Toward color image segmentation in analog VLSI: Algorithm and hardware

Holographic 3D Display Using Depth Maps Generated by 2D-to-3D Rendering Approach

Holographic display has the potential to be utilized in many 3D application scenarios because it provides all the depth cues that human eyes can perceive. However, the shortage of 3D content has limited the application of holographic 3D displays. To enrich 3D content for holographic display, a 2D to 3D rendering approach is presented. In this method, 2D images are firstly classified into three categories, including distant view images, perspective view images and close-up images. For each category, the computer-generated depth map (CGDM) is calculated using a corresponding gradient model. The resulting CGDMs are applied in a layer-based holographic algorithm to obtain computer-generated holograms (CGHs). The correctly reconstructed region of the image changes with the reconstruction distance, providing a natural 3D display effect. The realistic 3D effect makes the proposed approach can be applied in many applications, such as education, navigation, and health sciences in the future.

Transformation of a high-dimensional color space for material classification

Images in red-green-blue (RGB) color space need to be transformed to other color spaces for image processing or analysis. For example, the well-known hue-saturation-intensity (HSI) color space, which separates hue from saturation and intensity and is similar to the color perception of humans, can aid many computer vision applications. For high-dimensional images, such as multispectral or hyperspectral images, transformation images to a color space that can separate hue from saturation and intensity would be useful; however, the related works are limited. Some methods could interpret a set of high-dimensional images to hue, saturation, and intensity, but these methods need to reduce the dimension of original images to three images and then map them to the trichromatic color space of RGB. Generally, dimension reduction could cause loss or distortion of original data, and, therefore, the transformed color spaces could not be suitable for material classification in critical conditions. This paper describes a method that can transform high-dimensional images to a color space called hyper-hue-saturation-intensity (HHSI), which is analogous to HSI in high dimensions. The transformation does not need dimension reduction, and, therefore, it can preserve the original information. Experimental results indicate that the hyper-hue is independent of saturation and intensity and it is more suitable for material classification of proximal or remote sensing images captured in a natural environment where illumination usually cannot be controlled.

Directional line detectors in correlated noisy environments

In this research, we present a methodology for extracting very narrow lines in correlated noisy environments. The approach is a generalization of the analysis of variance applied to the symmetrically balanced incomplete-blocks design. It encompasses many well-known algorithms when subjected to more restrictive conditions. The detector is robust and superior to the polynomial-approximation-based detector and the classical Prewitt detector. The procedure detects narrow lines embedded in nonuniform background without compromising resolution, and performs satisfactorily in severe corruptive noise. Extensive computer simulations demonstrate the practicality of the detector on real imaging environments.