Retail Technologies

Introduction to Image Processing

image processing

Nowadays, image processing systems that are used by various aspects of companies are among the rapidly growing technologies. Image Processing is also the main research area in the disciplines of engineering and computer science.

What is Image Processing?

Image processing aims to transform an image into digital form and performs some process on it, to get an enhanced image or take some utilized information from it. It is a method that develops to convert the image into digital form and perform some operations to obtain specific models or to extract useful information from it. The input of this method is a video section or an image, such as a photograph. The output corresponds to the desired or attention part of the picture. Generally, the Image Processing system treats images as two-dimensional signals when applying predetermined signal processing methods.

Image processing basically involves the following three steps.
  • Image acquisition with optical scanner or digital photos.
  • Analyze and use images that include non-human staining patterns such as data compression, image enhancement, and satellite photographs.
  • Output, modified based on image analysis of results, ready to use.

Purposes

There are different purposes of image processing:

  1. Visualization – Observing objects that are difficult to see.
  2. Image sharpening and restoration – Improving noisy images.
  3. Image retrieval – Attractive and high-resolution image search.
  4. Pattern recognition – Defining various objects in an image.
  5. Image recognition – Detecting objects in an image.

Kinds

The two methods used for Image Processing are Analog and Digital.  Analog or visual image processing techniques can be used for printed copies, such as photocopies and photographs. Image analysts place an interpretation on a variety of backgrounds when using these visual techniques. Image processing should not be limited to technical knowledge but should be based on the imagination and thinking ability of engineers. Another essential tool in the field of image processing with visual techniques is raw data, that is, past collected and unprocessed images. Analysts teach the previous operations of the products they want to identify to the system. As a deep learning type, Image Processing works in the light of historical data.

Digital Processing techniques help manipulate digital images with computers. Images from the satellite platform are incomplete due to sensor error. To overcome these flaws and to obtain the authenticity of the information, it has to go through various processing stages. There are three general steps that all kinds of data must go through when using the digital technique; Pre-processing, development, and imaging are information extraction.

Face Detection

Face detection is one of the most used image processing applications in the world. Technically following the deep learning methodology, the machine is first taught the specific features of human faces. Descriptive features, such as the distance between the two eyes, the shape of the average human face, serve as metrics to form the face shape. After teaching the human-specific criteria of the face, it accepts all objects in the image that resemble the same shape as the face. The detection of the face is made by making the specific metrics that make up the face human. Face detection is a vital tool for tracking customers in the shopping journey. After the face detection process, customers can be grouped to some clusters to define their specifications. By this process, the number of customers and their main features can be known in order to determine the most efficient way to increase sales.

Did you check our business intelligence solution with Heat Mapping Analysis? You can start using Udentify that analyzes the costumers’ behaviors at the brick and mortar shops. Udentify provides local conversion rate, head counting, heat maps, and order analysis. Thanks to the image processing technology, it can follow the customers anonymously, with which it gives meaning to the data that is gathered from the tracking by converting them to statistical models. It enables company managers to take data-oriented decisions by its management interface.

About author

Şerif Ali Enes Yolcu works as a Marketing Associate for Udentify the Camera-based real-time physical store productivity measurement platform that can help retailers find the right shelf share and store location for the products, avoid unnecessary staff and rental costs, make the right planning for new products and have the opportunity to quickly identify and solve in-store problems.
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