Multi-Lingual Character Recognition and Extraction using Recurrent Neural Networks
A Novel Approach to Smart Autonomous Monitoring of Indoor Plant Health Based on Leaf Color
Edge Preserving Image Denoising with Deep Networks and Ensemble Convolutional Neural Network Architectures
Neural Network Approach to Detect Renal Calculi
Enhancing Fabric Quality Control: Implementing Real-Time Defect Detection with Image Processing Techniques and Arduino
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
Synthetic Aperture Radar (SAR) imaging has been an exciting field owing to its wide application in military/non military fields. This paper is aimed at investigating effects of using different types of signals and emission parameters on the accuracy and resolution of simulated SAR image and performing simulation on a reconstructed SAR image of an fully polarimetric SAR for identifying different land features. The speckles in the image are removed using the Lee filter and then the image is decomposed using Pauli decomposition method. After feature extraction, neural network techniques are used for training, validating and testing the network. The final image classified in RGB, representing features like urban, vegetation and water has been achieved. The percentage classification of each feature pixels in the image has been presented using confusion matrix.
Application of biometrics in computer security is nothing new and with the advancement of new age technologies, time has come that we make our ATM transactions more secured through biometric security processes. Currently, security of ATM transaction is based on card punch and PIN combination. This paper proposes a 3 layered security measure for ATM which may replace carrying today's plastic card. Starting with a PIN the proposed ATM system will apply face recognition as level two security measure and fingerprint recognition as level three security measure. Face recognition is the task of creating an identification of a face during a photo or video image against existing database. It begins with detection, then distinguishing human faces from other objects within the image, and then works on identification of the detected face. Fingerprint recognition is the process of determining a person's identity by examining their dermal ridges. It is economical technique and unique feature. This will help in making more informed choices, whether it's about determining motive, promoting deals, or avoiding security threats. The aim of this research is to develop an automatic face and fingerprint recognition system to reduce frauds while transacting with an ATM. The end result is an improved biometric ATM system, which will be a defensive strategy in the coming year and will increase consumer trust in the banking sector.
In a biometric system, there are physical or behavioral traits of humans used for the identification and verification of humans. It has the ability to improve the security of any system. This system provides the result in Boolean logic, i.e., 0 and 1 in which there can be possibility of identification and verification of wrong person or genuine person due to external condition or any other reason. In this situation, fuzzy logic gives us a solution to handle the inconsistency. Fuzzy logic is an approach to computation that involves the use of degrees of truth instead of the traditional positive or negative. In this paper, using face modality we applied the fuzzy logic rule and determined the accuracy of the identification in the biometrics system.
In this digital era, multimedia such as images and videos have become one of the principal means of information carrier because of ease in acquisition, distribution and storage. Hence, they are used as a common source of evidence in everyday life controversies and trials. However, the accessibility of this multimedia brings a major drawback. It can be easily edited with a variety of common editing tools like Adobe Photoshop. Therefore, it is easy to modify its content and meaning without leaving any visually detectable traces. In the literature, many instances of tampering or forgery can be found and are very common nowadays. Hence, there is a need to confirm the authenticity of multimedia documents before relying on their content. In response to this, researchers have begun to develop digital multimedia forensic techniques which are capable of identifying multimedia forgeries. Digital multimedia forensics analyses the multimedia by making use of the fact that most of the image and video processing operations leave visually undetectable traces in the altered multimedia content. These undetectable traces are detected to reveal tampering. Researchers have addressed two main problems in digital multimedia forensics. The first one is to identify the device which is used to capture the multimedia by performing some kind of ballistic analysis. The second is to detect the various traces of multimedia forgeries by studying inconsistencies in the multimedia statistics. To address these two main problems, various techniques have been proposed in literature. In this paper, we will discuss the most common steps which are performed in the image acquisition and storage. We will also discuss existing source camera identification tools and techniques including their advantages and drawbacks and the future scope in this field.
Image binarization is the process of representing an image pixel in binary format by assigning a value to the pixel as either 0 or 1. Before conversion to binary, the image can be in either gray-scale having pixel value between 0 to 255 or color, i.e., a pixel having value between 0 to 255 for each of the red, green, blue (RGB) channels separately. The method through which this conversion is implemented over an image is called as the binarization method. This paper reviews the methodology, contributions, advantages, and disadvantages of the existing studies on binarization methods. Further, the paper also highlights the problems in image processing with the scope for future enhancements.
