Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining Practical Machine Learning Tools and Techniques

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

The PASCAL Visual Object Classes Challenge

https://bura.brunel.ac.uk/bitstream/2438/14221/1/FullText.pdf

A survey of deep neural network architectures and their applications

A pixel-level image fusion method based on turbo iterative for Synthetic Aperture Radar (SAR) and other sensor images is proposed. In this method, a wavelet-based fusion algorithm is employed at first. Then the output image is used to estimate the prior information of the ideal fusion result which is needed in the next step of adoption of a model-based fusion method. Such iterative is performed until the export image's change is at the convergence. The fusion results for SAR images show that the proposed method has a good performance.

Pixel-level SAR image fusion based on turbo iterative

This thesis focuses on the studies of the extraction and characterization of local image structures, in the context of images and texture analysis. Relying on the level lines of images or on the somehow dual and less structured notion of gradient orientation, the contributions of the thesis concentrate on following three themes: The first part of this thesis presents a new method for texture analysis that in spirit is similar to morphological granulometries, while allowing a high degree of geometrical and radiometric invariances. Also using the topographic map representation, the second part of this thesis develops a general approach for the abstraction of images, the aim of which is to automatically generate abstract images from realistic photographs. The subject of the last part of this thesis is the detection of junctions in natural images. The approach relies on the local directions of level lines through the orientation of image gradient. We introduce a generic junction analysis scheme. The first asset of the proposed procedure is an automatic criterion for the detection of junctions, permitting to deal with textured parts in which no detection is expected. Second, the method yields a characterization of L-, Y- and X- junctions, including a precise computation of their type, localization and scale. Contrary to classical approaches, scale characterization does not rely on the linear scale-space, therefore enabling geometric accuracy.

Some Geometric Methods for the Analysis of Images and Textures

This paper aims to address the problem of detecting buildings from remote sensing images with very high resolution (VHR). Inspired by the observation that buildings are always more distinguishable in geometries than in texture or spectral, we propose a new geometric building index (GBI) for accurate building detection, which relies on the geometric saliency of building structures. The geometric saliency of buildings is derived from a mid-level geometric representations based on meaningful junctions that can locally describe anisotropic geometrical structures of images. The resulting GBI is measured by integrating the derived geometric saliency of buildings. Experiments on three public datasets demonstrate that the proposed GBI achieves very promising performance, and meanwhile shows impressive generalization capability.

Accurate Building Detection in VHR Remote Sensing Images using Geometric Saliency

Object tracking usually suffers from the geometrical deformations and occlusions of objects. This paper presents a new method for accurate object tracking by combining the multi-angle discriminative correlation filters and key-points under the framework of Discriminative Scale Space Tracker (DSST) tracker. Experimental results demonstrate that the proposed method can produce promising tracking results and outperform the-state-of-the-art methods using correlation filters.

Accurate object tracking by combining correlation filters and keypoints

This paper studies the problem of polygonal mapping of buildings by tackling the issue of mask reversibility that leads to a notable performance gap between the predicted masks and polygons from the learning-based methods. We addressed such an issue by exploiting the hierarchical supervision (of bottom-level vertices, mid-level line segments and the high-level regional masks) and proposed a novel interaction mechanism of feature embedding sourced from different levels of supervision signals to obtain reversible building masks for polygonal mapping of buildings. As a result, we show that the learned reversible building masks take all the merits of the advances of deep convolutional neural networks for high-performing polygonal mapping of buildings. In the experiments, we evaluated our method on the two public benchmarks of AICrowd and Inria. On the AICrowd dataset, our proposed method obtains unanimous improvements on the metrics of AP, APboundary and PoLiS. For the Inria dataset, our proposed method also obtains very competitive results on the metrics of IoU and Accuracy. The models and source code are available at https://github.com/SarahwXU.

Gui-Song Xia

Papers

Pixel-level SAR image fusion based on turbo iterative

Some Geometric Methods for the Analysis of Images and Textures

Accurate Building Detection in VHR Remote Sensing Images using Geometric Saliency

Accurate object tracking by combining correlation filters and keypoints

Accurate Polygonal Mapping of Buildings in Satellite Imagery