This is an informal review of the principle techniques and issues associated with prestack depth imaging. The intended audience for this book would be those seismic interpreters, processors, managers, and explorationists who require basic familiarity with the technology that has so greatly expanded the range of geologic structures that can be successfully imaged. The emphasis of the book is on velocity-model building techniques that are the key to successful depth imaging.

Cameras for 3D depth imaging, using either time-of-flight (ToF) or structured light sensors, have received a lot of attention recently and have been improved considerably over the last few years. The present techniques make full-range 3D data available at video frame rates, and thus pave the way for a much broader application of 3D vision systems. A series of workshops have closely followed the developments within ToF imaging over the years. Today, depth imaging workshops can be found at every major computer vision conference. The papers presented in this volume stem from a seminar on Time-of-Flight Imaging held at Schloss Dagstuhl in October 2012. They cover all aspects of ToF depth imaging, from sensors and basic foundations, to algorithms for low level processing, to important applications that exploit depth imaging. In addition, this book contains the proceedings of a workshop on Imaging New Modalities, which was held at the German Conference on Pattern Recognition in Saarbrücken, Germany, in September 2013. A state-of-the-art report on the Kinect sensor and its applications is followed by two reports on local and global ToF motion compensation and a novel depth capture system using a plenoptic multi-lens multi-focus camera sensor.

Riding on the success of 3D cinema blockbusters and advances in stereoscopic display technology, 3D video applications have gathered momentum in recent years. 3D-TV System with Depth-Image-Based Rendering: Architectures, Techniques and Challenges surveys depth-image-based 3D-TV systems, which are expected to be put into applications in the near future. Depth-image-based rendering (DIBR) significantly enhances the 3D visual experience compared to stereoscopic systems currently in use. DIBR techniques make it possible to generate additional viewpoints using 3D warping techniques to adjust the perceived depth of stereoscopic videos and provide for auto-stereoscopic displays that do not require glasses for viewing the 3D image. The material includes a technical review and literature survey of components and complete systems, solutions for technical issues, and implementation of prototypes. The book is organized into four sections: System Overview, Content Generation, Data Compression and Transmission, and 3D Visualization and Quality Assessment. This book will benefit researchers, developers, engineers, and innovators, as well as advanced undergraduate and graduate students working in relevant areas.

As one of the world's most important crops, potatoes play an important role in maintaining the stability of the global food supply. Many countries, including China, believe that food supply security is a basic condition for maintaining national stability and development. Therefore, potatoes can not only solve the problem of international food shortage, but also promote the development of international trade. In recent years, with the continuous improvement of planting technology, the global production and trade volume of potatoes have also been continuously increasing. However, the development of traditional potato quality grading technology is relatively slow. Currently, it still relies on manual sorting in many countries and regions. Because workers can not keep their attention for a long time under huge work pressure and their understanding of grading standards is inconsistent, large amount of wrong potato grading often occurs. This result not only affects farmers' income, but also causes serious waste in the potato processing due to unqualified raw potatoes. In addition, with the continuous increase of manual wages, the cost of manual grading of potatoes has under challenge. Therefore, achieving automation of potato quality grading is imperative. Traditional grading system mainly uses cameras to capture potato color images, and achieves potato quality grading through color information analysis. This method can reach high success rate for certain defects detection, such as green skin, surface rot and mechanical damage. Due to the variety of shapes of potatoes growing underground, the appearance defects, such as bending, bump and hollow, are widely existing. These abnormal samples may fail to be detected and grade to wrong quality groups, the 3D appearance information cannot be fully perceived in 2D color images. In response to such issues, we have decided to build a machine vision system based on depth cameras, which can obtain depth images of potatoes with 3D shape information. Unlike each pixel in a color image that stores color information, each pixel in a depth image stores the distance from the target to the camera. Therefore, the potato 3D surface features can be sensed and used for bump and hollow defects detection. To capture high-quality depth images, we have constructed a specialized depth imaging system, and developed the image acquisition software based on OpenCV and OpenNI framework. Then, each potato surface features are analyzed and extracted for shape analysis, defect detection, and overall quality grading. In recent years, machine learning technology has developed rapidly and has been widely applied in fields such as object recognition and feature detection. Hence, we also apply machine learning technology to the field of potato quality grading. By developing a machine learning model based on convolutional neural networks, we can directly input potato depth images and get the corresponding quality level of the samples. The experiment achieved good grading results. Since color and depth images of potatoes are actually collected simultaneously in data collection step, a novel algorithm is developed for potato 3D model rebuilding. The method is based on Point Cloud Library and OpenGL technology, and it shows the advantage in solving the problem of data traceability, especially when users have objections to automatic quality classification results. This model not only displays 3D potato shape model, but also supports scaling and 360-degree rotation operations. Overall, we believe that with the development of machine learning and depth sensing, potato quality grading systems will become more intelligent, efficient and low-cost.

Compilation of material published 1983-2004.

Get a broad overview of the different modalities of immersive video technologies—from omnidirectional video to light fields and volumetric video—from a multimedia processing perspective. From capture to representation, coding, and display, video technologies have been evolving significantly and in many different directions over the last few decades, with the ultimate goal of providing a truly immersive experience to users. After setting up a common background for these technologies, based on the plenoptic function theoretical concept, Immersive Video Technologies offers a comprehensive overview of the leading technologies enabling visual immersion, including omnidirectional (360 degrees) video, light fields, and volumetric video. Following the critical components of the typical content production and delivery pipeline, the book presents acquisition, representation, coding, rendering, and quality assessment approaches for each immersive video modality. The text also reviews current standardization efforts and explores new research directions. With this book the reader will a) gain a broad understanding of immersive video technologies that use three different modalities: omnidirectional video, light fields, and volumetric video; b) learn about the most recent scientific results in the field, including the recent learning-based methodologies; and c) understand the challenges and perspectives for immersive video technologies. - Describes the whole content processing chain for the main immersive video modalities (omnidirectional video, light fields, and volumetric video) - Offers a common theoretical background for immersive video technologies based on the concept of plenoptic function - Presents some exemplary applications of immersive video technologies

This book constitutes the refereed proceedings of the 37th Computer Graphics International Conference, CGI 2020, held in Geneva, Switzerland, in October 2020. The conference was held virtually. The 43 full papers presented together with 3 short papers were carefully reviewed and selected from 189 submissions. The papers address topics such as: virtual reality; rendering and textures; augmented and mixed reality; video processing; image processing; fluid simulation and control; meshes and topology; visual simulation and aesthetics; human computer interaction; computer animation; geometric computing; robotics and vision; scientific visualization; and machine learning for graphics.