Allows anyone with basic OpenCV experience to rapidly obtain skills in many computer vision topics, for research or commercial use
Each chapter is a separate project covering a computer vision problem, written by a professional with proven experience on that topic.
All projects include a step-by-step tutorial and full source-code, using the C++ interface of OpenCV.
Learn how to build your own computer vision (CV) applications quickly and easily with SimpleCV, an open source framework written in Python. Through examples of real-world applications, this hands-on guide introduces you to basic CV techniques for collecting, processing, and analyzing streaming digital images. You’ll then learn how to apply these methods with SimpleCV, using sample Python code. All you need to get started is a Windows, Mac, or Linux system, and a willingness to put CV to work in a variety of ways. Programming experience is optional....
This paper introduces a novel generation system that composes humanlike descriptions of images from computer vision detections. By leveraging syntactically informed word co-occurrence statistics, the generator ﬁlters and constrains the noisy detections output from a vision system to generate syntactic trees that detail what the computer vision system sees. Results show that the generation system outperforms state-of-the-art systems, automatically generating some of the most natural image descriptions to date. ...
Image formation, feature detection and matching, segmentation, feature-based alignment, structure from motion is the main content of the book "Computer vision algorithms and applications". Invite you to consult the detailed content lectures to capture details.
What kind of information can we extract from an image, why study computer vision, why is computer vision difficult,... To help you answer the questions above, you are invited to refer to the content of the curriculum ''Comp 776: Computer vision". Hope this is useful references for you.
Face plays an important role in human communication. Facial expressions and gestures
incorporate nonverbal information which contributes to human communication. By
recognizing the facial expressions from facial images, a number of applications in the field of
human computer interaction can be facilitated. Last two decades, the developments, as well
as the prospects in the field of multimedia signal processing have attracted the attention of
many computer vision researchers to concentrate in the problems of the facial expression
This paper presents GpuCV, an open source multi-platform library for easily developing GPU-accelerated image processing and Computer Vision operators and applications. It is meant for computer vision scientist not familiar with GPU technologies. It is designed to be compatible with Intel’s OpenCV library by oﬀering GPU-accelerated operators that can be integrated into native OpenCV applications.
Computer vision uses digital computer techniques to extract, characterize, and interpret
information in visual images of a three-dimensional world. The goal of computer vision is
primarily to enable engineering systems to model and manipulate the environment by using
The field of computer vision can be characterized as immature and diverse. Even
though earlier work exists, it was not until the late 1970s that a more focused study of the
field started when computers could manage the processing of large data sets such as images....
Research in computer vision has exponentially increased in the last two decades due to the
availability of cheap cameras and fast processors. This increase has also been accompanied
by a blurring of the boundaries between the different applications of vision, making it truly
interdisciplinary. In this book we have attempted to put together state-of-the-art research
and developments in segmentation and pattern recognition.