scholarly journals A Biologically Inspired Approach for Robot Depth Estimation

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Ester Martinez-Martin ◽  
Angel P. del Pobil
Keyword(s):  
Early Stage ◽  
Depth Estimation ◽  
Stereo Image ◽  
Service Robots ◽  
Disparity Map ◽  
Biologically Inspired ◽  
Scene Representation ◽  
Depth Cue ◽  
The Right ◽  
Robotic Tasks

Aimed at building autonomous service robots, reasoning, perception, and action should be properly integrated. In this paper, the depth cue has been analysed as an early stage given its importance for robotic tasks. So, from neuroscience findings, a hierarchical four-level dorsal architecture has been designed and implemented. Mainly, from a stereo image pair, a set of complex Gabor filters is applied for estimating an egocentric quantitative disparity map. This map leads to a quantitative depth scene representation that provides the raw input for a qualitative approach. So, the reasoning method infers the data required to make the right decision at any time. As it will be shown, the experimental results highlight the robust performance of the biologically inspired approach presented in this paper.

scholarly journals Digital watermarking of 3D disparity map based images for transaction tracking

2021 ◽  
Author(s):  
Ramzy Jaber
Keyword(s):  
Image Watermarking ◽  
Depth Estimation ◽  
3D Image ◽  
Disparity Map ◽  
Decision Matrix ◽  
Dct Transform ◽  
Watermark Embedding ◽  
Binary Information ◽  
Left Image ◽  
The Right

In this thesis, the basics of disparity map and watermarking are reviewed extensively. In order to embed binary information into images, a 3D image watermarking system was proposed. This embedded information was to survive the 3D Image rendering process of Disparity maps, to help identify malicious user who would distribute the watermarked image through an unauthorized system. The proposed system adopted the concept of hidden pixel and introduced an algorithm that identifies all known hidden pixels within the image. This information is combined with the Disparity map to generate a hidden pixel disparity map (HPDM); using the information in the HPDM a decision matrix is generated. This decision matrix is used to guide the watermark embedding process to ensure that information embedded in the Left Image can survive the 3D rendering process. Using the decision matrix, the watermark detector is capable of extracting the image from either the left or right image with no effect on the overall bit rate. This achievement is due to two original additions to the detection process: (1) Reverse rendering and (2) Cyclical Redundancy check. The proposed reverse rendering process expands the decision matrix into a reduced disparity map. This reduced disparity map is used to reverse the right image into a reduced left image. The identification of the image (left or right) is achieved through the use of a CRC check, which is also capable of detecting any errors in the extracted message, thus reducing the number of misidentification. The proposed system was implemented and tested using MATLAB. The bit efficiency of the proposed system varied between 38% and 88%. This variance is caused by the complexity of the depth scene as well as the cost function used in the depth estimation process. The watermark embedding system proposed had a PSNR of 45 dB (when no mark was embedded); this value is primarily attributed to some of the quantization that occurs during the DCT transform. However, a PSNR of 33dB is attained when the watermark was added at full strength.

scholarly journals Digital watermarking of 3D disparity map based images for transaction tracking

2021 ◽  
Author(s):  
Ramzy Jaber
Keyword(s):  
Image Watermarking ◽  
Depth Estimation ◽  
3D Image ◽  
Disparity Map ◽  
Decision Matrix ◽  
Dct Transform ◽  
Watermark Embedding ◽  
Binary Information ◽  
Left Image ◽  
The Right

In this thesis, the basics of disparity map and watermarking are reviewed extensively. In order to embed binary information into images, a 3D image watermarking system was proposed. This embedded information was to survive the 3D Image rendering process of Disparity maps, to help identify malicious user who would distribute the watermarked image through an unauthorized system. The proposed system adopted the concept of hidden pixel and introduced an algorithm that identifies all known hidden pixels within the image. This information is combined with the Disparity map to generate a hidden pixel disparity map (HPDM); using the information in the HPDM a decision matrix is generated. This decision matrix is used to guide the watermark embedding process to ensure that information embedded in the Left Image can survive the 3D rendering process. Using the decision matrix, the watermark detector is capable of extracting the image from either the left or right image with no effect on the overall bit rate. This achievement is due to two original additions to the detection process: (1) Reverse rendering and (2) Cyclical Redundancy check. The proposed reverse rendering process expands the decision matrix into a reduced disparity map. This reduced disparity map is used to reverse the right image into a reduced left image. The identification of the image (left or right) is achieved through the use of a CRC check, which is also capable of detecting any errors in the extracted message, thus reducing the number of misidentification. The proposed system was implemented and tested using MATLAB. The bit efficiency of the proposed system varied between 38% and 88%. This variance is caused by the complexity of the depth scene as well as the cost function used in the depth estimation process. The watermark embedding system proposed had a PSNR of 45 dB (when no mark was embedded); this value is primarily attributed to some of the quantization that occurs during the DCT transform. However, a PSNR of 33dB is attained when the watermark was added at full strength.

scholarly journals PDANet: Self-Supervised Monocular Depth Estimation Using Perceptual and Data Augmentation Consistency

2021 ◽  
Vol 11 (12) ◽  
pp. 5383
Author(s):  
Huachen Gao ◽  
Xiaoyu Liu ◽  
Meixia Qu ◽  
Shijie Huang
Keyword(s):  
Data Augmentation ◽  
State Of The Art ◽  
Depth Estimation ◽  
Input Image ◽  
Depth Information ◽  
Disparity Map ◽  
Estimation Model ◽  
Absolute Relative Error ◽  
Texture Region ◽  
Monocular Depth

In recent studies, self-supervised learning methods have been explored for monocular depth estimation. They minimize the reconstruction loss of images instead of depth information as a supervised signal. However, existing methods usually assume that the corresponding points in different views should have the same color, which leads to unreliable unsupervised signals and ultimately damages the reconstruction loss during the training. Meanwhile, in the low texture region, it is unable to predict the disparity value of pixels correctly because of the small number of extracted features. To solve the above issues, we propose a network—PDANet—that integrates perceptual consistency and data augmentation consistency, which are more reliable unsupervised signals, into a regular unsupervised depth estimation model. Specifically, we apply a reliable data augmentation mechanism to minimize the loss of the disparity map generated by the original image and the augmented image, respectively, which will enhance the robustness of the image in the prediction of color fluctuation. At the same time, we aggregate the features of different layers extracted by a pre-trained VGG16 network to explore the higher-level perceptual differences between the input image and the generated one. Ablation studies demonstrate the effectiveness of each components, and PDANet shows high-quality depth estimation results on the KITTI benchmark, which optimizes the state-of-the-art method from 0.114 to 0.084, measured by absolute relative error for depth estimation.

scholarly journals Deep Learning Based BCI Control of a Robotic Service Assistant Using Intelligent Goal Formulation

2018 ◽  
Author(s):  
D. Kuhner ◽  
L.D.J. Fiederer ◽  
J. Aldinger ◽  
F. Burget ◽  
M. Völker ◽  
...  
Keyword(s):  
Deep Learning ◽  
User Study ◽  
Human Robot Interaction ◽  
Service Robots ◽  
Referring Expressions ◽  
Non Invasive ◽  
Neuronal Signal ◽  
High Level ◽  
User Friendly ◽  
Robotic Tasks

AbstractAs autonomous service robots become more affordable and thus available for the general public, there is a growing need for user-friendly interfaces to control these systems. Control interfaces typically get more complicated with increasing complexity of the robotic tasks and the environment. Traditional control modalities as touch, speech or gesture commands are not necessarily suited for all users. While non-expert users can make the effort to familiarize themselves with a robotic system, paralyzed users may not be capable of controlling such systems even though they need robotic assistance most. In this paper, we present a novel framework, that allows these users to interact with a robotic service assistant in a closed-loop fashion, using only thoughts. The system is composed of several interacting components: non-invasive neuronal signal recording and co-adaptive deep learning which form the brain-computer interface (BCI), high-level task planning based on referring expressions, navigation and manipulation planning as well as environmental perception. We extensively evaluate the BCI in various tasks, determine the performance of the goal formulation user interface and investigate its intuitiveness in a user study. Furthermore, we demonstrate the applicability and robustness of the system in real world scenarios, considering fetch-and-carry tasks and tasks involving human-robot interaction. As our results show, the system is capable of adapting to frequent changes in the environment and reliably accomplishes given tasks within a reasonable amount of time. Combined with high-level planning using referring expressions and autonomous robotic systems, interesting new perspectives open up for non-invasive BCI-based human-robot interactions.

scholarly journals Quantitative and dosimetric analysis for treating synchronous bilateral breast cancer using two radiotherapy planning techniques

2021 ◽  
Vol 27 (3) ◽  
pp. 201-206
Author(s):  
Özlem Mermut ◽  
Aysun Ozsoy Ata ◽  
Didem Can Trabulus
Keyword(s):  
Breast Cancer ◽  
Early Stage ◽  
Organs At Risk ◽  
Bilateral Breast Cancer ◽  
Radiotherapy Planning ◽  
Conformity Index ◽  
Treatment Unit ◽  
Significant Difference ◽  
Synchronous Bilateral Breast Cancer ◽  
The Right

Abstract Objective: We compared mono-isocenter and dual-isocenter plans in synchronous bilateral breast cancer (SBBC), which is defined as tumours occurring simultaneously in both breasts, and evaluated the effects of these differences in plans on organs-at-risk (OARs). Materials and methods: We evaluated 10 women with early stage, nod negative (Tis-2N0M0) SBBC. The treatment dose was determined to be 50 Gy. We used mean dose and VXGy to evaluate the OARs. To evaluate the effectiveness of treatment plans, Homogeneity index (HI), conformity index (CI) and sigma index (SI) and monitor units (MU) of monoisocenter (MIT) and dual-isocenter (DIT) plans were compared. During bilateral breast planning, for the single-centre plan, the isocenter was placed at the center of both breasts at a depth of 3-4 cm. For the two-center plan, dual-isocenters were placed on the right and left breasts. Results: No significant difference between the techniques in terms of the scope of the target volume was observed. Statistically significant results were not achieved in MIT and DIT plans for OARs. Upon comparing MIT and DIT, the right-side monitor unit (MU) value in DIT (p = 0.011) was statistically significantly lower than that in MIT. Upon comparing right-left side MIT and DIT, the MU value (p = 0.028) was significantly lower in DIT than MIT. Conclusion: SBBC irradiation is more complex than unilateral breast radiotherapy. No significant difference between both techniques and OARs was observed. However, we recommend MIT as a priority technique due to the ability to protect OARs, ease of administration during treatment, and the fact that the patient stays in the treatment unit for a shorter period of time.

scholarly journals Towards improved precision and a new classification of diabetes mellitus

2021 ◽  
Author(s):  
Emma Ahlqvist ◽  
Rashmi B Prasad ◽  
Leif Groop
Keyword(s):  
Insulin Resistance ◽  
Personalized Medicine ◽  
Fatty Liver ◽  
Early Stage ◽  
Age At Onset ◽  
Clinical Manifestations ◽  
Insulin Resistant ◽  
Increased Risk ◽  
Patient Health ◽  
The Right

Type 2 diabetes (T2D) is one of the fastest increasing diseases worldwide. Although it is defined by a single metabolite, glucose, it is increasingly recognized as a highly heterogeneous disease with varying clinical manifestations. Identification of different subtypes at an early stage of disease when complications might still be prevented could hopefully allow for more personalized medicine. An important step towards precision medicine would be to target the right resources to the right patients, thereby improving patient health and reducing health costs for the society. More well-defined disease populations also offer increased power in experimental, genetic and clinical studies. In a recent study, we used six clinical variables (GAD autoantibodies, age at onset of diabetes, HbA1c, BMI, and simple measures of insulin resistance and insulin secretion (so called HOMA estimates) to cluster adult-onset diabetes patients into five subgroups. These subgroups have been robustly reproduced in several populations worldwide and are associated with different risks of diabetic complications and responses to treatment. Importantly, the group with severe insulin-deficient diabetes (SIDD) had increased risk of retinopathy and neuropathy, whereas the severe insulin-resistant diabetes (SIRD) group has the highest risk for diabetic kidney disease (DKD) and fatty liver. This emphasizes the key role of insulin resistance in the pathogenesis of DKD and fatty liver in T2D. In conclusion, this novel sub-classification, breaking down T2D in clinically meaningful subgroups, provides the prerequisite framework for expanded personalized medicine in diabetes beyond what is already available for monogenic and to some extent type 1 diabetes.

Primary Pericardial Angiosarcoma: A Case Report and Literature Review

2021 ◽  
Author(s):  
hong sun ◽  
min zhao
Keyword(s):  
Literature Review ◽  
Early Stage ◽  
Radical Resection ◽  
Primary Angiosarcoma ◽  
Positron Emission ◽  
Contrast Enhanced ◽  
Wide Range ◽  
The Right ◽  
Enhanced Computed Tomography ◽  
Contrast Enhanced Computed Tomography

Abstract Primary angiosarcoma is extremely rare malignant tumor that has no typical symptoms and progress rapidly with poor prognosis. It is mesenchymal in origin and observed most frequently in the right atrium, cases in the pericardium is much more rare. Only few can detected in the early-stage allowing complete radical resection with a mean survival of 3 months to 1 year. There is few pericardial angiosarcoma reported among these years. The present study reports a case of a 44-year-old woman with primary pericardial angiosarcoma, who underwent a wide range of imaging methods, including transthoracic echocardiography, contrast-enhanced computed tomography (CT) and positron emission tomography-magnetic resonance imaging (PET-MRI). The patient recovered well after operation in two years and died due to the recrudescence and pulmonary metastases in April, 2020. We report the case for its rarity and revealing the early detection of primary pericardial angiosarcoma with imaging examinations is critical for prognosis. Finally a literature review is done.

AI and Statistical Technologies for Manufacturing and Maintenance Strategies Improvement

2020 ◽  
pp. 569-588
Author(s):  
Susana Ferrerio Del Río ◽  
Santiago Fernández ◽  
Iñaki Bravo-Imaz ◽  
Egoitz Konde ◽  
Aitor Arnaiz Irigaray
Keyword(s):  
Health Monitoring ◽  
Early Stage ◽  
Electromechanical Actuators ◽  
Maintenance Strategies ◽  
Hydraulic Actuators ◽  
Predictive Algorithms ◽  
Maintenance Activities ◽  
The Right ◽  
Additional Value ◽  
Actuation Systems

The development and the implementation of advanced actuation systems has increased in recent years, as many factors are driving the migration from hydraulic actuators to electromechanical actuators (EMAs) in aeronautics. But not only do we have to consider the right design to customize the system from the requirements oriented to the final application, also additional functions that can provide the system with additional value, to make it more competitive in this market. This is the case of the Health Monitoring (HM) systems. The development, implementation and integration of predictive algorithms into the environment of the EMA provide the system with an additional functionality, from which it is possible to detect failures at an early stage in order to avoid catastrophic accidents and improve maintenance activities. This work shows how to develop HM algorithms based on AI and Statistical technologies to detect and predict early stages of failure in a gearbox, which can directly affect to the transmission of power in EMAs.

AI and Statistical Technologies for Manufacturing and Maintenance Strategies Improvement

2017 ◽  
pp. 215-233 ◽  
Author(s):  
Susana Ferrerio Del Río ◽  
Santiago Fernández ◽  
Iñaki Bravo-Imaz ◽  
Egoitz Konde ◽  
Aitor Arnaiz Irigaray
Keyword(s):  
Health Monitoring ◽  
Early Stage ◽  
Electromechanical Actuators ◽  
Maintenance Strategies ◽  
Hydraulic Actuators ◽  
Predictive Algorithms ◽  
Maintenance Activities ◽  
The Right ◽  
Additional Value ◽  
Actuation Systems

The development and the implementation of advanced actuation systems has increased in recent years, as many factors are driving the migration from hydraulic actuators to electromechanical actuators (EMAs) in aeronautics. But not only do we have to consider the right design to customize the system from the requirements oriented to the final application, also additional functions that can provide the system with additional value, to make it more competitive in this market. This is the case of the Health Monitoring (HM) systems. The development, implementation and integration of predictive algorithms into the environment of the EMA provide the system with an additional functionality, from which it is possible to detect failures at an early stage in order to avoid catastrophic accidents and improve maintenance activities. This work shows how to develop HM algorithms based on AI and Statistical technologies to detect and predict early stages of failure in a gearbox, which can directly affect to the transmission of power in EMAs.

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