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
In last decade, many real world applications were developed based on unimodal biometric systems. Generally it is used for identifying human's physiological characteristic like face, fingerprints, and thumb for person verification. Biometric provides a solution for security, where unimodal systems have significant limitations due to sensitivity to noise, intraclass variation, non-universality, Spoof attacks, and other factors. To improve the performance of matchers in such various situations may not prove to be highly effective. Multibiometric systems seek some of these problems by providing multiple pieces of evidence with same identity and help to increase the performance which may not be possible in singlebiometric indicator. Aadhar card is a best idea to check the authenticity of a person, which is more secure and identical. This paper presents the limitations of biometric and proves the privacy and security of an Aadhaar card.
The agile growth and demand of high quality multimedia has been raised drastically in last half decade. To storage and process this huge data over internet is a big challenge. Efficient transmission with less storage space demands the compression for such type of data. The performance of compression is closely related to the performance of any mathematical transforms in terms of energy compaction and spatial frequency isolation by majorly exploiting inter-pixel redundancies. The Fractional Fourier Transform (FRFT) is the generalization of the Fourier transform which may use in signal compression due to its property of establishing high correlation among the coefficients and its beauty of compact signal representation in FRFT domain along with noise immunity. The Discrete Fractional Fourier Transform (DFRFT) is derived form of Discrete Fourier transforms (DFT). In this article, a compression scheme based on Discrete Fractional Fourier Transform is proposed with superior performance over Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and other Fractional Transforms based Compression schemes. The convincing feature of discrete fractional transforms is that it benefitted us with an extra degree of freedom that is provided by its fractional orders. In this scheme, an image is subdivided and DFRFT is applied on each subdivided image to transformed coefficients and quantize these transformed coefficients with reduced size subsequently, run length encoding is applied for further compression. Later, decompression is achieved by applying decoding and reverse order DFRFT on each sub-images and reconstruction of original image is done by merging all sub-images. The performance of the proposed scheme is evaluated on parameters, such as Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and Compression Ratio (CR) using MATLAB software environment.
The primary objective of this work is image processing based blood groups identification that utilizes the digital image matching process without using needles. Now-a-days, the blood group testing uses needles and also utilizes some chemicals, optical plates, cotton cloths, etc. Above biomaterial disposal in the environment is very dangerous and also creates environmental soil pollution. Non-bio degradable materials are the main pollution sources in soil. Technology and various researches have dominated to save human blood and to control the soil pollution and thus the present situation is met. The novel blood group testing is done by finding blood group of a patient without piercing the skin. This article explains a method to determine the human blood type by applying digital image processing to understand the image of artificial vessels underlying the skin. The research includes Multicore wavelength light sprinkling method, where light passes through the vessels for classifying the blood cells based on exact antigens on the red blood cell surface. The transferrable camera along with photo-detectors forms the basic detector structure and is used to detect the light distribution produced by blood cell to determine the blood type. This research presents a current state-of-the-art in optimizing digital image processing based blood group identification, which provides a clear vision of the latest top research advances in image processing with the help of MatLab and Embedded C program.
In this paper, various types of VLSI architectures for image compression using Discrete Wavelet Transform (DWT) were reviewed. Images are the most convenient way of transmitting information. Compression is done to reduce the redundancy of the image and to store or transmit the data in an efficient manner. The DWT is popularly used due to its perfect reconstruction, multiresolution, and scaling property. The different architectures for convolution and lifting based schemes that are very much essential to design a new efficient hardware architecture for image compression are discussed. The DWT is the mathematical tool of choice, when digital images are to be viewed or processed at multiple resolutions. The signal compression and processing applications using wavelet based coding are of major concern.
Image fusion is a process of combining two or more images of same scene captured by different sensors and converting into single image to get the detailed information of an image. Image fusion method is used to improve the quality of an image. Image fusion is applicable in image analysis applications, such as in medical, remote sensing application, robotics, etc. Many fusion methods are used for image fusion, such as Brovey, Multiplicative and Principal Component Analysis (PCA), etc.
