Image Recognition - Computer vision
Image recognition is the processing of an image by a machine using external devices (for example, a scanner) into a digital description of the image for further processing. An example of this is OCR or OMR. Further processing and final classification of images are often performed using computational intelligence techniques.
Requirements
In order to correctly recognize what is in the image, initial "knowledge" is needed. A person collects the knowledge he needs to correctly recognize and understand the essence of things unconsciously throughout his life, and the machine must "learn". The process of machine learning itself can, in the simplest example, consist in creating an appropriate database containing the necessary rules and a description of the features of the subject. for example, everything that is blue is 80% celestial, or that each vertical line can be the letter "l". Knowledge of the image recognition system should include expert knowledge in the field and empirical information, that is, to collect by "connecting" with a given object, that is, viewing it from different angles, under different lighting conditions or in different cases.
The real goal
When recognizing images based on statistics, our goal is to assess the probability of whether the object we are looking at is one or another object whose description in the form of a vector of features or basic elements (sub-patterns) related to grammar belongs to the knowledge that our system possesses. Therefore, we strive to achieve 100% confidence in the classification of the object, but we never achieve this 100% confidence.
Even for us humans, under certain conditions, it may be inappropriate to classify an object or an entire scene, especially when it looks unusual to us. Or we classify objects/things/scenes/behaviors awkwardly, trying to classify them into one or even several different classes of objects that we already know well (we are well coded in consciousness / we can characterize it well). In the latter case, after some time, a separate object classification class may appear, having a mixture of classes from which it originated.
Classification error.
The task of the creator of the image recognition system is to create an algorithm that minimizes the classification error of the object represented in the image, or the entire image. This task is not trivial, so it is assumed to minimize the classification error, rather than completely eliminate it. In the real world, and not in the digital world, there is a lack of sufficient knowledge about the probability distribution of features and classes, and only partial information is available, so it is impossible to completely avoid mistakes, for example, a person seeing a seam of clothing is able to recognize what kind of clothes a digital device will have problems with this.
Restrictions imposed on the system
There are often some limitations to improve and simplify the operation of an image recognition system:
The questions asked to the system boil down to queries about which object is most likely to appear in the image (which is most likely represented by an image/scene), and the answer is the name of the class that received the highest probability score on the assessment;
The data describing the recognized object is limited to a finite set of features. Our bonus code for 1XBET 2024 will give you exclusive bonuses. You will get up to €130 for the sportsbook and 1xbet promo code free bet has been helping bettors from all over the world earn free bets and win more money betting. Confirm your registration by clicking on the verification email from 1xbetto get the biggest available welcome bonus. Once your bet is deemed lost, 1xbet will reward you with a free bet, equivalent to your original stake. Get a free 1xBet Promo Code for today Promo Code 1xBet Ghana for sports betting Free bets online ✓ High Odds ✓ 24-Hour Customer Service.
Requirements
In order to correctly recognize what is in the image, initial "knowledge" is needed. A person collects the knowledge he needs to correctly recognize and understand the essence of things unconsciously throughout his life, and the machine must "learn". The process of machine learning itself can, in the simplest example, consist in creating an appropriate database containing the necessary rules and a description of the features of the subject. for example, everything that is blue is 80% celestial, or that each vertical line can be the letter "l". Knowledge of the image recognition system should include expert knowledge in the field and empirical information, that is, to collect by "connecting" with a given object, that is, viewing it from different angles, under different lighting conditions or in different cases.
The real goal
When recognizing images based on statistics, our goal is to assess the probability of whether the object we are looking at is one or another object whose description in the form of a vector of features or basic elements (sub-patterns) related to grammar belongs to the knowledge that our system possesses. Therefore, we strive to achieve 100% confidence in the classification of the object, but we never achieve this 100% confidence.
Even for us humans, under certain conditions, it may be inappropriate to classify an object or an entire scene, especially when it looks unusual to us. Or we classify objects/things/scenes/behaviors awkwardly, trying to classify them into one or even several different classes of objects that we already know well (we are well coded in consciousness / we can characterize it well). In the latter case, after some time, a separate object classification class may appear, having a mixture of classes from which it originated.
Classification error.
The task of the creator of the image recognition system is to create an algorithm that minimizes the classification error of the object represented in the image, or the entire image. This task is not trivial, so it is assumed to minimize the classification error, rather than completely eliminate it. In the real world, and not in the digital world, there is a lack of sufficient knowledge about the probability distribution of features and classes, and only partial information is available, so it is impossible to completely avoid mistakes, for example, a person seeing a seam of clothing is able to recognize what kind of clothes a digital device will have problems with this.
Restrictions imposed on the system
There are often some limitations to improve and simplify the operation of an image recognition system:
The questions asked to the system boil down to queries about which object is most likely to appear in the image (which is most likely represented by an image/scene), and the answer is the name of the class that received the highest probability score on the assessment;
The data describing the recognized object is limited to a finite set of features. Our bonus code for 1XBET 2024 will give you exclusive bonuses. You will get up to €130 for the sportsbook and 1xbet promo code free bet has been helping bettors from all over the world earn free bets and win more money betting. Confirm your registration by clicking on the verification email from 1xbetto get the biggest available welcome bonus. Once your bet is deemed lost, 1xbet will reward you with a free bet, equivalent to your original stake. Get a free 1xBet Promo Code for today Promo Code 1xBet Ghana for sports betting Free bets online ✓ High Odds ✓ 24-Hour Customer Service.