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    Vitaliy_Kiselev
    Good quote: On crisis and triumth
    • Despite the bourgeois howls "about the crisis of communism" there is no force in the world to stop the destruction of the old capitalist system, there is no force in the world to prevent the historical development of mankind to communism: The socialist October revolution has triumphed, and will always triumph.

      Enver Hoxha

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    Vitaliy_Kiselev
    Sensorless camera for digits recognition
    • image

      Machine learning (ML) has been widely applied to image classification. Here, we extend this application to data
      generated by a camera comprised of only a standard CMOS image sensor with no lens. We first created a
      database of lensless images of handwritten digits. Then, we trained a ML algorithm on this dataset. Finally, we demonstrated that the trained ML algorithm is able to classify the digits with accuracy as high as 99% for 2
      digits. Our approach clearly demonstrates the potential for non-human cameras in machine-based decisionmaking scenarios.

      image

      https://arxiv.org/abs/1709.00408

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    Glenn7
    Photo is enough to tell if you are gay to everyone
    • We show that faces contain much more information about sexual orientation than can be perceived and interpreted by the human brain. We used deep neural networks to extract features from 35,326 facial images. These features were entered into a logistic regression aimed at classifying sexual orientation. Given a single facial image, a classifier could correctly distinguish between gay and heterosexual men in 81% of cases, and in 74% of cases for women. Human judges achieved much lower accuracy: 61% for men and 54% for women. The accuracy of the algorithm increased to 91% and 83%, respectively, given five facial images per person. Facial features employed by the classifier included both fixed (e.g., nose shape) and transient facial features (e.g., grooming style). Consistent with the prenatal hormone theory of sexual orientation, gay men and women tended to have gender-atypical facial morphology, expression, and grooming styles. Prediction models aimed at gender alone allowed for detecting gay males with 57% accuracy and gay females with 58% accuracy. Those findings advance our understanding of the origins of sexual orientation and the limits of human perception. Additionally, given that companies and governments are increasingly using computer vision algorithms to detect people’s intimate traits, our findings expose a threat to the privacy and safety of gay men and women.

      https://psyarxiv.com/hv28a/

    2 comments 3 comments Vitaliy_KiselevSeptember 8Last reply - September 10 by Glenn7 Subscribe to this blog
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    Glenn7
    PV Lab: Issues measuring sensor spectrum response
    • Interesting piece

      The common approach is to use a wide spectrum light source, for example a tungsten source, and filter the light source with a monochromator to only keep a narrow band spectrum centered around the test wavelength. The FWHM (Full Width Half Maximum) of such a spectrum is typically 5nm, 10nm, 20nm or more.

      This beam then hits the sensor on a small area and the center of the spot, typically about 10 to 50% of its area, is used to measure the average sensor output for each color channel. After calibration, this average value represents the responsivity or the QE (Quantum Efficiency) of the sensor for that central wavelength.

      The measurement is then repeated in steps equal to or smaller than the FWHM until the complete response range of the sensor is covered.

      Most companies will then report the R, G, B curves or the monochrome curve of the sensor's response and claim that this is the sensor's response.

      That statement is wrong! The R, G, B or the monochrome curve is not the response curve of the sensor but only some information about it.

      First of all, it is usually only measured for an incident beam perpendicular to the sensor, or in other words at a zero chief ray angle. But we know that the spectral response varies in intensity and in shape with the angle of incidence. It also depends on the direction of the incidence as changing the angle in X (horizontal or along a row) does not give the same results as changing the angle in Y (vertical or along a column). This is highly related to the internal pixel structure, mostly metal layers, but also the technology and the design of the microlenses. Optical crosstalk and material absorption can also play a role and their effect is not symmetrical.

      Secondly, it is a noisy measurement and therefore the precision depends on the number of samples taken. The measurement is noisy because the amount of light after the monochromator is limited as only a tiny fraction of the source's spectrum reaches the sensor. Therefore the signal level is small and its SNR is therefore low. In order to get more signal, it is possible to use more gain or a longer exposure time but this won't solve the SNR problem. In some cases, it could even make it worse. This is more extreme at the edge of the spectrum, both NUV (Near UltraViolet) and NIR (Near InfraRed), where the sensor's sensitivity is very low and a lot more signal would be required.

      The shape of the spectrum also depends on the bandwidth of the measuring instrument. As the response spectrum can exhibit oscillations at several scales (see this other publication about spectral response), only a very small bandwidth, i.e. a narrow monochromator lid, can reproduce the actual shape of the response curve, any other approach will only produce a smoothed curve without any detail. This is very true for FSI (Front Side Illuminated) CMOS and less critical for BSI (Back Side Illuminated) sensors and CCD sensors. CCDs have a less complex structure that causes less oscillations and BSI sensors have the complexity of the metal structure after the photodiode and therefore this structure has a lot less influence. However, some level of details might be required for some applications, especially the applications that involve a laser or a luminescence or fluorescence phenomenon at a specific wavelength. It is also important for multispectral and hyperspectral applications for which each band is narrow and therefore high frequency oscillations or pixel to pixel variations of the spectrum are more critical.

      Finally, the wide band light source will change over time and its spectrum will change over temperature, therefore requiring regular calibration, sometimes even calibration during the test itself. The light source may also flicker and this effect is more visible at shorter exposure times.

      Therefore, these curves only make sense if the incidence angle, the slid's width (or similarly the bandwidth) of the monochromator, the temperature, the size of the measured spot and the exposure time and mentioned with the plot.

      From http://www.aphesa.com/blogart.php?id=20

      This guys offer to use laser, but it is really overkill.

      the wide band light source will change over time and its spectrum will change over temperature, therefore requiring regular calibration, sometimes even calibration during the test itself.

      This part has easy solution they never mention.

    1 comment 2 comments Vitaliy_KiselevSeptember 7Last reply - September 7 by Glenn7 Subscribe to this blog
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    Glenn7
    Samsung VA panels pixels
    • Dark colors

      image

      Brighter colors

      image

    1 comment 2 comments Vitaliy_KiselevSeptember 6Last reply - September 7 by Glenn7 Subscribe to this blog
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