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Multiple View Geometry in Computer Vision.

We propose a flexible technique to easily calibrate a camera. It only requires the camera to observe a planar pattern shown at a few (at least two) different orientations. Either the camera or the planar pattern can be freely moved. The motion need not be known. Radial lens distortion is modeled. The proposed procedure consists of a closed-form solution, followed by a nonlinear refinement based on the maximum likelihood criterion. Both computer simulation and real data have been used to test the proposed technique and very good results have been obtained. Compared with classical techniques which use expensive equipment such as two or three orthogonal planes, the proposed technique is easy to use and flexible. It advances 3D computer vision one more step from laboratory environments to real world use.

https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/tr98-71.pdf

A flexible new technique for camera calibration

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

This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms are among the most influential data mining algorithms in the research community. With each algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and review current and further research on the algorithm. These 10 algorithms cover classification, clustering, statistical learning, association analysis, and link mining, which are all among the most important topics in data mining research and development.

/pdf/top-10-algorithms-in-data-mining-70qtv03lc6.pdf

Top 10 algorithms in data mining

Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

/pdf/computer-vision-algorithms-and-applications-25dn6wu83j.pdf

Computer Vision: Algorithms and Applications

https://www.dcs.bbk.ac.uk/~sjmaybank/Neurocomputing_2014.pdf

Action classification using a discriminative multilevel HDP-HMM

Human behavior analysis is an important area of research in computer vision and is also driven by a wide spectrum of applications, such as smart video surveillance and human-computer interface. In this paper, we present a novel approach for human behavior analysis. Two research challenges, motion representation and behavior recognition, are addressed. A novel motion descriptor, which is an improved feature based on optical flow, is proposed for motion representation. Optical flow is improved with a motion filter, and feature fusion with the shape and trajectory information. To recognize the behavior, the support vector machine is employed to train the classifier where the concatenation of histograms is formed as the input features. Experimental results on the Weizmann behavior database and the Institute of Automation, Chinese Academy of Science real-world multiview behavior database demonstrate the robustness and effectiveness of our method.

Human Behavior Analysis Based on a New Motion Descriptor

Visible and infrared image registration is required for multi-sensor fusion and cooperative processing. However, traditional single-sensor image registration methods are generally not feasible as multi-sensor images are often loosely related and show different properties in imaging. This paper presents a coarse-to-fine procedure for registering visible and infrared images based on stable region features and edginess. Zernike moments are used to describe salient region features for a coarse registration, and an entropy optimal process based on edginess is used to refine the registration to achieve a more accurate result. Experiments show that the proposed method provides more robust and accurate registration than the existing methods.

Visible and infrared image registration based on region features and edginess

This document describes a method for classifying images of blood cells. Three different classes of cells are used: Band Neutrophils, Eosinophils and Lymphocytes. The image pattern is projected down to a lower dimensional sub space using PCA; the probability density function for each class is modeled with a Gaussian mixture using the EM-Algorithm. A new cell image is classified using the maximum a posteriori decision rule.

http://aurea-dt.com/mchc/LeukocyteRecognition.pdf

Leukocyte Recognition Using EM-Algorithm

In this paper, we present a new geometric-temporal representation for visual action recognition based on local spatio-temporal features. First, we propose a modified covariance descriptor under the log-Euclidean Riemannian metric to represent the spatio-temporal cuboids detected in the video sequences. Compared with previously proposed covariance descriptors, our descriptor can be measured and clustered in Euclidian space. Second, to capture the geometric-temporal contextual information, we construct a directional pyramid co-occurrence matrix (DPCM) to describe the spatio-temporal distribution of the vector-quantized local feature descriptors extracted from a video. DPCM characterizes the co-occurrence statistics of local features as well as the spatio-temporal positional relationships among the concurrent features. These statistics provide strong descriptive power for action recognition. To use DPCM for action recognition, we propose a directional pyramid co-occurrence matching kernel to measure the similarity of videos. The proposed method achieves the state-of-the-art performance and improves on the recognition performance of the bag-of-visual-words (BOVWs) models by a large margin on six public data sets. For example, on the KTH data set, it achieves 98.78% accuracy while the BOVW approach only achieves 88.06%. On both Weizmann and UCF CIL data sets, the highest possible accuracy of 100% is achieved.

/pdf/modeling-geometric-temporal-context-with-directional-pyramid-1pf95foz91.pdf

Stephen J. Maybank

Papers

Action classification using a discriminative multilevel HDP-HMM

Human Behavior Analysis Based on a New Motion Descriptor

Visible and infrared image registration based on region features and edginess

Leukocyte Recognition Using EM-Algorithm

Modeling Geometric-Temporal Context With Directional Pyramid Co-Occurrence for Action Recognition