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  2. Kanade–Lucas–Tomasi feature tracker - Wikipedia

    en.wikipedia.org/wiki/Kanade–Lucas–Tomasi...

    A low-resolution smoothed version of the image can be used to obtain an approximate match. Applying the algorithm to higher resolution images will refine the match obtained at lower resolution. As smoothing extends the range of convergence, the weighting function improves the accuracy of approximation, speeding up the convergence.

  3. Speeded up robust features - Wikipedia

    en.wikipedia.org/wiki/Speeded_up_robust_features

    Non-maximum suppression in a 3×3×3 neighborhood is applied to localize interest points in the image and over scales. The maxima of the determinant of the Hessian matrix are then interpolated in scale and image space with the method proposed by Brown, et al. Scale space interpolation is especially important in this case, as the difference in ...

  4. Facial recognition system - Wikipedia

    en.wikipedia.org/wiki/Facial_recognition_system

    Facial recognition software at a US airport Automatic ticket gate with face recognition system in Osaka Metro Morinomiya Station. A facial recognition system [1] is a technology potentially capable of matching a human face from a digital image or a video frame against a database of faces.

  5. Object detection - Wikipedia

    en.wikipedia.org/wiki/Object_detection

    Objects detected with OpenCV's Deep Neural Network module (dnn) by using a YOLOv3 model trained on COCO dataset capable to detect objects of 80 common classes. Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. [1]

  6. Face Recognition Vendor Test - Wikipedia

    en.wikipedia.org/wiki/Face_Recognition_Vendor_Test

    FRVT Ongoing now has roughly 200 face recognition algorithms and tests against at least six collections of photographs [5] with multiple photographs of more than 8 million people. The best algorithms for 1:1 verification gives False Non Match Rates of 0.0003 at False Match Rates of 0.0001 on high quality visa images. [6] Additional programs:

  7. Viola–Jones object detection framework - Wikipedia

    en.wikipedia.org/wiki/Viola–Jones_object...

    F(0) = 1.0; D(0) = 1.0; i = 0 while F(i) > Ftarget increase i n(i) = 0; F(i)= F(i-1) while F(i) > f × F(i-1) increase n(i) use P and N to train a classifier with n(i) features using AdaBoost Evaluate current cascaded classifier on validation set to determine F(i) and D(i) decrease threshold for the ith classifier (i.e. how many weak ...

  8. 9 Items You Should Actually Store In The Freezer, According ...

    www.aol.com/9-items-actually-store-freezer...

    Bread. Once you bake those grains into bread, it’s wise to store it in the freezer if you don’t anticipate that you can make it through the whole loaf in 2 to 4 days, per the USDA.While you ...

  9. Face detection - Wikipedia

    en.wikipedia.org/wiki/Face_detection

    Examples include upper torsos, pedestrians, and cars. Face detection simply answers two question, 1. are there any human faces in the collected images or video? 2. where is the face located? Face-detection algorithms focus on the detection of frontal human faces. It is analogous to image detection in which the image of a person is matched bit ...