Light Detection And Ranging (LiDAR), as well as many other applications and sensors, involve segmenting sparse sets of points (point clouds) for which point density is the only discriminating feature. The segmentation of these point clouds is challenging for several reasons, including the fact that the points are not associated with a regular grid. Moreover, the presence of noise, particularly impulsive noise with varying density, can make it difficult to obtain a good segmentation using traditional techniques, including the algorithms that had been developed to process LiDAR data. This dissertation introduces novel algorithms and frameworks based on statistical techniques and image analysis in order to segment and extract surfaces from sparse noisy point clouds. We introduce an adaptive method for mapping point clouds onto an image grid followed by a contour detection approach that is based on an enhanced version of region-based Active Contours Without Edges (ACWE). We also proposed a noise reduction method using Bayesian approach and incorporated it, along with other noise reduction approaches, into a joint framework that produces robust results. We combined the aforementioned techniques with a statistical surface refinement method to introduce a novel framework to detect ground and canopy surfaces in micropulse photon-counting LiDAR data. The algorithm is fully automatic and uses no prior elevation or geographic information to extract surfaces. Moreover, we propose a novel segmentation framework for noisy point clouds in the plane based on a Markov random field (MRF) optimization that we call Point Cloud Densitybased Segmentation (PCDS). We also developed a large synthetic dataset of in plane point clouds that includes either a set of randomly placed, sized and oriented primitive objects (circle, rectangle and triangle) or an arbitrary shape that forms a simple approximation for the LiDAR point clouds. The experiment performed on a large number of real LiDAR and synthetic point clouds showed that our proposed frameworks and algorithms outperforms the state-of-the-art algorithms in terms of segmentation accuracy and surface RMSE. / Ph. D. / The increasing concerns about the global warming have raised the interest about studying and understanding the global ecosystem components including the carbon cycle. The interaction between forests and earth atmosphere is one major component of the global carbon cycle. Thus, quantifying the global forest biomass is an important factor in studying carbon cycle and its dynamics. Therefore repeated large-scale estimates of forest biomass are critically important. LIDAR (Light Detection and Ranging) is a active remote sensing method that uses light in the form of a pulsed laser to measure ranges and distances based on the time-of-flight concept (similar to radar systems). LiDAR systems can generate precise, three-dimensional information about the shape of the Earth and its surface characteristics. Therefore, LiDAR remote sensing is much more suitable for forest studies than photogrammetry because of the laser’s ability to penetrate tree crowns allowing the system to find ground returns under dense canopies. This property allows us to estimate tree heights which is a major factor for estimating the forest biomass. In order to track forest biomass changes at the global scale, recurring high-altitude observations are needed. Satellite-based LiDAR systems can provide these observations, although no such systems are currently operational. The situation will change with the launch of NASAs ICESat-2, which is planned for July 2017. However, although LiDAR technology allows for rapid and inexpensive measurements over broad geographical areas, ICESat-2 will be equipped with a new sensor known as photon-counting micropulse LiDAR system. This new LiDAR technology is expected to produce measurements that include high levels of noise. The data produced by this sensor will be in the form of a cloud of points in which the signal points are expected to be much more dense than noise points. Analysis of data from the ICESat-2 satellite will therefore need to be robust with respect to noise, as well as fast and automatic because of the large quantity of data that will be generated. The problem of segmentation in point clouds is challenging for several reasons, including the fact that the points are not associated with a regular grid, as is the case with most image data. Moreover, the presence of noise particularly impulsive noise with varying density, can make it difficult to obtain a good segmentation using traditional techniques, including the algorithms that had been developed to process LiDAR data. This dissertation introduces novel algorithms and approaches based on statistical techniques and image analysis in order to segment sparse noisy point clouds to extract contours and surfaces in order to detect meaningful measurements and information.

The purpose of this study is to develop an automatic facial feature extraction system, which is able to identify the detailed shape of eyes, eyebrows and mouth from facial images. The developed system not only extracts the location information of the features, but also estimates the parameters pertaining the contours and parts of the features using parametric deformable templates approach. In order to extract facial features, deformable models for each of eye, eyebrow, and mouth are developed. The development steps of the geometry, imaging model and matching algorithms, and energy functions for each of these templates are presented in detail, along with the important implementation issues. In addition, an eigenfaces based multi-scale face detection algorithm which incorporates standard facial proportions is implemented, so that when a face is detected the rough search regions for the facial features are readily available. The developed system is tested on JAFFE (Japanese Females Facial Expression Database), Yale Faces, and ORL (Olivetti Research Laboratory) face image databases. The performance of each deformable templates, and the face detection algorithm are discussed separately.