From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

A three-dimensional imaging system which distributes the optical illumination over the full field-of-view is sought after. Here, the authors demonstrate the capability of reconstructing 128 × 128 pixel resolution three-dimensional scenes to an accuracy of 3 mm as well as real-time video with a frame-rate up to 12 Hz.

Single-pixel three-dimensional imaging with time-based depth resolution

How to image objects that are hidden from a camera's view is a problem of fundamental importance to many fields of research, with applications in robotic vision, defence, remote sensing, medical imaging and autonomous vehicles. Non-line-of-sight (NLOS) imaging at macroscopic scales has been demonstrated by scanning a visible surface with a pulsed laser and a time-resolved detector. Whereas light detection and ranging (LIDAR) systems use such measurements to recover the shape of visible objects from direct reflections, NLOS imaging reconstructs the shape and albedo of hidden objects from multiply scattered light. Despite recent advances, NLOS imaging has remained impractical owing to the prohibitive memory and processing requirements of existing reconstruction algorithms, and the extremely weak signal of multiply scattered light. Here we show that a confocal scanning procedure can address these challenges by facilitating the derivation of the light-cone transform to solve the NLOS reconstruction problem. This method requires much smaller computational and memory resources than previous reconstruction methods do and images hidden objects at unprecedented resolution. Confocal scanning also provides a sizeable increase in signal and range when imaging retroreflective objects. We quantify the resolution bounds of NLOS imaging, demonstrate its potential for real-time tracking and derive efficient algorithms that incorporate image priors and a physically accurate noise model. Additionally, we describe successful outdoor experiments of NLOS imaging under indirect sunlight.

Confocal non-line-of-sight imaging based on the light-cone transform.

A system and method for obtaining a dimension measurement that conforms to a conformance criteria is disclosed. The dimensioning system provides either (i) feedback to confirm that the measurement complies with the criteria or (ii) information on how the measurement geometry could be adjusted in order to provide a compliant measurement in a subsequent dimension measurement.

Handheld dimensioning system with measurement-conformance feedback

A dimensioning system including a computing device running an alignment software program is disclosed. The alignment software uses range information from a range sensor in order to generate alignment messages. The alignment messages may help a user define a frame of reference and align the dimensioning system's range sensor for improved dimensioning performance.

Dimensioning system with guided alignment

Imagers that use their own illumination can capture three-dimensional (3D) structure and reflectivity information. With photon-counting detectors, images can be acquired at extremely low photon fluxes. To suppress the Poisson noise inherent in low-flux operation, such imagers typically require hundreds of detected photons per pixel for accurate range and reflectivity determination. We introduce a low-flux imaging technique, called first-photon imaging, which is a computational imager that exploits spatial correlations found in real-world scenes and the physics of low-flux measurements. Our technique recovers 3D structure and reflectivity from the first detected photon at each pixel. We demonstrate simultaneous acquisition of sub-pulse duration range and 4-bit reflectivity information in the presence of high background noise. First-photon imaging may be of considerable value to both microscopy and remote sensing.

https://science.sciencemag.org/content/sci/343/6166/58.full.pdf

First-Photon Imaging

Range acquisition systems such as light detection and ranging (LIDAR) and time-of-flight (TOF) cameras operate by measuring the time difference of arrival between a transmitted pulse and the scene reflection. We introduce the design of a range acquisition system for acquiring depth maps of piecewise-planar scenes with high spatial resolution using a single, omnidirectional, time-resolved photodetector and no scanning components. In our experiment, we reconstructed 64 × 64-pixel depth maps of scenes comprising two to four planar shapes using only 205 spatially-patterned, femtosecond illuminations of the scene. The reconstruction uses parametric signal modeling to recover a set of depths present in the scene. Then, a convex optimization that exploits sparsity of the Laplacian of the depth map of a typical scene determines correspondences between spatial positions and depths. In contrast with 2D laser scanning used in LIDAR systems and low-resolution 2D sensor arrays used in TOF cameras, our experiment demonstrates that it is possible to build a non-scanning range acquisition system with high spatial resolution using only a standard, low-cost photodetector and a spatial light modulator.

Exploiting sparsity in time-of-flight range acquisition using a single time-resolved sensor

We present Mime, a compact, low-power 3D sensor for unencumbered free-form, single-handed gestural interaction with head-mounted displays (HMDs). Mime introduces a real-time signal processing framework that combines a novel three-pixel time-of-flight (TOF) module with a standard RGB camera. The TOF module achieves accurate 3D hand localization and tracking, and it thus enables motion-controlled gestures. The joint processing of 3D information with RGB image data enables finer, shape-based gestural interaction. Our Mime hardware prototype achieves fast and precise 3D gestural control. Compared with state-of-the-art 3D sensors like TOF cameras, the Microsoft Kinect and the Leap Motion Controller, Mime offers several key advantages for mobile applications and HMD use cases: very small size, daylight insensitivity, and low power consumption. Mime is built using standard, low-cost optoelectronic components and promises to be an inexpensive technology that can either be a peripheral component or be embedded within the HMD unit. We demonstrate the utility of the Mime sensor for HMD interaction with a variety of application scenarios, including 3D spatial input using close-range gestures, gaming, on-the-move interaction, and operation in cluttered environments and in broad daylight conditions.

Mime: compact, low power 3D gesture sensing for interaction with head mounted displays

Active range acquisition systems such as light detection and ranging (LIDAR) and time-of-flight (TOF) cameras achieve high depth resolution but suffer from poor spatial resolution. In this paper we introduce a new range acquisition architecture that does not rely on scene raster scanning as in LIDAR or on a two-dimensional array of sensors as used in TOF cameras. Instead, we achieve spatial resolution through patterned sensing of the scene using a digital micromirror device (DMD) array. Our depth map reconstruction uses parametric signal modeling to recover the set of distinct depth ranges present in the scene. Then, using a convex program that exploits the sparsity of the Laplacian of the depth map, we recover the spatial content at the estimated depth ranges. In our experiments we acquired 64×64-pixel depth maps of fronto-parallel scenes at ranges up to 2.1 M using a pulsed laser, a DMD array and a single photon-counting detector. We also demonstrated imaging in the presence of unknown partially-transmissive occluders. The prototype and results provide promising directions for non-scanning, low-complexity range acquisition devices for various computer vision applications.

/pdf/compressive-depth-map-acquisition-using-a-single-photon-cg6u31opro.pdf

Compressive depth map acquisition using a single photon-counting detector: Parametric signal processing meets sparsity

A process generates lookup tables for estimating spatial depth in a scene. The process identifies subsets of illuminators of a camera system that has a 2-dimensional array of image sensors and illuminators in fixed locations relative to the array, and partitions the image sensors into a plurality of pixels. For each pixel, and for each of m distinct depths from the respective pixel, the process simulates a virtual surface at the respective depth. For each of the subsets of illuminators, the process determines an expected light intensity at the pixel based on the respective depth. The process forms an intensity vector using the expected light intensities for each of the distinct subsets and normalizes the intensity vector. For each pixel, the process constructs a lookup table comprising the normalized vectors corresponding to the pixel. The lookup table associates each normalized vector with the depth of the corresponding simulated surface.

Andrea Colaco

Papers

First-Photon Imaging

Exploiting sparsity in time-of-flight range acquisition using a single time-resolved sensor

Mime: compact, low power 3D gesture sensing for interaction with head mounted displays

Compressive depth map acquisition using a single photon-counting detector: Parametric signal processing meets sparsity

Simulating an infrared emitter array in a video monitoring camera to construct a lookup table for depth determination