Statistical/Geometric Techniques for Object Representation and Recognition

Object modeling and recognition are key areas of research in computer vision and graphics with wide range of applications. Though research in these areas is not new, traditionally most of it has focused on analyzing problems under controlled environments. The challenges posed by real life applications demand for more general and robust solutions. The wide variety of objects with large intra-class variability makes the task very challenging. The difficulty in modeling and matching objects also vary depending on the input modality. In addition, the easy availability of sensors and storage have resulted in tremendous increase in the amount of data that needs to be processed which requires efficient algorithms suitable for large-size databases. In this dissertation, we address some of the challenges involved in modeling and matching of objects in realistic scenarios. Object matching in images require accounting for large variability in the appearance due to changes in illumination and view point. Any real world object is characterized by its underlying shape and albedo, which unlike the image intensity are insensitive to changes in illumination conditions. We propose a stochastic filtering framework for estimating object albedo from a single intensity image by formulating the albedo estimation as an image estimation problem. We also show how this albedo estimate can be used for illumination insensitive object matching and for more accurate shape recovery from a single image using standard shape from shading formulation. We start with the simpler problem where the pose of the object is known and only the illumination varies. We then extend the proposed approach to handle unknown pose in addition to illumination variations. We also use the estimated albedo maps for another important application, which is recognizing faces across age progression. Many approaches which address the problem of modeling and recognizing objects from images assume that the underlying objects are of diffused texture. But most real world objects exhibit a combination of diffused and specular properties. We propose an approach for separating the diffused and specular reflectance from a given color image so that the algorithms proposed for objects of diffused texture become applicable to a much wider range of real world objects. Representing and matching the 2D and 3D geometry of objects is also an integral part of object matching with applications in gesture recognition, activity classification, trademark and logo recognition, etc. The challenge in matching 2D/3D shapes lies in accounting for the different rigid and non-rigid deformations, large intra-class variability, noise and outliers. In addition, since shapes are usually represented as a collection of landmark points, the shape matching algorithm also has to deal with the challenges of missing or unknown correspondence across these data points. We propose an efficient shape indexing approach where the different feature vectors representing the shape are mapped to a hash table. For a query shape, we show how the similar shapes in the database can be efficiently retrieved without the need for establishing correspondence making the algorithm extremely fast and scalable. We also propose an approach for matching and registration of 3D point cloud data across unknown or missing correspondence using an implicit surface representation. Finally, we discuss possible future directions of this research

Symmetry for face analysis.

Yuan Tianqiang.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.Includes bibliographical references (leaves 51-55).Abstracts in English and Chinese.abstract --- p.iacknowledgments --- p.ivtable of contents --- p.vlist of figures --- p.viilist of tables --- p.ixChapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Reflectional Symmetry Detection --- p.1Chapter 1.2 --- Research Progress on Face Analysis --- p.2Chapter 1.2.1 --- Face Detection --- p.3Chapter 1.2.2 --- Face Alignment --- p.4Chapter 1.2.3 --- Face Recognition --- p.6Chapter 1.3 --- Organization of this thesis --- p.8Chapter Chapter 2 --- Local reflectional symmetry detection --- p.9Chapter 2.1 --- Proposed Method --- p.9Chapter 2.1.1 --- Symmetry measurement operator --- p.9Chapter 2.1.2 --- Potential regions selection --- p.10Chapter 2.1.3 --- Detection of symmetry axes --- p.11Chapter 2.2 --- Experiments --- p.13Chapter 2.2.1 --- Parameter setting and analysis --- p.13Chapter 2.2.2 --- Experimental Results --- p.14Chapter Chapter 3 --- Global perspective reflectional symmetry detection --- p.16Chapter 3.1 --- Introduction of camera models --- p.16Chapter 3.2 --- Property of Symmetric Point-Pair --- p.18Chapter 3.3 --- analysis and Experiment --- p.20Chapter 3.3.1 --- Confirmative Experiments --- p.20Chapter 3.3.2 --- Face shape generation with PSI --- p.22Chapter 3.3.3 --- Error Analysis --- p.24Chapter 3.3.4 --- Experiments of Pose Estimation --- p.25Chapter 3.4 --- Summary --- p.28Chapter Chapter 4 --- Pre-processing of face analysis --- p.30Chapter 4.1 --- Introduction of Hough Transform --- p.30Chapter 4.2 --- Eye Detection --- p.31Chapter 4.2.1 --- Coarse Detection --- p.32Chapter 4.2.2 --- Refine the eyes positions --- p.34Chapter 4.2.3 --- Experiments and Analysis --- p.35Chapter 4.3 --- Face Components Detection with GHT --- p.37Chapter 4.3.1 --- Parameter Analyses --- p.38Chapter 4 3.2 --- R-table Construction --- p.38Chapter 4.3.3 --- Detection Procedure and Voting Strategy --- p.39Chapter 4.3.4 --- Experiments and Analysis --- p.41Chapter Chapter 5 --- Pose estimation with face symmetry --- p.45Chapter 5.1 --- Key points selection --- p.45Chapter 5.2 --- Face Pose Estimation --- p.46Chapter 5.2.1 --- Locating eye corners --- p.46Chapter 5.2.2 --- Analysis and Summary --- p.47Chapter Chapter 6 --- Conclusions and future work --- p.49bibliography --- p.5

Pairwise geometric histograms for object recognition : developments and analysis.

One of the fundamental problems in the field of computer vision is the task of classifying objects, which are present in an image or sequence of images, based on their appearance. This task is commonly referred to as the object recognition problem. A system designed to perform this task must be able to learn visual cues such as shape, colour and texture from examples of objects presented to it. These cues are then later used to identify examples of the known objects in previously unseen scenes. The work presented in this thesis is based on a statistical representation of shape known as a pairwise geometric histogram which has been demonstrated by other researchers in 2-dimensional object recognition tasks. An analysis of the performance of recognition based on this representation has been conducted and a number of contributions to the original recognition algorithm have been made. An important property of an object recognition system is its scalability. This is the. ability of the system to continue performing as the number of known objects is increased. The analysis of the recognition algorithm presented here considers this issue by relating the classification error to the number of stored model objects. An estimate is also made of the number of objects which can be represented uniquely using geometric histograms. One of the main criticisms of the original recognition algorithm based on geometric histograms was the inability to recognise objects at different scales. An algorithm is presented here that is able to recognise objects over a range of scale using the geometric histogram representation. Finally, a novel pairwise geometric histogram representation for arbitrary surfaces has been proposed. This inherits many of the advantages of the 2-dimensional shape descriptor but enables recognition of 3-dimensional object from arbitrary viewpoints

Object recognition using multi-view imaging

Single view imaging data has been used in most previous research in computer vision and image understanding and lots of techniques have been developed. Recently with the fast development and dropping cost of multiple cameras, it has become possible to have many more views to achieve image processing tasks. This thesis will consider how to use the obtained multiple images in the application of target object recognition. In this context, we present two algorithms for object recognition based on scale- invariant feature points. The first is single view object recognition method (SOR), which operates on single images and uses a chirality constraint to reduce the recognition errors that arise when only a small number of feature points are matched. The procedure is extended in the second multi-view object recognition algorithm (MOR) which operates on a multi-view image sequence and, by tracking feature points using a dynamic programming method in the plenoptic domain subject to the epipolar constraint, is able to fuse feature point matches from all the available images, resulting in more robust recognition. We evaluated these algorithms using a number of data sets of real images capturing both indoor and outdoor scenes. We demonstrate that MOR is better than SOR particularly for noisy and low resolution images, and it is also able to recognize objects that are partially occluded by combining it with some segmentation techniques

Study of object recognition and identification based on shape and texture analysis

The objective of object recognition is to enable computers to recognize image patterns without human intervention. According to its applications, it is mainly divided into two parts: recognition of object categories and detection/identification of objects. My thesis studied the techniques of object feature analysis and identification strategies, which solve the object recognition problem by employing effective and perceptually important object features. The shape information is of particular interest and a review of the shape representation and description is presented, as well as the latest research work on object recognition. In the second chapter of the thesis, a novel content-based approach is proposed for efficient shape classification and retrieval of 2D objects. Two object detection approaches, which are designed according to the characteristics of the shape context and SIFT descriptors, respectively, are analyzed and compared. It is found that the identification strategy constructed on a single type of object feature is only able to recognize the target object under specific conditions which the identifier is adapted to. These identifiers are usually designed to detect the target objects which are rich in the feature type captured by the identifier. In addition, this type of feature often distinguishes the target object from the complex scene. To overcome this constraint, a novel prototyped-based object identification method is presented to detect the target object in the complex scene by employing different types of descriptors to capture the heterogeneous features. All types of descriptors are modified to meet the requirement of the detection strategy’s framework. Thus this new method is able to describe and identify various kinds of objects whose dominant features are quite different. The identification system employs the cosine similarity to evaluate the resemblance between the prototype image and image windows on the complex scene. Then a ‘resemblance map’ is established with values on each patch representing the likelihood of the target object’s presence. The simulation approved that this novel object detection strategy is efficient, robust and of scale and rotation invariance

Fast and robust image feature matching methods for computer vision applications

Service robotic systems are designed to solve tasks such as recognizing and manipulating objects, understanding natural scenes, navigating in dynamic and populated environments. It's immediately evident that such tasks cannot be modeled in all necessary details as easy as it is with industrial robot tasks; therefore, service robotic system has to have the ability to sense and interact with the surrounding physical environment through a multitude of sensors and actuators. Environment sensing is one of the core problems that limit the deployment of mobile service robots since existing sensing systems are either too slow or too expensive. Visual sensing is the most promising way to provide a cost effective solution to the mobile robot sensing problem. It's usually achieved using one or several digital cameras placed on the robot or distributed in its environment. Digital cameras are information rich sensors and are relatively inexpensive and can be used to solve a number of key problems for robotics and other autonomous intelligent systems, such as visual servoing, robot navigation, object recognition, pose estimation, and much more. The key challenges to taking advantage of this powerful and inexpensive sensor is to come up with algorithms that can reliably and quickly extract and match the useful visual information necessary to automatically interpret the environment in real-time. Although considerable research has been conducted in recent years on the development of algorithms for computer and robot vision problems, there are still open research challenges in the context of the reliability, accuracy and processing time. Scale Invariant Feature Transform (SIFT) is one of the most widely used methods that has recently attracted much attention in the computer vision community due to the fact that SIFT features are highly distinctive, and invariant to scale, rotation and illumination changes. In addition, SIFT features are relatively easy to extract and to match against a large database of local features. Generally, there are two main drawbacks of SIFT algorithm, the first drawback is that the computational complexity of the algorithm increases rapidly with the number of key-points, especially at the matching step due to the high dimensionality of the SIFT feature descriptor. The other one is that the SIFT features are not robust to large viewpoint changes. These drawbacks limit the reasonable use of SIFT algorithm for robot vision applications since they require often real-time performance and dealing with large viewpoint changes. This dissertation proposes three new approaches to address the constraints faced when using SIFT features for robot vision applications, Speeded up SIFT feature matching, robust SIFT feature matching and the inclusion of the closed loop control structure into object recognition and pose estimation systems. The proposed methods are implemented and tested on the FRIEND II/III service robotic system. The achieved results are valuable to adapt SIFT algorithm to the robot vision applications

Vision-based techniques for gait recognition

Global security concerns have raised a proliferation of video surveillance devices. Intelligent surveillance systems seek to discover possible threats automatically and raise alerts. Being able to identify the surveyed object can help determine its threat level. The current generation of devices provide digital video data to be analysed for time varying features to assist in the identification process. Commonly, people queue up to access a facility and approach a video camera in full frontal view. In this environment, a variety of biometrics are available - for example, gait which includes temporal features like stride period. Gait can be measured unobtrusively at a distance. The video data will also include face features, which are short-range biometrics. In this way, one can combine biometrics naturally using one set of data. In this paper we survey current techniques of gait recognition and modelling with the environment in which the research was conducted. We also discuss in detail the issues arising from deriving gait data, such as perspective and occlusion effects, together with the associated computer vision challenges of reliable tracking of human movement. Then, after highlighting these issues and challenges related to gait processing, we proceed to discuss the frameworks combining gait with other biometrics. We then provide motivations for a novel paradigm in biometrics-based human recognition, i.e. the use of the fronto-normal view of gait as a far-range biometrics combined with biometrics operating at a near distance