Doug DeCarlo

Bio: Doug DeCarlo is an academic researcher from Rutgers University. The author has contributed to research in topics: Rendering (computer graphics) & Eye tracking. The author has an hindex of 24, co-authored 33 publications receiving 3676 citations. Previous affiliations of Doug DeCarlo include University of Pennsylvania & Technical University of Berlin.

Suggestive contours for conveying shape

Abstract: In this paper, we describe a non-photorealistic rendering system that conveys shape using lines. We go beyond contours and creases by developing a new type of line to draw: the suggestive contour. Suggestive contours are lines drawn on clearly visible parts of the surface, where a true contour would first appear with a minimal change in viewpoint. We provide two methods for calculating suggestive contours, including an algorithm that finds the zero crossings of the radial curvature. We show that suggestive contours can be drawn consistently with true contours, because they anticipate and extend them. We present a variety of results, arguing that these images convey shape more effectively than contour alone.

Stylization and abstraction of photographs

Abstract: Good information design depends on clarifying the meaningful structure in an image. We describe a computational approach to stylizing and abstracting photographs that explicitly responds to this design goal. Our system transforms images into a line-drawing style using bold edges and large regions of constant color. To do this, it represents images as a hierarchical structure of parts and boundaries computed using state-of-the-art computer vision. Our system identifies the meaningful elements of this structure using a model of human perception and a record of a user's eye movements in looking at the photo; the system renders a new image using transformations that preserve and highlight these visual elements. Our method thus represents a new alternative for non-photorealistic rendering both in its visual style, in its approach to visual form, and in its techniques for interaction.

Gaze-based interaction for semi-automatic photo cropping

Abstract: We present an interactive method for cropping photographs given minimal information about important content location, provided by eye tracking. Cropping is formulated in a general optimization framework that facilitates adding new composition rules, and adapting the system to particular applications. Our system uses fixation data

The integration of optical flow and deformable models with applications to human face shape and motion estimation

Abstract: We present a formal methodology for the integration of optical flow and deformable models. The optical flow constraint equation provides a non-holonomic constraint on the motion of the deformable model. In this augmented system, forces computed from edges and optical flow are used simultaneously. When this dynamic system is solved, a model-based least-squares solution for the optical flow is obtained and improved estimation results are achieved. The use of a 3-D model reduces or eliminates problems associated with optical flow computation. This approach instantiates a general methodology for treating visual cues as constraints on deformable models. We apply this framework to human face shape and motion estimation. Our 3-D deformable face model uses a small number of parameters to describe a rich variety of face shapes and facial expressions. We present experiments in extracting the shape and motion of a face from image sequences.

Robust clustering of eye movement recordings for quantification of visual interest

Abstract: Characterizing the location and extent of a viewer's interest, in terms of eye movement recordings, informs a range of investigations in image and scene viewing. We present an automatic data-driven method for accomplishing this, which clusters visual point-of-regard (POR) measurements into gazes and regions-of-interest using the mean shift procedure. Clusters produced using this method form a structured representation of viewer interest, and at the same time are replicable and not heavily influenced by noise or outliers. Thus, they are useful in answering fine-grained questions about where and how a viewer examined an image.

Computer Vision: Algorithms and Applications

Abstract: 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.

Global contrast based salient region detection

Abstract: Automatic estimation of salient object regions across images, without any prior assumption or knowledge of the contents of the corresponding scenes, enhances many computer vision and computer graphics applications. We introduce a regional contrast based salient object detection algorithm, which simultaneously evaluates global contrast differences and spatial weighted coherence scores. The proposed algorithm is simple, efficient, naturally multi-scale, and produces full-resolution, high-quality saliency maps. These saliency maps are further used to initialize a novel iterative version of GrabCut, namely SaliencyCut, for high quality unsupervised salient object segmentation. We extensively evaluated our algorithm using traditional salient object detection datasets, as well as a more challenging Internet image dataset. Our experimental results demonstrate that our algorithm consistently outperforms 15 existing salient object detection and segmentation methods, yielding higher precision and better recall rates. We also show that our algorithm can be used to efficiently extract salient object masks from Internet images, enabling effective sketch-based image retrieval (SBIR) via simple shape comparisons. Despite such noisy internet images, where the saliency regions are ambiguous, our saliency guided image retrieval achieves a superior retrieval rate compared with state-of-the-art SBIR methods, and additionally provides important target object region information.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Abstract: From the Publisher: The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Learning to Detect a Salient Object

Abstract: In this paper, we study the salient object detection problem for images. We formulate this problem as a binary labeling task where we separate the salient object from the background. We propose a set of novel features, including multiscale contrast, center-surround histogram, and color spatial distribution, to describe a salient object locally, regionally, and globally. A conditional random field is learned to effectively combine these features for salient object detection. Further, we extend the proposed approach to detect a salient object from sequential images by introducing the dynamic salient features. We collected a large image database containing tens of thousands of carefully labeled images by multiple users and a video segment database, and conducted a set of experiments over them to demonstrate the effectiveness of the proposed approach.

Learning to predict where humans look

Abstract: For many applications in graphics, design, and human computer interaction, it is essential to understand where humans look in a scene Where eye tracking devices are not a viable option, models of saliency can be used to predict fixation locations Most saliency approaches are based on bottom-up computation that does not consider top-down image semantics and often does not match actual eye movements To address this problem, we collected eye tracking data of 15 viewers on 1003 images and use this database as training and testing examples to learn a model of saliency based on low, middle and high-level image features This large database of eye tracking data is publicly available with this paper