Fast full-color computational imaging with single-pixel detectors

Single-pixel detectors can be used as imaging devices by making use of structured illumination. These systems work by correlating a changing incident light field with signals measured on a photodiode to derive an image of an object. In this work we demonstrate a system that utilizes a digital light projector to illuminate a scene with approximately 1300 different light patterns every second and correlate these with the back scattered light measured by three spectrally-filtered single-pixel photodetectors to produce a full-color high-quality image in a few seconds of data acquisition. We utilize a differential light projection method to self normalize the measured signals, improving the reconstruction quality whilst making the system robust to external sources of noise. This technique can readily be extended for imaging applications at non-visible wavebands

COLOR MULTIPLEXED SINGLE PATTERN SLI

Structured light pattern projection techniques are well known methods of accurately capturing 3-Dimensional information of the target surface. Traditional structured light methods require several different patterns to recover the depth, without ambiguity or albedo sensitivity, and are corrupted by object movement during the projection/capture process. This thesis work presents and discusses a color multiplexed structured light technique for recovering object shape from a single image thus being insensitive to object motion. This method uses single pattern whose RGB channels are each encoded with a unique subpattern. The pattern is projected on to the target and the reflected image is captured using high resolution color digital camera. The image is then separated into individual color channels and analyzed for 3-D depth reconstruction through use of phase decoding and unwrapping algorithms thereby establishing the viability of the color multiplexed single pattern technique. Compared to traditional methods (like PMP, Laser Scan etc) only one image/one-shot measurement is required to obtain the 3-D depth information of the object, requires less expensive hardware and normalizes albedo sensitivity and surface color reflectance variations. A cosine manifold and a flat surface are measured with sufficient accuracy demonstrating the feasibility of a real-time system

Mobile Robot Range Sensing through Visual Looming

This article describes and evaluates visual looming as a monocular range sensing method for mobile robots. The looming algorithm is based on the relationship between the displacement of a camera relative to an object, and the resulting change in the size of the object's image on the focal plane of the camera. We have carried out systematic experiments to evaluate the ranging accuracy of the looming algorithm using a Pioneer I mobile robot equipped with a color camera. We have also performed noise sensitivity for the looming algorithm, obtaining theoretical error bounds on the range estimates for given levels of odometric and visual noise, which were verified through experimental data. Our results suggest that looming can be used as a robust, inexpensive range sensor as a complement to sonar.Defense Advanced Research Projects Agency; Office of Naval Research; Navy Research Laboratory (00014-96-1-0772, 00014-95-1-0409

Capturing 3D textured inner pipe surfaces for sewer inspection

Inspection robots equipped with TV camera technology are commonly used to detect defects in sewer systems. Currently, these defects are predominantly identified by human assessors, a process that is not only time-consuming and costly but also susceptible to errors. Furthermore, existing systems primarily offer only information from 2D imaging for damage assessment, limiting the accurate identification of certain types of damage due to the absence of 3D information. Thus, the necessary solid quantification and characterisation of damage, which is needed to evaluate remediation measures and the associated costs, is limited from the sensory side. In this paper, we introduce an innovative system designed for acquiring multimodal image data using a camera measuring head capable of capturing both color and 3D images with high accuracy and temporal availability based on the single-shot principle. This sensor head, affixed to a carriage, continuously captures the sewer's inner wall during transit. The collected data serves as the basis for an AI-based automatic analysis of pipe damages as part of the further assessment and monitoring of sewers. Moreover, this paper is focused on the fundamental considerations about the design of the multimodal measuring head and elaborates on some application-specific implementation details. These include data pre-processing, 3D reconstruction, registration of texture and depth images, as well as 2D-3D registration and 3D image fusion