Cascade Classifier — OpenCV v2.4.2 documentation Goal In this tutorial you will learn how to: Use the CascadeClassifier class to detect objects in a video stream. Particularly, we will use the functions:load to load a .xml classifier file. It can be either a Haar or a LBP classiferdetectMultiScale to perform the detection. Code This tutorial code’s is shown lines below. Result Here is the result of running the code above and using as input the video stream of a build-in webcam: Remember to copy the files haarcascade_frontalface_alt.xml and haarcascade_eye_tree_eyeglasses.xml in your current directory. Help and Feedback You did not find what you were looking for?
OpenTSDB - A Distributed, Scalable Monitoring System FaceDetection Note: This tutorial uses the OpenCV 1 interface and (as far as I can tell) is not compatible with the version of haarcascade_frontalface_alt.xml included in the OpenCV 2 code source. See for the OpenCV 2 version of the tutorial which is compatible with the current XML files. How to compile and run the facedetect.c is one of the frequently asked question in the OpenCV Yahoo! Groups. I'll try to explain it to my best. Feel free to edit it if you have some more details.. Haar Like Features: What is that? A recognition process can be much more efficient if it is based on the detection of features that encode some information about the class to be detected. The object detector of OpenCV has been initially proposed by Paul Viola and improved by Rainer Lienhart. After a classifier is trained, it can be applied to a region of interest (of the same size as used during the training) in an input image. Ok.
D3.js - Data-Driven Documents Face Detection and Face Recognition with Real-time Training from a Camera To improve the recognition performance, there are MANY things that can be improved here, some of them being fairly easy to implement. For example, you could add color processing, edge detection, etc. You can usually improve the face recognition accuracy by using more input images, atleast 50 per person, by taking more photos of each person, particularly from different angles and lighting conditions. If you cant take more photos, there are several simple techniques you could use to obtain more training images, by generating new images from your existing ones: You could create mirror copies of your facial images, so that you will have twice as many training images and it wont have a bias towards left or right. You could translate or resize or rotate your facial images slightly to produce many alternative images for training, so that it will be less sensitive to exact conditions. You could add image noise to have more training images that improve the tolerance to noise.
The R Project for Statistical Computing Tutorial: OpenCV haartraining (Rapid Object Detection With A Cascade of Boosted Classifiers Based on Haar-like Features) - Naotoshi Seo Tutorial: OpenCV haartraining (Rapid Object Detection With A Cascade of Boosted Classifiers Based on Haar-like Features) Objective The OpenCV library provides us a greatly interesting demonstration for a face detection. Furthermore, it provides us programs (or functions) that they used to train classifiers for their face detection system, called HaarTraining, so that we can create our own object classifiers using these functions. However, I could not follow how OpenCV developers performed the haartraining for their face detection system exactly because they did not provide us several information such as what images and parameters they used for training. My working environment is Visual Studio + cygwin on Windows XP, or on Linux. FYI: I recommend you to work haartrainig with something different concurrently because you have to wait so many days during training (it would possibly take one week). A picture from the OpenCV website History Tag: SciSoftware ComputerVision FaceDetection OpenCV .
Creative Inspiration: 10 Principles of Design Graphic design is much more than learning how to use the tools within Photoshop. It requires an intimate understanding of the relationship between different objects. This series of paper art poster designs by Efil Türk covers 10 design principles that are core to any designer's success. 1. Balance "Balance as a design principle, places the parts of a visual in an aesthetically pleasing arrangement." 2. "Visual hierarchy is the order in which the human eye perceives what it sees. 3. "Pattern uses the art elements in planned or random repetition to enhance surfaces or paintings." 4. "Rhythm is the repetition of visual movement of the elements-colors, shapes, values, forms, spaces, texture." 5. "Space is an empty place or surface in or around a work of art. 6. "Proportion refers to the relative size and scale of the various elements in a design. 7. "It creates a focal point in a design; it is how we bring attention to what is most important." 8. 9. 10.