Perception – Engineering AI Agents

Author

Pantelis Monogioudis

CNN Example Architectures

This is a very high level view of practical structures of CNNs before the advent of more innovative architectures such as ResNets.

CNN Layers

In the convolutional layer the first operation a 3D image with its two spatial dimensions and its third dimension due to the primary colors, typically Red Green and Blue is…

Faster RCNN Object Detection

With Faster RCNN, the 3rd generation in the family of region-based detectors, we are replacing the selective search algorithm that is considered computationally expensive…

Feature Extraction via Residual Networks

In the figure below we plot the top-5 classification error as a function of depth in CNN architectures. Notice the big jump due to the introduction of the ResNet architecture.

Introduction to Convolutional Neural Networks

(content:cnn-intro)= # Introduction to Convolutional Neural Networks

Introduction to Scene Understanding

In the previous chapters we have treated the perception subsystem mainly from starting the first principles that govern supervised learning to the deep learning…

Mask R-CNN Semantic Segmentation

The semantic segmentation approach described in this section is Mask R-CNN paper. Mask R-CNN is an extension of Faster R-CNN that adds a mask head to the detector. The mask…

Object Detection

In the introductory section, we have seen examples of what object detection is. In this section we will treat the detection pipeline itself, summarized below:

Object Detection and Semantic Segmentation Metrics

NOTE: The following example is based on here and its corresponding implementation

Region-CNN (RCNN) Object Detection

This section describes the 1st generation of the so-called Region based CNN detectors. Despite the fact that the RCNN detector is slow and has been replaced by faster…