Steganography is one of the secure techniques of protecting data inside a cover object. Images are the most popular cover objects for Steganography. It provides secret message between users. The current paper presents an enhanced Most Significant Bit (MSB) technique. In this paper, a Private Domains Approach (PDA) is proposed; each domain consists of RGB of a pixel of cover image. Bit No.5 is applied to store the secret information in light of the bit that achieved highest steganography rate and the less probability of error rate.Consequently, this technique is allowing an improved version of MSB technique based on Mean-Squared Error (MSE), Peak Signalto-Noise Ratio (PSNR). The experimental results show that our schemes perform well in terms of image quality. Generally; MSB technique produced the best stego-image quality in this paper.

Three-dimensional (3D) information of capturing and reconstructing an object existing in its environment is a big challenge. In this work, we discuss the 3D laser scanning techniques, which can obtain a high density of data points by an accurate and fast method. This work considers the previous developments in this area to propose a developed cost-effective system based on pinhole projection concept and commercial hardware components taking into account the current achieved accuracy. A laser line auto-scanning system was designed to perform close-range 3D reconstructions for home/office objects with high accuracy and resolution. The system changes the laser plane direction with a microcontroller to perform automatic scanning and obtain continuous laser strips for objects’ 3D reconstruction. The system parameters were calibrated with Matlab’s built-in camera calibration toolbox to find camera focal length and optical center constraints. The pinhole projection equation was defined to optimize the prototype rotating axis equation. The developed 3D environmental laser scanner with pinhole projection proved the system’s effectiveness on close-range stationary objects with high resolution and accuracy with a measurement error in the range (0.05–0.25) mm. The 3D point cloud processing of the Matlab computer vision toolbox has been employed to show the 3D object reconstruction and to perform the camera calibration, which improves efficiency and highly simplifies the calibration method. The calibration error is the main error source in the measurements, and the errors of the actual measurement are found to be influenced by several environmental parameters. The presented platform can be equipped with a system of lower power consumption, and compact smaller size