Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

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Computer Vision: Algorithms and Applications

We present a unified model for face detection, pose estimation, and landmark estimation in real-world, cluttered images. Our model is based on a mixtures of trees with a shared pool of parts; we model every facial landmark as a part and use global mixtures to capture topological changes due to viewpoint. We show that tree-structured models are surprisingly effective at capturing global elastic deformation, while being easy to optimize unlike dense graph structures. We present extensive results on standard face benchmarks, as well as a new “in the wild” annotated dataset, that suggests our system advances the state-of-the-art, sometimes considerably, for all three tasks. Though our model is modestly trained with hundreds of faces, it compares favorably to commercial systems trained with billions of examples (such as Google Picasa and face.com).

Face detection, pose estimation, and landmark localization in the wild

A mobile terminal including a body; a touchscreen provided to a front and extending to side of the body and configured to display content; and a controller configured to detect one side of the body comes into contact with one side of an external terminal, display a first area on the touchscreen corresponding to a contact area of the body and the external terminal and a second area including the content, receive an input of moving the content displayed in the second area to the first area, display the content in the first area, and share the content in the first area with the external terminal.

Mobile terminal and method for controlling the same

The capacity to estimate the head pose of another person is a common human ability that presents a unique challenge for computer vision systems. Compared to face detection and recognition, which have been the primary foci of face-related vision research, identity-invariant head pose estimation has fewer rigorously evaluated systems or generic solutions. In this paper, we discuss the inherent difficulties in head pose estimation and present an organized survey describing the evolution of the field. Our discussion focuses on the advantages and disadvantages of each approach and spans 90 of the most innovative and characteristic papers that have been published on this topic. We compare these systems by focusing on their ability to estimate coarse and fine head pose, highlighting approaches that are well suited for unconstrained environments.

Head Pose Estimation in Computer Vision: A Survey

We present an algorithm for simultaneous face detection, landmarks localization, pose estimation and gender recognition using deep convolutional neural networks (CNN). The proposed method called, HyperFace, fuses the intermediate layers of a deep CNN using a separate CNN followed by a multi-task learning algorithm that operates on the fused features. It exploits the synergy among the tasks which boosts up their individual performances. Additionally, we propose two variants of HyperFace: (1) HyperFace-ResNet that builds on the ResNet-101 model and achieves significant improvement in performance, and (2) Fast-HyperFace that uses a high recall fast face detector for generating region proposals to improve the speed of the algorithm. Extensive experiments show that the proposed models are able to capture both global and local information in faces and performs significantly better than many competitive algorithms for each of these four tasks.

HyperFace: A Deep Multi-Task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition

Driver distraction and inattention are prominent causes of automotive collisions. To enable driver-assistance systems to address these problems, we require new sensing approaches to infer a driver's focus of attention. In this paper, we present a new procedure for static head-pose estimation and a new algorithm for visual 3-D tracking. They are integrated into the novel real-time (30 fps) system for measuring the position and orientation of a driver's head. This system consists of three interconnected modules that detect the driver's head, provide initial estimates of the head's pose, and continuously track its position and orientation in six degrees of freedom. The head-detection module consists of an array of Haar-wavelet Adaboost cascades. The initial pose estimation module employs localized gradient orientation (LGO) histograms as input to support vector regressors (SVRs). The tracking module provides a fine estimate of the 3-D motion of the head using a new appearance-based particle filter for 3-D model tracking in an augmented reality environment. We describe our implementation that utilizes OpenGL-optimized graphics hardware to efficiently compute particle samples in real time. To demonstrate the suitability of this system for real driving situations, we provide a comprehensive evaluation with drivers of varying ages, race, and sex spanning daytime and nighttime conditions. To quantitatively measure the accuracy of system, we compare its estimation results to a marker-based cinematic motion-capture system installed in the automotive testbed.

https://cvrr.ucsd.edu/publications/2010/erik_ITS10.pdf

Head Pose Estimation and Augmented Reality Tracking: An Integrated System and Evaluation for Monitoring Driver Awareness

Recognizing driver awareness is an important prerequisite for the design of advanced automotive safety systems. Since visual attention is constrained to a driver's field of view, knowing where a driver is looking provides useful cues about his activity and awareness of the environment. This work presents an identity-and lighting-invariant system to estimate a driver's head pose. The system is fully autonomous and operates online in daytime and nighttime driving conditions, using a monocular video camera sensitive to visible and near-infrared light. We investigate the limitations of alternative systems when operated in a moving vehicle and compare our approach, which integrates Localized Gradient Orientation histograms with support vector machines for regression. We estimate the orientation of the driver's head in two degrees-of-freedom and evaluate the accuracy of our method in a vehicular testbed equipped with a cinematic motion capture system.

http://cvrr.ucsd.edu/publications/2007/MurphyChutorian_Trivedi_ITSC07.pdf

Head Pose Estimation for Driver Assistance Systems: A Robust Algorithm and Experimental Evaluation

Driver distraction and inattention are prominent causes of automotive collisions. To enable driver assistance systems to address these problems, we require new sensing approaches to infer a driverpsilas focus of attention. In this paper, we present a new 3D tracking algorithm and integrate it into HyHOPE, a real-time (30 fps) hybrid head orientation and position estimation system for driver head tracking. With a single video camera, the system continuously tracks the head in six degrees-of-freedom, initializing itself automatically with separate modules for head detection and head pose estimation. The tracking module provides a fine estimate of the 3D motion of the head, using a new appearance-based algorithm for 3D model tracking by particle filtering in an augmented reality environment. We describe our implementation, which utilizes OpenGL-optimized graphics hardware to efficiently compute particle samples in real-time. To quantitatively evaluate the accuracy of our system, we compare its estimation results to a marker-based cinematic motion capture system installed in an automotive testbed. We evaluate the system on real daytime and nighttime drives with drivers of varying ages, race, and sex.

http://cvrr.ucsd.edu/publications/2008/MurphyChutorian_Trivedi_IV08.pdf

HyHOPE: Hybrid Head Orientation and Position Estimation for vision-based driver head tracking

Methods, systems and apparatus for identifying result images for a query image. One or more labels that are associated with the query image are obtained. Candidate images matching the query labels are identified. Visual similarity scores are generated for the candidate images. Each visual similarity score represents the visual similarity of a respective candidate image to the query image. Relevance scores are generated for each of the candidate images based on the visual similarity scores. Each relevance score represents a measure of relevance of the respective candidate images to the query image. The candidate images are ranked based on the relevance scores, a highest ranking subset of the candidate images being identified as result images and referenced by image search results. The result images can be candidate images that satisfy a similarity condition relative to the query image and other result images.

Erik Murphy-Chutorian

Papers

Head Pose Estimation in Computer Vision: A Survey

Head Pose Estimation and Augmented Reality Tracking: An Integrated System and Evaluation for Monitoring Driver Awareness

Head Pose Estimation for Driver Assistance Systems: A Robust Algorithm and Experimental Evaluation

HyHOPE: Hybrid Head Orientation and Position Estimation for vision-based driver head tracking

Method, system, and computer readable medium for identifying result images based on an image query