Is Computer Vision Difficult To Use?
We humans see objects, places, and people using our eyes. We have been gifted with a natural object-analyzing, detection tool that helps us to identify things in the vicinity. But have you wondered how face lock works in both Android and iPhones? Do computers also have an eye that keeps looking at the world like humans?
Computer Vision
Computer vision is a type of artificial intelligence that helps the computer to see the world, make interpretations, and analyze the visual world. It also uses the machine learning concept to identify different objects that it sees and classify them with similar objects. The machine learning model used here is already well-trained to do this job.
But, in the process of identifying and classifying objects, a few difficulties can have a major effect on the final result.
1) Information Loss During Conversion of 3D to 2D
In this case, when the object is being captured by the camera, the main trouble is with the pinhole that we use. A pinhole is a box with a small hole in it that is used for perspective projection.
The real trouble with the pinhole model is that when the image is being captured, the projective transformation sees a relatively small object close to the camera. In this case, we humans require a ‘yardstick’ to predict the actual size of the object. But this won’t work out for computers.
The actual image of the object is not captured in the computer so the size of a coin, a bat, and a building is the same when seen as an image in the computer.
2) Interpretation
When we humans try to analyze or understand an image, we use all of our previous long-gathered knowledge and experience to fully interpret the image and get insights from it. We have invested several years in training an Artificial intelligence model to understand observations, but the ability of the model to understand observations is still limited. To increase the level of interpretation, several mathematical tools are being utilized.
3) Noise
Noise is present in each of the measurements of the image. We use the mathematical tools that deal with such unreliability. Noise can’t be removed to some extent but the usage of such tools can complicate the image analysis.
4) Large Data
The image and audio files that we use are huge in memory. An A4 sheet of paper is scanned monochromatically at 300 dots per inch corresponding to 8.5MB. Non-interlaced RGB 24-bit color video 512 * 768 pixels, makes a data stream of 225MB per second.
If the processing we conduct is not very simple then it is hard for it to achieve a real-time performance like processing 25 to 30 images per second.
5) Local View vs Global View
An image analysis algorithm analyses small storage in the local memory like a pixel in the image, the computer sees the image through a keyhole. When we see the image through a keyhole, it’s more difficult to understand what the image is depicting. But It is easy for humans to interpret an image if it is seen globally
Conclusion
In this blog, you can get a clear picture of the various difficulties faced while processing images using computer vision. Once we overcome these difficulties, we can make computer vision accessible for all.
I hope you enjoyed reading this blog. Please do like and comment on your views on today’s topic. Go to my profile for more such blogs.
Happy learning!!
We humans see objects, places, and people using our eyes. We have been gifted with a natural object-analyzing, detection tool that helps us to identify things in the vicinity. But have you wondered how face lock works in both Android and iPhones? Do computers also have an eye that keeps looking at the world like humans?
Computer Vision
Computer vision is a type of artificial intelligence that helps the computer to see the world, make interpretations, and analyze the visual world. It also uses the machine learning concept to identify different objects that it sees and classify them with similar objects. The machine learning model used here is already well-trained to do this job.
But, in the process of identifying and classifying objects, a few difficulties can have a major effect on the final result.
1) Information Loss During Conversion of 3D to 2D
In this case, when the object is being captured by the camera, the main trouble is with the pinhole that we use. A pinhole is a box with a small hole in it that is used for perspective projection.
The real trouble with the pinhole model is that when the image is being captured, the projective transformation sees a relatively small object close to the camera. In this case, we humans require a ‘yardstick’ to predict the actual size of the object. But this won’t work out for computers.
The actual image of the object is not captured in the computer so the size of a coin, a bat, and a building is the same when seen as an image in the computer.
2) Interpretation
When we humans try to analyze or understand an image, we use all of our previous long-gathered knowledge and experience to fully interpret the image and get insights from it. We have invested several years in training an Artificial intelligence model to understand observations, but the ability of the model to understand observations is still limited. To increase the level of interpretation, several mathematical tools are being utilized.
3) Noise
Noise is present in each of the measurements of the image. We use the mathematical tools that deal with such unreliability. Noise can’t be removed to some extent but the usage of such tools can complicate the image analysis.
4) Large Data
The image and audio files that we use are huge in memory. An A4 sheet of paper is scanned monochromatically at 300 dots per inch corresponding to 8.5MB. Non-interlaced RGB 24-bit color video 512 * 768 pixels, makes a data stream of 225MB per second.
If the processing we conduct is not very simple then it is hard for it to achieve a real-time performance like processing 25 to 30 images per second.
5) Local View vs Global View
An image analysis algorithm analyses small storage in the local memory like a pixel in the image, the computer sees the image through a keyhole. When we see the image through a keyhole, it’s more difficult to understand what the image is depicting. But It is easy for humans to interpret an image if it is seen globally
Conclusion
In this blog, you can get a clear picture of the various difficulties faced while processing images using computer vision. Once we overcome these difficulties, we can make computer vision accessible for all.
I hope you enjoyed reading this blog. Please do like and comment on your views on today’s topic. Go to my profile for more such blogs.
Happy learning!!
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