Computer vision

In smart cities, information and communication technologies are integrated to exchange real-time data between citizens, governments, and organizations.

Solving problems with deep neural networks typically relies on massive amounts of labeled training data to achieve high performance/b>.

Human visual perception is limited to the visual-optical spectrum.

Person re-identification is the problem of associating observations of targets in different non-overlapping cameras.

This book introduces zero-effort technologies (ZETs), an emerging class of technologies that require little or no effort from the people who use them.

In the human quest for scientific knowledge, empirical evidence is collected by visual perception.

As we increase our reliance on computer-generated information, often using it as part of our decision-making process, we must devise tools to assess the correctness of that information.

This volume presents novel computational models for representing digital humans and their interactions with other virtual characters and meaningful environments.

Our society has entered a data-driven era, one in which not only are enormous amounts of data being generated daily but there are also growing expectations placed on the analysis of this data.

Visualization has become a valuable means for data exploration and analysis.

A common feature of many approaches to modeling sensory statistics is an emphasis on capturing the "e;average.

Interest in visualization design has increased in recent years.

This book focuses on the core question of the necessary architectural support provided by hardware to efficiently run virtual machines, and of the corresponding design of the hypervisors that run them.

This book discusses semantic interaction, a user interaction methodology for visual analytic applications that more closely couples the visual reasoning processes of people with the computation.

Outlier-contaminated data is a fact of life in computer vision.

Heritage sites across the world have witnessed a number of natural calamities, sabotage and damage from visitors, resulting in their present ruined condition.

Because circular objects are projected to ellipses in images, ellipse fitting is a first step for 3-D analysis of circular objects in computer vision applications.

Prevalent types of data in scientific visualization are volumetric data, vector field data, and particle-based data.

This book serves as a practical guide to simulation of 3D deformable solids using the Finite Element Method (FEM).

Background subtraction is a widely used concept for detection of moving objects in videos.

One of the grand challenges of artificial intelligence is to enable computers to interpret 3D scenes and objects from imagery.

Analytical reasoning techniques are methods by which users explore their data to obtain insight and knowledge that can directly support situational awareness and decision making.

As networks of video cameras are installed in many applications like security and surveillance, environmental monitoring, disaster response, and assisted living facilities, among others, image understanding in camera networks is becoming an important area of research and technology development.

Displaying multiple levels of data visually has been proposed to address the challenge of limited screen space.

Being able to recover the shape of 3D deformable surfaces from a single video stream would make it possible to field reconstruction systems that run on widely available hardware without requiring specialized devices.

Plants and trees are among the most complex natural objects.

In its early years, the field of computer vision was largely motivated by researchers seeking computational models of biological vision and solutions to practical problems in manufacturing, defense, and medicine.

Modeling data from visual and linguistic modalities together creates opportunities for better understanding of both, and supports many useful applications.

Face detection, because of its vast array of applications, is one of the most active research areas in computer vision.

The visual recognition problem is central to computer vision research.

This lecture presents research on a general framework for perceptual organization that was conducted mainly at the Institute for Robotics and Intelligent Systems of the University of Southern California.

In Understanding Image Registration, two applications of image registration are discussed: the retinal image registration method and the far infrared image registration method.

This book focuses on research trends in image processing and recognition and corresponding developments.