Super depth-map rendering by converting holoscopic viewpoint to perspective projection

Alazawi, E.; Abbod, Maysam F.; Aggoun, A.; Swash, Mohammad Rafiq; Fatah, O. Abdul; Fernández, J. C.

doi:10.1109/3dtv.2014.6874714

Cited by 6 publications

(1 citation statement)

References 9 publications

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“…The disparity displacement (d) is extracted, which is the physical distance between corresponding pixels in pairs of VPIs. To determine the initial disparity * c d of the central pixel "c" of the window block, we calculate the score disparity (cost match function) for all neighboring pixels of "c" in horizontal direction for unidirectional holoscopic 3D images and both horizontal and vertical directions for omnidirectional holoscopic 3D images using Equation (5). The cost match function for the initial disparity map in horizontal direction for 3DM estimation ∀ d in x direction is mathematically described as:…”

Section: Hybrid Depthmap Estimation Stagementioning

confidence: 99%

3D Depth Measurement for Holoscopic 3D Imaging System

Alazawi¹,

Swash²,

Abbod³

2016

JCC

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Holoscopic 3D imaging is a true 3D imaging system mimics fly's eye technique to acquire a true 3D optical model of a real scene. To reconstruct the 3D image computationally, an efficient implementation of an Auto-Feature-Edge (AFE) descriptor algorithm is required that provides an individual feature detector for integration of 3D information to locate objects in the scene. The AFE descriptor plays a key role in simplifying the detection of both edge-based and region-based objects. The detector is based on a Multi-Quantize Adaptive Local Histogram Analysis (MQALHA) algorithm. This is distinctive for each Feature-Edge (FE) block i.e. the large contrast changes (gradients) in FE are easier to localise. The novelty of this work lies in generating a free-noise 3D-Map (3DM) according to a correlation analysis of region contours. This automatically combines the exploitation of the available depth estimation technique with edge-based feature shape recognition technique. The application area consists of two varied domains, which prove the efficiency and robustness of the approach: a) extracting a set of setting feature-edges, for both tracking and mapping process for 3D depthmap estimation, and b) separation and recognition of focus objects in the scene. Experimental results show that the proposed 3DM technique is performed efficiently compared to the state-of-the-art algorithms.

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Section: Hybrid Depthmap Estimation Stagementioning

confidence: 99%

3D Depth Measurement for Holoscopic 3D Imaging System

Alazawi¹,

Swash²,

Abbod³

2016

JCC

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Spatial Augmented Reality Based Customer Satisfaction Enhancement and Monitoring System

et al. 2021

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The research proposes a novel scheme that enhances customer satisfaction through spatial augmented reality and a customer expression-based deep learning network to customize the dining environment in high-grade restaurants. The perception of the quality of food was also enhanced by the recreation of the live environment on the preparation of the menu ordered utilizing the waiting time after each ordering session. Initially, a virtual menu driven by a web page is projected onto the table using a Three-dimensional (3D) projection by which the customers can interactively choose dishes after examining how the dish looks in quality and quantity. The dishes are being portrayed in 3D also adding Spatial Augmented Reality (SAR) features in a special angle creating a 3D illusion to the naked eye. By using photogrammetry we could portrait an identical 3D model of restaurant dishes. With the use of software's parallax effect was added to the video animation to bring out the 3D effect. A virtual restaurant background is created on the walls with relaxing videos and soothing soundtracks to enhance the restaurant ambiance as the restaurant ambiance deals more with the sensory and emotional aspects. On the analysis of the ordered menu and based on preferences available within the database, menu suggestions are also proposed depending on the recommendations of the customer satisfaction analysis model. . This provides customers with a firsthand experience at the restaurant; known as "Business to Customer" (B2C) strategy in marketing. The deep learning-based facial expression recognition system is running in the background to monitor customer satisfaction levels through facial expressions. The captured data will be processed loaded into a database containing a ranking system, which the restaurant management can use for future analytics, to re-arrange prices and upgrade profit margins. The performance was analyzed with numerous experimental evaluations with ambient light levels and desired viewing angles, where we found optimal angle values for spatial augmented reality applications. A survey was done on a sample with different ages, gender, and social status to analyze user perception on evaluating the proposed system. The results revealed 87.5% of responders were very satisfied with the 3D virtual experience. 79.2% indicated rareness of feedback on food and service and more than 50% liked a system that will automatically measure their satisfaction. The outcome and resulting insights may lay a foundation for a novel approach towards the management of customer experience.

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Portable holoscopic 3D camera adaptor for Raspberry Pi

Albar

Swash

2016

2016 Digital Media Industry &Amp; Academic Forum (DMIAF)

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Super depth-map rendering by converting holoscopic viewpoint to perspective projection

Cited by 6 publications

References 9 publications

3D Depth Measurement for Holoscopic 3D Imaging System

3D Depth Measurement for Holoscopic 3D Imaging System

Spatial Augmented Reality Based Customer Satisfaction Enhancement and Monitoring System

Portable holoscopic 3D camera adaptor for Raspberry Pi

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