In the present Artificial Intelligence and Machine Learning industry, “Image Recognition”, and “Computer Vision” are two of the most well liked trends. Each of those fields involve working with identifying visual characteristics, which is the explanation more often than not, these terms are sometimes used interchangeably. Despite some similarities, each computer vision and image recognition represent different technologies, concepts, and applications. 

In this text, we shall be comparing Computer Vision & Image Recognition by delving into their differences, similarities, and methodologies used. So let’s start. 

What’s Image Recognition?

Image Recognition is a branch in modern artificial intelligence that permits computers to discover or recognize patterns or objects in digital images. Image Recognition gives computers the flexibility to discover objects, people, places, and texts in any image. 

The important aim of using Image Recognition is to categorise images on the idea of pre-defined labels & categories after analyzing & interpreting the visual content to learn meaningful information. For instance, when implemented appropriately, the image recognition algorithm can discover & label the dog within the image. 

How Image Recognition Works?

Fundamentally, a picture recognition algorithm generally uses machine learning & deep learning models to discover objects by analyzing every individual pixel in a picture. The image recognition algorithm is fed as many labeled images as possible in an try and train the model to acknowledge the objects in the pictures. 

The image recognition process generally comprises the next three steps. 

Gathering and s Data

Step one is to assemble and label a dataset with images. For instance, a picture with a automobile in it have to be labeled as a “automobile”. Generally, larger the dataset, higher the outcomes. 

Training the Neural Networks on the Dataset

Once the pictures have been labeled, they shall be fed to the neural networks for training on the pictures. Developers generally prefer to make use of Convolutional Neural Networks or CNN for image recognition because CNN models are able to detecting features with none additional human input. 

Testing & Prediction

After the model trains on the dataset, it’s fed a “Test” dataset that incorporates unseen images to confirm the outcomes. The model will use its learnings from the test dataset to predict objects or patterns present within the image, and check out to acknowledge the item. 

What’s Computer Vision?

Computer Vision is a branch in modern artificial intelligence that permits computers to discover or recognize patterns or objects in digital media including images & videos. Computer Vision models can analyze a picture to acknowledge or classify an object inside a picture, and likewise react to those objects. 

The important aim of a pc vision model goes further than simply detecting an object inside a picture, it also interacts & reacts to the objects. For instance, within the image below, the pc vision model can discover the item within the frame (a scooter), and it could possibly also track the movement of the item inside the frame. 

How Computer Vision Works?

A pc vision algorithm works just as a picture recognition algorithm does, through the use of machine learning & deep learning algorithms to detect objects in a picture by analyzing every individual pixel in a picture. The working of a pc vision algorithm will be summed up in the next steps. 

Data Acquisition and Preprocessing

Step one is to assemble a sufficient amount of information that may include images, GIFs, videos, or live streams. The information is then preprocessed to remove any noise or unwanted objects. 

Feature Extraction

The training data is then fed to the pc vision model to extract relevant features from the info. The model then detects and localizes the objects inside the data, and classifies them as per predefined labels or categories. 

Semantic Segmentation & Evaluation

The image is then segmented into different parts by adding semantic labels to every individual pixel. The information is then analyzed and processed as per the necessities of the duty. 

Image Recognition v/s Computer Vision : How Do They Differ?

Although each image recognition and computer vision function on the identical basic principle of identifying objects, they differ by way of their scope & objectives, level of information evaluation, and techniques involved. Let’s discuss each of them individually. 

Scope and Objectives

The important objective of image recognition is to discover & categorize objects or patterns inside a picture. The first goal is to detect or recognize an object inside a picture. However, computer vision goals at analyzing, identifying or recognizing patterns or objects in digital media including images & videos. The first goal is to not only detect an object inside the frame, but in addition react to them.  

Level of Evaluation

Essentially the most significant difference between image recognition & data evaluation is the extent of study. In image recognition, the model is anxious only with detecting the item or patterns inside the image. On the flip side, a pc vision model not only goals at detecting the item, however it also tries to grasp the content of the image, and discover the spatial arrangement. 

For instance, within the above image, a picture recognition model might only analyze the image to detect a ball, a bat, and a toddler within the frame. Whereas, a pc vision model might analyze the frame to find out whether the ball hits the bat, or whether it hits the kid, or it misses all of them together. 

Complexity

Image recognition algorithms generally are inclined to be simpler than their computer vision counterparts. It’s because image recognition is mostly deployed to discover easy objects inside a picture, and thus they depend on techniques like deep learning, and convolutional neural networks (CNNs)for feature extraction. 

Computer vision models are generally more complex because they detect objects and react to them not only in images, but videos & live streams as well. A pc vision model is mostly a mixture of techniques like image recognition, deep learning, pattern recognition, semantic segmentation, and more. 

Image Recognition Vs. Computer Vision: Are They Similar?

Despite their differences, each image recognition & computer vision share some similarities as well, and it will be protected to say that image recognition is a subset of computer vision. It’s essential to grasp that each these fields are heavily reliant on machine learning techniques, they usually use existing models trained on labeled dataset to discover & detect objects inside the image or video. 

Final Thoughts

To sum things up, image recognition is used for the precise task of identifying & detecting objects inside a picture. Computer vision takes image recognition a step further, and interprets visual data inside the frame.