. This paper describes an experimental investigation of the recognition performance of two approaches to the representation of objects for recognition. The first representation, generally known as appearance modelling, describes an object by a set of images. The image set is acquired for a range of views and illumination conditions which are expected to be encountered in subsequent recognition. This image database provides a description of the object. Recognition is carried out by constructing an eigenvector space to compute efficiently the distance between a new image and any image in the database. The second representation is a geometric description based on the projected boundary of an object. General object classes such as planar objects, surfaces of revolution and repeated structures support the construction of invariant descriptions and invariant index functions for recognition. In this paper we present an investigation of the relative performance of the two approaches. Two objec...

Abstract—We advance new active object recognition algorithms that classify rigid objects and estimate their pose from intensity images. Our algorithms automatically detect if the class or pose of an object is ambiguous in a given image, reposition the sensor as needed, and incorporate data from multiple object views in determining the final object class and pose estimate. A probabilistic feature space trajectory (FST) in a global eigenspace is used to represent 3D distorted views of an object and to estimate the class and pose of an input object. Confidence measures for the class and pose estimates, derived using the probabilistic FST object representation, determine when additional observations are required as well as where the sensor should be positioned to provide the most useful information. We demonstrate the ability to use FSTs constructed from images rendered from computer-aided design models to recognize real objects in real images and present test results for a set of metal machined parts. Index Terms—Active vision, classification, object recognition, pose estimation. 1

Eigendecomposition is a common technique that is performed on sets of correlated images in a number of computer vision and robotics applications. Unfortunately, the computation of an eigendecomposition can become prohibitively expensive when dealing with very high-resolution images. While reducing the resolution of the images will reduce the computational expense, it is not known a priori how this will affect the quality of the resulting eigendecomposition. The work presented here provides an analysis of how different resolution reduction techniques affect the eigendecomposition. A computationally efficient algorithm for calculating the eigendecomposition based on this analysis is proposed. Examples show that this algorithm performs well on arbitrary video sequences.

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Methods for mobile robot localization that use eigenspaces of panoramic snapshots of the environment are in general sensitive to changes in the illumination of the environment. Therefore, we propose in this paper an approach which achieves a reliable localization under severe illumination conditions by illumination insensitive eigenspaces. The method in question uses gradient filtering of the eigenspaces. The method was tested on images obtained by a mobile robot and, as we show, it outperforms by far the other known methods.

A vision-based robot localization system must be robust: able to keep track of the position of the robot at any time even if illumination conditions change and, in the extreme case of a failure, able to efficiently recover the correct position of the robot. With this objective in mind, we enhance the existing appearance-based robot localization framework in two directions by exploiting the use of a stereo camera mounted on a pan-and-tilt device. First, we move from the classical passive appearance-based localization framework to an active one where the robot sometimes executes actions with the only purpose of gaining information about its location in the environment. Along this line, we introduce an entropy-based criterion for action selection that can be efficiently evaluated in our probabilistic localization system. The execution of the actions selected using this criterion allows the robot to quickly find out its position in case it gets lost. Secondly, we introduce the use of depth maps obtained with the stereo cameras. The information provided by depth maps is less sensitive to changes of illumination than that provided by plain images. The main drawback of depth maps is that they include missing values: points for which it is not possible to reliably determine depth information. The presence of missing values makes Principal Component Analysis (the standard method used to compress images in the appearance-based framework) unfeasible. We describe a novel Expectation-Maximization algorithm to determine the principal components of a data set including missing values and we apply it to depth maps. The experiments we present show that the combination of the active localization with the use of depth maps gives an efficient and robust appearance-based robot localization system.

Abstract — In this paper, we propose an original approach to control camera position and/or lighting conditions in an environment using image gradient information. Our goal is to ensure a good viewing condition and good illumination of an object to perform vision-based task (recognition, tracking, etc.). Within the visual servoing framework, we propose solutions to two different issues: maximizing the brightness of the scene and maximizing the contrast in the image. Solutions are proposed to consider either a static light and a moving camera, eitheror a moving light and a static/moving camera. The proposed method is independent of the structure, color and aspect of the objects. Experimental results on both synthetic and real images are finally presented. I. OVERVIEW In this paper we investigate the problem of relative placement

In this paper, an approach towards head pose estimation is introduced based on Gabor eigenspace modeling. Gabor filter is used to enhance pose information and eliminate other distractive information like variable face appearance or changing environmental illumination. We discuss the selection of optimal Gabor filter’s orientation to each pose, which leads to more compact pose clustering. Then we use a distributionbased pose model (DBPM) to model each pose cluster in Gabor eigenspace. Thus to each pose cluster, a 2Ddistance space is established where the distance from centroid (DFC) could be used to estimate head pose. Experimental results demonstrate the algorithm’s robustness and generalization. We also try our algorithm on real scene sequences to detect human face and estimate its pose. In this way, user can control an intelligent wheelchair just by his head poses. 1.

We present a novel approach to face recognition by constructing facial identity structures across views and over time, referred to as identity surfaces, in a Kernel Discriminant Analysis (KDA) feature space. This approach is aimed at addressing three challenging problems in face recognition: modelling faces across multiple views, extracting non-linear discriminatory features, and recognising faces over time. First, a multi-view face model is designed which can be automatically fitted to face images and sequences to extract the normalised facial texture patterns. This model is capable of dealing with faces with large pose variation. Second, KDA is developed to compute the most significant non-linear basis vectors with the intention of maximising the between-class variance and minimising the within-class variance. We applied KDA to the problem of multi-view face recognition, and a significant improvement has been achieved in reliability and accuracy. Third, identity surfaces are constructed in a pose-parameterised discriminatory feature space. Dynamic face recognition is then performed by matching the object trajectory computed from a video input and model trajectories constructed on the identity surfaces. These two types of trajectories encode the spatio-temporal dynamics of moving faces.

Large collections of images can be indexed by their projections on a few "primary" images. The optimal primary images are the eigenvectors of a large covariance matrix. We address the problem of computing primary images when access to the images is expensive. This is the case when the images cannot be kept locally, but must be accessed through slow communication such as the Internet, or stored in a compressed form. A distributed algorithm that computes optimal approximations to the eigenvectors (known as Ritz vectors) in one pass through the image set is proposed. When iterated, the algorithm can recover the exact eigenvectors. The widely used SVD technique for computing the primary images of a small image set is a special case of the proposed algorithm. In applications to image libraries and learning it is necessary to compute different primary images for several sub-categories of the image set. The proposed algorithm can compute these additional primary images "offline", without the ...