A New Wavelet Completed Local Ternary Count (WCLTC) for Image Classification

To overcome noise sensitivity and increase the discriminative quality of the Local Binary Pattern, a Completed Local Ternary Count (CLTC) was developed by combining the Local Ternary Pattern (LTP) with the Completed Local Binary Count (CLBC) (LBP). Furthermore, by integrating the proposed CLTC with the Redundant Discrete Wavelet Transform (RDWT) to construct a Wavelet Completed Local Ternary Count, the proposed CLTC's discriminative property is improved (WCLTC). As a result, more accurate local texture feature capture inside the RDWT domain is possible. The proposed WCLTC is utilised to perform texture and medical image classification tasks. The WCLTC performance is evaluated using two benchmark texture datasets, CUReT and Outex, as well as three medical picture databases, 2D Hela, VIRUS Texture, and BR datasets. With several of these datasets, the experimental findings demonstrate a remarkable classification accuracy. In several cases, the WCLTC performance outperformed the prior descriptions. With the 2D Hela, CUReT, and Virus datasets, the WCLTC achieves the highest classification accuracy of 96.91%, 97.04 percent, and 72.89%, respectively

Iris Recognition System Using Convolutional Neural Network

Identification system is one of the important parts in security domains of the present time. The traditional protection methods considered to be inefficient and unreliable as they are subjected to the theft, imitation or forgetfulness. In contrast, biometrics such as facial recognition, fingerprints and the retina have emerged as modern protection methods, but still also suffer from some defects and violations. However, Iris recognition is an automated method that considered as a promising biometric identification due to the stability and the uniqueness of its patterns. In this paper, an iris recognition system based on Convolutional Neural Network (CNN) model was proposed. CNN is used to perform the required processes of feature extraction and classification. The proposed system was evaluated through CASIA-V1 and ATVS datasets, after the required pre-processing steps taken place, and achieved 98% and 97.83% as a result, respectively

Performance evaluation of wavelet SVD-based watermarking schemes for color images

Digital image watermarking techniques have enabled imperceptible information in images to be hidden to ensure the information can be extracted later from those images. For any watermarking scheme, there are four main requirements which are imperceptibility, Robustness, capacity and security. Recently, hybrid Singular Value Decomposition (SVD) based watermarking schemes in the transform domain have significantly gained a lot of attention. This is due to the characteristics of SVD and the wavelet. Most of these schemes were tested under different conditions using grey images only. However, due to the growth of digital technology and the huge use of the colour images, it is important to consider the colour images in the watermarking area. Three different SVD-based image watermarking schemes with different wavelet transforms are selected in this paper to be tested and evaluated for colour images. Two colour models are used to represent the colour images to perform the embedding and the extraction watermarking process to study these colour models’ performances and effectiveness in the watermarking area. These colour models are RGB and YCbCr. All these colour models’ channels are used as an embedding channel and then are evaluated under different attacks types. The experimental results of the selected Wavelet SVD-based watermarking schemes proved that the embedding in the RGB and YCbCr colour channels are achieved high imperceptibility. These colour channels also showed good robustness against different attacks such as cropping, cutting, rotation and JPEG compression