DDRNet: Depth Map Denoising and Refinement for Consumer Depth Cameras Using Cascaded CNNs

2018 ◽  
pp. 155-171 ◽  
Author(s):  
Shi Yan ◽  
Chenglei Wu ◽  
Lizhen Wang ◽  
Feng Xu ◽  
Liang An ◽  
...  
Keyword(s):  
Depth Map ◽  
Depth Cameras

Interaction Detection with Depth Sensing and Body Tracking Cameras in Physical Rehabilitation

2016 ◽  
Vol 55 (01) ◽  
pp. 70-78 ◽  
Author(s):  
B. Jansen ◽  
B. Bonnechère ◽  
M. Oravec ◽  
P. Jarmila ◽  
S. Van Sint Jan ◽  
...  
Keyword(s):  
Face Recognition ◽  
Serious Games ◽  
Adaptive Systems ◽  
Depth Map ◽  
Local Binary Patterns ◽  
Minimal Distance ◽  
Automated Identification ◽  
Depth Sensing ◽  
Depth Cameras ◽  
Interaction Detection

SummaryIntroduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Methodologies, Models and Algorithms for Patients Rehabilitation”. Objectives: This paper presents a camera based method for identifying the patient and detecting interactions between the patient and the therapist during therapy. Detecting interactions helps to discriminate between active and passive motion of the patient as well as to estimate the accuracy of the skeletal data. Methods: Continuous face recognition is used to detect, recognize and track the patient with other people in the scene (e.g. the therapist, or a clinician). We use a method based on local binary patterns (LBP). After identifying users in the scene we identify interactions between the patient and other people. We use a depth map/point cloud for estimating the distance between two people. Our method uses the association of depth regions to user identities and computes the minimal distance between the regions. Results: Our results show state-of-the-art performance of real-time face recognition using low-resolution images that is sufficient to use in adaptive systems. Our proposed approach for detecting interactions shows 91.9% overall recognition accuracy what is sufficient for applications in the context of serious games. We also discuss limitations of the proposed method as well as general limitations of using depth cameras for serious games. Conclusions: We introduced a new method for frame-by-frame automated identification of the patient and labeling reliable sequences of the patient’s data recorded during rehabilitation (games). Our method improves automated rehabilitation systems by detecting the identity of the patient as well as of the therapist and by detecting the distance between both over time.

Interfered depth map recovery with texture guidance for multiple structured light depth cameras

2015 ◽  
Vol 31 ◽  
pp. 34-46 ◽  
Author(s):  
Sen Xiang ◽  
Li Yu ◽  
You Yang ◽  
Qiong Liu ◽  
Jialiang Zhou
Keyword(s):  
Structured Light ◽  
Depth Map ◽  
Depth Cameras

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.

3D facial depth map recognition based on manifold learning-LOGMAP algorithm

2013 ◽  
Vol 26 (2) ◽  
pp. 138-143 ◽  
Author(s):  
Shu Zhan ◽  
Zhihua Zhang ◽  
Changming Ye ◽  
Jianguo Jiang ◽  
S Ando
Keyword(s):  
Manifold Learning ◽  
Depth Map

Depth Map Intra Coding Based on Virtual View Rendering Distortion Estimation

2011 ◽  
Vol 33 (11) ◽  
pp. 2541-2546 ◽  
Author(s):  
Qiu-wen Zhang ◽  
Ping An ◽  
Yan Zhang ◽  
Zhao-yang Zhang
Keyword(s):  
Depth Map ◽  
Intra Coding ◽  
Virtual View ◽  
Distortion Estimation ◽  
Virtual View Rendering

Fusing Dynamic Images and Depth Motion Maps for Action Recognition in Surveillance Systems

2019 ◽  
Vol 09 ◽  
Author(s):  
Rajat Khurana ◽  
Alok Kumar Singh Kushwaha
Keyword(s):  
Action Recognition ◽  
Depth Map ◽  
Good Choice ◽  
Surveillance Systems ◽  
Activity Detection ◽  
Learning Capability ◽  
Proposed Model ◽  
Care Activity ◽  
Increasing Demand ◽  
Depth Motion Maps

Background & Objective: Identification of human actions from video has gathered much attention in past few years. Most of the computer vision tasks such as Health Care Activity Detection, Suspicious Activity detection, Human Computer Interactions etc. are based on the principle of activity detection. Automatic labelling of activity from videos frames is known as activity detection. Motivation of this work is to use most out of the data generated from sensors and use them for recognition of classes. Recognition of actions from videos sequences is a growing field with the upcoming trends of deep neural networks. Automatic learning capability of Convolutional Neural Network (CNN) make them good choice as compared to traditional handcrafted based approaches. With the increasing demand of RGB-D sensors combination of RGB and depth data is in great demand. This work comprises of the use of dynamic images generated from RGB combined with depth map for action recognition purpose. We have experimented our approach on pre trained VGG-F model using MSR Daily activity dataset and UTD MHAD Dataset. We achieve state of the art results. To support our research, we have calculated different parameters apart from accuracy such as precision, F score, recall. Conclusion: Accordingly, the investigation confirms improvement in term of accuracy, precision, F-Score and Recall. The proposed model is 4 Stream model is prone to occlusion, used in real time and also the data from the RGB-D sensor is fully utilized.

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