scholarly journals Hybrid Image-based Rendering for Free-view Synthesis

2021 ◽  
Vol 4 (1) ◽  
pp. 1-20
Author(s):  
Siddhant Prakash ◽  
Thomas Leimkühler ◽  
Simon Rodriguez ◽  
George Drettakis
Keyword(s):  
Image Quality ◽  
View Synthesis ◽  
Image Region ◽  
Good Balance ◽  
Image Based Rendering ◽  
Geometric Reconstruction ◽  
Outdoor Scenes ◽  
Filtering Approach ◽  
Indoor And Outdoor

Image-based rendering (IBR) provides a rich toolset for free-viewpoint navigation in captured scenes. Many methods exist, usually with an emphasis either on image quality or rendering speed. In this paper we identify common IBR artifacts and combine the strengths of different algorithms to strike a good balance in the speed/quality tradeoff. First, we address the problem of visible color seams that arise from blending casually-captured input images by explicitly treating view-dependent effects. Second, we compensate for geometric reconstruction errors by refining per-view information using a novel clustering and filtering approach. Finally, we devise a practical hybrid IBR algorithm, which locally identifies and utilizes the rendering method best suited for an image region while retaining interactive rates. We compare our method against classical and modern (neural) approaches in indoor and outdoor scenes and demonstrate superiority in quality and/or speed.

ROI Based Post Image Quality Assessment Technique on Multiple Localized Filtering Method On Kinect HD Sensor Mobile Robot

2018 ◽  
Vol 3 (2) ◽  
pp. 145
Author(s):  
Kholilatul Wardani ◽  
Aditya Kurniawan
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Similarity Index ◽  
Assessment Model ◽  
Sensor Data ◽  
Edge Region ◽  
Kinect Sensor ◽  
Image Region ◽  
Filtering Technique

 The ROI (Region of Interest) Image Quality Assessment is an image quality assessment model based on the SSI (Structural Similarity Index) index used in the specific image region desired to be assessed. Output assessmen value used by this image assessment model is 1 which means identical and -1 which means not identical. Assessment model of ROI Quality Assessment in this research is used to measure image quality on Kinect sensor capture result used in Mobile HD Robot after applied Multiple Localized Filtering Technique. The filter is applied to each capture sensor depth result on Kinect, with the aim to eliminate structural noise that occurs in the Kinect sensor. Assessment is done by comparing image quality before filter and after filter applied to certain region. The kinect sensor will be conditioned to capture a square black object measuring 10cm x 10cm perpendicular to a homogeneous background (white with RGB code 255,255,255). The results of kinect sensor data will be taken through EWRF 3022 by visual basic 6.0 program periodically 10 times each session with frequency 1 time per minute. The results of this trial show the same similar index (value 1: identical) in the luminance, contrast, and structural section of the edge region or edge region of the specimen. The value indicates that the Multiple Localized Filtering Technique applied to the noise generated by the Kinect sensor, based on the ROI Image Quality Assessment model has no effect on the image quality generated by the sensor.

A BACKGROUND MODELING-BASED FAITHFUL APPROACH TO THE DISOCCLUSION PROBLEM IN DEPTH IMAGE-BASED RENDERING

2013 ◽  
Vol 27 (02) ◽  
pp. 1354005 ◽  
Author(s):  
MICHAEL SCHMEING ◽  
XIAOYI JIANG
Keyword(s):  
Background Modeling ◽  
View Synthesis ◽  
Depth Image ◽  
Image Based Rendering ◽  
Texture Information ◽  
Depth Image Based Rendering ◽  
Compression Artifacts

In this paper, we address the disocclusion problem that occurs during view synthesis in depth image-based rendering (DIBR). We propose a method that can recover faithful texture information for disoccluded areas. In contrast to common disocclusion filling methods, which usually work frame-by-frame, our algorithm can take information from temporally neighboring frames into account. This way, we are able to reconstruct a faithful filling for the disocclusion regions and not just an approximate or plausible one. Our method avoids artifacts that occur with common approaches and can additionally reduce compression artifacts at object boundaries.

Novel Scenes Improve Recollection and Recall of Words

2008 ◽  
Vol 20 (7) ◽  
pp. 1250-1265 ◽  
Author(s):  
Daniela B. Fenker ◽  
Julietta U. Frey ◽  
Hartmut Schuetze ◽  
Dorothee Heipertz ◽  
Hans-Jochen Heinze ◽  
...  
Keyword(s):  
Specific Activity ◽  
Activity Patterns ◽  
Long Term Potentiation ◽  
Contextual Memory ◽  
Memory Enhancement ◽  
Term Potentiation ◽  
Outdoor Scenes ◽  
Indoor And Outdoor ◽  
Hippocampus Dependent Memory

Exploring a novel environment can facilitate subsequent hippocampal long-term potentiation in animals. We report a related behavioral enhancement in humans. In two separate experiments, recollection and free recall, both measures of hippocampus-dependent memory formation, were enhanced for words studied after a 5-min exposure to unrelated novel as opposed to familiar images depicting indoor and outdoor scenes. With functional magnetic resonance imaging, the enhancement was predicted by specific activity patterns observed during novelty exposure in parahippocampal and dorsal prefrontal cortices, regions which are known to be linked to attentional orienting to novel stimuli and perceptual processing of scenes. Novelty was also associated with activation of the substantia nigra/ventral tegmental area of the midbrain and the hippocampus, but these activations did not correlate with contextual memory enhancement. These findings indicate remarkable parallels between contextual memory enhancement in humans and existing evidence regarding contextually enhanced hippocampal plasticity in animals. They provide specific behavioral clues to enhancing hippocampus-dependent memory in humans.

Image quality evaluation for smart-phone displays at lighting levels of indoor and outdoor conditions

2012 ◽  
Vol 51 (8) ◽  
pp. 084001-1 ◽  
Author(s):  
Rui Gong ◽  
Haisong Xu ◽  
Binyu Wang ◽  
Ming Ronnier Luo
Keyword(s):  
Image Quality ◽  
Quality Evaluation ◽  
Smart Phone ◽  
Image Quality Evaluation ◽  
Indoor And Outdoor

scholarly journals Fusing Multimodal Video Data for Detecting Moving Objects/Targets in Challenging Indoor and Outdoor Scenes

2019 ◽  
Vol 11 (4) ◽  
pp. 446 ◽  
Author(s):  
Zacharias Kandylakis ◽  
Konstantinos Vasili ◽  
Konstantinos Karantzalos
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Local Coordinate System ◽  
Image Plane ◽  
Hyperspectral Data ◽  
Video Data ◽  
Outdoor Scenes ◽  
Indoor Scenes ◽  
Video Sensors ◽  
Indoor And Outdoor

Single sensor systems and standard optical—usually RGB CCTV video cameras—fail to provide adequate observations, or the amount of spectral information required to build rich, expressive, discriminative features for object detection and tracking tasks in challenging outdoor and indoor scenes under various environmental/illumination conditions. Towards this direction, we have designed a multisensor system based on thermal, shortwave infrared, and hyperspectral video sensors and propose a processing pipeline able to perform in real-time object detection tasks despite the huge amount of the concurrently acquired video streams. In particular, in order to avoid the computationally intensive coregistration of the hyperspectral data with other imaging modalities, the initially detected targets are projected through a local coordinate system on the hypercube image plane. Regarding the object detection, a detector-agnostic procedure has been developed, integrating both unsupervised (background subtraction) and supervised (deep learning convolutional neural networks) techniques for validation purposes. The detected and verified targets are extracted through the fusion and data association steps based on temporal spectral signatures of both target and background. The quite promising experimental results in challenging indoor and outdoor scenes indicated the robust and efficient performance of the developed methodology under different conditions like fog, smoke, and illumination changes.

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