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

Introduction to linear and nonlinear programming

50 Years of Image Analysis

The diffraction tomography theorem is adapted to one-dimensional length measurement. The resulting spectral interferometry technique is described and the first length measurements using this technique on a model eye and on a human eye in vivo are presented.

Measurement of intraocular distances by backscattering spectral interferometry

If I provide you a face image of mine (without telling you the actual age when I took the picture) and a large amount of face images that I crawled (containing labeled faces of different ages but not necessarily paired), can you show me what I would look like when I am 80 or what I was like when I was 5? The answer is probably a No. Most existing face aging works attempt to learn the transformation between age groups and thus would require the paired samples as well as the labeled query image. In this paper, we look at the problem from a generative modeling perspective such that no paired samples is required. In addition, given an unlabeled image, the generative model can directly produce the image with desired age attribute. We propose a conditional adversarial autoencoder (CAAE) that learns a face manifold, traversing on which smooth age progression and regression can be realized simultaneously. In CAAE, the face is first mapped to a latent vector through a convolutional encoder, and then the vector is projected to the face manifold conditional on age through a deconvolutional generator. The latent vector preserves personalized face features (i.e., personality) and the age condition controls progression vs. regression. Two adversarial networks are imposed on the encoder and generator, respectively, forcing to generate more photo-realistic faces. Experimental results demonstrate the appealing performance and flexibility of the proposed framework by comparing with the state-of-the-art and ground truth.

/pdf/age-progression-regression-by-conditional-adversarial-4o0qnp7kc0.pdf

Age Progression/Regression by Conditional Adversarial Autoencoder

Optical sectioning imaging with high spatial resolution deep inside scattering samples such as mammalian brain is of great interest in biological study. Conventional two-photon microscopy deteriorates in focus when light scattering increases. Here we develop an optical sectioning enhanced two-photon technique which incorporates structured illumination into line-scanning spatial-temporal focusing microscopy (LTSIM), and generate patterned illumination via laser intensity modulation synchronized with scanning. LTSIM brings scattering background elimination and in-focus contrast enhancement, and realizes nearly 2-fold increase in spatial resolution to ∼208 nm laterally and ∼0.94 µm axially. In addition, the intensity modulated line-scanning implementation of LTSIM enables fast and flexible generation of structured illumination, permitting adjustable spatial frequency profiles to optimize image contrast. The highly qualified optical sectioning ability of our system is demonstrated on samples including tissue phantom, C. elegans and mouse brain at depths over hundreds of microns.

Contrast and resolution enhanced optical sectioning in scattering tissue using line-scanning two-photon structured illumination microscopy

Limited by long acquisition time of 2D ghost imaging, current ghost imaging systems are so far inapplicable for dynamic scenes. However, it’s been demonstrated that nature images are spatiotemporally redundant and the redundancy is scene dependent. Inspired by that, we propose a content-adaptive computational ghost imaging approach to achieve high reconstruction quality under a small number of measurements, and thus achieve ghost imaging of dynamic scenes. To utilize content-adaptive inter-frame redundancy, we put the reconstruction under an iterative reweighted optimization, with non-uniform weight computed from temporal-correlated frame sequences. The proposed approach can achieve dynamic imaging at 16fps with 64×64-pixel resolution.

Content-adaptive ghost imaging of dynamic scenes.

From Compressed Sensing to Low-rank Matrix Recovery: Theory and Applications

In this paper, we consider scenes that are immersed in transparent refractive media with a dynamic surface. We take the first steps to reconstruct both the 3D fluid surface shape and the 3D structure of immersed scene simultaneously by utilizing distortion and defocus clues. We demonstrate that the images captured through a refractive dynamic fluid surface are the distorted and blurred versions of all-in-focused (AIF) images captured through a flat fluid surface. The amounts of distortion and refractive blur are formulated by the shape of fluid surface, scene depth and camera parameters, based on our refractive geometry model of a finite aperture imaging system. An iterative optimization algorithm is proposed to reconstruct the distortion and immersed scene depth, which are then used to infer the 3D fluid surface. We validate and demonstrate the effectiveness of our approach on a variety of synthetic and real scenes under different fluid surfaces.

/pdf/recovering-scene-geometry-under-wavy-fluid-via-distortion-1fc6llxxf3.pdf

Recovering Scene Geometry under Wavy Fluid via Distortion and Defocus Analysis

Ptychography is a predominant non-interferometric technique to image large complex fields but with quite a narrow working spectrum, because diffraction measurements require dense array detection with an ultra-high dynamic range. Here we report a single-pixel ptychography technique that realizes non-interferometric and non-scanning complex-field imaging in a wide waveband, where 2D dense detector arrays are not available. A single-pixel detector is placed in the far field to record the DC-only component of the diffracted wavefront scattered from the target field, which is illuminated by a sequence of binary modulation patterns. This decreases the measurements' dynamic range by several orders of magnitude. We employ an efficient single-pixel phase-retrieval algorithm to jointly recover the field's 2D amplitude and phase maps from the 1D intensity-only measurement sequence. No a priori object information is needed in the recovery process. We validate the technique's quantitative phase imaging nature using both calibrated phase objects and biological samples and demonstrate its wide working spectrum with both 488-nm visible light and 980-nm near-infrared light.

Jinli Suo

Papers

Contrast and resolution enhanced optical sectioning in scattering tissue using line-scanning two-photon structured illumination microscopy

Content-adaptive ghost imaging of dynamic scenes.

From Compressed Sensing to Low-rank Matrix Recovery: Theory and Applications

Recovering Scene Geometry under Wavy Fluid via Distortion and Defocus Analysis

Single-pixel ptychography