Optic design and image processing considering angle of incidence via end-to-end optimization method

2019 ◽  
Author(s):  
Byoungho Lee ◽  
Youngmo Jeong ◽  
Dongheon Yoo ◽  
Jaebum Cho
Keyword(s):  
Image Processing ◽  
Optimization Method ◽  
Angle Of Incidence ◽  
End To End

scholarly journals End-To-End Computer Vision Framework: An Open-Source Platform for Research and Education

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3691
Author(s):  
Ciprian Orhei ◽  
Silviu Vert ◽  
Muguras Mocofan ◽  
Radu Vasiu
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Computer Vision ◽  
Open Source ◽  
Visual Processing ◽  
Research Field ◽  
Learning Models ◽  
Research Activity ◽  
End To End ◽  
Machine Learning Models

Computer Vision is a cross-research field with the main purpose of understanding the surrounding environment as closely as possible to human perception. The image processing systems is continuously growing and expanding into more complex systems, usually tailored to the certain needs or applications it may serve. To better serve this purpose, research on the architecture and design of such systems is also important. We present the End-to-End Computer Vision Framework, an open-source solution that aims to support researchers and teachers within the image processing vast field. The framework has incorporated Computer Vision features and Machine Learning models that researchers can use. In the continuous need to add new Computer Vision algorithms for a day-to-day research activity, our proposed framework has an advantage given by the configurable and scalar architecture. Even if the main focus of the framework is on the Computer Vision processing pipeline, the framework offers solutions to incorporate even more complex activities, such as training Machine Learning models. EECVF aims to become a useful tool for learning activities in the Computer Vision field, as it allows the learner and the teacher to handle only the topics at hand, and not the interconnection necessary for visual processing flow.

scholarly journals Deep Colorization for Surveillance Images

2018 ◽  
Vol 228 ◽  
pp. 02009
Author(s):  
Chen Yao ◽  
Yan Xia
Keyword(s):  
Image Processing ◽  
Video Surveillance ◽  
Experimental Results ◽  
Grayscale Image ◽  
End To End ◽  
Surveillance Application

In video surveillance application, grayscale image often influences the image processing results. In order to solve the colorization problem for surveillance images, this paper propose a fully end-to-end approach to obtain a reasonable colorization results. A CNN learning structure and gradient prior are be used for chromatic space inferring. Finally, our experimental results show our advantage.

scholarly journals DeepISP: Toward Learning an End-to-End Image Processing Pipeline

2019 ◽  
Vol 28 (2) ◽  
pp. 912-923 ◽  
Author(s):  
Eli Schwartz ◽  
Raja Giryes ◽  
Alex M. Bronstein
Keyword(s):  
Image Processing ◽  
Processing Pipeline ◽  
End To End

Differentiable Compound Optics and Processing Pipeline Optimization for End-to-end Camera Design

2021 ◽  
Vol 40 (2) ◽  
pp. 1-19
Author(s):  
Ethan Tseng ◽  
Ali Mosleh ◽  
Fahim Mannan ◽  
Karl St-Arnaud ◽  
Avinash Sharma ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Optical Design ◽  
Neural Nets ◽  
Fine Tuning ◽  
Optical Systems ◽  
Design Tools ◽  
End To End ◽  
Application Specific ◽  
Network Processing

Most modern commodity imaging systems we use directly for photography—or indirectly rely on for downstream applications—employ optical systems of multiple lenses that must balance deviations from perfect optics, manufacturing constraints, tolerances, cost, and footprint. Although optical designs often have complex interactions with downstream image processing or analysis tasks, today’s compound optics are designed in isolation from these interactions. Existing optical design tools aim to minimize optical aberrations, such as deviations from Gauss’ linear model of optics, instead of application-specific losses, precluding joint optimization with hardware image signal processing (ISP) and highly parameterized neural network processing. In this article, we propose an optimization method for compound optics that lifts these limitations. We optimize entire lens systems jointly with hardware and software image processing pipelines, downstream neural network processing, and application-specific end-to-end losses. To this end, we propose a learned, differentiable forward model for compound optics and an alternating proximal optimization method that handles function compositions with highly varying parameter dimensions for optics, hardware ISP, and neural nets. Our method integrates seamlessly atop existing optical design tools, such as Zemax . We can thus assess our method across many camera system designs and end-to-end applications. We validate our approach in an automotive camera optics setting—together with hardware ISP post processing and detection—outperforming classical optics designs for automotive object detection and traffic light state detection. For human viewing tasks, we optimize optics and processing pipelines for dynamic outdoor scenarios and dynamic low-light imaging. We outperform existing compartmentalized design or fine-tuning methods qualitatively and quantitatively, across all domain-specific applications tested.

Hardware-in-the-Loop End-to-End Optimization of Camera Image Processing Pipelines

2020 ◽  
Author(s):  
Ali Mosleh ◽  
Avinash Sharma ◽  
Emmanuel Onzon ◽  
Fahim Mannan ◽  
Nicolas Robidoux ◽  
...  
Keyword(s):  
Image Processing ◽  
Hardware In The Loop ◽  
Camera Image ◽  
End To End

scholarly journals Parameters configuration for interest points in images obtained by Drone

2018 ◽  
Author(s):  
Rodrigo A. Rebouças ◽  
Elcio H. Shiguemori ◽  
Lamartine N. F. Guimarães
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Object Recognition ◽  
3D Reconstruction ◽  
Optimization Method ◽  
Input Image ◽  
Interest Points ◽  
Image Navigation ◽  
Mosaic Generation ◽  
Best Parameters

Drone use has grown with the use of image processing and computer vision techniques, such as autonomous image navigation, mosaic generation, elevation modeling, 3D reconstruction, and object recognition. In all techniques, an important step is an extraction of features, such as methods of interest points. This work addresses the modes of application of interest points, such as BRISK, ORB, FREAK, AKAZE and LATCH with the parameters configured automatically using the optimization method for images with different textures. This process is one of the pieces of final software that selects the use of a meta heuristic the best parameters automatically according to an input image.

scholarly journals 3D Video Image Processing Effect Optimization Method Based on Virtual Reality Technology

2022 ◽  
Vol 16 ◽  
pp. 385-390
Author(s):  
Zhihong He ◽  
Wenjie Jia ◽  
Erhua Sun ◽  
Huilong Sun
Keyword(s):  
Image Processing ◽  
Virtual Reality ◽  
Optimization Methods ◽  
Optimization Method ◽  
3D Video ◽  
Video Image ◽  
Virtual Reality Technology ◽  
Processing Effect ◽  
Image Edge ◽  
Video Image Processing

The existing optimization methods have the problem of image edge blur, which leads to a high degree of shadow residue. In order to address this problem, reduce the shadow residual degree, this paper designs a 3D video image processing effect optimization method supported by virtual reality technology. Coding was used to eliminate redundant data in video and eliminate image noise using median filtering. The virtual reality technology detects the image edge and determines the motion offset between the image frames. According to the motion parameters of the camera carrier obtained from the motion estimation, the feature point matching algorithm constructs the video image motion model, and uses the camera calibration technology to set the processing effect optimization mode. It is optimized by perspective projection transformation. Experimental results: the average shadow residual degree of the optimization method and the two existing optimization methods are 3.108%, 6.167% and 6.396% respectively, which proves that the optimization method combined with virtual reality technology has higher practical application value.

Sign in / Sign up

Export Citation Format

Share Document