scholarly journals 3D Perception Adjustment of Stereoscopic Images Based upon Depth Map

2011 ◽  
pp. 13-21
Author(s):  
Jong In Gil ◽  
Seung Eun Jang ◽  
Manbae Kim
Keyword(s):  
Depth Map ◽  
3D Perception ◽  
Stereoscopic Images

scholarly journals Transformation of Stereoscopic Images for 3D Perception Improvement

2012 ◽  
Vol 17 (6) ◽  
pp. 911-923
Author(s):  
Jong In Gil ◽  
Hwang Kyu Choi ◽  
Manbae Kim
Keyword(s):  
3D Perception ◽  
Stereoscopic Images

scholarly journals Obstacles Regions 3D-Perception Method for Mobile Robots Based on Visual Saliency

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Tao Xu ◽  
Songmin Jia ◽  
Zhengyin Dong ◽  
Xiuzhi Li
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Depth Map ◽  
Visual Saliency ◽  
Kinect Sensor ◽  
Original Image ◽  
3D Perception ◽  
Pulse Input ◽  
Region Extraction ◽  
Salience Map

A novel mobile robots 3D-perception obstacle regions method in indoor environment based on Improved Salient Region Extraction (ISRE) is proposed. This model acquires the original image by the Kinect sensor and then gains Original Salience Map (OSM) and Intensity Feature Map (IFM) from the original image by the salience filtering algorithm. The IFM was used as the input neutron of PCNN. In order to make the ignition range more exact, PCNN ignition pulse input was further improved as follows: point multiplication algorithm was taken between PCNN internal neuron and binarization salience image of OSM; then we determined the final ignition pulse input. The salience binarization region abstraction was fulfilled by improved PCNN multiple iterations finally. Finally, the binarization area was mapped to the depth map obtained by Kinect sensor, and mobile robot can achieve the obstacle localization function. The method was conducted on a mobile robot (Pioneer3-DX). The experimental results demonstrated the feasibility and effectiveness of the proposed algorithm.

scholarly journals Analysis of Compression Techniques for Stereoscopic Images

2018 ◽  
Vol 6 (61) ◽  
pp. 197-220 ◽  
Author(s):  
Ivana Vasiljevic ◽  
Dinu Dragan ◽  
Ratko Obradovic ◽  
Veljko Petrović
Keyword(s):  
Image Compression ◽  
Data Transfer ◽  
Stereo Image ◽  
Discrete Wavelet ◽  
3D Display ◽  
3D Perception ◽  
Stereoscopic Images ◽  
Transfer Rates ◽  
Head Mounted Displays ◽  
Transform Coefficients

Virtual Reality (VR) and Augmented Reality (AR) Head-Mounted Displays (HMDs) have been emerging in the last years and they are gaining an increased popularity in many industries. HMDs are generally used in entertainment, social interaction, education, but their use for work is also increasing in domains such as medicine, modeling and simulation. Despite the recent release of many types of HMDs, two major problems are hindering their widespread adoption in the mainstream market: the extremely high costs and the user experience issues [1]. The illusion of a 3D display in HMDs is achieved with a technique called stereoscopy. Applications of stereoscopic imagining are such that data transfer rates and—in mobile applications—storage quickly become a bottleneck. Therefore, efficient image compression techniques are required. Standard image compression techniques are not suitable for stereoscopic images due to the discrete differences that occur between the compressed and uncompressed images. The issue is that the loss in lossy image compression may blur the minute differences between the left-eye and right-eye images that are crucial in establishing the illusion of 3D perception. However, in order to achieve more efficient coding, there are various coding techniques that can be adapted to stereoscopic images. Stereo image compression techniques that can be found in the literature utilize discrete Wavelet transformation and the morphological compression algorithm applied to the transform coefficients. This paper provides an overview and comparison of available techniques for the compression of stereoscopic images, as there is still no technique that is accepted as best for all criteria. We want to test the techniques with users who would actually be potential users of HMDs and therefore would be exposed to these techniques. Also, we focused our research on low-priced, consumer grade HMDs which should be available for larger population.

Real-time monocular 3D perception with ORB-Features

2018 ◽  
Vol 45 (6) ◽  
pp. 776-783
Author(s):  
Babing Ji ◽  
Qixin Cao
Keyword(s):  
Real Time ◽  
3D Structure ◽  
Depth Map ◽  
Depth Estimation ◽  
Industrial Robots ◽  
3D Perception ◽  
Content Type ◽  
Structure Information ◽  
Monocular Camera ◽  
Indoor Scenarios

Purpose This paper aims to propose a new solution for real-time 3D perception with monocular camera. Most of the industrial robots’ solutions use active sensors to acquire 3D structure information, which limit their applications to indoor scenarios. By only using monocular camera, some state of art method provides up-to-scale 3D structure information, but scale information of corresponding objects is uncertain. Design/methodology/approach First, high-accuracy and scale-informed camera pose and sparse 3D map are provided by leveraging ORB-SLAM and marker. Second, for each frame captured by a camera, a specially designed depth estimation pipeline is used to compute corresponding 3D structure called depth map in real-time. Finally, depth map is integrated into volumetric scene model. A feedback module has been designed for users to visualize intermediate scene surface in real-time. Findings The system provides more robust tracking performance and compelling results. The implementation runs near 25 Hz on mainstream laptop based on parallel computation technique. Originality/value A new solution for 3D perception is using monocular camera by leveraging ORB-SLAM systems. Results in our system are visually comparable to active sensor systems such as elastic fusion in small scenes. The system is also both efficient and easy to implement, and algorithms and specific configurations involved are introduced in detail.

scholarly journals A Real-Time Infrared Stereo Matching Algorithm for RGB-D Cameras’ Indoor 3D Perception

2020 ◽  
Vol 9 (8) ◽  
pp. 472
Author(s):  
Jiageng Zhong ◽  
Ming Li ◽  
Xuan Liao ◽  
Jiangying Qin
Keyword(s):  
Mutual Information ◽  
Stereo Matching ◽  
Low Cost ◽  
Depth Map ◽  
Cost Calculation ◽  
3D Perception ◽  
Detection Range ◽  
Matching Algorithm ◽  
Map Resolution ◽  
The Cost

Low-cost, commercial RGB-D cameras have become one of the main sensors for indoor scene 3D perception and robot navigation and localization. In these studies, the Intel RealSense R200 sensor (R200) is popular among many researchers, but its integrated commercial stereo matching algorithm has a small detection range, short measurement distance and low depth map resolution, which severely restrict its usage scenarios and service life. For these problems, on the basis of the existing research, a novel infrared stereo matching algorithm that combines the idea of the semi-global method and sliding window is proposed in this paper. First, the R200 is calibrated. Then, through Gaussian filtering, the mutual information and correlation between the left and right stereo infrared images are enhanced. According to mutual information, the dynamic threshold selection in matching is realized, so the adaptability to different scenes is improved. Meanwhile, the robustness of the algorithm is improved by the Sobel operators in the cost calculation of the energy function. In addition, the accuracy and quality of disparity values are improved through a uniqueness test and sub-pixel interpolation. Finally, the BundleFusion algorithm is used to reconstruct indoor 3D surface models in different scenarios, which proved the effectiveness and superiority of the stereo matching algorithm proposed in this paper.

4 Pattern Recognition for Healing Burns to Compute Evidence: Space Syntax and Machine Learning Analysis of Burns Center Karachi

2013 ◽  
Vol 1 (1) ◽  
pp. 13
Author(s):  
Javaria Manzoor Shaikh ◽  
JaeSeung Park
Keyword(s):  
Pattern Recognition ◽  
Prediction Models ◽  
Uv Light ◽  
Healing Process ◽  
Space Syntax ◽  
Depth Map ◽  
Routine Tasks ◽  
Hyper Plane ◽  
First Time ◽  
Learning Analysis

Usually elongated hospitalization is experienced byBurn patients, and the precise forecast of the placement of patientaccording to the healing acceleration has significant consequenceon healthcare supply administration. Substantial amount ofevidence suggest that sun light is essential to burns healing andcould be exceptionally beneficial for burned patients andworkforce in healthcare building. Satisfactory UV sunlight isfundamental for a calculated amount of burn to heal; this delicaterather complex matrix is achieved by applying patternclassification for the first time on the space syntax map of the floorplan and Browder chart of the burned patient. On the basis of thedata determined from this specific healthcare learning technique,nurse can decide the location of the patient on the floor plan, hencepatient safety first is the priority in the routine tasks by staff inhealthcare settings. Whereas insufficient UV light and vitamin Dcan retard healing process, hence this experiment focuses onmachine learning design in which pattern recognition andtechnology supports patient safety as our primary goal. In thisexperiment we lowered the adverse events from 2012- 2013, andnearly missed errors and prevented medical deaths up to 50%lower, as compared to the data of 2005- 2012 before this techniquewas incorporated.In this research paper, three distinctive phases of clinicalsituations are considered—primarily: admission, secondly: acute,and tertiary: post-treatment according to the burn pattern andhealing rate—and be validated by capable AI- origin forecastingtechniques to hypothesis placement prediction models for eachclinical stage with varying percentage of burn i.e. superficialwound, partial thickness or full thickness deep burn. Conclusivelywe proved that the depth of burn is directly proportionate to thedepth of patient’s placement in terms of window distance. Ourfindings support the hypothesis that the windowed wall is mosthealing wall, here fundamental suggestion is support vectormachines: which is most advantageous hyper plane for linearlydivisible patterns for the burns depth as well as the depth map isused.

Framework of a Contour Based Depth Map Coding Method

2012 ◽  
Vol E95.A (8) ◽  
pp. 1270-1279
Author(s):  
Minghui WANG ◽  
Xun HE ◽  
Xin JIN ◽  
Satoshi GOTO
Keyword(s):  
Depth Map ◽  
Coding Method

Automated Detection of Pin Defects on Counterfeit Microelectronics

2018 ◽  
Author(s):  
Pallabi Ghosh ◽  
Domenic Forte ◽  
Damon L. Woodard ◽  
Rajat Subhra Chakraborty
Keyword(s):  
Integrated Circuits ◽  
Depth Map ◽  
Optical Microscope ◽  
Automated Detection ◽  
Detection Methods ◽  
Global Supply Chains ◽  
Subject Matter Experts ◽  
Security Breaches ◽  
The Past ◽  
Skilled Subject

Abstract Counterfeit electronics constitute a fast-growing threat to global supply chains as well as national security. With rapid globalization, the supply chain is growing more and more complex with components coming from a diverse set of suppliers. Counterfeiters are taking advantage of this complexity and replacing original parts with fake ones. Moreover, counterfeit integrated circuits (ICs) may contain circuit modifications that cause security breaches. Out of all types of counterfeit ICs, recycled and remarked ICs are the most common. Over the past few years, a plethora of counterfeit IC detection methods have been created; however, most of these methods are manual and require highly-skilled subject matter experts (SME). In this paper, an automated bent and corroded pin detection methodology using image processing is proposed to identify recycled ICs. Here, depth map of images acquired using an optical microscope are used to detect bent pins, and segmented side view pin images are used to detect corroded pins.

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