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

Hyperspectral imaging is an important tool having been applied in various fields, but still limited in observation of dynamic scenes. In this paper, we propose a snapshot hyperspectral imaging technique which exploits both spectral and spatial sparsity of natural scenes. Under the computational imaging scheme, we conduct spectral dimension reduction and spatial frequency truncation to the hyperspectral data cube and snapshot it in a low cost manner. Specifically, we modulate the spectral variations by several broadband spectral filters, and then map these modulated images into different regions in the Fourier domain. The encoded image compressed in both spectral and spatial are finally collected by a monochrome detector. Correspondingly, the reconstruction is essentially a Fourier domain extraction and spectral dimensional back projection with low computational load. This Fourier-spectral multiplexing in a 2D sensor simplifies both the encoding and decoding process, and makes hyperspectral data captured in a low cost manner. We demonstrate the high performance of our method by quantitative evaluation on simulation data and build a prototype system experimentally for further validation.

Snapshot hyperspectral imaging via spectral basis multiplexing in Fourier domain

In this paper, we design a real-time 2D to 3D conversion chip. Architecture of this chip includes three key modules: 2D to 3D conversion core, 3D format conversion and peripheral I/O circuit. Specially, calculation precision and smooth filter are carefully designed and implemented according to the features of chip, such that the quality of output 3D video is largely improved. The experiment results on the prototypes show that our chip can give promising conversion results and has a broad application foreground.

A 2D-to-3D chip and its application to TV systems

Massive collection and explosive growth of biomedical data, demands effective compression for efficient storage, transmission and sharing. Readily available visual data compression techniques have been studied extensively but tailored for natural images/videos, and thus show limited performance on biomedical data which are of different features and larger diversity. Emerging implicit neural representation (INR) is gaining momentum and demonstrates high promise for fitting diverse visual data in target-data-specific manner, but a general compression scheme covering diverse biomedical data is so far absent. To address this issue, we firstly derive a mathematical explanation for INR's spectrum concentration property and an analytical insight on the design of INR based compressor. Further, we propose a Spectrum Concentrated Implicit neural compression (SCI) which adaptively partitions the complex biomedical data into blocks matching INR's concentrated spectrum envelop, and design a funnel shaped neural network capable of representing each block with a small number of parameters. Based on this design, we conduct compression via optimization under given budget and allocate the available parameters with high representation accuracy. The experiments show SCI's superior performance to state-of-the-art methods including commercial compressors, data-driven ones, and INR based counterparts on diverse biomedical data. The source code can be found at https://github.com/RichealYoung/ImplicitNeuralCompression.git.

SCI: A spectrum concentrated implicit neural compression for biomedical data

We introduce VERTEX, an effective solution to recover 3D shape and intrinsic texture of vehicles from uncalibrated monocular input in real-world street environments. To fully utilize the template prior of vehicles, we propose a novel geometry and texture joint representation, based on implicit semantic template mapping. Compared to existing representations which infer 3D texture distribution, our method explicitly constrains the texture distribution on the 2D surface of the template as well as avoids limitations of fixed resolution and topology. Moreover, by fusing the global and local features together, our approach is capable to generate consistent and detailed texture in both visible and invisible areas. We also contribute a new synthetic dataset containing 830 elaborate textured car models labeled with sparse key points and rendered using Physically Based Rendering (PBRT) system with measured HDRI skymaps to obtain highly realistic images. Experiments demonstrate the superior performance of our approach on both testing dataset and in-the-wild images. Furthermore, the presented technique enables additional applications such as 3D vehicle texture transfer and material identification.

Vehicle Reconstruction and Texture Estimation Using Deep Implicit Semantic Template Mapping.

The limited throughput of a digital image correlation (DIC) system hampers measuring deformations at both high spatial resolution and high temporal resolution. To address this dilemma, in this paper we propose to integrate snapshot compressive imaging (SCI)-a recently proposed computational imaging approach-into DIC for high-speed, high-resolution deformation measurement. Specifically, an SCI-DIC system is established to encode a sequence of fast changing speckle patterns into a snapshot and a high-accuracy speckle decompress SCI (Sp-DeSCI) algorithm is proposed for computational reconstruction of the speckle sequence. To adapt SCI reconstruction to the unique characteristics of speckle patterns, we propose three techniques under SCI reconstruction framework to secure high-precision reconstruction, including the normalized sum squared difference criterion, speckle-adaptive patch search strategy, and adaptive group aggregation. For efficacy validation of the proposed Sp-DeSCI, we conducted extensive simulated experiments and a four-point bending SCI-DIC experiment on real data. Both simulation and real experiments verify that the Sp-DeSCI successfully removes the deviations of reconstructed speckles in DeSCI and provides the highest displacement accuracy among existing algorithms. The SCI-DIC system together with the Sp-DeSCI algorithm can offer temporally super-resolved deformation measurement at full spatial resolution, and can potentially replace conventional high-speed DIC in real measurements.

Jinli Suo

Papers

Snapshot hyperspectral imaging via spectral basis multiplexing in Fourier domain

A 2D-to-3D chip and its application to TV systems

SCI: A spectrum concentrated implicit neural compression for biomedical data

Vehicle Reconstruction and Texture Estimation Using Deep Implicit Semantic Template Mapping.

Snapshot compressive imaging based digital image correlation: temporally super-resolved full-resolution deformation measurement.