DIGITAL IMAGE PROCESSING

Image analysis is the automated detection, measurement, segmentation and classification of image data. "Automatic" implies some semiautomatic or fully-automated process. In general the aim of image analysis is to extract information from images and/or reconstruct an image from the analysis. Image analysis techniques can be divided into three major application areas:.

 

Image Segmentation

Automated image segmentation is concerned with dividing up an image into sub-regions which represent meaningful information about the imaged scene. An example is applying an algorithm which detects brown objects on a green background, then separating the two (ie. detecting the boundary).

 

Image Classification

Automated image classification is the process of classifying an image into one of a fixed number of categories. An example is applying an algorithm to classify an image into one of several hundred human poses.
 

Object Tracking

Automated object tracking is the process of identifying one or more moving objects in a sequence of images, automatically animating over time as the object (or objects) move through the scene in question. An example would be applying an algorithm to detect and track persons in an airport security line.

 

As AI encounters and combines with the real world, something extraordinary happens…

For a while now, computer vision researchers have been wrestling with the problem of inferring accurate camera parameters from images. To interpret these images as physical reality, we (humans) do so by inferring various properties of cameras: lens size, focal length, principal point, optical center, so on. Cameras are used in many challenging applications where accessing such information would be highly useful – ie. applications in robot vision/micro drone vision/self driving cars/VR products etc. The problem has been that there are at least four different methods for estimating these parameters named "Active Camera Model", "Distortion Model", "Global 2nd Order Model" and "Global 3rd Order model", and each has its own strengths and limitations.

 

Modern digital cameras have access to two noisy measurements: images, and lens metadata. Research has focused on how to solve inverse problems in computer vision using models trained on known data, where the noise is variously interpolated or semi-parametric. Active Camera Models are particularly challenging to estimate because they are large, often non-linear, functions of possibly thousands of parameters. Assuming some prior knowledge, it may seem like a daunting task to extract any usable information from a camera.

 

Digital image processing allows for the use of much more advanced algorithms, allowing for more complicated performance on basic tasks as well as the application of methods that would be difficult to apply using conventional methods.
 

Different Taks of Digital Image Processing

 

1. Classification

On the basis of a training set of data containing observations whose group membership is defined, classification is the problem of deciding which of a set of categories a new observation belongs to. Assigning a diagnosis to a particular patient based on observable characteristics and categorizing a given email as "spam" or "non-spam" are two examples. Pattern recognition us an example

 

2. Feature Extraction

In image processing, feature extraction begins from a collection of calculated data and generates derived values that are meant to be descriptive and non-redundant, making understanding and generalization simpler and, in some situations, leading to improved human interpretations. Dimensionality reduction is linked to feature extraction.
 

3. Multi-Scale Signal Analysis

Analyzing, manipulating, and synthesizing signals such as sound, images, and experimental measurements is the subject of the field of signal processing. Signal processing methods may be used to enhance reception, storage performance, and subjective accuracy of a measured signal, as well as to emphasize or detect components of interest.
 

Digital Image Processing allows for a much broader variety of algorithms to be applied to the input data, as well as the elimination of issues like noise and distortion during processing.

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