Understanding the specific needs of a machine vision task goes beyond technical know-how; it's about grasping the unique challenges and objectives of each project. For instance, in agriculture, recognizing the ripeness of fruit involves not just color analysis but also texture and size differentiation. Each sector demands a tailored approach, blending technical skills with deep insights into the sector's specific needs. This fusion of expertise ensures solutions are not just technologically sound but truly aligned with real-world applications, enhancing efficiency and accuracy in tasks ranging from quality control to automation.

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Making Robots do cool things || Research @Autonomy & Intelligence lab, Northeastern || MS Robotics and ML @Khoury college || Former Machine Learning Intern @UMemphis & VIT || Ambassador @Google Women Techmakers

Computer vision: Algorithms and Applications

Mein Fazit: Maschinelles Lernen und Deep Learning sind unverzichtbare Technologien in der Bildverarbeitung, die es Maschinen ermöglichen, komplexe Probleme effektiv zu lösen. Ingenieure müssen fundierte Kenntnisse in verschiedenen ML/DL-Konzepten und Tools wie TensorFlow und PyTorch besitzen, um innovative Lösungen zu entwickeln und weiter voranzutreiben.

Computer vision is the core of machine vision, and it requires a solid understanding of the principles and techniques of image processing, analysis, and understanding. Computer vision fundamentals include topics such as image representation, filtering, segmentation, feature extraction, object detection, recognition, tracking, and classification. Machine vision engineers and developers need to know how to apply these techniques to various machine vision problems, such as defect detection, face recognition, optical character recognition, and barcode scanning.

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Programming languages and frameworks are the tools that enable machine vision engineers and developers to implement computer vision algorithms and applications. There are many programming languages and frameworks to choose from, but some of the most popular and widely used ones are Python, C/C++, MATLAB, OpenCV, TensorFlow, PyTorch, and Keras. Python is a high-level, versatile, and easy-to-learn language that offers many libraries and packages for computer vision, such as NumPy, SciPy, scikit-image, scikit-learn, and Pillow. C/C++ is a low-level, fast, and powerful language that can interact with hardware and optimize performance. MATLAB is a numerical computing environment that provides many built-in functions and toolboxes for computer vision, such as Image Processing Toolbox, Computer Vision Toolbox, and Deep Learning Toolbox. OpenCV is an open-source library that provides a comprehensive set of computer vision functions and modules for various platforms and languages. TensorFlow, PyTorch, and Keras are frameworks that enable machine vision engineers and developers to build and train deep learning models for computer vision tasks, such as object detection, segmentation, face recognition, and pose estimation.

Proficiency in Python and C++ is essential due to their widespread use in machine vision. Knowledge of frameworks like OpenCV, TensorFlow, and PyTorch allows engineers to build and deploy models efficiently. Understanding how to use these tools for tasks like image recognition and classification is key.

Domain knowledge and problem-solving skills are the abilities to understand the specific context and goals of the machine vision applications and to devise effective solutions for them. Machine vision engineers and developers need to have a good grasp of the domain knowledge and problem-solving skills related to the industries and sectors they work in, such as manufacturing, automotive, healthcare, agriculture, security, and entertainment. They need to know the characteristics and requirements of the objects, scenes, and events they deal with, such as shape, size, color, texture, motion, lighting, noise, occlusion, and variation. They also need to know how to formulate the machine vision problems, select the appropriate methods and techniques, design the system architecture, test and evaluate the results, and optimize the performance.

Making Robots do cool things || Research @Autonomy & Intelligence lab, Northeastern || MS Robotics and ML @Khoury college || Former Machine Learning Intern @UMemphis & VIT || Ambassador @Google Women Techmakers

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Hardware and software platforms form the backbone of machine vision systems, ranging from versatile options like Raspberry Pi to powerful devices like Jetson Nano. In my experience, optimizing hardware can complement software efforts. For instance, investing in quality lenses reduces computational load, while adjusting sensors can streamline processes. Considerations like energy efficiency and compliance are crucial for industrial applications. By harmonizing hardware and software, we can unlock the full potential of machine vision technology.

Computer vision basics are vital for machine vision, involving tasks like understanding images and identifying objects. However, expertise extends beyond algorithms; proficiency in math, coding, and industry knowledge is essential. Understanding the hardware and software used in machine vision systems is crucial. Additionally, mastery of deep learning algorithms enables innovative solutions.

Staying updated with the latest research and trends is important, as the field evolves rapidly. Familiarity with cloud platforms like AWS or Azure for deploying and scaling machine vision applications is increasingly relevant. Finally, soft skills like communication and teamwork are essential, as these projects often require collaboration across multidisciplinary teams.

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Common Vision Blox (CVB) is a software development kit (SDK) for machine vision applications by STEMMER IMAGING.

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Machine vision is the application of computer vision to industrial and manufacturing processes, such as quality control, inspection, automation, and robotics. It involves capturing, processing, and analyzing images of objects, scenes, and events to extract useful information and perform tasks. Machine vision engineers and developers need a combination of skills and tools to design, implement, and optimize machine vision systems. In this article, we will explore some of the essential skills and tools for machine vision engineers and developers.

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Machine vision often involves specific hardware, like cameras and sensors, and platforms such as NVIDIA’s Jetson for edge computing. Engineers should be comfortable with these, along with GPUs for accelerating deep learning tasks. Experience with ROS (Robot Operating System) is a plus for robotics-related projects.

Machine learning and deep learning are subfields of artificial intelligence that enable machines to learn from data and perform tasks that are difficult or impossible to program explicitly. Machine learning and deep learning are widely used in machine vision to solve complex and challenging problems, such as semantic segmentation, face recognition, object detection, pose estimation, and scene understanding. Machine vision engineers and developers need to have a basic knowledge of machine learning and deep learning concepts, such as supervised learning, unsupervised learning, reinforcement learning, neural networks, convolutional neural networks, recurrent neural networks, generative adversarial networks, and transfer learning. They also need to know how to use machine learning and deep learning tools and frameworks, such as TensorFlow, PyTorch, Keras, scikit-learn, and pandas.

In-depth knowledge of Mathematics : Strong Skills in Pre-calculus+ calculus + linear algebra. What i see is non-mathematical students jumping on to the AI bandwagon with just learning python libs.This will hurt them as libraries are just sweet layers of abstractions only. Programming Expertise: Hands on C/C++. Depth of C++ can never be ignored as most python libs i.e. TensorFlow are built from C++. Just using top abstracted libraries without knowing under-the-hood will get engineers into trouble. ML-Frameworks:TensorFlow, Keras, and PyTorch. Computer Vision :Usage of OpenCV in C++ level and tweeking openCV libs. Pillow/scikit-image + Dlib for machine learning and data analysis. 3D modeling : MATLAB/Simulink for simulation+design.

Understanding the specific industry or application domain is critical. Whether in healthcare, manufacturing, or autonomous vehicles, knowing the unique challenges and requirements helps in tailoring solutions. Strong problem-solving skills enable engineers to adapt existing methods to new or complex scenarios.

A solid grasp of machine learning and deep learning is vital, especially for tasks like image classification, object detection, and pose estimation. Knowledge of convolutional neural networks (CNNs) and how to fine-tune pre-trained models is particularly important.

Making Robots do cool things || Research @Autonomy & Intelligence lab, Northeastern || MS Robotics and ML @Khoury college || Former Machine Learning Intern @UMemphis & VIT || Ambassador @Google Women Techmakers

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Several employers are seeking vision engineers/salespeople with training on PLC integration/programming. Software skills to back hardware knowledge is a winning combo!

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To start in programming for computer vision, begin with Python tutorials, focusing on libraries like OpenCV. Gain a solid understanding of image processing basics, then explore deeper into machine learning concepts. Practice with small projects, gradually increasing complexity, and seek out online courses and communities for guidance and support.

Mein Fazit: Die Wahl der Hardware- und Softwareplattformen für Bildverarbeitung ist entscheidend für den Erfolg von Anwendungen. Von Raspberry Pi bis hin zu Cloud-Diensten bieten diese Plattformen eine Vielzahl von Optionen, die je nach Anforderungen an Leistung, Speicher und Kosten angepasst werden können. Die richtige Auswahl ermöglicht eine optimale Balance zwischen Funktionalität, Skalierbarkeit und Effizienz in der industriellen Bildverarbeitung.

Mein Fazit: Programmiersprachen und Frameworks wie Python, C/C++, MATLAB, OpenCV, TensorFlow, PyTorch und Keras spielen eine entscheidende Rolle bei der Entwicklung von Algorithmen für maschinelles Sehen. Python bietet Vielseitigkeit und eine Fülle an Bibliotheken, während C/C++ für die Interaktion mit Hardware und Performanceoptimierung ideal ist. MATLAB punktet mit zahlreichen integrierten Funktionen speziell für Computer Vision, während OpenCV eine umfassende Open-Source-Lösung darstellt. TensorFlow, PyTorch und Keras ermöglichen es Entwicklern, leistungsstarke Deep-Learning-Modelle für komplexe Aufgaben wie Objekterkennung und Gesichtserkennung zu erstellen und zu trainieren.

One of the fundamental skills needed in the realm of machine vision is to be able to identify what an inspection needs to accomplish. The most common application types I've dealt with in automotive manufacturing can be broken down into these base categories: Presence/absence: Is the component there? Defect detection: Are there any abnormalities in the part? Character recognition: Do the characters on the part match what is expected? Measurement: Do the component's dimensions fall within expected tolerances? Identification: Is the correct style of part present? Once you have identified the base category of the application, you can consider what vision program tools (blob, pattern matching, edge detection, etc.) to use to achieve it.

Programming: Proficiency in programming languages like Python, Java, and R is necessary. Machine Learning and Deep Learning: Understanding of machine learning and deep learning concepts is vital. This includes knowledge of algorithms, data structures, data modeling, and software architecture. Computer Vision Libraries: Familiarity with computer vision libraries such as OpenCV, TensorFlow, and PyTorch is beneficial. Image Processing: Knowledge of image processing techniques, including image annotation, image and video segmentation, and image recognition, is important. Artificial Intelligence (AI): Understanding of AI concepts, including artificial neural networks and Edge AI, is essential.

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The raw material of machine vision is a good image, regardless of which vision algorithms are used, Rule-Based, Machine Learning or Deep Learning, the most important thing before starting any inspection is to have an adequate image so that it can be treated. The machine vision process is similar to an industrial production process, if the raw material is bad, top quality results cannot be demanded.

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Making Robots do cool things || Research @Autonomy & Intelligence lab, Northeastern || MS Robotics and ML @Khoury college || Former Machine Learning Intern @UMemphis & VIT || Ambassador @Google Women Techmakers

Making Robots do cool things || Research @Autonomy & Intelligence lab, Northeastern || MS Robotics and ML @Khoury college || Former Machine Learning Intern @UMemphis & VIT || Ambassador @Google Women Techmakers

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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?

Simplifying Hardware and Software in my Industrial #MachineVision World! 💡 Let's get to the core of it. I'm all about a holistic approach to hardware. While software can tackle almost anything, sometimes making minor adjustments on the hardware is more efficient. Take lens distortion, for example. Investing in quality lenses with minimal distortion is computationally lighter and more effective than running intensive distortion-free algorithms. Physically adjusting a sensor can also be a game-changer, saving on software development by canceling offsets or bypassing the need for a ring buffer. Stay tuned for more insights into the world of Machine Vision! 🚀 #AutoVisionInsights

In the realm of computer vision, machine learning and deep learning are indispensable tools, enabling systems to decipher and interpret visual data. From recognizing faces to understanding complex scenes, these techniques drive innovation in the field. In my experience, mastering machine learning concepts like neural networks and frameworks like TensorFlow is crucial for developing robust computer vision solutions. Experimentation and continuous learning are key to staying at the forefront of this rapidly evolving domain.

Mein Fazit: Domänenwissen und Problemlösungsfähigkeiten sind entscheidend für Bildverarbeitungsingenieure. Sie müssen nicht nur die spezifischen Anwendungsziele verstehen, sondern auch das Fachwissen über verschiedene Branchen beherrschen. Dies ermöglicht es ihnen, effektive Lösungen zu entwickeln, die auf die spezifischen Anforderungen von Bereichen wie Fertigung, Gesundheitswesen oder Sicherheit zugeschnitten sind. Die Fähigkeit, komplexe Probleme zu formulieren, geeignete Techniken auszuwählen und die Systemleistung zu optimieren, ist dabei unerlässlich.

Hardware and software platforms are the devices and systems that run machine vision applications and algorithms. They range from general-purpose computers and laptops to specialized embedded systems and cameras. Machine vision engineers and developers need to consider the requirements and constraints of the hardware and software platforms they use, such as processing power, memory, storage, speed, accuracy, reliability, scalability, and cost. Some of the common hardware and software platforms for machine vision are Raspberry Pi, Arduino, Jetson Nano, Intel RealSense, Microsoft Kinect, Google Coral, AWS, Azure, and Google Cloud.

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