Die Kennzeichenerkennung ist eine Bildverarbeitungstechnologie, mit der Fahrzeuge anhand ihrer Kennzeichen identifiziert werden. Diese Technologie wird in verschiedenen Sicherheits- und Verkehrsanwendungen eingesetzt.
How you can set up and install the OpenCV library and use it with Python. Learn how to install OpenCV with PyCharm IDE.We learn how to build and use OpenCV.
Congratulation !, you've got your new rasperry pi camera ! Isn't-it cute ? But after first try, you discover that it's not an usb-webcam. :-( Thus, OpenCV doesn't work natively. (forget cvCaptureFromCAM for example and all your wonderful apps you've thought up !) However, some nice apps (such as raspivid or raspistill) controls the pi…
Mat estimateRigidTransform(const Mat& srcpt, const Mat& dstpt, bool fullAffine)¶ Computes optimal affine transformation between two 2D point sets Parameters: * srcpt – The first input 2D point set * dst – The second input 2D point set of the same size and the same type as A * fullAffine – If true, the function finds the optimal affine transformation with no any additional resrictions (i.e. there are 6 degrees of freedom); otherwise, the class of transformations to choose from is limited to combinations of translation, rotation and uniform scaling (i.e. there are 5 degrees of freedom) The function finds the optimal affine transform [A|b] (a 2 \times 3 floating-point matrix) that approximates best the transformation from \texttt{srcpt}_i to \texttt{dstpt}_i : [A^*|b^*] = arg \min _{[A|b]} \sum _i \| \texttt{dstpt} _i - A { \texttt{srcpt} _i}^T - b \| ^2 where [A|b] can be either arbitrary (when fullAffine=true ) or have form
We propose a joint optical flow and principal component analysis (PCA) method for motion detection. PCA is used to analyze optical flows so that major optical flows corresponding to moving objects in a local window can be better extracted. This joint approach can efficiently detect moving objects and more successfully suppress small turbulence. It is particularly useful for motion detection from outdoor videos with low quality. It can also effectively delineate moving objects in both static and dynamic background. Experimental results demonstrate that this approach outperforms other existing methods by extracting the moving objects more completely with lower false alarms.
Image alignment is the process of matching one image called template (let's denote it as T) with another image, I (see the above figure). There are many applications for image alignment, such as tracking objects on video, motion analysis, and many other tasks of computer vision. In 1981, Bruse D. Lucas and Takeo Kanade proposed a new technique that used image intensity gradient information to search for the best match between a template T and another image I. The proposed algorithm has been widely used in the field of computer vision for the last 20 years, and has had many modifications and extensions. One of such modifications is an algorithm proposed by Simon Baker, Frank Dellaert, and Iain Matthews. Their algorithm is much more computationally effective than the original Lucas-Kanade algorithm.
This wiki is intended to provide a space for COMP4411 students (and staff) to share hints, tips, ideas, solutions and the like, related to the experimental robotics course.