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Course Level Date Clip# Line Interlocutor Content
Oral Assessments Graduate 2024-07-18 3 132 S01 photo of your face very near so the size of it uh
Oral Assessments Graduate 2024-07-18 3 133 S01 uh will change but here→ as we- as we can see
Oral Assessments Graduate 2024-07-18 3 134 S01 there are a- the same thing but not in the same size
Oral Assessments Graduate 2024-07-18 3 135 S01 so uh uh the image did not seem will be uh somehow
Oral Assessments Graduate 2024-07-18 3 136 S01 have some shape on it to make uh it works for
Oral Assessments Graduate 2024-07-18 3 137 S01 different size↘ so it's actually a very
Oral Assessments Graduate 2024-07-18 3 138 S01 important things good question↘ (0.9) okay↗
Oral Assessments Graduate 2024-07-18 3 139 S01 am I making ⌈clear↗
Oral Assessments Graduate 2024-07-18 3 140 T02 ⌊mm hmm
Oral Assessments Graduate 2024-07-18 3 141 S01 thank you↘
Oral Assessments Graduate 2024-07-18 3 142 S01 (0.4) so now ha- i- we now we have already get uh
Oral Assessments Graduate 2024-07-18 3 143 S01 uh not xxx but uh we have all the uh images
Oral Assessments Graduate 2024-07-18 3 144 S01 uh that's uh contains noise→ so we are supposed to
Oral Assessments Graduate 2024-07-18 3 145 S01 do for image denoising is that we can use some
Oral Assessments Graduate 2024-07-18 3 146 S01 uh tool some uh some uh math that gets uh (0.6)
Oral Assessments Graduate 2024-07-18 3 147 S01 uh get- that try to get this uh this this one hundred
Oral Assessments Graduate 2024-07-18 3 148 S01 percent clean image↘ although↘ still in theory→
Oral Assessments Graduate 2024-07-18 3 149 S01 it's impossible to get one hundred percent
Oral Assessments Graduate 2024-07-18 3 150 S01 clean image but we will try to make uh uh make uh
Oral Assessments Graduate 2024-07-18 3 151 S01 approximate explanation about (0.4) this one
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