scholarly journals 3D reconstruction of indoor and outdoor scenes using a mobile range scanner

2003 ◽  
Author(s):  
Y. Sun ◽  
J.K. Paik ◽  
A. Koschan ◽  
M.A. Abidi
Keyword(s):  
3D Reconstruction ◽  
Outdoor Scenes ◽  
Indoor And Outdoor

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.

scholarly journals Real-time 3D reconstruction from single-photon lidar data using plug-and-play point cloud denoisers

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Julián Tachella ◽  
Yoann Altmann ◽  
Nicolas Mellado ◽  
Aongus McCarthy ◽  
Rachael Tobin ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Real Time ◽  
Single Photon ◽  
Three Dimensional ◽  
Lidar Data ◽  
Computational Framework ◽  
Processing Times ◽  
Outdoor Scenes ◽  
3D Scene ◽  
Time Required

Abstract Single-photon lidar has emerged as a prime candidate technology for depth imaging through challenging environments. Until now, a major limitation has been the significant amount of time required for the analysis of the recorded data. Here we show a new computational framework for real-time three-dimensional (3D) scene reconstruction from single-photon data. By combining statistical models with highly scalable computational tools from the computer graphics community, we demonstrate 3D reconstruction of complex outdoor scenes with processing times of the order of 20 ms, where the lidar data was acquired in broad daylight from distances up to 320 metres. The proposed method can handle an unknown number of surfaces in each pixel, allowing for target detection and imaging through cluttered scenes. This enables robust, real-time target reconstruction of complex moving scenes, paving the way for single-photon lidar at video rates for practical 3D imaging applications.

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.

SEMANTIC VIDEO TRANSCODING USING CLASSES OF RELEVANCE

2003 ◽  
Vol 03 (01) ◽  
pp. 145-169 ◽  
Author(s):  
RITA CUCCHIARA ◽  
COSTANTINO GRANA ◽  
ANDREA PRATI
Keyword(s):  
Video Processing ◽  
Video Transcoding ◽  
User Requirements ◽  
User Interests ◽  
Object Based ◽  
Outdoor Scenes ◽  
Web Access ◽  
Evaluation Metric ◽  
Scene Object ◽  
Indoor And Outdoor

In this work we present a framework for on-the-fly video transcoding that exploits computer vision-based techniques to adapt the Web access to the user requirements. The proposed transcoding approach aims at coping with both user bandwidth and resources capabilities, and with user interests in the video's content. We propose an object-based semantic transcoding that, according to the user-defined classes of relevance, applies different transcoding techniques to the objects segmented in a scene. Object extraction is provided by on-the-fly video processing, without manual annotation. Multiple transcoding policies are reviewed and a performance evaluation metric based on the Weighted Mean Square Error (and corresponding PSNR), that takes into account the perceptual user requirements by means of classes of relevance, is defined. Results are analyzed by varying transcoding techniques, bandwidth requirements and video types (with indoor and outdoor scenes), showing that the use of semantics can dramatically improve the bandwidth to distortion ratio.

scholarly journals Signposts in the fog: objects facilitate scene representations in left scene-selective cortex

2017 ◽  
Author(s):  
Talia Brandman ◽  
Marius V. Peelen
Keyword(s):  
Visual Image ◽  
Object Perception ◽  
Neural Representation ◽  
Independent Experiment ◽  
Response Patterns ◽  
Outdoor Scenes ◽  
Scene Representation ◽  
Early Visual Cortex ◽  
Feature Overlap ◽  
Indoor And Outdoor

AbstractWe internally represent the structure of our surroundings even when there is little layout information available in the visual image, such as when walking through fog or darkness. One way in which we disambiguate such scenes is through object cues; for example, seeing a boat supports the inference that the foggy scene is a lake. Recent studies have investigated the neural mechanisms by which object and scene processing interact to support object perception. The current study examines the reverse interaction, by which objects facilitate the neural representation of scene layout. Photographs of indoor (closed) and outdoor (open) real-world scenes were blurred such that they were difficult to categorize on their own, but easily disambiguated by the inclusion of an object. fMRI decoding was used to measure scene representations in scene-selective parahippocampal place area (PPA) and occipital place area (OPA). Classifiers were trained to distinguish response patterns to fully visible indoor and outdoor scenes, presented in an independent experiment. Testing these classifiers on blurred scenes revealed a strong improvement in classification in left PPA and OPA when objects were present, despite the reduced low-level visual feature overlap with the training set in this condition. These findings were specific to left PPA/OPA, with no evidence for object-driven facilitation in right PPA/OPA, object-selective areas, and early visual cortex. These findings demonstrate separate roles for left and right scene-selective cortex in scene representation, whereby left PPA/OPA represents inferred scene layout, influenced by contextual object cues, and right PPA/OPA represents a scene’s visual features.

Mesh Based Semantic Modelling for Indoor and Outdoor Scenes

2013 ◽  
Author(s):  
Julien P.C. Valentin ◽  
Sunando Sengupta ◽  
Jonathan Warrell ◽  
Ali Shahrokni ◽  
Philip H.S. Torr
Keyword(s):  
Semantic Modelling ◽  
Outdoor Scenes ◽  
Indoor And Outdoor

scholarly journals Toward 3D Reconstruction of Outdoor Scenes Using an MMW Radar and a Monocular Vision Sensor

Sensors ◽  
2015 ◽  
Vol 15 (10) ◽  
pp. 25937-25967 ◽  
Author(s):  
Ghina Natour ◽  
Omar Ait-Aider ◽  
Raphael Rouveure ◽  
François Berry ◽  
Patrice Faure
Keyword(s):  
3D Reconstruction ◽  
Monocular Vision ◽  
Vision Sensor ◽  
Outdoor Scenes
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