IMAGE RECOGNITION ALGRORITHM

FILLING :

Image recognition is the ability of a system or software to identify objects, people, places, and actions in images.

People's visual performance is much better than computers, probably due to higher image comprehension, status information, and greater similar processing. But human power deteriorates dramatically after prolonged supervision, and some workplaces are inaccessible or extremely dangerous to humans. So for these reasons, automated alert systems are designed for a variety of applications. Driven by advances in computer technology and image processing technology, computer simulation of human vision has recently gained a foundation in many operating systems.

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What is Image recognition?

Image recognition refers to the technology that identifies places, logos, people, objects, buildings, and a few other variations in digital photography. It can be very easy for people like you and me to see different pictures, such as pictures of animals. We can easily see a picture of a cat and separate it from a picture of a horse. But it may not be so easy on a computer.

A digital image is a picture composed of image elements, also known as pixels, each with a limited, distinct numerical value for its intensity or gray level. So the computer sees the image as the numerical value of these pixels and in order to see a particular image, it must see patterns and norms in this numerical data.

How does Image recognition work?

Usually, the image recognition function involves the creation of a neural network that processes individual pixels in an image. These networks are provided with as many pre-labeled images as we can so that we can "teach ourselves" how to recognize the same images.

So let me break down the process with some simple steps:

We need a database containing images with their labels. For example, a picture of a dog should be written as a dog or something we can understand.

Next, these images will be uploaded to the Neural Network and trained on them. Typically, in photographic activities, we use convolutional neural networks. These networks include convolutional layers and layers of addition over Multiperceptron layers (MLP). The effectiveness of the integration and integration layers is described below.

We feed on an image that is not in the training set and get predictions.

In the next sections, by following these simple steps we will create a section that can see RGB images of 10 species of animals.

Working of Convolutional and Pooling layers:

Convolutional layers and integration layers are the major building blocks used in convolutional neural networks. Let's see them in more detail

How does Convolutional Layer work?
The convolutional layer parameters contain a set of readable filters (or kernels), with a small reception field. These filters scan through image pixels and collect information in a collection of photos/pictures. Variable layers combine inputs and transfer their effect to the next layer. This is similar to the response of a neuron to the visual cortex.

Below is an example of how convolution works in a photo. The same process is performed on all pixels.

Here is an example of an image in our test set combined with four different filters and that is why we get four different images.

Photos after installing convolution

How does the Pooling Layer work?

Integration function involves sliding a two-sided filter over each feature map channel and summarizing features within a filter-covered region. The composite layer is usually joined between two consecutive conversion layers. The aggregation layer reduces the number of parameters and calculations by taking a representative sample. Consolidation work can be quantitative or quantitative. High integration is often used as it works better

Integration function involves sliding a two-sided filter over each feature map channel and summarizing features within a filter-covered region. This process is shown below.

When we transfer the four images that we found after being converted by a large 2 × 2 compact mass, we find this as the output.

Photos after pooling

As we can see, the size is halved but the information in the picture is still saved.

Image recognition using Python:

Here I will use in-depth reading, especially convolutional emotional networks that can detect RGB images of ten species of animals. An RGB image can be viewed as three separate images (a red scale image, a green scale image, and a blue scale image) packed on top of each other, and when embedded in the red, green, and blue input of a color monitor, it produces a color image on the screen. We are using a database known as Animals-10 from Kaggle.

RGB image recognition
The image is shown on the red channel, on the blue channel, and on the green channel
So, let's start making a class using Python and Camera. We will use the program in Colab as we need more processing power and Goggle Colab provides free GPUs. The entire neural network structure we are using can be seen in this image.

Challenges of Image Recognition Challenges:

Differences of view: In the real world, image-sharing businesses are aligned with different bodies and when such images are fed to a system, the system predicts incorrect values. In short, the system fails to understand that changing the alignment of the image (left, right, bottom, top) will not make us different and that is why it creates challenges in image recognition.

Scale change: Size variations affect object classification. When you get closer to something it looks bigger in size and vice versa

Deformation: Things do not change even if they are disabled. The program learns from the complete picture and creates the impression that something can only be in a certain situation. We know that in the real world, shapes change and as a result, precision results when the system encounters a defective image.

Class Variations: Something varies between classes. They can be a different shape, size, but they still represent the same category. For example, buttons, chairs, bottles, bags come in different sizes and shapes.

Use of Image Visibility

Drones:

Drones equipped with image detection capabilities can provide automatic monitoring based on vision, exploration, and control of assets located in remote locations. Production:

Inspecting production lines, checking key points regularly within buildings. Monitoring the quality of the final products to reduce errors. Employee evaluation can help the manufacturing industry to have complete control over the various functions in the systems.

Independent vehicles with image recognition can identify activities on the road and take the necessary steps. Small robots can help the transportation industry to locate and transport objects from one place to another. It also maintains a database of product movement history to prevent the product from being lost or stolen. Military Surveillance:

Acquisition of unusual activities in border areas and the ability to make automatic decisions can help prevent intrusion and lead to the survival of soldiers. . It can also provide complete monitoring of large, inaccessible areas.

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IMAGE RECOGNITION ALGORITHM