{"id":9465,"date":"2023-05-22T15:19:41","date_gmt":"2023-05-22T08:19:41","guid":{"rendered":"https:\/\/bap-software.net\/?post_type=products&p=9465"},"modified":"2024-01-30T16:19:36","modified_gmt":"2024-01-30T09:19:36","slug":"human-semantic-segmentation","status":"publish","type":"products","link":"https:\/\/bap-software.net\/kr\/case-study\/human-semantic-segmentation\/","title":{"rendered":"Human Semantic Segmentation"},"content":{"rendered":"

<\/p>\n

Overview for Semantic Segmentation<\/strong><\/h2>\n

For segmentation, demonstrates the steps to use the DeepLab model to perform semantic segmentation on a sample input image. Expected outputs are semantic labels overlayed on the sample image, such as some of examples including.<\/p>\n

\"Case<\/p>\n

Atrous Spatial Pyramid Pooling (1\/2)<\/strong><\/h2>\n

– It is proposed such as the Atrous Spatial Pyramid Pooling where four parallel atrous convolutions with different atrous rates are applied on top of the feature map.<\/p>\n

– The resulting features from all the branches are then concatenated and pass through another 1 \u00d7 1 convolution (also with 256 filters and batch normalization) before the final 1 \u00d7 1 convolution which generates the final logits.<\/p>\n

Atrous Spatial Pyramid Pooling (2\/2)\u00a0<\/strong><\/h2>\n

\"Case<\/p>\n

How to segment every objects at each frames<\/strong><\/h2>\n

– Collecting data for training and Calibrating or loading the latest version of the pretrained DeepLab model (supported by Tensorflow)<\/p>\n

– Load the colormap from the PASCAL VOC dataset<\/p>\n

– Adds colors to various labels, such as “pink” for people, “green” for bicycle, e.t.c<\/p>\n

– Visualize an image, and add an overlay of colors on various regions<\/p>\n

Experimental Results<\/b><\/h2>\n

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