Attract experts in Computer Vision
 

Our deep understanding of the fundamental concepts, applications, and emerging trends in this constantly evolving technological field allows us to excel in the selection of highly qualified professionals. We have acquired specialized knowledge in image processing techniques, object detection, pattern recognition, and deep learning used in Computer Vision.

 
 
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At Steneg, we understand the importance of technical excellence in the field of Computer Vision, and we take pride in our ability to rigorously evaluate candidates and select the most outstanding professionals in this discipline. Furthermore, we recognize that the field of Computer Vision is constantly evolving, and therefore we also place a strong emphasis on evaluating skills in neural network models and deep learning techniques. We seek candidates who are proficient in convolutional architectures and other techniques crucial in the current Computer Vision environment.

 
Interview Process and Techniques
 
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Libraries & Tools
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Network of Contacts
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Processing
 
 
 
Classic Techniques
 

During our thorough interviews, we delve deeply into the essential skills that form the foundation of Computer Vision. This includes detailed analysis of classic techniques, such as manipulating Histograms for analyzing pixel intensity distributions and applying Canny Algorithms for accurate edge detection in images. We also assess the ability to apply Sobel and Prewitt operators for gradient processing in images and the use of Laplace filters to enhance features and contours. Furthermore, we examine knowledge in Fourier transforms for frequency analysis in images and the application of Gaussian filters to smooth and enhance image quality. We understand that these skills form the foundation of a strong and versatile computer vision capability.

 
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Libraries & Tools
OpenCV (Open Source Computer Vision Library): OpenCV is one of the most widely used libraries for Computer Vision in Python. It offers a wide range of functions and tools for image processing, object detection, object tracking, camera calibration, and more.
 
 
NumPy: Although not specific to Computer Vision, NumPy is essential for numerical processing in Python and is used in conjunction with other libraries to manipulate and process image matrices and arrays.
Pillow (PIL Fork): Pillow is a library for image processing in Python that allows opening, manipulating, and saving a variety of image formats. It is useful for basic image manipulation tasks.
 
 
TensorFlow and Keras: TensorFlow is a popular machine learning library that includes specific modules for Computer Vision tasks, such as the TensorFlow Object Detection API. Keras, now part of TensorFlow, facilitates the building of convolutional neural networks (CNN) for computer vision tasks.
PyTorch: PyTorch is another deep learning library used for computer vision and offers flexibility in model design and a growing ecosystem of computer vision-specific extensions.
 
 
Dlib: Dlib is a library that includes a variety of algorithms for image processing and computer vision, including facial detection and object tracking.
Mahotas: Mahotas is a Python image processing library that offers a wide range of computer vision algorithms, including feature extraction, segmentation, and more.
 
 
YOLO (You Only Look Once): YOLO is a popular architecture for real-time object detection. It offers an efficient and accurate approach to detect objects in images and video sequences.
Scikit-Image (skimage): Scikit-Image is a library that provides high-level image processing algorithms and is widely used for image processing tasks and feature analysis.
 
 
 
 
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Network of Contacts

Steneg stands out for its extensive and solid network of contacts in the field of Computer Vision. We have established connections with a wide range of professionals, including highly trained engineers, expert data scientists, leading researchers in the field, and developers specialized in critical areas such as Computer Vision algorithms, image processing, and deep learning, among other related disciplines. This network of contacts allows us to access a diversified group of highly qualified candidates, ensuring that we can select the best talent to meet the needs of our clients in the exciting field of Computer Vision.

 
 
 
Processing
 

CPU (Central Processing Unit)
  • Image processing on mobile devices: In mobile devices such as smartphones and tablets, where energy efficiency is crucial, CPUs enable augmented reality applications, document scanners, and photo filters.
  • Real-time image processing: CPUs can be suitable for real-time computer vision tasks, such as face detection in webcams or real-time video enhancement.
  • Medical image processing: In medical applications, CPUs are used for processing medical images, such as detecting anomalies in Magnetic Resonance Imaging (MRI) or Computed Tomography (CT) images.
 
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VPU (Vision Processing Unit)
  • Security and surveillance cameras: In security and surveillance systems, VPUs enable real-time object detection, tracking, and video analysis, identifying objects, faces, or suspicious behavior patterns in high-resolution videos.
  • Autonomous robotics: In autonomous robotics, such as delivery robots or autonomous vehicles, VPUs detect obstacles, pedestrians, and traffic signs, ensuring safe navigation.
  • Drones and Unmanned Aerial Vehicles (UAVs): VPUs are essential for UAVs, enabling them to perform tasks like tracking, mapping, and real-time object detection during flight.
 

GPU (Graphics Processing Unit)
  • Deep learning and model training: GPUs are essential for training deep learning models, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), used in image classification, voice recognition, and natural language processing.
  • Rendering and simulation: In applications of 3D rendering and simulation, such as video games and virtual environments, GPUs accelerate graphic processing, enhancing the user experience.
  • Real-time video processing: GPUs are ideal for real-time video processing applications, such as video transcoding, video enhancement, and tracking and analyzing objects in video streams.
 
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