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

An introduction to the Kalman filter

In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discrete-data linear filtering problem. Since that time, due in large part to advances in digital computing, the Kalman filter has been the subject of extensive research and application, particularly in the area of autonomous or assisted navigation. The Kalman filter is a set of mathematical equations that provides an efficient computational (recursive) means to estimate the state of a process, in a way that minimizes the mean of the squared error. The filter is very powerful in several aspects: it supports estimations of past, present, and even future states, and it can do so even when the precise nature of the modeled system is unknown. The purpose of this paper is to provide a practical introduction to the discrete Kalman filter. This introduction includes a description and some discussion of the basic discrete Kalman filter, a derivation, description and some discussion of the extended Kalman filter, and a relatively simple (tangible) example with real numbers & results.

/pdf/an-introduction-to-the-kalman-filter-4na56gymup.pdf

An Introduction to the Kalman Filter

This paper reviews the state-of-the art in the field of lock-in time-of-flight (ToF) cameras, their advantages, their limitations, the existing calibration methods, and the way they are being used, sometimes in combination with other sensors. Even though lock-in ToF cameras provide neither higher resolution nor larger ambiguity-free range compared to other range map estimation systems, advantages such as registered depth and intensity data at a high frame rate, compact design, low weight, and reduced power consumption have motivated their increasing usage in several research areas, such as computer graphics, machine vision, and robotics.

/pdf/lock-in-time-of-flight-tof-cameras-a-survey-365c0dnen2.pdf

Lock-in Time-of-Flight (ToF) Cameras: A Survey

Time-of-flight range sensors have error characteristics which are complementary to passive stereo. They provide real time depth estimates in conditions where passive stereo does not work well, such as on white walls. In contrast, these sensors are noisy and often perform poorly on the textured scenes for which stereo excels. We introduce a method for combining the results from both methods that performs better than either alone. A depth probability distribution function from each method is calculated and then merged. In addition, stereo methods have long used global methods such as belief propagation and graph cuts to improve results, and we apply these methods to this sensor. Since time-of-flight devices have primarily been used as individual sensors, they are typically poorly calibrated. We introduce a method that substantially improves upon the manufacturerpsilas calibration. We show that these techniques lead to improved accuracy and robustness.

Fusion of time-of-flight depth and stereo for high accuracy depth maps

An important field of reasearch in computer vision is the 3D analysis and reconstruction of objects and scenes for e.g. position determination, online object recognition or collision prevention. Known techniques thereby mainly rely on measuring devices such as laser scanners, stereo camera systems, or comparable algorithms like structure from motion. The rather new Photonic Mixing Device (PMD) technique is based on the time-of-flight principle and measures full-range distance information in real-time. Unfortunately, PMD-based devices have still limited resolution (e.g. 160 x 120px) and provide only graylevel information. This paper describes a fast algorithmic approach to combine high resolution RGB images with low resolution PMD distance data, acquired using a binocular camera setup. The approach relies completely on fast parallel Graphics Processing Units (GPUs) ressources. Additionally, we introduce a simple but efficient method to detect insufficient color assignments, which are due to the binocular setup. The resulting combined RGBZ-data not only enhances the visual result, but also represents a basis for advanced data processing in e.g. object recognition.

https://www.cg.informatik.uni-siegen.de/data/Publications/2007/lindner07fusion.pdf

Data-Fusion of PMD-Based Distance-Information and High-Resolution RGB-Images

How good are cheap depth cameras, namely the Microsoft Kinect, compared to state of the art Time-of- Flight depth cameras? In this paper several depth cameras of different types were put to the test on a variety of tasks in order to judge their respective performance and to find out their weaknesses. We will concentrate on the common area of applications for which both types are specified, i.e. near field indoor scenes. The characteristics and limitations of the different technologies as well as the concrete hardware implementations are discussed and evaluated with a set of experimental setups. Especially, the noise level and the axial and angular resolutions are compared. Additionally, refined formulas to generate depth values based on the raw measurements of the Kinect are presented.

http://www.grk1564.uni-siegen.de/publicationPDFbyID?ID=395

Depth camera technology comparison and performance evaluation

In this paper, a novel imaging technique for enhancing 3-D vision is proposed. The enhancement is achieved with the help of 3-D time-of-flight camera, this 3D camera delivers the intensity and depth information of the scene in real time. Although this kind of 3-D cameras provide accurate depth information, its low 2-D image res- olution tends to be a hindering factor for many image pro- cessing applications. This limitation can be inundated with the proposed setup; a 2-D sensor (CCD/CMOS) with higher resolution is used to improve the image resolution. The 2-D and 3-D cameras are placed in a special housing, so that the field-of-view (FOV) is nearly same for both the cameras. The 2D/3D data fusion becomes facile for mapping and/or image registration. Hence the new system provides better image quality with range data. Within this paper, we discuss the initial results and findings of the proposed system.

/pdf/first-steps-in-enhancing-3d-vision-technique-using-2d-3d-46762bxw2x.pdf

First steps in enhancing 3D vision technique using 2D/3D sensors

Modern time-of-flight measuring camera systems are able to provide accurate depth images but there are problems with regard to their usage in a dynamic environment due to their measurement principle. The Centre for Sensor Systems recently proposed a new 2D/3D camera avoiding this drawback by the monocular combination of a 2D and a 3D sensor. In this paper, the camera is presented and an approach for detecting erroneous depth values due to movement is made.

Movement Artefacts in Range Images of Time-of-Flight Cameras

This paper describes a fast and robust segmentation technique based on the fusion of 2D/3D images for gesture recognition. These images are provided by the novel 3D Time-of-Flight (TOF) camera which has been implemented in our research center (ZESS). Using modulated infrared lighting, this camera generates an intensity image with the range information for each pixel of a Photonic Mixer Device (PMD) sensor. The intensity and range data are fused to be used as the input information for the segmentation algorithm. Our proposed segmentation technique is based on the combination of two unsupervised clustering approaches: K-Means and Expectation Maximization (EM). They both attempt to find the centers of natural clusters in the fused data. The experimental results show that the proposed gesture segmentation technique can successfully segment the hand from user’s body, face, arm or other objects in the scene under variant illumination conditions in real time.

Klaus Hartmann

Papers

Data-Fusion of PMD-Based Distance-Information and High-Resolution RGB-Images

Depth camera technology comparison and performance evaluation

First steps in enhancing 3D vision technique using 2D/3D sensors

Movement Artefacts in Range Images of Time-of-Flight Cameras

Hand Segmentation Using 2D/3D Images