian noise can be removed using wavelet denoising technique. This paper presents a better method that controls the threshold (T) adaptively to eliminate noise. Experimental results of the proposed method are better than the existing denoising algorithm Such as NSTISM(19), SPBIDM(18), BayesShrink, NormalShrink, and ModifiedBayesShrink.

The paper presents a new multi-focus based image fusion method for enhancing the image processing component of the central computer system in the visual sensor network (VSN) for efficient surveillance and motion tracking. The proposed method is based on decomposing the input multi-focus images using stationary wavelet transform (SWT). The wavelet coefficients are fused on the basis of the selected threshold value. The threshold value is selected on the basis of various parameters such asloss of correlation, luminance distortion, and contrast distortion. On the basis of these parameters and residual information of the input images, the fusion of wavelet coefficients is performed. The proposed fusion approach is analyzed on the basis of qualitative and quantitative measures. It is also compared to some of the traditional and non-traditional methods. The proposed method is capable of being incorporated in the real-time application of surveillance in VSN.

In modern science there is a rapid development of artificial intelligence, image processing has gradually fascinated and inspired the attention of many researchers in the field of artificial intelligence and has become an interesting and demanding task. The main idea of Image caption is to automatically generate natural language descriptions according to the information observed in an image, this is an important portion of scene understanding, which combines all the knowledge and information available of computer vision and natural language processing. The use of image caption is broad and noteworthy, for example, the understanding of human-computer collaboration. This paper reviews the related methods and focuses on the attention mechanism, which plays a vital role in computer vision and is broadly used in image caption generation tasks. Furthermore, the advantages and the shortcomings of these methods are discussed, providing the commonly used datasets and evaluation criteria in this field. Finally, this paper proposes some open challenges in the image caption task.

The human voices are very flexible and contains a mess of sentiments or emotions. Feeling or emotions in speech incorporates extra vision about human activities. Recognition of various emotions from the human speech signal is very stretching ingredient in human computer interaction. Through in addition analysis, we can higher recognize the rationale of human beings or people, whether they are not happy with the service clients, happy customers, encouraging folks or inspiring fans. Deep Learning strategies have been as of lately proposed as an option in contrast to conventional techniques in Speech Emotion Recognition (SER). The Emotion of a speaker can be easily govern by the humans because it the human nature to understand the complexion of a person by just guessing the flow of speech, but the domain of emotion or sentiment recognition in the course of machine learning is an open circle of research. In this intended project, we perform speech information evaluation on speaker discriminated speech signals to hitupon the warmth of the person speakers involved inside the verbal exchange. we’re analyzing exceptional techniques to perform speaker discrimination and speech evaluation to locate efficient algorithms to carry out this task.

Face recognition is the most advanced way of authentication and authorization and it has been developed a lot since the last decade and such systems are now popular in fields such as security and commerce. This work revolves around facial recognition based automated user management system with active learning features the proposed work uses a combination of a couple of technologies to build such a system for user keeping which is foolproof and secure it also adds active learning to counter the problem of changing faces in youngsters. The system is recommended for schools, universities, corporate offices, and prisons.

Gaussian noise has been the bane of any and every denoising process under the sun. Being a very corrosive noise with huge disruptive potential, this has received much attention form the image restoration community. Building on the premise, a novel framework is proposed to leverage multi-level image denoising that iteratively removes gaussian noise while recovering details lost during processing. This framework uses existing deep learning based CNN systems whilst enhancing the same by the addition of method denoising to the process. This framework is habile in competing with state-of-the-art technologies and outperforming them in some cases.

From Generations, Security methods have always been improvised on the basis of modernisation of Technology. Now a day’s using man power for security have become less reliable as Humans can be manipulated using social/emotional hacking. Today most of the family members are employed and only the old aged members or toddlers of the family are left alone at home unattended and unguided. One of the best solutions to these problems is to use the things that are reliable and can’t be socially hacked. The primary aim that is achieved by this technology is to look after the unattended and other important activities. Problems like this can be handled by using IOT which involves Surveillance System, remote access, automated security systems, Cloud Computing and many more. Vast variety of problems can be solved by this technology. This can be used at personal level, organisational level or national level. This paper basically describes how Internet of things (i.e. Connecting multiple uncommon devices using internet and giving them instructions remotely as per required) can be used to achieve reliable high level security and can be used to make working simple and easy in some other important fields. IOT has limitless potential; it has the capacity to replace some previous technologies.

The unwanted data obtained through the medical image fusion is the main problem in biomedical applications, guided-image surgical and radiology. The Stationary Wavelet Transform (SWT) denoted the various advantages over conventional representation of imaging approach. In this research article we introduced innovative multi-modality fusion technique for medical image fusion based upon the SWT. In our approach it disintegrates of source images into approximation layers (coarse layer) and detail layers through the Stationary Wavelet Transform scheme, then applying of the Fuzzy Local Information C-means Clustering (FLICM) and Local contrast fusion approach to overcome the blurring effect, sensitiveness and conserve the quality evaluation in the distinguish layers. The demonstration shows that it preserves more detailed information in the source images and it enhances the more quality features and edge preserved of the final fused image obtained through the reconstruction procedure by recursive initial steps. The different methodologies with other techniques to evaluate performance such as mutual information, edge based similarity and blind image quality. This shows that in both the objective and subjective analysis our methodology results attained more supercilious performance.

Speckle noise is a ‘granular pattern’ special kind of multiplicative noise mainly found in the SAR images and medical images such as CT scan and an ultrasound image. SAR is Synthetic Aperture Radar, special kind of radar that mainly captures the high-resolution images of the broad area of the earth surface. Image despeckling using wavelet transform is one efficient approach as it generates a large number of the small coefficients and a small number of the large coefficients. DWT is applied to analyze the speckled image more efficiently in detail. This paper gives a brief introduction to DWT and experimentally analysed the hard and soft thresholding functions. Matlab Toolbox Main Menu (MATLAB R2014a) is used to realize the practical implementation of thresholding functions and to obtain the experimental results.

In the field of image restoration, noise plays the most prominent role. Speckle noise is a granular pattern, special kind of noise that mainly found in the satellite images, removing such noise is one of the major challenge and least touched issue. These satellite images are captured by special kind of radar named as Synthetic Aperture Radar. Speckle noise is an undesirable effect. The source of this type of noise is caused due to random interference between the coherent returns issued from the so many scatterers present on a earth surface, on the scale of a wavelength of the incident radar wave. In general, speckle noise is the grainy salt-and-pepper pattern present in radar imagery. This paper analyses the effects of speckle noise into the SAR image using probability density function.