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Articles related to "detector"


Face Detection Models: Which to Use and Why?

  • However, this article won’t be dwelling on those aspects and we will merely be trying to draw bounding boxes on faces using pre-trained models like Haar cascades, dlib frontal face detector, MTCNN, and a Caffe model using OpenCV’s DNN module.
  • We will be using Haar, dlib, Multi-task Cascaded Convolutional Neural Network (MTCNN), and OpenCV’s DNN module.
  • They were proposed way back in 2001 by Paul Viola and Micheal Jones in their paper, “Rapid Object Detection using a Boosted Cascade of Simple Features.” It is super fast to work with and like the simple CNN, it extracts a lot of features from images.
  • Dlib and MTCNN are both pip installable, whereas Haar Cascades and DNN face detectors require OpenCV.
  • Let’s give credit where it’s due as only Haar cascade was the only model able to detect the face in the dark in a couple of frames, while the DNN model provided a false positive during that time.

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Face Detection Models: Which to Use and Why?

  • However, this article won’t be dwelling on those aspects and we will merely be trying to draw bounding boxes on faces using pre-trained models like Haar cascades, dlib frontal face detector, MTCNN, and a Caffe model using OpenCV’s DNN module.
  • We will be using Haar, dlib, Multi-task Cascaded Convolutional Neural Network (MTCNN), and OpenCV’s DNN module.
  • They were proposed way back in 2001 by Paul Viola and Micheal Jones in their paper, “Rapid Object Detection using a Boosted Cascade of Simple Features.” It is super fast to work with and like the simple CNN, it extracts a lot of features from images.
  • Dlib and MTCNN are both pip installable, whereas Haar Cascades and DNN face detectors require OpenCV.
  • Let’s give credit where it’s due as only Haar cascade was the only model able to detect the face in the dark in a couple of frames, while the DNN model provided a false positive during that time.

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Quantum fluctuations have been shown to affect macroscopic objects

  • Light enters the cavity in an ‘unsqueezed’ state — that is, quantum fluctuations related to the phase and amplitude of light (uncertainties in the probability distribution of measurements) do not correlate with each other.
  • Phase-squeezed light, in which the uncertainty associated with the phase is squeezed, has been used to reduce shot noise for both LIGO3 and Virgo, the gravitational-wave detector located in Cascina, Italy4.
  • now confirm that the ponderomotive effect occurs in the optical cavities of the LIGO interferometer, and have investigated whether it can be used in combination with squeezed-vacuum states to reduce quantum noise below the SQL in measurements of mirror position in the cavities.
  • At present, such detectors use phase-squeezed vacuum states to reduce shot noise, without considering the correlations that are introduced by the interferometer mirrors.

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Curve Detecting Neurons

  • We found that practitioners generally had to choose between several algorithms, each with significant trade-offs such as robustness to different kinds of visual “noise” (for instance, texture), even in images much less complex than the natural images in ImageNet. For instance, this answer on StackOverflow claims “The problem [of curve detection], in general, is a very challenging one and, except for toy examples, there are no good solutions.” Additionally, many classical curve detection algorithms are too slow to run in real-time, or require often intractable amounts of memory..
  • Images that cause curve detectors to activate weakly, such as edges or angles, are a natural extension of the algorithm that InceptionV1 uses to implement curve detection.
  • Every time we use feature visualization to make curve neurons fire as strongly as possible we get images of curves, even when we explicitly incentivize the creation of different kinds of images using a diversity term.

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