In this paper, we describe the development of a rapidly reconfigurable system in which the users’ tacit knowledge and requirements are
elicited via a process of Interactive Evolution, finding the image processing parameters to achieve the required goals without any need for
specialised knowledge of the machine Vision system. We show that the resulting segmentation can be quickly and easily evolved from
scratch, and achieves detection rates comparable to those of a hand-tuned system on a hot-rolled steel defect recognition problem.
In this paper, we propose
a hierarchical clustering method using visual, textual and link
analysis. By using a Vision-based page segmentation algorithm, a
web page is partitioned into blocks, and the textual and link information
of an image can be accurately extracted from the block containing
that image.
object recognition using fast adaptive hough transform 快速自適應霍夫變換
作者：D.D. Haule. A.S. Malowany
Computer Vision and Robotics Laboratory
Department of Electrical Engineering
McGill University。IEEE 1989的文章，對指導霍夫變換檢測目標的識別有一定的參考意義
VideoMan is a very easy image acquisition library. It is able to manage many video inputs at the same time, such as WDM Firewire and USB cameras, PointGrey cameras and video files. It can also render the inputs using OpenGL. Suitable for computer Vision
VideoMan is a very easy image acquisition library. It is able to manage many video inputs at the same time, such as WDM Firewire and USB cameras, PointGrey cameras and video files. It can also render the inputs using OpenGL. Suitable for computer Vision
VideoMan is a very easy image acquisition library. It is able to manage many video inputs at the same time, such as WDM Firewire and USB cameras, PointGrey cameras and video files. It can also render the inputs using OpenGL. Suitable for computer Vision
VideoMan is a very easy image acquisition library. It is able to manage many video inputs at the same time, such as WDM Firewire and USB cameras, PointGrey cameras and video files. It can also render the inputs using OpenGL. Suitable for computer Vision
This library implements the KLT Tracking algorithm [2004] for Feature Tracking in Video useful in computer Vision tasks like object recognition, image indexing, tracking and structure from motion. This implementation uses programmable Graphics Hardware to achieve considerable speedup in the running time of the GPU-based implementation.
I would like to thank my advisor, Dr. A. Lynn Abbott, for helping me throughout
my research, Gary Fleming and the rest of the people at NASA Langley who provided all
the flight information and image sequences, and my parents who supported me in my
decision to enter graduate study. Also, thanks to Phichet Trisirisipal and Xiaojin Gong
for helping when I had computer Vision questions, and Nathan Herald for his help
creating an illustration.
