Computer vision is the field of artificial intelligence that enables machines to see, understand, and interact with the visual world. It is one of the most exciting and rapidly evolving technologies that has the potential to transform various industries and domains, such as healthcare, education, retail, security, agriculture, and entertainment.
In this article, I will share some of the applications and benefits of computer vision, as well as some of the challenges and opportunities that lie ahead for this technology.
What is Computer Vision?
Computer vision is the process of extracting meaningful information from digital images or videos. It involves techniques such as image processing, machine learning, deep learning, computer graphics, and natural language processing.
Some of the common tasks that AI vision can perform are:
- Object detection and recognition: identifying and locating objects in an image or video, such as faces, cars, animals, etc.
- Scene understanding: analyzing and interpreting the context and semantics of an image or video, such as indoor/outdoor, day/night, weather, etc.
- Face recognition: verifying or identifying a person’s identity based on their facial features.
- Optical character recognition (OCR): converting text in images or videos into machine-readable format.
- Image segmentation: dividing an image into regions or pixels that belong to different objects or categories.
- Image enhancement: improving the quality or appearance of an image by removing noise, adjusting contrast, sharpening edges, etc.
- Image synthesis: generating new images or videos from existing ones or from scratch, such as style transfer, super-resolution, face swapping, etc.
Why is Computer Vision Important?
Computer vision has many applications and benefits for various sectors and domains. Some of the examples are:
- Healthcare: AI vision can help diagnose diseases, monitor patients, perform surgeries, analyze medical images, etc. For instance, computer vision can detect skin cancer by analyzing skin lesions, or measure blood pressure by tracking facial blood flow.
- Education: computer vision can enhance learning outcomes, personalize education, assess students’ performance, etc. For example, AI vision can recognize students’ emotions and engagement levels by analyzing their facial expressions and eye movements, or provide feedback and guidance for physical activities such as yoga or dance.
- Retail: AI vision can improve customer experience, optimize inventory management, prevent theft, etc. For instance, computer vision can enable cashierless checkout by tracking the items that customers pick up and put back, or recommend products based on customers’ preferences and styles.
- Security: computer vision can enhance public safety, prevent crime, protect privacy, etc. For example, AI vision can detect suspicious activities or objects by analyzing surveillance footage, or blur faces or license plates in images or videos to protect personal information.
- Agriculture: computer vision can improve crop yield, reduce pesticide use, detect pests and diseases, etc. For instance, AI vision can identify weeds and spray herbicides selectively by using drones, or monitor plant growth and health by using satellite imagery.
- Entertainment: AI vision can create immersive and realistic experiences, generate novel and creative content, enable social interaction, etc. For example, computer vision can create virtual reality (VR) or augmented reality (AR) environments by using 3D reconstruction, or generate realistic faces or voices by using generative adversarial networks (GANs).
What are the Challenges and Opportunities for Computer Vision?
Computer vision is a fast-growing and dynamic field that faces many challenges and opportunities. Some of the main ones are:
- Data quality and quantity: computer vision relies on large amounts of labeled data to train its models. However, collecting and annotating data can be costly, time-consuming, and prone to errors. Moreover, data may not be representative of the real-world scenarios or diverse enough to capture all the variations. Therefore, improving data quality and quantity is essential for developing robust and accurate AI vision systems.
- Ethical and social issues: AI vision raises many ethical and social issues that need to be addressed carefully. For example,
- How to ensure fairness and accountability of computer vision systems?
- How to protect privacy and security of individuals and organizations?
- How to balance human and machine roles and responsibilities?
- How to deal with potential biases or discrimination in computer vision systems?
- How to foster trust and transparency in computer vision systems?
- Human-computer interaction: AI vision enables new ways of human-computer interaction that can enhance user experience and satisfaction. However, designing and evaluating such interactions can be challenging and complex. For example,
- How to design intuitive and natural interfaces for computer vision systems?
- How to provide effective and timely feedback and guidance for AI vision systems?
- How to measure and improve user engagement and satisfaction with AI vision systems?
- Innovation and creativity: computer vision offers many opportunities for innovation and creativity that can generate new value and impact for various domains and industries. However, fostering and harnessing such innovation and creativity can be difficult and uncertain. For example,
- How to identify and explore new problems and solutions for computer vision systems?
- How to leverage and integrate multiple modalities and technologies for AIvision systems?
- How to evaluate and validate the effectiveness and usefulness of AI vision systems?
Conclusion
Computer vision is the key to a smarter future that can enable many benefits and opportunities for various sectors and domains. However, it also poses many challenges and issues that need to be addressed carefully and responsibly. As an international marketer, I believe that computer vision is a powerful and promising technology that can create value and impact for both businesses and society. Therefore, I invite you to join me in exploring and embracing the potential of computer vision for a smarter future.
I hope you enjoyed reading my article and learned something new about AI vision. If you or your business need help or want to use computer vision to improve your business, feel free to contact me and let’s talk about it. I have experience and expertise in applying computer vision to various domains and industries.
Try Computer Vision now
As a bonus, and because I value your time and more than just talking I want to show you, see this small example of all the info and data that I got using my portrait picture that is in the home of this my site.
Face detection
This feature locates faces with bounding polygons, and identifies specific facial “landmarks” such as eyes, ears, nose, mouth, etc. along with their corresponding confidence values. It also returns likelihood ratings for emotion (joy, sorrow, anger, surprise) and general image properties (underexposed, blurred, headwear present). Likelihoods ratings are expressed as 6 different values: UNKNOWN, VERY_UNLIKELY, UNLIKELY, POSSIBLE, LIKELY, or VERY_LIKELY.
And yeah, as you may see, it’s very likely that I show signs of joy. And that the confidence of all the results is 100% (incredibly good, right?).
Object detection
This feature provides general label and bounding box annotations for multiple objects recognized in a single image. For each object detected the following elements are returned: a textual description, a confidence score, and normalized vertices [0,1] for the bounding polygon around the object.
In this case, computer vision has a confidence of 94% that the image is a person and confidence of 81% that there is a shirt.
Label detection
This feature provides generalized labels for an image. For each label returns a textual description, confidence score, and topicality rating.
Talking about my pic, you can see different labels such as: Forehead Cheek Smile Eyebrow Dress Shirt Flash Photography Jaw Neck Sleeve Gesture Collar
Image properties
This feature returns dominant colors in an image. Each color is represented in the RGBA color space, has a confidence score, and displays the fraction of pixels occupied by the color [0, 1].
In my situation since it is mainly a black and white picture, we are going to have different variants at levels of these colors. And the amount of presence that those colors have on the pic.
Explicit content detection (SafeSearch)
This feature provides likelihood ratings for the following explicit content categories: adult, spoof, medical, violence, and racy. Likelihoods ratings are expressed as 6 different values: UNKNOWN, VERY_UNLIKELY, UNLIKELY, POSSIBLE, LIKELY, or VERY_LIKELY. And yeah, my picture is of course a non-explicit picture and computer vision knows it. However, thanks to this technology and splitting it to nowadays legislations, we need to know very fast when something may not be appropriate to act accordingly.
Now, I invite you to try it yourself in the box below and let me know if you are happy with the results. Spoiler alert: I know that you will love it. 😊
If you or your business need help or want to use computer vision to improve your business, feel free to contact me and let’s talk about it. I have experience and expertise in applying computer vision to various domains and industries.