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Running Head: IMAGE RECOGNITION
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IMAGE RECOGNITION |
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Image recognition
Name
Institution Affiliation
Image recognition
To instructor
This skills will help to create a perfect future for the next generations since the current challenges will be solved and make the environment a suitable place to stay due to the perfect security measures. In conclusion, the study has enabled me to identify and understand the need for the use of image recognition in the current and future life.
Your truly,
Signature.
Table of content
Introduction …………………………………………………………………………………….. v
Operation of image recognition……………………………………………………………......vi
Impact of image recognition viii
Reference……………………………………………………………………..………………….xii
Abstract
The level of safety in various business sectors is improved by installation of security methods to protect the premises or the big data. The main aim for this study is to find out the main purpose of image recognition, its impact on the environment and the people and how the vision will be in the future.
Though the image recognition system may have several advantages, there are challenges of the system as discussed in the report. Data collected on the information concerning the image recognition is used to provide enough information on the report.
It is recommended that for the perfect security that has being desired by most of the people, the use of image recognition will help to accomplish the mission. Additionally, as technology develops in all the sectors, the security sector will also develop as the image recognition involve to use of advanced technology.
Image recognition
Introduction
Image recognition, also known as visual artificial intelligence refers to the implementation of computer software and hardware to perform various mandates and functions that relate to the identification of various aspects and objects in the environment such as animals and human beings. The implementation of various computer software and their respective hardware devices give rise to the image recognition. In this process, the computer software is integrated and merged in a particular setting that includes the methods and techniques that are helpful in the analysis and detection of images and other visuals. The particular process of detection and analysis of the images and visuals are implemented to help in the automation of some specific tasks and mandates. The Image recognition has been in innovation and processing form the early 1950, where researches and scientists found out that the manipulation of mechanics and machines can be done in such a way that they mimic the Human brain, where they can identify and name various objects in the environment. However, the ideas that the scientists had by they were hard to actuate and make reality because of the high prices of computer at the time. Since the particular time, advancements and innovations regarding the respective subject were implemented and led to the improvement and the actuation of the particular ideas.
The evolution and the growth of the implementation of artificial intelligence, particularly in processes such as image recognition have been realized in the recent past. Various requirement and demands for better results in different aspects and circumstances led to the demand for better systems that provided efficient automation processes using the image recognition and other related aspects. The various implementations and circumstances include the security departments, where the implementation of AI in image recognition is becoming an effective and essential requirement is circumstances such as access control. Image recognition is realized as a development that took several years to achieve the respective result and particular result that is accorded in the present time.
I this report, the main purpose is to address the respective experiences, implementations and impacts of artificial intelligence, which is image recognition and other experiences realized form the implementation and the actuation of image recognition in various circumstances and environments. In this report, the knowledge and the information is intended to reach various audiences, including the respective organization and companies that benefit from the implementation of the implementation of image recognition. The implementation of image recognition is realized and witnessed in various environments and circumstances such as security enforcement, were image recognition is used to automate access control and other related aspects. Besides, image recognition is also witnessed in other circumstances such as the automation of driverless cars, automation of bridges and the signing of attendance at working environments and institutions. Such are the audiences addressed by this report. Beside, other audiences such as students who seek knowledge and information about image recognition and AI can also benefit from this report. In this report the limitations witnessed includes the insufficient funds that led to the failure of the required exposure to various companies and organizations that employ and also implement the formulation and the implementation of image recognition and other artificial intelligence applications.
Operation of image recognition
At this point of image recognition systems and software, the various aspects and characters used to actuate and implement the functions of the image recognition activity is witnessed. The implementation and the operation of the image recognition theory and other related forms of artificial intelligence are witnessed and realized from the perspectives witnessed during the actuation and the particular implementation of the image recognition procedures. The use of artificial neural networks and other related technologies are among the processes that avails and produce the function and the operation of image recognition. Foremost, the neural networks in image recognition are used to help in the recognition and the analysis of various concepts and other characteristics found in any particular image ("Image Recognition : A Complete Guide - Deepomatic", 2021). To achieve this implementation, training of the neural networks is implemented using primary data collected from various circumstance and environments. After the particular data is availed and stored in the system, the annotation and the definition of various characters and other physical aspects of the image helps to build the image recognition process and its efficiency in operation.
After the annotation and the entry of the required data into the system, various examples of real world examples and images are then brought in to help tech the neural networks about the concepts and other various aspects witnessed in image recognition. When different examples and samples are used in the process, the neural network learns more about the aspects and the concepts witnessed in the image recognition process ("Image Recognition : A Complete Guide - Deepomatic", 2021). The image recognition software and system now identifies various images with and components in any image in form of digital representation, which are usually numerical values that stand for the pixels that build up the image. At this point, the particular system is able to recognize various images and components of images with the representation in numerical values. Each value represents different aspects and characters of different images. This makes it possible of the image recognition system to recognize different images and characters in a given time. An example of the numerical recognition of an image in the system can be witnessed bellow.
The numerical values in the representation stand for the different characteristics and aspects in the image. The different numerical expressions help to distinguish between various components and aspects of the image. The representation above changes from image to image. This implies that every image possesses its own representation that doesn’t resemble another. Form such aspects, the image recognition system becomes suitable in circumstances and points such as the automation of security systems in areas of access control.
The image recognition can be implemented and realized in various systems and software. Among the various examples is python, where the representation of an image is dine using various steps, where an image is accorded different dimensions in a decreasing manner, to a point where the respective representation of the image in the python software is diminished by half but all the aspects and characteristics are maintained. From such an implementation, the particular image recognition system is able to perform various tasks and mandates regarding to image recognition of various characters. In this aspect, the involvement of deep learning is implemented ("Image Recognition : A Complete Guide - Deepomatic", 2021). This is where the image recognition system is able to acquire all the required aspects and components of the image to be analyzed. The components include the color scale, size and other important aspects of every image. This allows for the accurate representation of the aspects and components of the respective image. As a result, accurate and precise recognition of the image is achieved. Below is an example of the image recognition process and illustration of in python.
Impact
The use of image recognition has brought about several impacts to the environment, mostly the business sector and to the individuals in general. Organizations are widely using image recognition for various activities such as to increase their level of security. The future of image recognition is likely to grow since most of the industries have visions on the services offered by image recognition. Moreover, there are strategies that will be made to solve the problems of image recognition to make it more effective in its services.
Findings
There are various findings associated with image recognition. These findings include advantages and the disadvantages of image recognition;
Advantages
Image recognition contributes in increasing the level of security designed to protect important information. Image recognition is widely used since it is installed in the private devises of individuals and it is possible for them to use the security method. Most of the phones are unlocked with the use of image recognition since this method is more effective as it is rare to find another person with the same features. Organizations use image recognition to enhance the security of the business since they deal with big data. Information about the employees in the place of work is protected by setting image recognition as security measures. This allows only few members to have access to the organizations data.
Image recognition is well known of its accuracy since the individuals’ features vary. Face ID has become more reliable since the advanced technology has introduced the use of 3D facial recognition. It is not simple for one to trick the system and hence this creates the confidence that the business premises are secure. Moreover, it is difficult for thieves or any stranger with bad motives to attack the organization since their images are not in the recognition system. Image recognition is fully automated as the only have to confirm their identity from the security system. This creates the advantage of saving time when checking on the security of the organization. The members of the organization do not have to spend a lot of time in signing in. Additionally, image recognition has a very high rate of accuracy and this makes the system more convenient to use. Likely, low cost is incurred in the process of installing the image recognition system. This makes it affordable by most of the organizations.
Above is a block diagram of face recognition system.
Disadvantages
However, image recognition has various disadvantages to the people since in most cases, the police department use hidden cameras to track the criminals in a given area hence there are other people in the same area that carry on their private businesses. This leads to lack of privacy in the areas since the people cannot carry out their activities in private. Likely, the image recognition systems can analyze millions of images in a given period since the people operation from the particular area are many. This makes it less effective to the police concerned since it is not easily possible track a criminal among a large number of people.
Moreover, since image recognition is likely to taking a sample of an individual’s DNA, most of the people are worried about their privacy since they have a lot of important information that they have protected through the face recognition. If incase the details on the image recognition of an individual are hankered, it impossible to reset since they are permanent. Other security methods such as passwords when hacked are easy to reset hence people prefer the use of passwords ("5 Pros and Cons of Face Recognition Technology", 2021). Moreover, some groups of people claim that face recognition not reliable since it may portray low illumination when used hence less effective. Likely, in a state where security cameras have been set to keep close watch on the property in the particular area, the videos captured in the stipulated period are not clear to identify the people in the clips.
It is proved that at times, image recognition in form of a video can display the false positivity. This happens when people are shown on the cameras and yet in actual status there was no one in the area. This creates a challenge to an organization that is in the process of maintaining a high security for its premises. Slight changes in the position of the cameras used for security lead to capturing the wrong angle and may give an advantage to the thieves to steal from the premises without being notified. With the increased cases of data breaches, image recognition is at risk since the data on recognition is stored in cloud and incase of hacking, the recognition information gets to unauthorized hands.
Interpretation of findings
Regardless of the benefits to image recognition, the challenges of using the system make it less effective to be used by all the people. Due to the big data in most of the organizations, security methods are used to protect the premises. The effective method is when the members of the particular organization give there recognition information to the security system. This helps to control the people who should enter the building of the organization. Maintaining strategies to protect the cloud system of the organization helps to protect the recognition information which is stored in the cloud. This is because there is not possible way to reset the facial recognitions of the individuals.
Future of image recognition
Due to the advanced technology, more devices are being invented that require the use of image recognition to operate. A good example is the driverless vehicles that can sense the presence of a person in front of it ("Image Recognition Applications: 7 Essential Future Uses – Data Science Society", 2021). This image recognition installed in the driverless vehicle help to reduce the accidents that happen as a result of a person or people crossing the road. Moreover, image recognition will be of great importance in future enrolling and admitting students in the specific schools. Instead of the long process where the school staff have to admit each student at a time taking their details, the process will be easier when the schools use the image recognition system. Moreover, the students with learning disabilities are able to rely on the applications that provide computer vision to read a given content.
The future of image recognition will lead to extensive use of the system in optimizing medical imagery hence making it more effective in the healthcare centers. Image recognition will be used in a situation of scanning the patients to identify the presences of a given infection in their body. The detection of severe diseases including cancer can be identified at their early stage hence creating a chance for the patient to get their treatment. Moreover, as time goes by, the challenges that are associated with image recognition will be solved to make it more effective. This gives most of the industries visions to increase their level of security in future.
Interpretation of findings
Image recognition as identified as one of the best security methods in all the sectors. It is relied on to make more positive impacts in future by improving the state of living of people in various ways. There are possibilities that the challenges associated with image recognition will be solved to increase its effectiveness ("What Will We Use Image Recognition for in the Future?", 2021). Companies are likely to depend on image recognition for more purposes apart from security measures. Its application is sectors such as disease identification in healthcare will lead to saving many lives that had diseases which were easy to detect. Driverless vehicles will become more effective due to the developed sensors to increase the safety of the passages.
Conclusion
In conclusion, image recognition can be used for various purposes including increasing security of a premises. This helps to specify the people who have the rightful access to the organization hence avoiding any issue of theft in the premises. Security cameras are placed in specific areas to increase the security of the area by getting the identification of the people who might have intruded in the place. Moreover, the self-driving vehicles are effective due to their ability to sense hence increasing the safety of the passengers. Despite to challenges of image recognition, organizations and industries have visions to improve their security through image recognition in future. It is recommendable that image recognition will grow in future due to its wide usage in the business sector to provide security and other services.
Reference
What is Image Recognition their functions, algorithm and its uses. (2021). Retrieved 27 February 2021, from https://www.mygreatlearning.com/blog/image-recognition/
Image Recognition : A Complete Guide - Deepomatic. (2021). Retrieved 27 February 2021, from https://deepomatic.com/en/what-is-image-recognition
What Is Image Recognition?. (2021). Retrieved 27 February 2021, from https://towardsdatascience.com/module-6-image-recognition-for-insurance-claim-handling-part-i-a338d16c9de0
What is image recognition? - Definition from WhatIs.com. (2021). Retrieved 27 February 2021, from https://searchenterpriseai.techtarget.com/definition/image-recognition
What Will We Use Image Recognition for in the Future?. LogoGrab Blog. (2021). Retrieved 27 February 2021, from https://blog.logograb.com/future-uses-image-recognition/.
Image Recognition Applications: 7 Essential Future Uses – Data Science Society. Data Science Society. (2021). Retrieved 27 February 2021, from https://www.datasciencesociety.net/image-recognition-applications-7-essential-future-uses/.
5 Pros and Cons of Face Recognition Technology. Techfunnel. (2021). Retrieved 27 February 2021, from https://www.techfunnel.com/information-technology/5-pros-and-cons-of-face-recognition-technology/.
input image
face detection
data base
face recognition