Computer Vision vs. Machine Learning: What are the Differences?
Computer vision and machine learning involve the absorption and interpretation of visual stimuli; therefore, it is essential to understand any overlapping technologies' strengths, limitations, and the most suitable use case scenarios.
Over the last twenty years, advanced technologies like Artificial Intelligence, Machine Learning, and Computer Vision have moved from Research and Development into commercial and mainstream environments. Commercial adaptation has witnessed automated robot production assembly lines, automated vehicle guidance systems, and the analysis of remotely captured imagery to facilitate automated visual inspection strategies. As a result, computer vision and machine learning applications are among the most alluring and fascinating technical topics nowadays. And most modern tech industry companies and ambitious technological start-ups are rushing to embrace the benefits from these advanced technologies.
What is Computer Vision?
Computer vision aims to provide computer systems with human-like visual perception capabilities. It is an interdisciplinary field that enables computer systems to process, analyze, and accurately interpret our visual world. For example, computer vision qualifies computers to identify meaningful information from images and video files in the same way humans do. The aim is to supply this 'natural' visual characteristic onto computers so they can understand and analyze complex digital systems - just like humans can - and perhaps even better.
Modern computer vision leverages machine learning, which is a component of artificial intelligence that focuses on 'teaching' machines to learn by themselves over time. However, unlike a system that will always act on a pre-defined set of rules or instructions, a machine learning system will analyze past experiences and decisions to decide on an appropriate response. Furthermore, all of this can be achieved with minimal or no human intervention required at all.
What is Machine Learning?
Machine learning is a straightforward subset or portion of artificial intelligence. Without human assistance, machines that embed machine learning can analyze and comprehend digital data autonomously.
Machine learning typically leverages both statistical principles and algorithms to produce models able to output decisions from input data. Therefore, machine learning is applied in several fields, ranging from supercomputers to complex software engineering.
So a key question will be - what is computer vision with regards to machine learning? Machine learning principles apply to computer vision, as computer vision nowadays heavily involve machine learning
After having examined both items separately, we can now discuss their technical fellowship.
Supervised Learning Defined
Supervised learning provides computers with a powerful toolset to classify and interpret digital data. To enable supervised learning, digital data has to be labeled manually. This labeled dataset is then used as the material for training the classification of other, similar unlabelled data using machine learning algorithms.
Supervised learning is a significant form of machine learning. It is termed supervised because the learning process is achieved using the previously obtained labels of observations, in contrast with unsupervised learning, where there is no manually labeled data available
As for the training data set, the input variables are the key features that influence the accuracy of a predicted label. It will contain both qualitative and quantitative variables, and the output variable will be the label (number or class).
According to the distinct types of output variables, supervised learning tasks are separated into two distinct categories:
1. A Classification task,
2. A Regression task.
* Classification task: the output variable of a classification task is a categorical variable. “Cat” or “Dog” are examples of the possible categories an image classification task
* Regression task: the output variable is continuous. The predicted movement of a share price on the stock market is noted as a regression task.
The Relationship between Machine Learning and Computer Vision
What precisely is computer vision within machine learning? Machine learning has strengthened the ability with which computer vision can correctly analyze visual data by swiftly identifying digital patterns. Machine learning has made computer vision image processing positively effective via instant recognition characteristics and efficient digital image processing.
Computer vision has significantly benefited from machine learning techniques involving machine learning's scale of digital operations. Key advancements within computer vision processes have permitted machine learning algorithms to function on a wider range of digital data sets.
Machine learning and artificial intelligence-based computer vision procedures have been developed to correctly identify and diagnose tumors and other growths appearing within the human body. While recent application results have been encouraging, there is always room for further improvement within this medical field.
Key Differences between Computer Vision and Machine Learning
In simple terms, computer vision is a technology that attempts to train computers to recognize patterns in visual data in a similar way as humans do. On the other hand, machine learning is a process that enables computers to learn how to process and react to data inputs based on precedents set by previous actions. In short, machine learning is more general and does not necessarily involve visual data.
In both computer vision and machine learning, the aim is for computer systems to learn how to process and react to data in a specific situation. However, computer vision is much more focused on imagery and visual data whilst machine learning focuses on other types of data and aims at tackling image classification, object detection, object segmentation, object tracking in videos. In both cases the outcome is the same, however, the type of input data determines which learning process would work best. The key outcome is that the computer system should be able to learn from previous data.
When is Computer Vision Used?
Data is used in a wide variety of applications and as such more than one type of data analysis method is required to teach computers how to react. Examples include medical diagnostic procedures, agriculture, and autonomous driving aids. In contrast, machine learning is also used when the data that is being analyzed consists of text- or speech-based data. Examples of this include speech recognition, financial data analysis, traffic data analysis, email analysis, and fintech applications.
Applications of Computer Vision using Machine Learning
Recognition Capability in Computer Vision
This specific application of machine learning within computer vision has changed the technological world's landscape. It is amazing to see how a simple application of machine learning - within computer vision systems - can have important implications.
Via the overlap of machine learning and computer vision competencies, artificial intelligence-based image processing is accomplished whereby data images are manipulated or altered to enhance the original image's quality or extract new and relevant information from the original image.
This important application of machine learning within computer vision is used in nearly every digital visual industry:
* Business analytics and market research,
* 3D data image mapping,
* Agriculture: and specifically the online and real-time monitoring of crops and produce for disease identification and prevention,
* Security: visual digital facial recognition for security systems and specifically used within airports, and
These examples show how critically important the overlap of computer vision and machine learning applications is in the real world.
Business Enterprises Rely Heavily on Computer Vision powered by Machine Learning
Computer Vision powered by Machine Learning (or Artificial Intelligence in general) has already provided enormous benefits and has seen wide applications within the real world.
The automobile manufacturing process is now predominantly robotized. Key automobile manufacturers such as Mercedes, Tesla, and BMW have largely shifted towards automated production and assembly lines. Automobile business leaders have realized that the efficiencies and cost-reduction outcomes they will earn from adopting such advanced Computer Vision are incomparable to other processes and manufacturing practices.
Indeed, almost every automation process within such assembly lines functions primarily due to Machine Learning-powered computer vision methodologies. However, without effective digital image sensory and the inability of Machine Learning based systems to interpret their environment and surroundings, the investment will not occur.
Nowadays, computer vision relies heavily on artificial intelligence because AI has continuously widened computer vision's scope of operations and permitted additional efficiency within computer visions' digital image processing.
A further example of enterprises adopting artificial intelligence is their use of process automation within their warehouses. Key e-commerce organizations, such as Alibaba and Amazon, have adopted AI-based automation practices in their warehouses. These important systems perform the majority of their 'picking and packing' customer chosen products.
The technical 'affinity' between computer vision and artificial intelligence has allowed noteworthy long-term cost-saving options for the corporations that have financed adoption. For example, with just-in-time (or JIT) stock operations, there are increased efficiencies and tangible competitive advantages seen.
Enterprises are beginning to recognize - and acknowledge - the potential of computer vision powered by artificial intelligence to save money and improve productivity.
The following key industries have radically commenced adopting the relationship between computer vision powered by artificial intelligence:
* Security and Surveillance, and
* Healthcare and Medicine.
Computer Vision and Machine learning are important aspects of the artificial intelligence landscape. They already improved the state-of-the-art levels in accuracy and performance in several tasks like image classification, object detection, and segmentation.
Additionally, the combination of computer vision and machine learning streamlines and simplifies the creation of efficient technical approaches, applications, and systems for all major industries and business sectors.
Machine learning and computer vision are two advanced technical fields that have evolved to become closely connected. Machine learning has improved computer vision regarding tracking and recognition. Additionally, it offers effective techniques for data acquisition, digital image processing, and data object focus - techniques that are all used within computer vision. Comparatively, machine learning is the broader technical field, and its more general algorithms can be used in other areas and fields.