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Human vision is simply spectacular, since we have a great understanding of images and knowledge of the context in which they are found. However, this capacity tends to wear out over time, or can take longer than we would like. This is where digital image processing comes in, to help us in this activity, being exactly what we want to show you in this article. But what are the stages of digital image processing and its objectives? How does it work? Come find out more about it! READ ALSO: • What is computer vision? Understand now!
• Meet a technology capable of monitoring highways
• Inventory stock through technology.
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What are the components of a digital image?
Digital image processing is the manipulation of digital data, with the help of computer hardware and software, to produce digital maps where specific information of interest is extracted and highlighted. Therefore, to truly understand how digital image processing works, we need to understand the components of an image, in this case, a digital image. But what is an image? Although we all know its meaning in practice, we can understand it in greater depth. An image is a spatial representation of a two-dimensional or three-dimensional scene. Thus, it forms an array set, or array pixels, organized in rows and columns. As for the digital image, we can say it is composed of photo elements, image elements, and pixels. In the matrix, each pixel appears as one of its elements. The numerical values in the pixel display vary uniformly, ranging from 0 (black pixels) to 255 (white pixels). In a binary image, there are only 0 or 1 pixels. That is, it is a monochromatic image, where only black and white are present. In the 8-bit format, there are 256 different color shades, with the number 127 being gray. The 16-bit format, which is more complex, contains different colors, being the highest color format. In 16-bit, the format is divided into red, green, and blue components. This is the more commonly known RGB format ("red", "green", and "blue").
What are the stages of digital image processing?
• Image acquisition: this is the pre-processing of the image, where things like scale are measured. It is a simple step, which can refer solely to the acquisition of an image that is already in digital format.
• Image enhancement: enhancement techniques work on the details of the image, emphasizing those that are obscured, as well as highlighting certain components of interest in the image. It can alter brightness, contrast, etc.
• Image restoration: this step deals with improving the appearance of the image. It is something objective, as its restoration techniques tend to use mathematical and probabilistic models of image degradation.
• Image color processing: this process has stood out across the internet, as there is an increasing number of digital images on networks. The step can include modeling and processing a digital domain, and is usually a subjective process.
• Multi-resolution and wavelet processing: the fundamental basis for representing images at various resolution levels is enabled by wavelets. The images are divided into smaller regions, in order to compress the data and provide a pyramidal representation.
• Compression: for use on the web, this step is very important for compressing data. Compression techniques reduce the space required to save an image, or the bandwidth to transmit it.
• Morphological processing: this process extracts the image components that are useful for describing and representing shapes.
• Segmentation: segmentation procedures partition the image into its constituent parts, or objects. It is one of the most difficult processes. However, a robust segmentation allows the process to steer towards an optimized solution for images that require, for example, objects to be individually identified.
• Representation and description: almost always occurs after segmentation, which typically uses raw pixel data constituting boundaries or regions, or all points of the region itself. Description extracts attributes that result in quantitative information of interest, or that differentiate one class of objects from another class.
• Object recognition: this process can label an image based on the descriptions made.
What are the objectives of digital image processing?
One of the main objectives of digital image processing is to improve pictorial information to enhance human interpretation. It also includes processing for storage, representation, and transmission for the perception of autonomous machines. Image recognition can identify places, logos, people, objects, buildings, among many other variables in digital images. Through this, a computer or device can see the image through the numerical values of the pixels, bringing a greater richness of data from that image. For example, a company conducting a forest inventory in a specific area can make use of image processing to obtain more precise data from the studied region. Similarly, a company seeks, through digital image processing, to analyze deforested areas in a specific environmental preservation area. In other words, the applications of digital image processing are numerous, and it can be used in various contexts.
Pix Force in digital image processing
Pix Force offers products that perform digital image processing, such as Pix Counter. With it, it is possible to count objects in real time, obtaining an instant and accurate result of the information you need to collect. Pix Counter counts items from an image, which can be taken from cameras or through the app. For some objects, Pix Counter also identifies the types, as well as the quality of the identified products. This is done in real time, so that the user has the result of the quantity of objects almost instantly. In addition to accelerating the process and being cheaper than traditional methods, the tool also allows for production quality control. Its accuracy is so high that Pix Counter is capable of counting countless objects or products, such as: • Quantity of cement bags on a truck
• Products on supermarket shelves
• Number of people at a given event
• Inventory count, etc.
Finally, the digital image processing done by Pix Force is completely customized. Each company needs unique service, since their needs are specific. Interested? Contact us !
Fabio Caraça
Fábio Caraça is the Chief Growth Officer at Pix Force. He leads Pix Force's transformation into a scalable SaaS operation, combining strategic vision, culture, and high-impact execution.
