Scale Space and Variational Methods in Computer Vision
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (20 February 2023) | Viewed by 12186
Special Issue Editor
Interests: image processing; computer vision; machine learning; deep learning; efficient convolutional neural network; facial emotion analysis; etc.
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Computer vision is a field of artificial intelligence that enables computers to extract meaningful information from visual inputs such as images or videos and take appropriate actions. Scale space and variational methods in computer vision is one of the techniques to achieve this goal. It mainly focuses on multiscale analysis of image content, partial differential equations, geometric methods, variational methods, and optimization. In the imaging world, distant objects are projected at a smaller size and near objects at a larger size. To extract meaningful information, images or videos should be processed at all levels of scales simultaneously. Scale–space theory in computer vision describes a formal way of representation and computation of features from image data at all scales. Variational methods are a specific class of optimization methods of cost functions in higher dimensions. Instead of defining a heuristic sequence of processing, the variational method enables the derivation of algorithms automatically. Many problems in computer vision can be formulated as variational methods. Examples are denoising, segmentation, super-resolution, tracking, object detection, optical flow estimation, depth estimation, 3D reconstruction, etc.
Dr. Deepak Ghimire
Guest Editor
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Keywords
- denoising and filtering
- image segmentation
- super-resolution
- 3D vision
- medical imaging
- feature extraction and analysis
- scale–space methods in computer vision
- motion estimation
- stereo vision
- 3D scene reconstruction
- optical flow estimation
- object detection and tracking
- variational methods in computer vision
- optimization
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