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

Super-resolution, the process of obtaining one or more high-resolution images from one or more low-resolution observations, has been a very attractive research topic over the last two decades. It has found practical applications in many real-world problems in different fields, from satellite and aerial imaging to medical image processing, to facial image analysis, text image analysis, sign and number plates reading, and biometrics recognition, to name a few. This has resulted in many research papers, each developing a new super-resolution algorithm for a specific purpose. The current comprehensive survey provides an overview of most of these published works by grouping them in a broad taxonomy. For each of the groups in the taxonomy, the basic concepts of the algorithms are first explained and then the paths through which each of these groups have evolved are given in detail, by mentioning the contributions of different authors to the basic concepts of each group. Furthermore, common issues in super-resolution algorithms, such as imaging models and registration algorithms, optimization of the cost functions employed, dealing with color information, improvement factors, assessment of super-resolution algorithms, and the most commonly employed databases are discussed.

/pdf/super-resolution-a-comprehensive-survey-xjl42yt9nt.pdf

Super-resolution: a comprehensive survey

[서평]「Digital Video Processing」

http://www.lmars.whu.edu.cn/prof_web/zhangliangpei/rs/publication/A%20Super-Resolution%20Reconstruction%20Algorithm%20for%20Surveillance%20Images.pdf

A super-resolution reconstruction algorithm for surveillance images

A fast and fully automatic registration approach based on point features for multi-source remote-sensing images

We propose a new adaptive filtering framework for local image registration, which compensates for the effect of local distortions/displacements without explicitly estimating a distortion/displacement field. To this effect, we formulate local image registration as a two-dimensional (2-D) system identification problem with spatially varying system parameters. We utilize a 2-D adaptive filtering framework to identify the locally varying system parameters, where a new block adaptive filtering scheme is introduced. We discuss the conditions under which the adaptive filter coefficients conform to a local displacement vector at each pixel. Experimental results demonstrate that the proposed 2-D adaptive filtering framework is very successful in modeling and compensation of both local distortions, such as Stirmark attacks, and local motion, such as in the presence of a parallax field. In particular, we show that the proposed method can provide image registration to: a) enable reliable detection of watermarks following a Stirmark attack in nonblind detection scenarios, b) compensate for lens distortions, and c) align multiview images with nonparametric local motion

/pdf/local-image-registration-by-adaptive-filtering-c9j2rfms59.pdf

Local Image Registration by Adaptive Filtering

In many surveillance video applications, it is of interest to recognize an object or a person, which occupies a small portion of a low-resolution, noisy video. This paper addresses the problem of super-resolution recovery of a region of interest from more than one low-resolution view of a scene recorded by multiple cameras. The multiple camera scenario alleviates the difficulty in registration of multiple frames of video that contain non-rigid or multiple object motion in the single camera case. With proper temporal registration of multiple videos, arbitrary scene motion can be handled. The success of super-resolution recovery from multiple views in real applications vitally depends on two factors: i) the accuracy of multiple view registration results, and ii) the accuracy of the camera and data acquisition model. We propose a system, which consists of a method for sub-pixel accurate spatio-temporal alignment of multiple video sequences for view registration and the projections onto convex sets method for super-resolution recovery. Experiments were implemented using two commercial analog video cameras, which do not perform on-board compression. Experimental results show that the super resolution recovery of dynamic scenes can be achieved as long as the multiple views of the scene can be registered with sub-pixel accuracy.

Super resolution recovery for multi-camera surveillance imaging

Image registration is a fundamental task in both image processing and computer vision. We present a novel method for local image registration based on adaptive filtering techniques. We utilize an adaptive filter to estimate and track correspondences among multiple images containing overlapping views of common scene regions. Image pixels are traversed in an order established by space-filling curves, to preserve the contiguity and hence track locally varying registration changes. The algorithm differs from pre-existing work on image registration in that it requires only local information and relatively low computational effort. These characteristics render the method suitable for deployment in imaging sensor networks, toward which the current work is directed. We evaluate the performance of the proposed algorithm using images captured with a digital camera in various real-world scenarios. Experimental results show that the proposed method can significantly improve accuracy and robustness over a global 2D parametric registration and can also outperform the local registration algorithm based on the Lucas-Kanade optical flow technique (Lucas, B. and Kanade, T., 1981).

/pdf/an-adaptive-filtering-framework-for-image-registration-31oykqti24.pdf

An adaptive filtering framework for image registration

We present a novel local image registration method based on adaptive 
filtering techniques. The proposed method utilizes an adaptive filter 
to track smoothly, locally varying changes in the motion field 
between the images. Image pixels are traversed following a scanning order established by Hilbert curves to preserve the contiguity in the 2-D image plane. We have performed experiments using both simulated images and real images captured by a digital camera. The proposed adaptive filtering framework has been shown by experimental results to give superior performance compared to global 2-D parametric registration and Lucas-Kanade optical flow technique when the image motion consists of mostly translational motion. The simulation experiments show that the proposed image registration technique can also handle small amounts of rotation, scale and perspectivity in the motion field.

/pdf/local-image-registration-an-adaptive-filtering-framework-xabl5cub8h.pdf

Local image registration: an adaptive filtering framework

We present a new dense multi-view registration technique for wide-baseline video/images that integrates a parametric optical flow-based approach with a sparse set of feature correspondences, based on a locally planar approximation of a nonplanar scene. The proposed method can deal with illuminance variations between the views, which is critically important for wide-baseline applications. It differs from existing work on wide-baseline image registration in that it requires only image information and provides dense matching with-out computing any camera calibration matrices or performing any prior scene segmentation. These characteristics render the method suitable for practical deployment in visual sensor networks, towards which the current work is directed. We demonstrate the performance of the proposed method on simulated multi-view images of a virtual 3D world composed of piece-wise smooth textured surfaces, as well as real wide-baseline images of nonplanar textured surfaces.

/pdf/multi-view-image-registration-for-wide-baseline-visual-4tsiaero4e.pdf

Gulcin Caner

Papers

Local Image Registration by Adaptive Filtering

Super resolution recovery for multi-camera surveillance imaging

An adaptive filtering framework for image registration

Local image registration: an adaptive filtering framework

Multi-View Image Registration for Wide-Baseline Visual Sensor Networks