Abstract: Maximizing both the area coverage and the resolution per target is highly desirable in many applications of computer vision. However, with a limited number of cameras viewing a scene, the two objectives are contradictory. This chapter is dedicated to active vision systems, trying to achieve a trade-off between these two aims and examining the use of high-level reasoning in such scenarios. The chapter starts by introducing different approaches to active cameras configurations. Later, a single active camera system to track a moving object is developed, offering the reader first-hand understanding of the issues involved. Another section discusses practical considerations in building an active vision platform, taking as an example a multi-camera system developed for a European project. The last section of the chapter reflects upon the future trends of using semantic factors to drive smartly coordinated active systems.

Abstract: In this paper we describe a novel approach to reactive tracking of moving targets with a pan-tilt-zoom camera. The approach uses an extended Kalman filter to jointly track the object position in the real world, its velocity in 3D and the camera intrinsics, in addition to the rate of change of these parameters. The filter outputs are used as inputs to PID controllers which continuously adjust the camera motion in order to reactively track the object at a constant image velocity while simultaneously maintaining a desirable target scale in the image plane. We provide experimental results on simulated and real tracking sequences to show how our tracker is able to accurately estimate both 3D object position and camera intrinsics with very high precision over a wide range of focal lengths.

Abstract: Humans can effortlessly perceive faces, follow them over space and time, and decode their rich content, such as pose, identity and expression. However, despite many decades of research on automatic facial perception in areas like face detection, expression recognition, pose estimation and face recognition, and despite many successes, a complete solution remains elusive. This thesis is dedicated to three problems in automatic face perception, namely face detection, face tracking and pose estimation.
In face detection, an initial simple model is presented that uses pixel-based heuristics to segment skin locations and hand-crafted rules to determine the locations of the faces present in an image. Different colorspaces are studied to judge whether a colorspace transformation can aid skin color detection. The output of this study is used in the design of a more complex face detector that is able to successfully generalize to different scenarios.
In face tracking, a framework that combines estimation and control in a joint scheme is presented to track a face with a single pan-tilt-zoom camera. While this work is mainly motivated by tracking faces, it can be easily applied atop of any detector to track different objects. The applicability of this method is demonstrated on simulated as well as real-life scenarios.
The last and most important part of this thesis is dedicate to monocular head pose estimation. In this part, a method based on partial least squares (PLS) regression is proposed to estimate pose and solve the alignment problem simultaneously. The contributions of this work are two-fold: 1) demonstrating that the proposed method achieves better than state-of-the-art results on the estimation problem and 2) developing a technique to reduce misalignment based on the learned PLS factors that outperform multiple instance learning (MIL) without the need for any re-training or the inclusion of misaligned samples in the training process, as normally done in MIL.

Abstract: We present a new methodology for symbol recognition, by employing a structural approach for representing visual associations in symbols and a statistical classifier for recognition. A graphic symbol is vectorized, its topological and geometrical details are encoded by an attributed relational graph and a signature is computed for it. Data adapted fuzzy intervals have been introduced for addressing the sensitivity of structural representations to noise. The joint probability distribution of signatures is encoded by a Bayesian network, which serves as a mechanism for pruning irrelevant features and choosing a subset of interesting features from structural signatures of underlying symbol set, and is deployed in a supervised learning scenario for recognizing query symbols. Experimental results on pre-segmented 2D linear architectural and electronic symbols from GREC databases are presented.

Abstract: We present a new method for explicit graph embedding. Our algorithm extracts a feature vector for an undirected attributed graph. The proposed feature vector encodes details about the number of nodes, number of edges, node degrees, the attributes of nodes and the attributes of edges in the graph. The first two features are for the number of nodes and the number of edges. These are followed by w features for node degrees, m features for k node attributes and n features for l edge attributes — which represent the distribution of node degrees, node attribute values and edge attribute values, and are obtained by defining (in an unsupervised fashion), fuzzy-intervals over the list of node degrees, node attributes and edge attributes. Experimental results are provided for sample data of ICPR2010 contest GEPR.

Abstract: Structural pattern recognition approaches offer the most expressive, convenient, powerful but computational expensive representations of underlying relational information. To benefit from mature, less expensive and efficient state-of-the-art machine learning models of statistical pattern recognition they must be mapped to a low-dimensional vector space. Our method of explicit graph embedding bridges the gap between structural and statistical pattern recognition. We extract the topological, structural and attribute information from a graph and encode numeric details by fuzzy histograms and symbolic details by crisp histograms. The histograms are concatenated to achieve a simple and straightforward embedding of graph into a low-dimensional numeric feature vector. Experimentation on standard public graph datasets shows that our method outperforms the state-of-the-art methods of graph embedding for richly attributed graphs.

Abstract: Ability to recognize patterns is among the most crucial capabilities of human beings for their survival, which enables them to employ their sophisticated neural and cognitive systems [1], for processing complex audio, visual, smell, touch, and taste signals. Man is the most complex and the best existing system of pattern recognition. Without any explicit thinking, we continuously compare, classify, and identify huge amount of signal data everyday [2], starting from the time we get up in the morning till the last second we fall asleep. This includes recognizing the face of a friend in a crowd, a spoken word embedded in noise, the proper key to lock the door, smell of coffee, the voice of a favorite singer, the recognition of alphabetic characters, and millions of more tasks that we perform on regular basis.

Abstract: Automatic analysis of visual art, such as paintings, is a challenging inter-disciplinary research problem. Conventional approaches only rely on global scene characteristics by encoding holistic information for computational painting categorization.We argue that such approaches are sub-optimal and that discriminative common visual structures provide complementary information for painting classification. We present an approach that encodes both the global scene layout and discriminative latent common structures for computational painting categorization. The region of interests are automatically extracted, without any manual part labeling, by training class-specific deformable part-based models. Both holistic and region-of-interests are then described using multi-scale dense convolutional features. These features are pooled separately using Fisher vector encoding and concatenated afterwards in a single image representation. Experiments are performed on a challenging dataset with 91 different painters and 13 diverse painting styles. Our approach outperforms the standard method, which only employs the global scene characteristics. Furthermore, our method achieves state-of-the-art results outperforming a recent multi-scale deep features based approach [11] by 6.4% and 3.8% respectively on artist and style classification.