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scikit-image is a collection of algorithms for image processing.
262
aziende
Abbiamo dati su 262 aziende che usano scikit-image. La nostra lista di clienti scikit-image è disponibile per il download ed è arricchita con specifiche vitali dell'azienda, incluse classificazione industriale, dimensioni organizzative, posizione geografica, round di finanziamenti e cifre di ricavi, tra gli altri.
Azienda | Paese | Settore | Dipendenti | Entrate |
---|---|---|---|---|
SomaDetect | Canada | Research Services | 25 | $1.5M |
![]() BenchSci | Canada | Software Development | 431 | $6.1M |
Future Fertility | Canada | Hospitals And Health Care | 12 | |
10x Genomics | Stati Uniti | Biotechnology Research | 2.1K | $490M |
Allen Institute | Stati Uniti | Research Services | 1.2K | $7M |
The Institute of Cancer Research | Regno Unito | Research Services | 1.4K | $35M |
Iterative Scopes | Stati Uniti | Hospitals And Health Care | 130 | $4.2M |
Stratagem Group LLC | Stati Uniti | Defense And Space Manufacturing | 78 | $4.4M |
Quartus Engineering | Stati Uniti | Engineering Services | 220 | $20M |
RWTH Aachen University | Germania | Higher Education | 13K | $731M |
RWTH Aachen | Germania | Education | 5.9K | $731M |
Noblis | Stati Uniti | It Services And It Consulting | 1.4K | $5.7M |
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Ci sono 18 alternative a scikit-image
scikit-image è utilizzata in 23 paesi
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Scikit-image is a powerful open-source image processing library based on SciPy that aims to provide a collection of algorithms for image processing. It is written in Python and is designed to work seamlessly with other scientific Python libraries such as NumPy, SciPy, and matplotlib. Scikit-image is widely used for tasks such as image segmentation, object detection, feature extraction, and more. With its rich set of functions and easy-to-use interface, it has become a popular choice among researchers, engineers, and data scientists working with image data.
Scikit-image falls under the category of PyPI Packages, focusing specifically on image processing within the Python ecosystem. It offers a comprehensive suite of tools for handling various aspects of digital image processing, making it an indispensable resource for anyone working with visual data. From simple operations like filtering and denoising to more advanced techniques like image registration and morphological transformations, scikit-image covers a wide range of functionalities to support diverse use cases in the field of image analysis.
Scikit-image was founded in 2009 by Stefan van der Walt, Juan Nunez-Iglesias, and contributors with the motivation to create a user-friendly and efficient image processing library for Python. Over the years, the project has grown substantially in terms of features, performance, and community support. It has evolved into a go-to solution for professionals and enthusiasts alike, driving innovation in the domain of image processing applications. The developers' commitment to quality and continuous improvement has solidified scikit-image's position as a leading library in the field.
In the current market scenario, scikit-image enjoys a significant market share within the PyPI Packages category due to its robust functionality and widespread adoption. As the demand for image processing solutions continues to rise across various industries such as healthcare, satellite imaging, and automation, the future outlook for scikit-image appears promising. With ongoing enhancements, community contributions, and integration with emerging technologies, scikit-image is poised to maintain its growth trajectory and further solidify its position as a top choice for image processing tasks.
Scikit-image is a powerful Python package commonly used by companies for image processing and computer vision tasks. Its versatile functionalities make it a top choice for businesses looking to enhance their image analysis capabilities. Below are some key benefits of using scikit-image:
Scikit-image offers a wide array of image processing techniques such as filtering, segmentation, and morphological operations. Its comprehensive library simplifies complex image manipulation tasks, making it an indispensable tool for companies requiring high-quality image analysis.
One of the standout features of scikit-image is its user-friendly API design. The intuitive interface allows users to quickly implement various image processing algorithms without the need for extensive code modifications. This seamless experience sets it apart from other similar technologies, where cumbersome syntax and steep learning curves are common barriers.
Scikit-image boasts extensive documentation and active community support, making it easier for companies to troubleshoot issues and explore advanced functionalities. This robust support system ensures that businesses can leverage the full potential of scikit-image for their image processing needs.
Scikit-image is optimized for performance, enabling companies to process images efficiently even with large datasets. Its underlying algorithms are designed to deliver fast and reliable results, making it a preferred choice for tasks that require quick turnaround times.
In conclusion, the versatile capabilities, user-friendly interface, extensive support, and performance optimization of scikit-image make it a superior choice for companies seeking robust image processing solutions within the PyPI Packages category.
Scikit-image is a popular Python package used for image processing and computer vision tasks by various companies across different industries. Here are a few real-life case studies showcasing how companies leverage scikit-image in their operations:
1. NASA - National Aeronautics and Space Administration: NASA utilizes scikit-image for analyzing satellite images of Earth, Mars, and other celestial bodies. The agency started using scikit-image in 2015 to enhance its image processing capabilities for tasks such as feature extraction, segmentation, and data visualization. By leveraging the advanced functionalities of scikit-image, NASA has been able to accelerate the analysis of vast amounts of satellite imagery data, leading to improved insights for research and space exploration missions.
2. Google DeepMind: Google DeepMind, known for its cutting-edge AI research, incorporates scikit-image into its projects involving computer vision and machine learning. The company began utilizing scikit-image in 2017 to preprocess and analyze medical imaging data for developing AI algorithms in healthcare. By harnessing the robust image processing tools offered by scikit-image, Google DeepMind enhances the accuracy and efficiency of its AI models, ultimately contributing to advancements in the field of medical diagnostics and treatment.
3. NVIDIA Corporation: NVIDIA, a global leader in visual computing technologies, integrates scikit-image into its solutions for image denoising and enhancement. Since 2016, NVIDIA has been leveraging scikit-image to optimize the performance of its graphics processing units (GPUs) by applying sophisticated image processing techniques. By incorporating scikit-image into its software development pipeline, NVIDIA has streamlined the image quality improvement process for its graphics cards, ensuring enhanced visual experiences for users across gaming, professional visualization, and artificial intelligence applications.
These case studies demonstrate the diverse applications of scikit-image in real-world scenarios, showcasing how prominent companies such as NASA, Google DeepMind, and NVIDIA leverage the capabilities of this PyPI package for enhancing their image processing and computer vision workflows.
Puoi accedere a un elenco aggiornato di aziende che utilizzano scikit-image visitando TheirStack.com. La nostra piattaforma fornisce un database completo di aziende che utilizzano varie tecnologie e strumenti interni.
Fino ad ora, abbiamo dati su 262 aziende che utilizzano scikit-image.
scikit-image è utilizzato da una vasta gamma di organizzazioni in vari settori, inclusi "Research Services", "Software Development", "Hospitals And Health Care", "Biotechnology Research", "Research Services", "Research Services", "Hospitals And Health Care", "Defense And Space Manufacturing", "Engineering Services", "Higher Education". Per un elenco completo di tutti i settori che utilizzano scikit-image, si prega di visitare TheirStack.com.
Alcune delle aziende che utilizzano scikit-image includono SomaDetect, BenchSci, Future Fertility, 10x Genomics, Allen Institute, The Institute of Cancer Research, Iterative Scopes, Stratagem Group LLC, Quartus Engineering, RWTH Aachen University e molte altre. Puoi trovare un elenco completo di 262 aziende che utilizzano scikit-image su TheirStack.com.
Secondo i nostri dati, scikit-image è più popolare in Stati Uniti (100 companies), Regno Unito (17 companies), Germania (12 companies), India (12 companies), Francia (9 companies), Canada (8 companies), Australia (4 companies), Brasile (4 companies), Singapore (4 companies), Irlanda (2 companies). Tuttavia, è utilizzato da aziende in tutto il mondo.
Puoi trovare aziende che utilizzano scikit-image cercandolo su TheirStack.com. Tracciamo le offerte di lavoro di milioni di aziende e le utilizziamo per scoprire quali tecnologie e strumenti interni stanno utilizzando.