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

Due to limited spatial bandwidth, one has to compromise between large field of view and high spatial resolution in both photography and microscopy. This dilemma largely hampers revealing fine details and global structures of the target scene simultaneously. Recently, a mainstream method is formed by utilizing multiple sensors for synchronous acquisition across different sub-FOVs with high resolution and stitching the patches according to the spatial position of the cameras. Various inpainting algorithms have been proposed to eliminate the intensity discontinuities, but conventional optimization methods are prone to misalignment, seaming artifacts or long processing time, and thus unable to achieve dynamic gap elimination. By taking advantage of generative adversarial networks (GANs) on image generation and padding, we propose a conditional GAN-based deep neural network for seamless gap inpainting. Specifically, a short series of displaced images are acquired to characterize the system configuration, under which we generate patch pairs with and without gap for deep network training. After supervised learning, we can achieve seamless inpainting in gap regions. To validate the proposed approach, we apply our approach on real data captured by large-scale imaging systems and demonstrate that the missing information at gaps can be retrieved successfully. We believe the proposed method holds potential for all-round observation in various fields including urban surveillance and systems biology.

Conditional GAN-based deep network for seamless large-FOV imaging by camera array

Existing multispectral imagers mostly use 2D array sensors to separately measure 2D data slices in a 3D spatialspectral data cube. They suffer from low photon efficiency, limited spectral range, and high cost. To address these issues, we propose to conduct multispectral imaging using a photodiode, to take full advantage of its high sensitivity, wide spectral range, low cost, and small size. Specifically, utilizing the photodiode’s fast response, a scene’s 3D spatial-spectral information is sinusoidally multiplexed into a dense 1D measurement sequence, and then demultiplexed computationally under the single-pixel imaging scheme. A proof-of-concept setup is built to capture multispectral data of 256 pixels × 256 pixels × 10 wavelength bands ranging from 450 nm to 650 nm. The imaging scheme holds great potentials for various biological applications such as fluorescence microscopy and endoscopy.

High-resolution multispectral imaging using a photodiode

Implicit neural representation (INR) can describe the target scenes with high fidelity using a small number of parameters, and is emerging as a promising data compression technique. However, limited spectrum coverage is intrinsic to INR, and it is non-trivial to remove redundancy in diverse complex data effectively. Preliminary studies can only exploit either global or local correlation in the target data and thus of limited performance. In this paper, we propose a Tree-structured Implicit Neural Compression (TINC) to conduct compact representation for local regions and extract the shared features of these local representations in a hierarchical manner. Specifically, we use Multi-Layer Perceptrons (MLPs) to fit the partitioned local regions, and these MLPs are organized in tree structure to share parameters according to the spatial distance. The parameter sharing scheme not only ensures the continuity between adjacent regions, but also jointly removes the local and non-local redundancy. Extensive experiments show that TINC improves the compression fidelity of INR, and has shown impressive compression capabilities over commercial tools and other deep learning based methods. Besides, the approach is of high flexibility and can be tailored for different data and parameter settings. The source code can be found at https://github.com/RichealYoung/TINC .

TINC: Tree-structured Implicit Neural Compression

The hyper-spectrum data exhibits the structure, materials, and semantic meaning of a nature scene and its fast acquisition is of great importance due to its potential for parse these properties of dynamic scenes. Targeting for high speed hyperspectrum imaging of a nature scene, this paper proposes to capture the coded hyper-spectrum reflectance of a nature scene using low cost hardware and reconstruct the latent data using a corresponding decoding algorithm. Except for a wide spectrum light source, the imaging system includes mainly a commercially available projector color wheel and a high speed camera, which work at their own constant periods and are self-synchronized by our algorithm. The introduced light source and color wheel cost less than 50 dollars and makes the proposed approach widely available. The results on the data captured by our prototype system show that, the proposed approach can reconstruct the high precision hyper-spectrum data at real time.

Self-synchronized fast reflectance acquisition

The invention discloses an error increase method aiming at gene correlation analysis of unbalanced samples. The method comprises the steps that healthy samples are randomly divided into L subsets, wherein the sample quantity of each subset is equal to the amount of sick samples; each healthy sample subset is paired with the sick sample, and thus L sample combinations are obtained, and key gene loci corresponding to the subsets are selected; according to the amount of finding times of each key gene locus under different sample combinations, an importance degree of each sample combination can be graded and assessed; a mean value of scores of all the healthy individuals distributed to the corresponding sample combinations is computed and taken as a confidence score of an individual grade; and the confidence score of each healthy individual is normalized, and a score obtained after normalization is analyzed through weight logistical regression, so that statistic significance of each key gene locus can be examined. The method disclosed by the invention can significantly enhance the analysis capacity under unbalanced data.

Jinli Suo

Papers

Conditional GAN-based deep network for seamless large-FOV imaging by camera array

High-resolution multispectral imaging using a photodiode

TINC: Tree-structured Implicit Neural Compression

Self-synchronized fast reflectance acquisition

Error increase method aiming at gene correlation analysis of unbalanced samples