Image processing is the science of creating, examining, synthesizing, enhancing, interpreting and extracting meaning from images. Pixels are the basic element used to build up an image. Each pixel can have various attributes that describe its color, intensity or location. These properties are the same as those that define other objects in our real world i.e. People have different measurements for their height etc... so pixels are used to group common properties together defining them as a single object which will be called "feature”. Image processing tries to find these features within images so they can be later organized into categories based on their commonness with others possessing similar traits e.g., human faces vs cars vs flowers etc. Since every possible feature one could think of has an infinite number of combinations, how do we define the features that are common to all objects/images? We use some mathematical functions (models) that assign numerical values to pixels based on their color intensity. Once these functions are able to represent pixel data with some degree of accuracy, it is possible for computers to process images by applying them. These models can be applied iteratively so several readings of the same image give us different perspectives but still keep the overall texture and structure intact.
Image processing example
Once processed, these signals can be analyzed using statistical methods e.g., variance or correlation coefficient which will provide information on how distribution of pixel measurements varies with respect to distance between them or how they distributed in space then displayed visually as a heat map. Also, each pixel reading will have a "measure of central tendency" which can be displayed with its corresponding set of pixels as a mean & std deviation, median & interquartile range, modal value etc... This is done to see how well or bad the model fits/describes data and then a better model is chosen. The goal is to find a good mathematical function that represents some given set of data (pixel measurements) representing an object. For instance, we can describe human faces by plotting several measurements for nose length, eye width, lip height etc... as histograms.
image vs object representation:
A single image contains several objects such as faces, cars, flowers etc. Each object has a set of measurements that defines it, for instance a face has several measurements e.g., nose length, mouth width etc. but these measurements do not have the same values across all faces. For example, the average size of the human eye is different from one person to another as well as differences in height or weight, these differences are called "variability" and there is no exact number showing how much variability occurs for each measurement e.g., eye width can vary from 35mm-50mm by about 1mm which is a big difference if you consider that the face will be represented by less than 1000 pixels (for color images). The goal is then to determine how much variability exists within an image and where it happens e.g., nose length will have less variability than eye width. The latter cannot be determined without comparing pairs of measurements between faces e.g., nose-mouth, eyes-mouth etc... Now that we can locate where this variability occurs within an image, the next step is to consider how information is coded between pixels so they can describe these variations.
Before you read further, I highly recommend you take a look to Aaron Neche’s paper on " Image Processing Theory and Machine Learning Algorithms " which provides easy to understand examples & explanations about several key concepts related to images that any newcomer should know before starting learning about machine learning for computer vision (MLCV). MLCV is super vast, you will need to have a clear picture of what it is or why you are learning about it. It's like saying "I'm interested in astronomy”, but not knowing exactly what you want to explore e.g., stars, planets, galaxies etc...
Features variables
1- The first step is to define the features (variables) & the measurements (observations) that were used for processing an image. These variables describe how pixel data was represented by some function(s) e.g., intensity/distance between colors that were assigned by the function to pixel values thus providing information about objects which can be displayed as images. This representation may happen through color distance calculations (Euclidean distance/Manhattan/brute force), Gabor filters, SIFT/HOG descriptors etc...
Pixel intensity
2- Now a function must be defined which will convert pixel values (pixel intensity) to measurements i.e., define the mapping from one space to another where the new space is most likely different from pixel data space. The function acts as a bridge between pixel representations and observations e.g., wanting to find distance between two points in 2D plane can be done by using Pythagoras theorem but this only works if the plane divides Earth into half, you won't get accurate results if it represents objects with round or irregular shape such as an apple or a human body. The same thing happens when looking for distances based on RGB color model that works fine for triangles or squares but not as good for irregular objects as a human face.
Function design
3- The last step is to design a function which compares observations and finds the variability within an image. This should be done by first comparing observed variables between pairs of images, then finding regions where differences occur e.g., nose will have less variation than eyes. These regions are called "clusters", the process of grouping these points into clusters is called "segmentation" because it aims at segmenting continuous variable space into segments/classes where each class has similar values. In other words, clustering works by figuring out how many different classes will fit or divide given data space (two in our case) using some distance function between points (Manhattan distance). Segmentation can be done using clustering algorithms such as k-means or PCA/ICP.
Image segmentation & clustering
Valid image
1- The first step is to define what is considered a valid image. E.g., I will use images where face is considered the main object, thus having at least one eye and nose on it. You can also design more complex conditions e.g., each face must have three eyes but this depends if you are learning about faces detection, finding similar faces within given data set etc... Defining these rules must be done before processing any image because your algorithm won't work on an invalid input (a badly cropped photo with no face on it).
Input variables
2- Selecting input variables that describe the image(face) . These variables must be simple to use and work fine on every type of images (three color channels, size & name of person). The same thing happened with features in machine learning (you need an input that is understandable by the algorithm you are using).
Variables measurements
3- Now define how to map these variables into measurements(features) e.g., distance between colors, computing orientation for eyes, nose & mouth regions. Try not to overfit your data by keeping things simple, some algorithms might fail if they need high dimensional space. For example, giving Euclidean distances as features will give inaccurate results because it needs only two-dimensional information so using Manhattan/Manhattan2D would be more reliable than using brute force calculation which requires many iterations.
Pixel variables
4- Define the mapping between pixel variables (intensity of each channel) and measurement space. This is the most important part where you need to make sure that your features are good enough for clustering & classification. For example, converting images into HSV will give more information about colors than just RGB because it contains all three-color channels but this comes with the cost of requiring more complex processing steps.
Group objects
5- Define how to group objects into classes/clusters using some distance function (Euclidean/Manhattan/brute force). You can use many different algorithms e.g. k-means or hierarchical methods such as PCA/ICA/ICP etc... Now it is time to break out Python!
Digital Image Processing relates to a process that involves the conversion of an image into its digital format. After that, technicians also perform different operations on the processed image.
This is a way to get an enhanced and clear image. It is also used to extract useful information out of the image. The image processing is similar to signal dispensation.
Here, the input is the image, video, or photograph. The output can be a refined image or other characteristics of such an image.
Normally, the procedure of image processing treats images as two-dimensional signals. This is done after applying signal-processing methods to such images.
There are five major purposes associated with image processing. These include:
1. Offers a clear image to observe, which isn’t visible without the image process.
2. Image processing ensures complete restoration and sharpening of the image. Hence, it helps to create a refined image.
3. It helps to retrieve an image that is of great importance.
4. This process makes it possible to measure different objects in a particular image.
5. The image processing also recognizes and distinguishes a specific object in the image.
Listed below are the different applications of image processing:
1. Face detection
2. Fingerprint sensor
3. License plate identification
4. Iris recognition
All these applications of image processing rely on an image produced by a digital camera or scanner. The image thus captured is converted into a digital file like JPEG.
When processing images in a video, the video data undergoes a decoding procedure. This creates video frames or full-scale images for better visualization.
Importance of image processing in business.
image processing can be defined as the way of manipulating images in order to get them to look better. To improve the quality of an image, various changes are made to it by changing its parameters like color, brightness or contrast. These changes could be done manually or through computer algorithms. Image processing is extremely beneficial for industrial projects like industrial inspection and satellite imaging since it helps in analyzing these images with ease and accuracy.
This field comes under factory automation where industrial processes are automated using robotics; one such process which needs the help of image processing is industrial inspection. For instance, while assembling electronic components on a circuit board many defects like bent pins, scratches etc., occur which hamper product quality eventually leading to wastage of non-usable components. This process can be done through image processing by locating these defects and repairing them before product finalization.
Satellite imaging is essential for agricultural purposes like identifying the drought prone areas, managing crop patterns, finding new area for farming etc. Image processing helps in analyzing satellite images to identify irregularities like water shortage or uneven distribution of crops among farmers which are vital for agriculture management. Image processing package “EVI” is being used by NASA to locate changes occurring in vegetation on earth surface caused due to natural factors like cyclones, forest fires etc., which help in bettering agricultural planning strategies further reducing the risk of crop loss due to unfavorable climatic conditions. With its powerful algorithms EVI is capable of identifying crop patterns even in high resolution satellite images.
This field of image processing deals with the analysis of video data to understand its underlying content by recognizing human gestures like hand waving, facial expressions etc., Computer vision is used in medical imaging like magnetic resonance imaging (MRI), computed tomography (CT) scan, ultrasound for diagnosis purpose which helps doctors find out diseases way before it can be diagnosed through traditional tests. Image processing also plays an important role in robot navigation by giving visual cues to these robots using computer algorithms. Apple iPhone X face ID feature uses “Face id” algorithm which scans users face and unlocks phone by matching their eyes or face with stored records; this system works on computer vision techniques only. There are many such algorithms like “corner detection”, “line detector” etc., which help in the process.
Thus, image processing is a very volatile technology that helps create impactful solutions for various industries and sectors of human lives.
benefits of image processing.
This field comes under factory automation where industrial processes are automated using robotics; one such process which needs the help of image processing is industrial inspection. For instance, while assembling electronic components on a circuit board many defects like bent pins, scratches etc., occur which hamper product quality eventually leading to wastage of non-usable components. This process can be done through image processing by locating these defects and repairing them before product finalization.
Image processing also plays an important role in the field of medical imaging like magnetic resonance imaging (MRI), computed tomography (CT) scan, ultrasound for diagnosis purpose which helps doctors find out diseases way before it can be diagnosed through traditional tests. Computer vision is used in medical imaging like magnetic resonance imaging (MRI), computed tomography (CT) scan, ultrasound for diagnosis purpose which helps doctors find out diseases way before it can be diagnosed through traditional tests. Image processing packages "EVI" and "Tomsk-903" play a vital role by identifying disease causing factors respectively to detect cancer cells or intracranial hemorrhage regaining CT scans at early stage.
Listed below are the steps associated with video processing.
This step involves acquiring an image in digital form. Usually, the image thus acquired passes through different steps such as scaling.
It is one of the simplest procedures linked with image processing. This process helps to clarify details that are obscured. It helps to highlight specific features that contain necessary information.
Image restoration tends to improve the overall appearance of an image. The restoration techniques rely on probabilistic or mathematical models.
Wavelets offer multiple resolutions of an image. The subdivision of images into relatively smaller parts helps during data compression. This in turn allows pyramidal representation.
This process includes techniques for minimizing the size of the image. This helps to transfer the image much quickly via the internet.
It deals with the extraction of image components. These are necessary for the description and representation of shape.
Segmentation relates to the partition of a specific image into different segments and parts. Usually, this is among the most difficult tasks associated with image processing.
This process includes labeling an object in an image. The object is located in the image through its descriptors.
It is a task of detailing regions of the image. The relevant information is located, which helps to limit the search.