Image processing using deep machine learning

Machine learning is a pretty old concept that everyone knows about. It’s a place where the machine will automatically train itself and do the process. Deep learning might be new for some of you. Deep learning (people also call it Neural networks) is a part (subset) of machine learning.

Deep learning is an AI function that can replicate the human brain. It can easily analyze and process the data like a human brain. This starts by detecting objects, recognizing speech, and can also make decisions. Further, it is also useful in translating language and similar purposes.

Here, we are going to talk about image processing using deep machine learning.

What is Image Processing with Deep Learning?

Image processing a technique where the machine will analyze the image and process it to give your further data.

Usually, image processing can be done in various ways. You can do it with the help of machine learning or if you want detailed processing, you can use machine learning.

Deep learning is a supervised way of processing images. You can do various things with the help of the processed data.

For example, if you give 100 animal images and start processing them with deep learning, it can categorize the data and give you the exact results. Deep learning is capable of categorizing animals. In the end, you will get the result where you can see the dog images aside and cat images aside.

Further, one can use image processing in many other ways. Further, deep learning can label the data and can do various other things.

Image Classification

The main usage of deep learning in image processing is image classification. The most effective way to classify images is with CNN. CNN stands for Convolutional Neural network is a class of deep neural network. This class is used to fetch data from the visual media or source. For example, if you want to capture the face of the person in an image, you can use CNN for the same.

To train the data set, we will need a couple of images. The images need to be in the set. Here, if we want to classify animals, we can create various sets such as dogs, cats, etc. You will also need a couple of more data such as the number of images, dimensions, channels, and also the number of levels per pixel. Once the image data is set, deep learning can automatically classify the data and give you the result you need.

Image Labeling

Now, let’s start with the next thing that is image labeling. Deep learning is also useful in labeling the image.

The machine will identify the image and then label it as per the instruction given. For example, if the actual image is of an animal, the software will mark it as an animal. It can be used in many other ways. It just needs proper data.

It can also find the text in the image and write the text. If you know about the OCR, it can also be done using this. This will extract the text from an image and give you all the text with ease.

Region-based Convulation

To localize the image processing, a different concept is used is RCNN. RCNN stands for Region-based Convolution Network.

Let’s see an example to know more about it. If someone wants to find an item located in a supermarket, one can use the deep learning image labeling to find out. Once the image is scanned, deep learning is capable of finding the right category of the image as well.

Many people use it for a variety of reasons starting from categorizing items in the supermarket to finding the person in an image.

How does Image Processing work with Deep learning?

There are some easy steps in which the image processing works.

1. Sensor
2. Segmentation
3. Feature extractions
4. Classification
5. Post Processing

Once the input is given, here are the phases.

Phase 1 — Sensor: Once the image is given to the machine, it will convert the input into the signal data. Only signal data is processed here. It will also fix the resolute, bandwidth, and all the similar things.

Phase 2 — Segmentation: The next phase is where the segmentation happens. This is where the machine will remove all the unnecessary things such as background and similar things. Segmentation is all about grouping various parts.

Phase 3 — Feature Extractions: This depends on the classification. It will detect the features in the image and classify the image accordingly.

Phase 4 — Classification: Once the image is classified, it will assign the image to a specific category.

Phase 5 — Post Processing: This is the place where the machine will decide if there are any other processing needed.

Thereafter, the final result is displayed.

Use Cases of Image Processing

Image processing has various use cases. There are unlimited examples of image processing where it can be implemented with ease. Here are some of the use cases of image processing.

• Automobile industry: To process parts image and categorize it.
• Gaming industry: Starting from image classification to labeling, it can be used in several other ways in the gaming industry.
• Retail industry: As we mentioned above, the retail industry uses it for labeling as well as classification.

There are various other similar examples where one can use image processing using deep learning.

Final Words

To conclude, this was all about image processing using deep learning. There are many other ways deep learning can process the image. However, the two main popular ways are classifications and labeling. You can surely use many other things such as RCNN and similar things for better processing of the image. You will need the data set at first to get started. Thereafter, the machine will do the work for you.

If you want to use image processing for your company, we can help you out with queries. Contact our team and see how they can help you.