Computer Vision: Everything You Need to Know When Assessing Computer Vision Skills

Computer Vision

Computer vision is a field of artificial intelligence that enables computers to understand the visual world. Computer vision algorithms are used to analyze and interpret images and videos, and to extract meaningful information from them.

Computer vision has a wide range of applications, including:

Image classification and detection: Computer vision algorithms can be used to classify images and detect objects within them. This is used in a variety of applications, such as facial recognition, object detection for self-driving cars, and product recognition for e-commerce.
Image processing and enhancement: Computer vision algorithms can be used to improve the quality of images and videos, and to extract specific features of interest. This is used in applications such as medical imaging, satellite imagery, and security surveillance.
3D reconstruction and tracking: Computer vision algorithms can be used to reconstruct 3D models from images and videos, and to track the movement of objects over time. This is used in applications such as augmented reality, virtual reality, and robotics.

How Does Computer Vision Work?

Computer vision algorithms typically work by following these steps:

Image acquisition: The first step is to acquire the image or video that will be analyzed. This can be done using a camera, scanner, or other device.
Feature extraction: Once the image or video has been acquired, the computer vision algorithm will extract features from it. Features are specific characteristics of the image or video, such as edges, corners, and textures.
Feature matching: The next step is to match the extracted features to known patterns in order to identify objects or scenes in the image or video.
Object recognition and tracking: Once the objects or scenes in the image or video have been identified, the computer vision algorithm can track their movement over time.

Challenges in Computer Vision

Computer vision is a challenging field because the visual world is complex and ambiguous. For example, the same object can look different depending on its lighting, pose, and occlusion. Additionally, there are many different types of objects in the world, and it can be difficult to create computer vision algorithms that can recognize all of them.

Recent Advances in Computer Vision

In recent years, there has been significant progress in the field of computer vision. This is due in part to the development of deep learning algorithms. Deep learning algorithms are able to learn complex patterns from data, and they have been shown to be very effective at computer vision tasks.

Future of Computer Vision

Computer vision is a rapidly developing field with a wide range of potential applications. As computer vision algorithms continue to improve, we can expect to see even more innovative and useful applications of computer vision in the future.

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How can you accurately assess somebody's technical skills, like the same way across the board, right? We had devised a Tableau-based assessment. So it wasn't like a past/fail. It was kind of like, hey, what do they send us? Did they understand the data or the values that they're showing accurate? Where we'd say, hey, here's the credentials to access the data set. And it just wasn't really a scalable way to assess technical - just administering it, all of it was manual, but the whole process sucked!

Cole Brickley, Avicado (Director Data Science & Business Intelligence)

I wouldn't dream of hiring somebody in a technical role without doing that technical assessment because the number of times where I've had candidates either on paper on the CV, say, I'm a SQL expert or in an interview, saying, I'm brilliant at Excel, I'm brilliant at this. And you actually put them in front of a computer, say, do this task. And some people really struggle. So you have to have that technical assessment.

Mike Yates, The British Psychological Society (Head of Data & Analytics)

I was at WooliesX (Woolworths) and we used Alooba and it was a highly positive experience. We had a large number of candidates. At WooliesX, previously we were quite dependent on the designed test from the team leads. That was quite a manual process. We realised it would take too much time from us. The time saving is great. Even spending 15 minutes per candidate with a manual test would be huge - hours per week, but with Alooba we just see the numbers immediately.

Shen Liu, Logickube (Principal at Logickube)

We get a high flow of applicants, which leads to potentially longer lead times, causing delays in the pipelines which can lead to missing out on good candidates. Alooba supports both speed and quality. The speed to return to candidates gives us a competitive advantage. Alooba provides a higher level of confidence in the people coming through the pipeline with less time spent interviewing unqualified candidates.

Scott Crowe, Canva (Lead Recruiter - Data)

How can you accurately assess somebody's technical skills, like the same way across the board, right? We had devised a Tableau-based assessment. So it wasn't like a past/fail. It was kind of like, hey, what do they send us? Did they understand the data or the values that they're showing accurate? Where we'd say, hey, here's the credentials to access the data set. And it just wasn't really a scalable way to assess technical - just administering it, all of it was manual, but the whole process sucked!

Cole Brickley, Avicado (Director Data Science & Business Intelligence)

I wouldn't dream of hiring somebody in a technical role without doing that technical assessment because the number of times where I've had candidates either on paper on the CV, say, I'm a SQL expert or in an interview, saying, I'm brilliant at Excel, I'm brilliant at this. And you actually put them in front of a computer, say, do this task. And some people really struggle. So you have to have that technical assessment.

Mike Yates, The British Psychological Society (Head of Data & Analytics)

I was at WooliesX (Woolworths) and we used Alooba and it was a highly positive experience. We had a large number of candidates. At WooliesX, previously we were quite dependent on the designed test from the team leads. That was quite a manual process. We realised it would take too much time from us. The time saving is great. Even spending 15 minutes per candidate with a manual test would be huge - hours per week, but with Alooba we just see the numbers immediately.

Shen Liu, Logickube (Principal at Logickube)