Understanding the CNN for Computer Vision Applications

Convolutional Neural Networks or CNN is a type of deep neural networks that are efficient at extracting meaningful information from visual imagery. As an experiential AI Development Company, Oodles AI decodes the underlying layers of CNN and how businesses can deploy CNN for computer vision applications. 

When it comes to us, humans, evolution has gifted us with very complex yet efficient techniques to view and detect several objects. Our brain keeps on learning continuously without our notice. There are several organs and parts of our brain involved in the process like eyes, receptors and visual cortex.

In the era, with the resources and immense computational power, it would be pointless not to explore computer vision. With so many applications of computer vision services, we can take current generation technology to the next level. A great example is the upcoming Tesla’s Robo-taxi which gives us a glimpse into the future. 

A very popular machine learning algorithm, especially for Object Detection, is Convolutional Neural Networks or CNN. CNN consists of four hidden layers such as-

  1. Convolutional layers
  2. Pooling layers
  3. fully connected layers, and
  4. Normalization layers.

Convolutional Layers takes two input layers - a part of the image and an equally sized filter called the kernal. The output of this layer is the dot product of both inputs. 

The idea of Pooling is to down-sample data. The Pooling Layer takes the input (an image) and reduces its size in terms of a number of pixels. There are two ways to perform this - Max Pooling and Min Pooling. Max Pooling picks the maximum value from the selected region, whereas Min Pooling picks up the minimum value.

Under Fully Connected Layers, as the name suggests, all the outputs from one layer are connected to the input of another layer. These layers are useful in the classification of the data. 

Normalization Layers are used to stabilize the neural networks. It performs normalization on the input data.

 CNN performs incredibly when it comes to analyzing a single image, but it lacks one essential quality - they only consider spatial features and visual data ignoring the temporal and time features i.e., how a frame is related to the previous frame. This is where Recurrent Neural Networks or RNN come into play. The term ‘recurrent’ suggests that the neural network repeats the same tasks for every sequence. RNN can also be used in Natural Language Processing.

Learn More: CNN for Computer Vision Applications


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