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== Overview ==
== Suggested* [http:/tested approaches == The pipeline consists of multiple steps where each step has a drastic influence on the resulting quality/sulc. The stage consist of a single fish or each fish can be tracked individually and independently on other fishes present in the scene. From the tracking stage, we have a bounding box. The image within the bounding box is then fed to a minimalistic encoder-decoded network where the latent space is used to detect different stages of the fish (either hearth or beat rate)one/QFB2018.pdf Presentation] The architecture of the network is very minimalistic due extremely small training dataset (3x3 consecutive convolutions, the image is rescaled to 64x64 pixels with three channels) which on average consist of no more than 2000 frames* [http://sulc. The overfitting is prevented by using a very short training stage (100 iterations is incomparably less than what is usually used). The bounding boxed from different videos cannot be used because of the setting (fishes, scene) or alignment is different for each video. Latent space usually consists of multiple dimensions, some of them are redundant, but we calculate the average of the entire latent space for each frame. The absolute value provides us a rough clue of at which stage the fish isone/QFB2018.zip Code]
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== Contact ==
== Aims ==
This project introduces the quantitative methods in computer vision which may be useful and interesting for biologists. The course has two main goals. The aim first is to show the state-of -the project -art computer vision techniques which may be potentially useful for various tasks addressed by biologists. And the second goal is to calculate show the frequency of hearth practical code snippets which are developed with respect to simplicity and breath rate ease of a fishuse for non-programmers.
== Provided data ==
