Consumer cameras are not scientific instruments *but* with proper calibration, they can acquire data useful for science. If you use cameras to capture colors = data (eg, you are not taking a photo to document presence/absence) you MUST work with raw images, not jpgs. Image
Unfortunately, esp. in marine science, I see very common use of jpgs for color-sensitive projects. Jpgs are not only specific to a camera but also to the settings/modes the user had when photographing. If color = data, others cannot reproduce a scene you photographed as a jpg.
Yes, RAW images have more burden - larger in size, colors are device-specific, therefore requires custom effort to process and therefore some (basic) programming/image analysis skills, but it is the only format output from a consumer camera that is not irreversibly biased.
If you use cameras for research and colors constitute data for you, please shoot RAW and develop your own image analysis pipeline, on which you can have full control at every step of the way.

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More from @dakkaynak

20 Nov
Teşekkür ederim. Fırsattan istifade tekrar belirteyim, yapay zeka değil, fizik temelli bir algoritma. Aradaki fark: yapay zeka olunca onbinlerce fotoğraftan renk öğreniyorsunuz; bir insan "bu yeşil, bu kırmızı, bu sarı" diye o fotoğraflarda belli pikselleri etiketlemiş oluyor +++
Yapay zeka o etiketlerden renkler neye benzer öğreniyor; öğrendiklerini yeni fotoğraflara bakıp "hah işte bu kırmızı, ben bunu biliyorum!" diye düzeltiyor. Eğer yapay zekanın karşısına çıkacak her şey çalıştığı yerlerden çıkarsa sorun yok. Ama daha önce görmediği bir renk +++
veya gördüğü bir rengin biraz solmuş hali karşısına çıkarsa çuvallayabiliyor.

Dünya genelinde denizlerdeki su ve dip rengi çok çeşitlilik gösteriyor. Bu çeşitliliği yakalayacak, kalibrasyonu yapılmış sualtı fotoğrafı çekmek aşırı maliyetli ve lojistik olarak imkansıza yakın.
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