Robust Detection of Non-overlapping Ellipses from Points with Applications to Circular Target Extraction in Images and Cylinder Detection in Point Clouds

This manuscript provides a collection of new methods for the automated detection of non-overlapping ellipses from edge points. The methods introduce new developments in: (i) robust Monte Carlo-based ellipse fitting to 2-dimensional (2D) points in the presence of outliers; (ii) detection of non-overlapping ellipse from 2D edge points; and (iii) extraction of cylinder from 3D point clouds. The proposed methods were thoroughly compared with established state-of-the-art methods, using simulated and real-world datasets, through the design of four sets of original experiments. It was found that the proposed robust ellipse detection was superior to four reliable robust methods, including the popular least median of squares, in both simulated and real-world datasets. The proposed process for detecting non-overlapping ellipses achieved F-measure of 99.3% on real images, compared to F-measures of 42.4%, 65.6%, and 59.2%, obtained using the methods of Fornaciari, Patraucean, and Panagiotakis, respectively. The proposed cylinder extraction method identified all detectable mechanical pipes in two real-world point clouds, obtained under laboratory, and industrial construction site conditions. The results of this investigation show promise for the application of the proposed methods for automatic extraction of circular targets from images and pipes from point clouds

UNSUPERVISED CLASSIFICATION AND CHOICE OF CLASSES: BAYESIAN APPROACH

We have given a solution to the problem of unsupervised classifica,tioll of multidinlensional data. Our approach is based on Bayesian estimation which regards the number of classes, the data partition and the parameter vectors that describe the density of classes as unknowns. We compute their MAP estimates simultaneously by maximizing their joint posterior -probability density given the data. The concept of partition as a variable to be estimated hasn\u27t been considered. This formulation also solves the problem of validating clusters obtained from various methods. Our method can also incorporate any additional information &about a class while assigning its prohability density. It can also ut,ilize any available training samples that arise from different classes. We provide a. descent algorithm that starts with an arbitrary partition of the data, and iteratively computes the MAP estimates. We also focus on robust regression which is a special case of unsupervised classification with two classes; inliers and outliers. The problem of intensity image segmentation is posed as an unsupervised classification problem and solved using the Bayesian formulation a multiscale set up. The proposed method is also applied to data sets that occur in statistical literature and target tracking. The results ohbtained demonstrate the power of Bayesian approach for unsupervised classification

Clustering Assisted Fundamental Matrix Estimation

In computer vision, the estimation of the fundamental matrix is a basic problem that has been extensively studied. The accuracy of the estimation imposes a significant influence on subsequent tasks such as the camera trajectory determination and 3D reconstruction. In this paper we propose a new method for fundamental matrix estimation that makes use of clustering a group of 4D vectors. The key insight is the observation that among the 4D vectors constructed from matching pairs of points obtained from the SIFT algorithm, well-defined cluster points tend to be reliable inliers suitable for fundamental matrix estimation. Based on this, we utilizes a recently proposed efficient clustering method through density peaks seeking and propose a new clustering assisted method. Experimental results show that the proposed algorithm is faster and more accurate than currently commonly used methods.Comment: 12 pages, 8 figures, 3 tables, Second International Conference on Computer Science and Information Technology (COSIT 2015) March 21~22, 2015, Geneva, Switzerlan

Intelligent image cropping and scaling

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 2011.Nowadays, there exist a huge number of end devices with different screen properties for watching television content, which is either broadcasted or transmitted over the internet. To allow best viewing conditions on each of these devices, different image formats have to be provided by the broadcaster. Producing content for every single format is, however, not applicable by the broadcaster as it is much too laborious and costly. The most obvious solution for providing multiple image formats is to produce one high resolution format and prepare formats of lower resolution from this. One possibility to do this is to simply scale video images to the resolution of the target image format. Two significant drawbacks are the loss of image details through ownscaling and possibly unused image areas due to letter- or pillarboxes. A preferable solution is to find the contextual most important region in the high-resolution format at first and crop this area with an aspect ratio of the target image format afterwards. On the other hand, defining the contextual most important region manually is very time consuming. Trying to apply that to live productions would be nearly impossible. Therefore, some approaches exist that automatically define cropping areas. To do so, they extract visual features, like moving reas in a video, and define regions of interest (ROIs) based on those. ROIs are finally used to define an enclosing cropping area. The extraction of features is done without any knowledge about the type of content. Hence, these approaches are not able to distinguish between features that might be important in a given context and those that are not. The work presented within this thesis tackles the problem of extracting visual features based on prior knowledge about the content. Such knowledge is fed into the system in form of metadata that is available from TV production environments. Based on the extracted features, ROIs are then defined and filtered dependent on the analysed content. As proof-of-concept, this application finally adapts SDTV (Standard Definition Television) sports productions automatically to image formats with lower resolution through intelligent cropping and scaling. If no content information is available, the system can still be applied on any type of content through a default mode. The presented approach is based on the principle of a plug-in system. Each plug-in represents a method for analysing video content information, either on a low level by extracting image features or on a higher level by processing extracted ROIs. The combination of plug-ins is determined by the incoming descriptive production metadata and hence can be adapted to each type of sport individually. The application has been comprehensively evaluated by comparing the results of the system against alternative cropping methods. This evaluation utilised videos which were manually cropped by a professional video editor, statically cropped videos and simply scaled, non-cropped videos. In addition to and apart from purely subjective evaluations, the gaze positions of subjects watching sports videos have been measured and compared to the regions of interest positions extracted by the system

Statistical Models and Optimization Algorithms for High-Dimensional Computer Vision Problems

Data-driven and computational approaches are showing significant promise in solving several challenging problems in various fields such as bioinformatics, finance and many branches of engineering. In this dissertation, we explore the potential of these approaches, specifically statistical data models and optimization algorithms, for solving several challenging problems in computer vision. In doing so, we contribute to the literatures of both statistical data models and computer vision. In the context of statistical data models, we propose principled approaches for solving robust regression problems, both linear and kernel, and missing data matrix factorization problem. In computer vision, we propose statistically optimal and efficient algorithms for solving the remote face recognition and structure from motion (SfM) problems. The goal of robust regression is to estimate the functional relation between two variables from a given data set which might be contaminated with outliers. Under the reasonable assumption that there are fewer outliers than inliers in a data set, we formulate the robust linear regression problem as a sparse learning problem, which can be solved using efficient polynomial-time algorithms. We also provide sufficient conditions under which the proposed algorithms correctly solve the robust regression problem. We then extend our robust formulation to the case of kernel regression, specifically to propose a robust version for relevance vector machine (RVM) regression. Matrix factorization is used for finding a low-dimensional representation for data embedded in a high-dimensional space. Singular value decomposition is the standard algorithm for solving this problem. However, when the matrix has many missing elements this is a hard problem to solve. We formulate the missing data matrix factorization problem as a low-rank semidefinite programming problem (essentially a rank constrained SDP), which allows us to find accurate and efficient solutions for large-scale factorization problems. Face recognition from remotely acquired images is a challenging problem because of variations due to blur and illumination. Using the convolution model for blur, we show that the set of all images obtained by blurring a given image forms a convex set. We then use convex optimization techniques to find the distances between a given blurred (probe) image and the gallery images to find the best match. Further, using a low-dimensional linear subspace model for illumination variations, we extend our theory in a similar fashion to recognize blurred and poorly illuminated faces. Bundle adjustment is the final optimization step of the SfM problem where the goal is to obtain the 3-D structure of the observed scene and the camera parameters from multiple images of the scene. The traditional bundle adjustment algorithm, based on minimizing the l_2 norm of the image re-projection error, has cubic complexity in the number of unknowns. We propose an algorithm, based on minimizing the l_infinity norm of the re-projection error, that has quadratic complexity in the number of unknowns. This is achieved by reducing the large-scale optimization problem into many small scale sub-problems each of which can be solved using second-order cone programming

Labeled Sampling Consensus A Novel Algorithm For Robustly Fitting Multiple Structures Using Compressed Sampling

The ability to robustly fit structures in datasets that contain outliers is a very important task in Image Processing, Pattern Recognition and Computer Vision. Random Sampling Consensus or RANSAC is a very popular method for this task, due to its ability to handle over 50% outliers. The problem with RANSAC is that it is only capable of finding a single structure. Therefore, if a dataset contains multiple structures, they must be found sequentially by finding the best fit, removing the points, and repeating the process. However, removing incorrect points from the dataset could prove disastrous. This thesis offers a novel approach to sampling consensus that extends its ability to discover multiple structures in a single iteration through the dataset. The process introduced is an unsupervised method, requiring no previous knowledge to the distribution of the input data. It uniquely assigns labels to different instances of similar structures. The algorithm is thus called Labeled Sampling Consensus or L-SAC. These unique instances will tend to cluster around one another allowing the individual structures to be extracted using simple clustering techniques. Since divisions instead of modes are analyzed, only a single instance of a structure need be recovered. This ability of L-SAC allows a novel sampling procedure to be presented “compressing” the required samples needed compared to traditional sampling schemes while ensuring all structures have been found. L-SAC is a flexible framework that can be applied to many problem domains

Human robot interaction in a crowded environment

Human Robot Interaction (HRI) is the primary means of establishing natural and affective communication between humans and robots. HRI enables robots to act in a way similar to humans in order to assist in activities that are considered to be laborious, unsafe, or repetitive. Vision based human robot interaction is a major component of HRI, with which visual information is used to interpret how human interaction takes place. Common tasks of HRI include finding pre-trained static or dynamic gestures in an image, which involves localising different key parts of the human body such as the face and hands. This information is subsequently used to extract different gestures. After the initial detection process, the robot is required to comprehend the underlying meaning of these gestures [3]. Thus far, most gesture recognition systems can only detect gestures and identify a person in relatively static environments. This is not realistic for practical applications as difficulties may arise from people‟s movements and changing illumination conditions. Another issue to consider is that of identifying the commanding person in a crowded scene, which is important for interpreting the navigation commands. To this end, it is necessary to associate the gesture to the correct person and automatic reasoning is required to extract the most probable location of the person who has initiated the gesture. In this thesis, we have proposed a practical framework for addressing the above issues. It attempts to achieve a coarse level understanding about a given environment before engaging in active communication. This includes recognizing human robot interaction, where a person has the intention to communicate with the robot. In this regard, it is necessary to differentiate if people present are engaged with each other or their surrounding environment. The basic task is to detect and reason about the environmental context and different interactions so as to respond accordingly. For example, if individuals are engaged in conversation, the robot should realize it is best not to disturb or, if an individual is receptive to the robot‟s interaction, it may approach the person. Finally, if the user is moving in the environment, it can analyse further to understand if any help can be offered in assisting this user. The method proposed in this thesis combines multiple visual cues in a Bayesian framework to identify people in a scene and determine potential intentions. For improving system performance, contextual feedback is used, which allows the Bayesian network to evolve and adjust itself according to the surrounding environment. The results achieved demonstrate the effectiveness of the technique in dealing with human-robot interaction in a relatively crowded environment [7]