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​Computer Vision Services
Move Your Business Forward With Our Computer Vision Solutions
RidgeRun specializes in developing high-performance Computer Vision solutions, from real-time object detection and image segmentation to advanced video analytics.
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By leveraging cutting-edge algorithms and optimizing for embedded systems, we help businesses integrate intelligent vision capabilities into their products with speed, accuracy, and efficiency.
What is Computer Vision?
The goal of Computer Vision is to use the observed image data to infer something about the world by developing methods that attempt to reproduce the capability of human vision. The understanding of digital images content may involve extracting information from the image, given by an object, a text description, a three-dimensional model and so on. This problem seems to be simply solved by people, even by very young children. However, it represents an unsolved computing problem based on the limited understanding of biological vision and the complexity of vision perception in a dynamic and nearly infinitely varying physical world.
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​Computer Vision services are a multidisciplinary field that could be considered as a subfield of Artificial Intelligence. It may even use Machine Learning and Deep Learning techniques that may involve the use of specialized methods and make use of general learning algorithms.
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​Some high-level problems where we have seen success with computer vision are shown here.
Deep Learning and Computer Vision Solutions
RidgeRun Deep Learning (DL) is a subset of Machine Learning techniques originally inspired by the human brain. DL is used in the domain of digital image processing to solve difficult problems such as image colorization, classification, segmentation and detection. DL methods such as Convolutional Neural Networks (CNNs) mostly improve prediction performance using big data and high-amount computing resources and have pushed the limits of what could be possible.
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​Fast growth in DL and improvements in device capabilities including power consumption, memory, processing, image sensor resolution, and optics have improved the performance and cost-effectiveness and further quickened the spread of vision-based applications.​
Compared to traditional Computer Vision (CV) solutions, DL enables businesses to achieve greater accuracy in tasks such as image classification, semantic segmentation, object detection, and Simultaneous Localization and Mapping (SLAM). Since neural networks used in DL are trained rather than programmed, applications using this approach often require less expert analysis and fine-tuning and exploit the tremendous amount of video data available in today’s systems. DL also provides superior flexibility because CNN models and frameworks can be re-trained using a custom dataset for any use case, contrary to CV algorithms, which tend to be more domain-specific.
Lens Distortion Correction
Nowadays, most modern camera sensors, regardless of current technology precision, have geometric manufacturing defects. Although these defects typically go unnoticed by the naked eye, many Computer Vision algorithms require them to be corrected to obtain appropriate results.
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​RidgeRun's computer vision development services include camera calibration and lens correction solutions for a variety of embedded platforms. We design these solutions to use the available hardware resources in order to meet real-time performance requirements on resource-constrained devices.
FPGA Image Processing
RidgeRun developed an Image Signal Processor (ISP) fully on FPGA. It offers video processing accelerators commonly found in integrated ISPs, such as demosaicing, histogram equalization, auto white balancing, color space conversion, and geometric transformations. It is also possible to integrate your own accelerators into FPGA ISP, which allows you to connect FPGA ISP directly to your camera, preprocess the image, and send the final result to your CPU, reducing the transmission overhead and producing an image ready to use.
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As part of our Computer Vision services, FPGA ISP is powered by Xilinx High-Level Synthesis, a powerful framework that enables us to implement complex image processing solutions faster than Verilog or VHDL. It also makes it easier to adapt FPGA ISP to your needs, leading to less time to market and exploiting the potential of FPGAs.
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​​For more information, please visit our FPGA ISP wiki page.
Bird’s Eye View
Bird's Eye View is an algorithm that creates a top-down view of the scene based on several input frontal views. To do so, it performs a perspective transformation called Inverse Perspective Mapping (IPM). IPM takes the frontal view, applies a homography and creates a top-down view of the captured scene by mapping pixels to a 2D frame (Bird's eye view).
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​This algorithm is intended to help the drivers with perspective effects produced by on-vehicle cameras. These can cause a misunderstanding of the real distance between the vehicle and other objects. To obtain the output bird's eye view image, the algorithm uses a projection matrix to map the relationship between a pixel of the bird's eye view image and a pixel from the input image. The IPM transformation works great in the immediate proximity of the car, assuming the road surface is planar.
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​For details, please see our Bird's Eye View wiki page.
Video Stabilization
RidgeRun is aware of the importance and quality of digital imaging tools such as video stabilization. Video stabilization refers to video quality improvements by removing unwanted camera shakes and jitters due to hand jiggling and unintentional camera panning.
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Video stabilization is especially useful for real-time applications. Our solution is based on Electronic Image Stabilization (EIS) and supports live video (or real-time) stabilization, thanks to movement data from accelerometers and gyroscopes in an Inertial Measurement Unit (IMU) and cutting-edge and state-of-the-art algorithms.
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​For more information, please visit our Video Stabilizer wiki page.
Motion Detection
RidgeRun has developed a Motion Detection GStreamer element that is able to detect motion from an incoming video image. The element implements the approximate median method for background subtraction algorithm with adapting background.
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The Motion Detection GStreamer element generates a start and stop motion signal when it detects movement and when it stops respectively. There is an option that allows the video frame data to be modified by making the movement trail visible, so a kind of movement wave can be seen in the displayed video.
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For additional details on this segment of our Computer Vision services, please see our Motion Detection page.
GstDispTEC
Another project in the Motion Detection area that RidgeRun has developed is GstDispTEC. This is a GStreamer plug-in that integrates the DispTEC library's algorithms, making it possible to incorporate their functionality into GStreamer pipelines, such as:
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Motion detection in a video sequence
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Multi-objective object tracking
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Static gesture recognition
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GstDispTEC has the ability to show where the object is located on a frame, even with non-stationary cameras.
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​GstDispTEC provides functionalities to add several DispTEC-based elements in the same pipeline to strengthen image analysis, connect your own applications to the pipeline for receiving the analysis data, and much more. The algorithms can be run either on CPU or GPU, making the most of the computational resources and reducing the processing time.
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​For more information on this aspect of our Computer Vision services, please visit our GstDispTEC wiki page.
GstCUDA
GstCUDA is a RidgeRun developed GStreamer plug-in enabling easy CUDA algorithm integration into GStreamer pipelines. GstCUDA offers a framework that allows users to develop custom GStreamer elements that execute any CUDA algorithm. The GstCUDA framework is a series of base classes abstracting the complexity of both CUDA and GStreamer.
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GstCUDA offers a GStreamer plugin that contains a set of elements that are ideal for GStreamer/CUDA quick prototyping. Those elements consist in a set of filters with different input/output pad combinations that are run-time loadable with an external custom CUDA library that contains the algorithm to be executed on the GPU on each video frame that passes through the pipeline. With GstCUDA, developers avoid writing elements from scratch, allowing them to focus on the algorithm logic, thus accelerating time to market.
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For additional details, please visit our GstCUDA page.
Motion Detection With GstDispTEC
See how GstDispTEC can detect video motion in a GStreamer pipeline.
Turn to RidgeRun for Computer Vision Services
From real-time object detection to advanced video analytics, RidgeRun’s Computer Vision services help you turn visual data into powerful, actionable insights. Whether you’re optimizing for embedded systems or building AI-driven imaging solutions, our team delivers the precision and performance your project demands. Ready to build smarter, faster, and more efficient vision-based systems? Contact RidgeRun now and see what’s possible.
Frequently Asked Questions About our Computer Vision Services
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The Computer Vision field can be challenging to navigate, but RidgeRun has the information you're looking for. Here are some common questions about our Computer Vision services.