Time-of-Flight Cameras: Principles, Methods and Applications

Springer Science & Business Media
4
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Time-of-flight (TOF) cameras provide a depth value at each pixel, from which the 3D structure of the scene can be estimated. This new type of active sensor makes it possible to go beyond traditional 2D image processing, directly to depth-based and 3D scene processing. Many computer vision and graphics applications can benefit from TOF data, including 3D reconstruction, activity and gesture recognition, motion capture and face detection. It is already possible to use multiple TOF cameras, in order to increase the scene coverage, and to combine the depth data with images from several colour cameras. Mixed TOF and colour systems can be used for computational photography, including full 3D scene modelling, as well as for illumination and depth-of-field manipulations. This work is a technical introduction to TOF sensors, from architectural and design issues, to selected image processing and computer vision methods.
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Publisher
Springer Science & Business Media
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Published on
Nov 6, 2012
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Pages
96
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ISBN
9781447146582
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Language
English
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Genres
Computers / Computer Graphics
Computers / Optical Data Processing
Computers / Software Development & Engineering / General
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This content is DRM protected.
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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.

Our knowledge of the surrounding world is obtained by our senses of perception. Among them, vision is undoubtedly the most important for the information it can provide. In artificial systems, this discipline, known as Computer Vision, mainly tries to identify physical objects and scenes from captured images to be able to make useful decisions. For that, the processing and analysis of images, video sequences, views from multiple cameras, or multi-dimensional data like a medical scanner, are carried out.

In this context, motion plays a main role since it provides a stimulus for detecting objects in movement within the observed scene. Moreover, motion allows other characteristics to be obtained such as, for instance, object shape, speed or trajectory, which are meaningful for detection and recognition. Nevertheless, the motion observable in a visual input could be due to different factors: movement of the imaged objects (targets and/or vacillating background elements), movement of the observer, motion of the light sources or a combination of (some of) them. Therefore, image analysis for motion detection will be conditional upon the considered factors. In particular, in this work, there is a focus on motion detection from images captured by perspective and fisheye still cameras. As cameras are still, ego-motion is not considered, although all the other factors can occur at any time.

With that assumption, the work proposes a complete sensor-independent visual system which provides robust target motion detection. So, firstly, the way sensors obtain images of the world, in terms of resolution distribution and pixel neighbourhood, is studied. In that way, a proper spatial analysis of motion can be carried out. Then, a novel background maintenance approach for robust target motion detection is implemented. On this matter, two different situations are considered: (1) a fixed camera observing a constant background where interest objects are moving; and, (2) a still camera observing interest objects in movement within a dynamic background. The reason for this distinction lies on developing, from the first analysis, a surveillance mechanism which removes the constraint of observing a scene free of foreground elements during several seconds when a reliable initial background model is obtained, since that situation cannot be guaranteed when a robotic system works in an unknown environment. Furthermore, on the way to achieve an ideal background maintenance system, other canonical problems are addressed such that the proposed approach successfully deals with (gradual and global) changes in illumination, the distinction between foreground and background elements in terms of motion and motionless, and non-uniform vacillating backgrounds.

In this context, motion plays a main role since it provides a stimulus for detecting objects in movement within the observed scene. Moreover, motion allows other characteristics to be obtained such as, for instance, object shape, speed or trajectory, which are meaningful for detection and recognition. Nevertheless, the motion observable in a visual input could be due to different factors: movement of the imaged objects (targets and/or vacillating background elements), movement of the observer, motion of the light sources or a combination of (some of) them. Therefore, image analysis for motion detection will be conditional upon the considered factors. In particular, in this work, there is a focus on motion detection from images captured by perspective and fisheye still cameras. As cameras are still, ego-motion is not considered, although all the other factors can occur at any time.

With that assumption, the work proposes a complete sensor-independent visual system which provides robust target motion detection. So, firstly, the way sensors obtain images of the world, in terms of resolution distribution and pixel neighbourhood, is studied. In that way, a proper spatial analysis of motion can be carried out. Then, a novel background maintenance approach for robust target motion detection is implemented. On this matter, two different situations are considered: (1) a fixed camera observing a constant background where interest objects are moving; and, (2) a still camera observing interest objects in movement within a dynamic background. The reason for this distinction lies on developing, from the first analysis, a surveillance mechanism which removes the constraint of observing a scene free of foreground elements during several seconds when a reliable initial background model is obtained, since that situation cannot be guaranteed when a robotic system works in an unknown environment. Furthermore, on the way to achieve an ideal background maintenance system, other canonical problems are addressed such that the proposed approach successfully deals with (gradual and global) changes in illumination, the distinction between foreground and background elements in terms of motion and motionless, and non-uniform vacillating backgrounds.

In this context, motion plays a main role since it provides a stimulus for detecting objects in movement within the observed scene. Moreover, motion allows other characteristics to be obtained such as, for instance, object shape, speed or trajectory, which are meaningful for detection and recognition. Nevertheless, the motion observable in a visual input could be due to different factors: movement of the imaged objects (targets and/or vacillating background elements), movement of the observer, motion of the light sources or a combination of (some of) them. Therefore, image analysis for motion detection will be conditional upon the considered factors. In particular, in this work, there is a focus on motion detection from images captured by perspective and fisheye still cameras. As cameras are still, ego-motion is not considered, although all the other factors can occur at any time.

With that assumption, the work proposes a complete sensor-independent visual system which provides robust target motion detection. So, firstly, the way sensors obtain images of the world, in terms of resolution distribution and pixel neighbourhood, is studied. In that way, a proper spatial analysis of motion can be carried out. Then, a novel background maintenance approach for robust target motion detection is implemented. On this matter, two different situations are considered: (1) a fixed camera observing a constant background where interest objects are moving; and, (2) a still camera observing interest objects in movement within a dynamic background. The reason for this distinction lies on developing, from the first analysis, a surveillance mechanism which removes the constraint of observing a scene free of foreground elements during several seconds when a reliable initial background model is obtained, since that situation cannot be guaranteed when a robotic system works in an unknown environment. Furthermore, on the way to achieve an ideal background maintenance system, other canonical problems are addressed such that the proposed approach successfully deals with (gradual and global) changes in illumination, the distinction between foreground and background elements in terms of motion and motionless, and non-uniform vacillating backgrounds.

With that assumption, the work proposes a complete sensor-independent visual system which provides robust target motion detection. So, firstly, the way sensors obtain images of the world, in terms of resolution distribution and pixel neighbourhood, is studied. In that way, a proper spatial analysis of motion can be carried out. Then, a novel background maintenance approach for robust target motion detection is implemented. On this matter, two different situations are considered: (1) a fixed camera observing a constant background where interest objects are moving; and, (2) a still camera observing interest objects in movement within a dynamic background. The reason for this distinction lies on developing, from the first analysis, a surveillance mechanism which removes the constraint of observing a scene free of foreground elements during several seconds when a reliable initial background model is obtained, since that situation cannot be guaranteed when a robotic system works in an unknown environment. Furthermore, on the way to achieve an ideal background maintenance system, other canonical problems are addressed such that the proposed approach successfully deals with (gradual and global) changes in illumination, the distinction between foreground and background elements in terms of motion and motionless, and non-uniform vacillating backgrounds.
Underwater surveys have numerous scientific applications, and optical imaging by underwater vehicles can provide high-resolution visual information of the ocean floor. However, the particular challenges of the underwater medium, such as light attenuation, require the imaging to be performed as close to the seabed as possible. Hence, optically mapping large seafloor areas can only be achieved by building image mosaics from a set of reduced-area pictures. Unfortunately, the seams along image boundaries are often noticeable, requiring image blending, the merging step in which these artifacts are minimized. Yet processing tools and bottlenecks have restricted underwater photo-mosaics to small areas despite the hundreds of thousands of square meters that modern surveys can cover.

This work proposes strategies and solutions to tackle the problem of building photo-mosaics of very large underwater optical surveys, presenting contributions to the image preprocessing, enhancing and blending steps, and resulting in an improved visual quality of the final photo-mosaic.

The text opens with a comprehensive review of mosaicing and blending techniques, before proposing an approach for large scale underwater image mosaicing and blending. In the image preprocessing step, a depth dependent illumination compensation function is used to solve the non-uniform illumination appearance due to light attenuation. For image enhancement, the image contrast variability due to different acquisition altitudes is compensated using an adaptive contrast enhancement based on an image quality reference selected through a total variation criterion. In the blending step, a graph-cut strategy operating in the image gradient domain over the overlapping regions is suggested. Next, an out-of-core blending strategy for very large scale photo-mosaics is presented and tested on real data. Finally, the performance of the approach is evaluated and compared with other approaches.
Much of our understanding of the relationships among geometric struc tures in images is based on the shape of these structures and their relative orientations, positions and sizes. Thus, developing quantitative methods for capturing shape information from digital images is an important area for computer vision research. This book describes the theory, implemen tation, and application of two multi resolution image shape description methods. The author begins by motivating the need for quantitative methods for describing both the spatial and intensity variations of struc tures in grey-scale images. Two new methods which capture this informa tion are then developed. The first, the intensity axis of symmetry, is a collection of branching and bending surfaces which correspond to the skeleton of the image. The second method, multiresolution vertex curves, focuses on surface curvature properties as the image is blurred by a sequence of Gaussian filters. Implementation techniques for these image shape descriptions are described in detail. Surface functionals are mini mized subject to symmetry constraints to obtain the intensity axis of symmetry. Robust numerical methods are developed for calculating and following vertex curves through scale space. Finally, the author demon strates how grey-scale images can be segmented into geometrically coher ent regions using these shape description techniques. Building quantita tive analysis applications in terms of these visually sensible image regions promises to be an exciting area of biomedical computer vision research. v Acknowledgments This book is a corrected and revised version of the author's Ph. D.
Learn how to model and train advanced neural networks to implement a variety of Computer Vision tasksKey Features Train different kinds of deep learning model from scratch to solve specific problems in Computer Vision Combine the power of Python, Keras, and TensorFlow to build deep learning models for object detection, image classification, similarity learning, image captioning, and more Includes tips on optimizing and improving the performance of your models under various constraintsBook Description

Deep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning.

In this book, you will learn different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more. You will also explore their applications using popular Python libraries such as TensorFlow and Keras. This book will help you master state-of-the-art, deep learning algorithms and their implementation.

What you will learnSet up an environment for deep learning with Python, TensorFlow, and KerasDefine and train a model for image and video classificationUse features from a pre-trained Convolutional Neural Network model for image retrievalUnderstand and implement object detection using the real-world Pedestrian Detection scenarioLearn about various problems in image captioning and how to overcome them by training images and text togetherImplement similarity matching and train a model for face recognitionUnderstand the concept of generative models and use them for image generationDeploy your deep learning models and optimize them for high performanceWho this book is for

This book is targeted at data scientists and Computer Vision practitioners who wish to apply the concepts of Deep Learning to overcome any problem related to Computer Vision. A basic knowledge of programming in Python—and some understanding of machine learning concepts—is required to get the best out of this book.

Many textbooks on computer vision can be unwieldy and intimidating in their coverage of this extensive discipline. This textbook addresses the need for a concise overview of the fundamentals of this field.

Concise Computer Vision provides an accessible general introduction to the essential topics in computer vision, highlighting the role of important algorithms and mathematical concepts. Classroom-tested programming exercises and review questions are also supplied at the end of each chapter.

Topics and features: provides an introduction to the basic notation and mathematical concepts for describing an image, and the key concepts for mapping an image into an image; explains the topologic and geometric basics for analysing image regions and distributions of image values, and discusses identifying patterns in an image; introduces optic flow for representing dense motion, and such topics in sparse motion analysis as keypoint detection and descriptor definition, and feature tracking using the Kalman filter; describes special approaches for image binarization and segmentation of still images or video frames; examines the three basic components of a computer vision system, namely camera geometry and photometry, coordinate systems, and camera calibration; reviews different techniques for vision-based 3D shape reconstruction, including the use of structured lighting, stereo vision, and shading-based shape understanding; includes a discussion of stereo matchers, and the phase-congruency model for image features; presents an introduction into classification and learning, with a detailed description of basic AdaBoost and the use of random forests.

This concise and easy to read textbook/reference is ideal for an introductory course at third- or fourth-year level in an undergraduate computer science or engineering programme.

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