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Start with the fundamentals of Image processing, programming in Python and AI algorithms. For this, there are many globally recognized MOOCs available free (with nominal charges). Having good knowledge of programming logics and concepts, average mathematical problem solving skills, good creative thinking and visualization capability, basic understaing of Python script syntax and certain libraries like Open CV, Keras, Scikit learn, matplotlib etc. may be a good start. Next step may be to learn the computer vision & pattern recognition methods and solve related problems using Python scripts. Try to reimplement the already solved computer vision problems by your own to boost up your confidence and later, try some innovative problems to solve.

Learning computer vision can be challenging and rewarding, but it can also be lonely and frustrating. That's why it's important to join a community of learners and experts who can help you, inspire you, and motivate you. You can find many online forums, groups, blogs, podcasts, and newsletters that are dedicated to computer vision, such as Reddit, Stack Overflow, Medium, Computer Vision News, and Computer Vision Foundation. You can also attend events, meetups, webinars, and conferences that are related to computer vision, such as CVPR, ICCV, and ECCV.

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If you have a good background on math, I would highly recommend to follow Stanford's Deep Learning for Computer Vision online courses. Led by Fei-Fei Li, it is an awesome deep dive into the details on how Computer Vision works.

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Kaggle is a great option to get experience and to get information. Is a place you can find: datasets, projects, code, discussions, courses and competitions. Some of the competitions have Prizes & Awards. You can create / participate in a team for the competitions.

Absolutely you can. Microsoft Learn has a number of valuable labs including an entire segment of the Azure AI-102 "AI Engineer" certification course where you can use an online lab environment without having to pay for any Azure credits to build a Custom Computer Vision (ML) solution as well as a no-code Computer Vision solution, with the benefit of working towards your certification. Highly recommended!

Computer vision is a branch of artificial intelligence that deals with the analysis and understanding of visual data, such as images and videos. It has many applications in various fields, such as face recognition, self-driving cars, medical imaging, and augmented reality. But how can you start learning computer vision with no experience? Here are some tips to help you get started.

Following a structured and comprehensive course is essential for learning computer vision effectively. Several online courses cater to different levels and objectives. For beginners, Coursera's "Computer Vision Basics" covers fundamental concepts like image representation, filtering, and segmentation using OpenCV. Udemy's "Deep Learning for Computer Vision" delves into advanced techniques such as convolutional neural networks and object detection with TensorFlow and Keras. For more advanced learners, Udacity's "Computer Vision Nanodegree" explores topics like feature extraction, object tracking, and facial recognition using PyTorch.

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Learning computer vision requires a structured and comprehensive course that covers the theory and practice of the field. There are various online courses to choose from, depending on your level and goals. For instance, Coursera's Computer Vision Basics introduces the fundamentals of computer vision, such as image representation, filtering, edge detection, and segmentation. It also covers how to use OpenCV. Udemy's Deep Learning for Computer Vision focuses on deep learning techniques like convolutional neural networks and object detection with TensorFlow and Keras. For a more advanced program, Udacity's Computer Vision Nanodegree covers topics like advanced image processing, feature extraction, object tracking, face detection, facial landmarks, and PyTorch applications.

Start with openCV. But, do not "just use it", try to understand about each technique you going to try! Probably you need to learn a bit about neural networks, but not only AI... just try an exercise using openCV and go to find information abou the technique it uses!

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There are many great free resources on Microsoft Learn where you can learn and try out basics. With easy-to-learn web-based services you can easily train your AI model for tasks such as classification or object detection. Achieving first visual results will motivate you to move forward, dig deeper into more advanced courses, and expand your knowledge in this area.

Sensor sizes are expressed in inches notation because at the time of the popularization of digital image sensors they were used to replace video camera tubes. The common 1" circular video camera tubes have a rectangular photo sensitive area about 16 mm diagonal, so a digital sensor with a 16 mm diagonal size is a 1" video tube equivalent. The name of a 1" digital sensor should more accurately be read as "one inch video camera tube equivalent" sensor. Current digital image sensor size descriptors are the video camera tube equivalency size, not the actual size of the sensor. For example, a 1" sensor has a diagonal measurement of 16 mm.[26][27]

The working distance (WD) is determined by the linear measurement of the objective front lens to the focal plane.

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To learn the basic for computer vision , I would recommend the following 1. Understand linear algebra, calculus, probability, and statistics. 2. Learn Python and libraries like OpenCV, PyTorch, TensorFlow, or Keras. 3. Study basic techniques like filtering and feature extraction. 4. Gain knowledge in supervised and unsupervised learning, classification, regression, clustering 5.Deep Learning - Learn about convolutional neural networks (CNNs). 8. **Stay Updated**: Keep abreast of the latest research and advancements in the field.

Sizes are often expressed as a fraction of an inch, with a one in the numerator, and a decimal number in the denominator. For example, 1/2.5 converts to 2/5 as a simple fraction, or 0.4 as a decimal number. This "inch" system brings a result approximately 1.5 times the length of the diagonal of the sensor. This "optical format" measure goes back to the way image sizes of video cameras used until the late 1980s were expressed, referring to the outside diameter of the glass envelope of the video camera tube. David Pogue of The New York Times states that "the actual sensor size is much smaller than what the camera companies publish – about one-third smaller." For example, a camera advertising a 1/2.7" sensor does not have a sensor with a diagonal of 0.37"; instead, the diagonal is closer to 0.26".[28][29][30] Instead of "formats", these sensor sizes are often called types, as in "1/2-inch-type CCD."

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I advocate for following a structured course in computer vision. It offers a guided pathway through the basics to more advanced concepts, ensuring a solid foundation and systematic learning.

Like other areas of machine learning, computer vision faces similar challenges. As you embark on such a project, keep the following in mind. 1️⃣ You'll need a GPU. Plus different strategies to balance memory with batch size 2️⃣ Data quality of generally low 3️⃣ Missing or mis-labelled images 4️⃣ Unbalanced dataset 5️⃣ Unsafe use of image augmentation to increase test data size 6️⃣ Challenging images, E.g., too much contrast, poor colour balance, too much noise, etc Regardless of framework, keep these in mind.

Computer vision is a fascinating and fast-growing field that offers many opportunities and challenges. By following these tips, you can start learning computer vision with no experience and become a competent and confident computer vision practitioner.

Integrating into a community is vital. Engage with online forums, social media groups, and attend webinars or virtual meetups. Platforms like GitHub, Reddit, and LinkedIn are great for connecting with experts and peers. This interaction boosts learning, provides support, and keeps you updated on the latest in computer vision. Networking opportunities can also arise, enhancing both knowledge and career prospects.

After learning the basics and doing course. Here's the important step and most effective step comes building valuable projects. Building projects is the best way to learn new techniques. First decide what project you want to built like want to built object detection project then collect the data annotate it the best platform I know for that is cvat.ai or you can also collect already annotated data from kaggle or roboflow. After that train the model on the annotated data and evalaute it. For training yolo is popular framework. You can get its details form ultralytics website.

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Be mindful of the biases that AI could bring into your project! Always validate the sources that have been used to train the models you use! Making AI ethical depends on every single one of us everyday!

Due to inch-based sensor formats being not standardized, their exact dimensions may vary, but those listed are typical.[29] The listed sensor areas span more than a factor of 1000 and are proportional to the maximum possible collection of light and image resolution (same lens speed, i.e., minimum F-number), but in practice are not directly proportional to image noise or resolution due to other limitations. See comparisons.[31][32] Film format sizes are included for comparison. The following comparison is with respect to the aspect ratio of 4:3. The application examples of phone or camera may not show the exact sensor sizes.

Begin by grasping foundational concepts in linear algebra, calculus, and programming languages like Python. Familiarize yourself with libraries such as OpenCV, TensorFlow, or Pytorch. Engage in online courses, like those on Coursera or edX, offering comprehensive insights into computer vision. Practice is paramount; work on small projects to hone your skills. Leveraging online communities, such as GitHub and Stack Overflow, will enhance your learning through shared experiences and collaborative problem-solving.

Learning computer vision from scratch may seem daunting, but it's totally doable! Start by grasping the basics of math and coding. Online courses like Khan Academy offer free tutorials on algebra and calculus, while platforms like Codecademy teach Python programming. Once you're comfortable with these fundamentals, dive into computer vision-specific resources. Websites like OpenCV provide beginner-friendly guides and tutorials, and YouTube channels like Sentdex offer step-by-step video tutorials. Practice coding simple projects like image classification or object detection to reinforce your learning. With dedication and practice, you'll soon be on your way to mastering computer vision.

Before diving into computer vision, you need to have some background knowledge in mathematics, programming, and data structures. You should be familiar with concepts such as linear algebra, calculus, statistics, probability, and optimization. You should also be able to code in Python, which is the most popular language for computer vision projects. Additionally, you should learn how to use data structures such as arrays, lists, dictionaries, and matrices, which are essential for storing and manipulating visual data.

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Dive into projects. It's a hands-on way to apply what you've learned, tackle real-world problems, and build a portfolio that showcases your skills in computer vision.

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Computer vision is a multidisciplinary field that intersects with areas such as machine learning, signal processing, and neuroscience. When starting with no experience, it's crucial to build a strong foundation in both the theoretical underpinnings and practical applications. Courses like those mentioned provide a guided pathway from basic concepts to more complex techniques. The use of frameworks like OpenCV, TensorFlow, and PyTorch is essential for hands-on learning, as they are industry-standard tools that offer a wide range of functionalities for image processing and neural network implementation.

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The best project to start working on is one that aligns with your interests and challenges you at a comfortable level. For tech beginners, a simple web or mobile app, like a to-do list, offers a practical introduction to coding and design principles. Data enthusiasts might explore analyzing a public dataset to learn about data cleaning and basic analytics. Web development learners can benefit from building a responsive website, learning HTML, CSS, and JavaScript. For those interested in AI or machine learning, a project using pre-trained models for tasks like image classification can introduce key concepts without overwhelming complexity.

When selecting a framework for computer vision projects, consider options like OpenCV, TensorFlow, PyTorch, Keras, and Scikit-learn. Each framework offers unique advantages and drawbacks, so choose based on your specific requirements and preferences. You can utilize multiple frameworks based on task demands; for instance, leverage OpenCV for image processing and manipulation, while employing TensorFlow for deep learning and neural networks. This approach ensures flexibility and optimization tailored to diverse computer vision tasks.

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The best way to learn computer vision is by doing. Working on projects will help you apply what you have learned, test your skills, and improve your understanding of the field. You can start with simple projects, such as image classification, color detection, or face detection, and then move on to more complex ones, such as optical character recognition, face recognition, or image captioning. You can also find many datasets and challenges online that you can use for your projects, such as MNIST, CIFAR-10, ImageNet, COCO, and Kaggle.

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

There are many frameworks and libraries that can help you implement computer vision algorithms and models. Some of the most popular ones are OpenCV, TensorFlow, PyTorch, Keras, and Scikit-learn. Each framework has its own advantages and disadvantages, so you should choose one that suits your needs and preferences. You can also use more than one framework, depending on the task. For example, you can use OpenCV for image processing and manipulation, and TensorFlow for deep learning and neural networks.

Many suggest starting with portfolio projects but do not to mention how to do that. Great resource and platform to learn by doing is RoboFlow(.)com. It’s full of projects, source code and access to inference. All things you’ll need to get started!

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When selecting a framework for computer vision, consider the project requirements and your familiarity with the language it supports. OpenCV is excellent for real-time image processing, while TensorFlow and PyTorch offer extensive resources for deep learning tasks. Keras, with its high-level API, can simplify neural network construction, and Scikit-learn is ideal for traditional machine learning algorithms. Combining frameworks can leverage their unique strengths, enhancing the capability and efficiency of your computer vision solutions.

Start with a beginner-friendly framework like TensorFlow or PyTorch, leveraging their documentation and community for learning computer vision from scratch.

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Joining a community is crucial in the AI field, not only for support but also for staying updated with the latest advancements and trends. Engaging with a community provides practical insights that complement academic knowledge, fostering a more holistic understanding of computer vision. Moreover, networking within these communities can lead to collaborative opportunities and potential career advancements.

Surprisingly Azure is not mentioned as the platforms you can use! Azure AI Vision offers a innovative unified service that provides computer vision capabilities. Give your apps the ability to analyze images, read text, and detect faces with prebuilt image tagging, text extraction with optical character recognition (OCR), and responsible facial recognition. Incorporate vision features into your projects with no machine learning experience required.

Starting to learn computer vision with no experience can be daunting, but it's important to remember that practical experience is invaluable. While following courses and tutorials, try to apply what you learn to real-world problems. This could be as simple as writing a program to filter or detect edges in an image, or as complex as building a facial recognition system. Additionally, understanding the math behind algorithms can greatly enhance your ability to innovate and troubleshoot. Lastly, don't overlook the importance of soft skills such as problem-solving and critical thinking, which are crucial in the field of AI.

1. Choose a use case that genuinely excites you. Example, if autonomous vehicles fascinate you, that can be your starting point. 2. Before diving into project, get familiar with the foundational concepts of computer vision through online learning platforms. 3. Based on your interest, define a simple project. For autonomous cars, it could be something like detecting and classifying road signs using images. 4. Then build a simple model, using platforms like Hugging Face or PyTorch. 5. Look for datasets relevant to your project on websites like Kaggle. 6. Engage with communities on Stack Overflow or Reddit for support. 7. Iterate on your project, gradually tackling more complex problems. 8. Document your progress to solidify your learning.

Join a community to navigate the computer vision learning journey. Engage with online forums, groups, and resources like Reddit, Stack Overflow, and Medium. Follow Computer Vision News and the Computer Vision Foundation. Attend events like CVPR, ICCV, and ECCV to connect with peers and experts, gaining inspiration and support. This approach enriches your learning experience, providing motivation and insights from the computer vision field.

- Gain a basic understanding of deep learning techniques, such as convolutional neural networks - Utilize resources such as books, online courses, or tutorials to learn about computer vision - Build a strong foundation in programming, particularly in languages such as Python - Acquire knowledge of linear algebra and image processing - Start with simple projects and gradually work towards more complex ones - Stay updated on the latest advancements and techniques in computer vision - Network with other professionals in the field - Attend conferences or workshops related to computer vision.

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