Enhancing Focus Volume through Perceptual Focus Factor in Shape-from-Focus
Mathematics, 2023 Shape From Focus (SFF) reconstructs a scene’s shape using a series of images with varied focus settings. However, the effectiveness of SFF largely depends on the Focus Measure (FM) used, which is prone to noise-induced inaccuracies in focus values.
Khurram Ashfaq, Muhammad Tariq Mahmood
doaj +1 more source
A novel switching bilateral filtering algorithm for depth map [PDF]
Компьютерная оптика, 2019 In this paper, we propose a novel switching bilateral filter for depth map from a RGB-D sensor. The switching method works as follows: the bilateral filter is applied not at all pixels of the depth map, but only in those where noise and holes are ...
Alexey Ruchay +2 more
doaj +1 more source
Fast 3D-HEVC Depth Map Encoding Using Machine Learning
IEEE transactions on circuits and systems for video technology (Print), 2020 This paper presents a fast depth map encoding for 3D-High Efficiency Video Coding (3D-HEVC) based on static decision trees. We used data mining and machine learning to correlate the encoder context attributes, building the static decision trees.
Mário Saldanha +3 more
semanticscholar +1 more source
FastMDE: A Fast CNN Architecture for Monocular Depth Estimation at High Resolution
IEEE Access, 2022 A depth map helps robots and autonomous vehicles (AVs) visualize the three-dimensional world to navigate and localize neighboring obstacles. However, it is difficult to develop a deep learning model that can estimate the depth map from a single image in ...
Thien-Thanh Dao +2 more
doaj +1 more source
Depth Map Completion by Jointly Exploiting Blurry Color Images and Sparse Depth Maps [PDF]
2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018 Accepted by WACV ...
Liyuan Pan +3 more
openaire +2 more sources
Deeply Supervised Depth Map Super-Resolution as Novel View Synthesis [PDF]
IEEE transactions on circuits and systems for video technology (Print), 2018 Deep convolutional neural network (DCNN) has been successfully applied to depth map super-resolution and outperforms existing methods by a wide margin. However, there still exist two major issues with these DCNN-based depth map super-resolution methods ...
Xibin Song, Yuchao Dai, Xueying Qin
semanticscholar +1 more source
Three-dimensional mapping of soil organic matter content using soil type-specific depth functions
, 2010 This paper proposes a method for mapping depth functions of soil organic matter (SOM) that combines general pedological knowledge with geostatistical modeling.
Brus, D.J., Stoorvogel, J.J., Kempen, B.
core +1 more source
DEPTH MAP ESTIMATION IN LIGHT FIELDS USING AN STEREO-LIKE TAXONOMY
Revista de Investigaciones Universidad del Quindío, 2016 The light field or LF is a function that describes the amount of light traveling in every direction (angular) through every point (spatial) in a scene, this LF can be captured in several ways, using arrays of cameras, or more recently using a single ...
Francisco C. Calderon +2 more
doaj +1 more source
Two independent mechanisms for motion-in-depth perception : evidence from individual differences
, 2013 Our forward-facing eyes allow us the advantage of binocular visual information: using the tiny differences between right and left eye views to learn about depth and location in three dimensions.
Harris, Julie +2 more
core +1 more source
Improving posture classification accuracy for depth sensor-based human activity monitoring in smart environments [PDF]
, 2016 Smart environments and monitoring systems are popular research areas nowadays due to its potential to enhance the quality of life. Applications such as human behaviour analysis and workspace ergonomics monitoring are automated, thereby improving well ...
Yuen, Pong C. +9 more
core +1 more source