Voici comment vous pouvez maîtriser les compétences clés de la vision par ordinateur et du traitement d’images.

Based on my advice to my Computer Science students at AOU, mastering computer vision and image processing requires a strategic approach. Start with a strong foundation in mathematics, particularly linear algebra and calculus. Learn programming languages like Python and libraries such as OpenCV and TensorFlow. Engage in practical projects to apply theoretical knowledge. Stay updated with the latest research and advancements. Participate in online courses and join relevant communities. Combining theoretical study with hands-on practice is key to success.

Deep knowledge of mathematics is crucial for working in computer vision for several reasons: 1. Many computer vision algorithms, such as those for image filtering, feature detection, and object recognition, are based on mathematical principles. 2. Techniques such as image transformations, filtering, and edge detection require a solid understanding of concepts like Fourier transforms, matrix operations, and differential equations. 3. Many computer vision tasks involve training machine learning models and require a deep understanding of statistics and probability theory. So, a solid mathematical foundation allows for better design, analysis, and optimization of computer vision systems, leading to more efficient solutions.

To reinforce understanding and application, you can employ coding exercises or projects that focus on image processing and utilize mathematical concepts. If you want to understand more about more complex topics, such as eigenvectors in computer vision and convolution techniques, consider engaging academic resources, online courses or workshops. Collaborating with peers or seeking mentorship will help you gain insights into challenging concepts in the computer vision field.

If you want to speed up your learning, develop faster and it applies to specific cases while using pre-trained algorithms then think about transfer methods in convolutional neural networks. It features up-to-date research directions from various fields so you could also use this resource as your professional skill development tool. There is a scheme where it is possible for people interested in learning theoretical basis which would assist persons who strive for mastery at advanced levels. A new initiative called Kaggle allows experts to provide all kinds of computing power and data access so that they can do what they are good at.

Ask your friends to comment on your work so you can perfect your methods and learn from other ways as well. Keep yourself informed by attending conferences, doing guided research or reading books that are regularly published concerning the progress of computer vision. Also, use social networks such as GitHub to publish some of yours and work in harmony with others worldwide. Continually analyze where you stand about set goals as well as what is difficult for you to always get better at them.

Avant même d'envisager l'utilisation d'un algorithme de traitement il est important de vérifier la qualité des images utilisées. L'objet ou la caractéristique que l'on cherche à évaluer est elle différenciable à l'oeil du reste de l'image ? Obtenir le meilleur contraste est déterminant pour limiter les opérations à réaliser par la suite. De même les niveaux de gris ou de couleurs sont-ils stables ? Quelles sont les sources de perturbation et comment les supprimer/limiter ? Une bonne image c'est plus de 80% de travail en moins par la suite. Il convient aussi d'identifier le ou les caractéristiques qui définissent notre objet : dimension, texture, forme, couleur ... sont d'autant d'éléments qui vont orienter les outils à employer.