A Historical Review into Computer Vision

When compared to Artificial Intelligence, Machine Learning, and the Neural Networks, Computer Vision has not been around nearly as long, just because the advancements made in it have taken longer than the others. But it, too, has had a rather rich history, and in this section, we will review some of the major highlights of it.

■ The 1970s:

This is deemed to be the very first starting point for Computer Vision. The main thought here was that Machine Learning would merely mimic the visual component and aspect of the human brain. But, it was not realized back then just how complicated this process would actually be. So instead, the primary focus was on building Computer Vision (CV) systems as part of the overall ANN system that could analyze just about any kind of visual input, and use that to help produce the desired outputs. In fact, the first known major efforts in CV took place when a well-known MIT researcher known as Marvin Minsky asked one of his research associates to merely link up a camera to a computer and get that to deliver outputs as to what it literally saw. At this time, a strong distinction was made between CV and the field of Digital Image Processing. In this regard, various 3-Dimensional images were extrapolated from the 2-Dimensional images themselves. Other key breakthroughs that occurred in this time period also include the following: *The development Line Labeling Algorithms;

  • *The development of Edge Detection formulas to be used in static images; *The implementation of 3-Dimensional modeling of non-Polyhedral Objects, making use of Generalized Cylinders;
  • *The creation of Elastic Patterns to create automated Pictorial Structures; *The first qualitative approaches to Computer Vision started with the use of Intrinsic Images;
  • *More quantitative approaches to Computer Vision were created such as Stereo Correspondence Algorithms and Intensity-based Optical Flow Algorithms. ‘Three key theories of Computer Vision were also formulated, which are:

The Computational Theory:

This questions the purpose of what a specific Computer Vision task is, and from there, ascertains what the mathematical permutations would be to get to the desired outputs from the ANN system.

The Image Representation and the Corresponding Algorithms Theory:

This theory aims to answer the fundamental questions as to how the input, output, and the intermediate datasets are used to calculate the desired outputs. The Hardware Implementation Theory:

This particular theory tries to determine how the hardware of the Computer Vision system can be associated with the hardware of the ANN system in order to compute the desired outputs. The reverse of this is also true, in that it tries to determine how the hardware can be associated with the CV algorithms in the most efficient manner.

■ The 1980s:

In this specific time frame, much more work was being done on refining and advancing the mathematical aspects of Computer Vision, whose groundwork was already established in the 1970s. The key developments in this era include the following:

  • *The development of Image Pyramids for use in what is known as “Image Blending”;
  • *The development of Space Scale Processing, in which created pyramids can be displaced into CV applications other than those they were originally intended for;
  • *The creation of the stereo-based Quantitative Shape Cue to be used in many types of X-Ray applications;
  • *The refinement of both Edge and Contour Detection-based mathematical algorithms (this also led to the creation of “Contour Trackers”);
  • *The development of various types of 3-Dimensional-based Physical Models; *The development of the discrete Markov Random Field Model, in which stereo, flow, and edge detection mathematical algorithms could be unified and optimized as one cohesive set to be used by the ANN system;
  • *Other, further refinements were also made to the Markov Random Field Model, which include the following;
  • *The mapping of the “Kalman Vision Filter”;
  • *The automated mapping of the Markov Random Field Model so that it can be used as a precursor to parallel processing to take place from within ANN systems;
  • *The development of 3-Dimensional Range Data Processing techniques, to be used for the acquisition, merging, mathematical modeling, and recognition of various images to be inputted into the ANN system.

■ The 1990s:

This time era in Computer Vision also witnessed the following key developments:

  • *The development of what are known as “Projective Reconstruction” algorithms which have been primarily used for exacting the calibrations of the camera for it to take the necessary images to be used by the ANN system; *The creation and implementation of “Factorization Techniques” in order to accurately calculate the needed approximations for Orthographic based cameras;
  • *The development of the “Bundle Adjustment Techniques” to be used in just about all types of Photogrammetry techniques;
  • *The development of using color and intensity in specific images, which made use of what is known as “Radiance Transport” and “Color Image Formation” that could be directly applied to a new subset of Computer Vision at that time known as “Physics based Vision”;
  • *The continued refinement of a majority of the Optical Flow Methods that are used by the Computer Vision component that come from within the ANN system;
  • *The refinement of the Dense Stereo Correspondence Algorithms;

‘Much more active and dynamic research started to take place in the implementation of Multi-View Stereo Algorithms that could be applied to replicate and easily produce 3-Dimensional pictures;

*The development of mathematical algorithms that could be used to record and produce various 3-Dimensional Volumetric Descriptions from upon various Binary-type silhouettes;

‘Techniques were also established for the development of the construction of what are known as “Smooth Occluding Contours”;

‘Image Tracking algorithms were greatly improved upon in which various Contour Tracking algorithms such as “Snakes,” “Particle Filters,” and “Level Sets” were primarily established;

‘Much more active research also started to precipitate a subset field of Computer Vision known as “Image Segmentation.” Such techniques that were developed in this area included Minimum Energy, the Minimum Description Length, Normalized Cuts, and Mean Shifts that could be applied to image analysis from within the ANN system;

‘This specific time period also saw the birth of the first statistical-based algorithms that were used in Computer Vision. These were first applied to such ANN system applications such as Principle Component Analysis (aka “PCA”), which relies upon the heavy usage of Eigenfaces, and the development of the Linear-Based Dynamical Systems, which were used in Curve Tracking;

‘Probably the most lasting development in Computer Vision that occurred during this time period was the increased interaction with Computer Graphics, which could also be used in the subfields of Image-based Modeling and even Rendering;

‘Various kinds of Image Morphing algorithms were also created, in order to create computer animation from both static and dynamic images. These specific algorithms could also be applied to Image Photo Stitching, and Full Light Field Rendering;

‘Other kinds of both mathematical and statistical algorithms were developed so that 3-Dimensional Image Models could be automatically created from a series of static images.

■ The 2000s and Beyond:

This specific time period witnessed probably the biggest interactions between Computer Vision and Computer Graphics. Here is what has transpired thus far:

‘The subfields of Computer Vision, which include Image Stitching, Light Field Capturing, and Rendering, as well as High Dynamic Range (aka HDR) techniques were combined into one specific field of Computer Vision, which became known as “Computational Photography.” From its emergence, various kinds of “Tone Mapping” algorithms were developed;

‘Various other kinds of both statistical- and mathematical-based algorithms were also created so that Flash-based Images could be easily combined with Non-Flash-based Images, as well as to segregate overlapping segments in both static and dynamic images into their own unique entities;

‘The techniques ofTexture Synthesis and Inpainting were developed in order to create new images from sample images;

^Numerous principles, which became known as “Feature-based Techniques” also evolved, which can used for Object Recognition by the ANN system. This included the development of the Constellation Model and the Pictorial Structures Techniques, as well as Interest Point-based Techniques, which make use of contours and region segmentation in both static and dynamic images;

  • *The “Looping Belief Propagation” theory was also established in which both static and dynamic images can be embedded and further analyzed onto a Cartesian Geometric Plane and other complex graphing planes;
  • * Finally, this time period has also witnessed the combination of the techniques of Machine Learning into Computer Vision that can be used by the ANN system in order to derive the generated outputs.

So far in this chapter, we have provided a technical definition for Computer Vision and some of the various applications it serves, as well as given a historical background as to how it became the field it is today, and explored its sheer dominance in the field of Artificial Intelligence. The remainder of this chapter is now devoted to doing a deeper dive into the theoretical constructs of Computer Vision.

 
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