110 research outputs found
Video Acceleration Magnification
The ability to amplify or reduce subtle image changes over time is useful in
contexts such as video editing, medical video analysis, product quality control
and sports. In these contexts there is often large motion present which
severely distorts current video amplification methods that magnify change
linearly. In this work we propose a method to cope with large motions while
still magnifying small changes. We make the following two observations: i)
large motions are linear on the temporal scale of the small changes; ii) small
changes deviate from this linearity. We ignore linear motion and propose to
magnify acceleration. Our method is pure Eulerian and does not require any
optical flow, temporal alignment or region annotations. We link temporal
second-order derivative filtering to spatial acceleration magnification. We
apply our method to moving objects where we show motion magnification and color
magnification. We provide quantitative as well as qualitative evidence for our
method while comparing to the state-of-the-art.Comment: Accepted paper at CVPR 2017. Project webpage:
http://acceleration-magnification.github.io
ΠΡΠΏΠΈΡΠ°ΡΠΈΡ ΠΌΠ΅ΠΊΠΎΠ½ΠΈΡ: ΡΠ°ΠΊΡΠΎΡΡ ΡΠΈΡΠΊΠ° ΠΈ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ Π»Π΅Π³ΠΎΡΠ½ΠΎΠΉ ΡΠ΅Π°Π½ΠΈΠΌΠ°ΡΠΈΠΈ
IMSP Institutul Mamei Εi CopiluluiRespiratory pathology is quite frequent during the neonatal period. Meconium aspiration syndrome is diagnosed in 5-15% of children born naturally.
The purpose of this study is to highlight the causes of this pathology and ways to decrease perinatal mortality in case of meconium aspiration.
It was studied a group of 35 newborns at "ICΕOSM si C" with Meconium Aspiration Syndrome. The main cause of neonatal mortality was the untimely detection of fetal distress. Was studied a primary volume of resuscitation in the delivery room depending on the status of newborn
It recommends more closely monitoring of pregnant women who belong to risk groups for Meconial aspiration syndrome, monitoring the fetal status of cardiovascular system reactivity, frequency of breathing, spontaneous motility, and neonatal muscular tone.
Neonatal resuscitation needs to be performed depending on the overall condition of the child.ΠΡΡ
Π°ΡΠ΅Π»ΡΠ½Π°Ρ ΠΏΠ°ΡΠΎΠ»ΠΎΠ³ΠΈΡ Π΄ΠΎΠ²ΠΎΠ»ΡΠ½ΠΎ ΡΠ°ΡΡΠΎ Π²ΡΡΡΠ΅ΡΠ°Π΅ΡΡΡ Π² Π½Π΅ΠΎΠ½Π°ΡΠ°Π»ΡΠ½ΠΎΠΌ ΠΏΠ΅ΡΠΈΠΎΠ΄Π΅. Π‘ΠΈΠ½Π΄ΡΠΎΠΌ Π°ΡΠΏΠΈΡΠ°ΡΠΈΠΈ ΠΌΠ΅ΠΊΠΎΠ½ΠΈΡ
Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΡΠ΅ΡΡΡ Ρ 5-15% Π΄Π΅ΡΠ΅ΠΉ, ΡΠΎΠΆΠ΄Π΅Π½Π½ΡΡ
Π΅ΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΠΌ ΠΏΡΡΠ΅ΠΌ.
Π¦Π΅Π»ΡΡ Π΄Π°Π½Π½ΠΎΠΉ ΡΠ°Π±ΠΎΡΡ ΡΠ²Π»ΡΠ»ΠΎΡΡ Π²ΡΠ΄Π΅Π»ΠΈΡΡ ΠΏΡΠΈΡΠΈΠ½Ρ ΡΡΠΎΠΉ ΠΏΠ°ΡΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΈ ΠΏΡΡΠΈ ΡΠ½ΠΈΠΆΠ΅Π½ΠΈΡ ΠΏΠ΅ΡΠΈΠ½Π°ΡΠ°Π»ΡΠ½ΠΎΠΉ ΡΠΌΠ΅ΡΡΠ½ΠΎΡΡΠΈ Π² ΡΠ»ΡΡΠ°Π΅ aΡΠΏΠΈΡΠ°ΡΠΈΠΈ ΠΌΠ΅ΠΊΠΎΠ½ΠΈΡ.
ΠΡΠ»Π° ΠΈΠ·ΡΡΠ΅Π½Π° Π³ΡΡΠΏΠΏΠ° ΠΈΠ· 35 Π½ΠΎΠ²ΠΎΡΠΎΠΆΠ΄Π΅Π½Π½ΡΡ
ΠΈΠ· Π Π¦ΠΠΠΠΈΠ Ρ ΡΠΈΠ½Π΄ΡΠΎΠΌΠΎΠΌ Π°ΡΠΏΠΈΡΠ°ΡΠΈΠΈ ΠΌΠ΅ΠΊΠΎΠ½ΠΈΡ. ΠΡΠ½ΠΎΠ²Π½ΠΎΠΉ ΠΏΡΠΈΡΠΈΠ½ΠΎΠΉ ΡΠΌΠ΅ΡΡΠ½ΠΎΡΡΠΈ Π½ΠΎΠ²ΠΎΡΠΎΠΆΠ΄Π΅Π½Π½ΡΡ
Π±ΡΠ»ΠΎ Π½Π΅ΡΠ²ΠΎΠ΅Π²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ΅ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅ Π΄ΠΈΡΡΡΠ΅ΡΡΠ° ΠΏΠ»ΠΎΠ΄Π°. ΠΡΠ» ΠΈΠ·ΡΡΠ΅Π½ ΠΎΠ±ΡΡΠΌ ΠΏΠ΅ΡΠ²ΠΈΡΠ½ΠΎΠΉ ΡΠ΅Π°Π½ΠΈΠΌΠ°ΡΠΈΠΈ Π² ΡΠΎΠ΄Π·Π°Π»Π΅ Π² Π·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΠΈ ΠΎΡ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ Π½ΠΎΠ²ΠΎΡΠΎΠΆΠ΄Π΅Π½Π½ΡΡ
.
Π Π΅ΠΊΠΎΠΌΠ΅Π½Π΄ΠΎΠ²Π°Π½ΠΎ Π±ΠΎΠ»Π΅Π΅ Π²Π½ΠΈΠΌΠ°ΡΠ΅Π»ΡΠ½ΠΎ ΡΠ»Π΅Π΄ΠΈΡΡ Π·Π° Π±Π΅ΡΠ΅ΠΌΠ΅Π½Π½ΡΠΌΠΈ ΠΆΠ΅Π½ΡΠΈΠ½Π°ΠΌΠΈ, ΠΊΠΎΡΠΎΡΡΠ΅ Π²Ρ
ΠΎΠ΄ΡΡ Π² Π³ΡΡΠΏΠΏΡ ΡΠΈΡΠΊΠ° ΠΏΠΎ Π‘ΠΠ, ΠΊΠΎΠ½ΡΡΠΎΠ»ΠΈΡΠΎΠ²Π°ΡΡ ΡΠΎΡΡΠΎΡΠ½ΠΈΠ΅ ΠΏΠ»ΠΎΠ΄Π°: ΡΠ΅ΡΠ΄Π΅ΡΠ½ΠΎ-ΡΠΎΡΡΠ΄ΠΈΡΡΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ, ΡΠ°ΡΡΠΎΡΡ Π΄ΡΡ
Π°Π½ΠΈΡ, ΡΠΏΠΎΠ½ΡΠ°Π½Π½ΠΎΠΉ ΠΌΠΎΡΠΎΡΠΈΠΊΠΈ ΠΈ ΠΌΡΡΠ΅ΡΠ½ΠΎΠ³ΠΎ ΡΠΎΠ½ΡΡΠ°. ΠΠ΅ΠΎΠ½Π°ΡΠ°Π»ΡΠ½Π°Ρ ΡΠ΅Π°Π½ΠΈΠΌΠ°ΡΠΈΡ Π΄ΠΎΠ»ΠΆΠ½Π° Π±ΡΡΡ Π²ΡΠΏΠΎΠ»Π½Π΅Π½Π° Π² Π·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΠΈ ΠΎΡ ΠΎΠ±ΡΠ΅Π³ΠΎ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ ΡΠ΅Π±Π΅Π½ΠΊΠ°
Cercopithecine and Colobine Abundance Across Protected and Unprotected Land in the Greater Mahale Ecosystem, Western Tanzania
Most primates live in unprotected land where abundances and threats may differ from those in protected areas. We therefore need to establish population densities in both unprotected and protected areas to effectively inform conservation planning. The Greater Mahale Ecosystem in western Tanzania is a region of mixed protected status with seven cercopithecine and colobine species: blue (Cercopithecus mitis doggetti), red-tailed (C. ascanius schmidi), and vervet (Chlorocebus pygerythrus) monkeys; ashy red colobus (Piliocolobus tephrosceles); black-and-white colobus (Colobus angolensis); and olive (Papio anubis) and yellow (P. cynocephalus) baboons. These species may be threatened by increasing human activity; however, except for ashy red colobus, no data on local abundances are available. We walked over 350 km of line transects in legally protected (Village Forest Reserves) and unprotected general land between August 2011 and October 2012 to estimate densities of primates and human activity. Primate densities were consistently low across the Greater Mahale Ecosystem. Blue and red-tailed monkey and ashy red colobus densities were especially low compared to populations in predominantly forested landscapes. Primate and human activity densities did not differ significantly inside and outside of reserves. Low primate densities could be natural responses to the lower proportions and quality of riparian forest habitat in the region. High levels of human activity and the absence of significantly higher primate densities in reserves suggest unprotected land could provide important refuges for primates in the Greater Mahale Ecosystem. This result further reinforces a broad need to include unprotected areas in primate conservation strategies. Β© 2019, The Author(s)
A step towards understanding why classification helps regression
A number of computer vision deep regression approaches report improved
results when adding a classification loss to the regression loss. Here, we
explore why this is useful in practice and when it is beneficial. To do so, we
start from precisely controlled dataset variations and data samplings and find
that the effect of adding a classification loss is the most pronounced for
regression with imbalanced data. We explain these empirical findings by
formalizing the relation between the balanced and imbalanced regression losses.
Finally, we show that our findings hold on two real imbalanced image datasets
for depth estimation (NYUD2-DIR), and age estimation (IMDB-WIKI-DIR), and on
the problem of imbalanced video progress prediction (Breakfast). Our main
takeaway is: for a regression task, if the data sampling is imbalanced, then
add a classification loss.Comment: Accepted at ICCV-202
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