scholarly journals Weakly Supervised Body Part Segmentation with Pose based Part Priors

2021 ◽  
Author(s):  
Zhengyuan Yang ◽  
Yuncheng Li ◽  
Linjie Yang ◽  
Ning Zhang ◽  
Jiebo Luo
Keyword(s):  
Body Part ◽  
Part Segmentation ◽  
Weakly Supervised

Efficient and robust deep networks for semantic segmentation

2017 ◽  
Vol 37 (4-5) ◽  
pp. 472-491 ◽  
Author(s):  
Gabriel L Oliveira ◽  
Claas Bollen ◽  
Wolfram Burgard ◽  
Thomas Brox
Keyword(s):  
Human Body ◽  
Semantic Segmentation ◽  
Body Part ◽  
Human Robot Interaction ◽  
Network Architectures ◽  
Convolutional Networks ◽  
Human Body Part ◽  
Aerial Vehicle ◽  
Part Segmentation ◽  
Computation Load

This paper explores and investigates deep convolutional neural network architectures to increase the efficiency and robustness of semantic segmentation tasks. The proposed solutions are based on up-convolutional networks. We introduce three different architectures in this work. The first architecture, called Part-Net, is designed to tackle the specific problem of human body part segmentation and to provide robustness to overfitting and body part occlusion. The second network, called Fast-Net, is a network specifically designed to provide the smallest computation load without losing representation power. Such an architecture is capable of being run on mobile GPUs. The last architecture, called M-Net, aims to maximize the robustness characteristics of deep semantic segmentation approaches through multiresolution fusion. The networks achieve state-of-the-art performance on the PASCAL Parts dataset and competitive results on the KITTI dataset for road and lane segmentation. Moreover, we introduce a new part segmentation dataset, the Freiburg City dataset, which is designed to bring semantic segmentation to highly realistic robotics scenarios. Additionally, we present results obtained with a ground robot and an unmanned aerial vehicle and a full system to explore the capabilities of human body part segmentation in the context of human–robot interaction.

Serialized Keypoint Estimation Using Body Part Segmentation

2019 ◽  
Author(s):  
Ho Gyeong Lee ◽  
Yong Chae Cho ◽  
Jeong Hoon Han ◽  
Woo Jin Jeong ◽  
Ye Jin Park ◽  
...  
Keyword(s):  
Body Part ◽  
Part Segmentation

scholarly journals Weakly-supervised hand part segmentation from depth images

2021 ◽  
Author(s):  
'Mohammad' 'Rezaei' ◽  
'Farnaz' 'Farahanipad' ◽  
'Alex' 'Dillhoff' ◽  
'Ramez' 'Elmasri' ◽  
'Vassilis' 'Athitsos'
Keyword(s):  
Depth Images ◽  
Hand Part ◽  
Part Segmentation ◽  
Weakly Supervised

scholarly journals Gesture Recognition Based on 3D Human Pose Estimation and Body Part Segmentation for RGB Data Input

2020 ◽  
Vol 10 (18) ◽  
pp. 6188
Author(s):  
Ngoc-Hoang Nguyen ◽  
Tran-Dac-Thinh Phan ◽  
Guee-Sang Lee ◽  
Soo-Hyung Kim ◽  
Hyung-Jeong Yang
Keyword(s):  
Gesture Recognition ◽  
Short Term Memory ◽  
Body Part ◽  
Human Action ◽  
Data Input ◽  
Novel Approach ◽  
Rgb Images ◽  
Part Segmentation ◽  
3D Skeleton ◽  
Public Datasets

This paper presents a novel approach for dynamic gesture recognition using multi-features extracted from RGB data input. Most of the challenges in gesture recognition revolve around the axis of the presence of multiple actors in the scene, occlusions, and viewpoint variations. In this paper, we develop a gesture recognition approach by hybrid deep learning where RGB frames, 3D skeleton joint information, and body part segmentation are used to overcome such problems. Extracted from the RGB images are the multimodal input observations, which are combined by multi-modal stream networks suited to different input modalities: residual 3D convolutional neural networks based on ResNet architecture (3DCNN_ResNet) for RGB images and color body part segmentation modalities; long short-term memory network (LSTM) for 3D skeleton joint modality. We evaluated the proposed model on four public datasets: UTD multimodal human action dataset, gaming 3D dataset, NTU RGB+D dataset, and MSRDailyActivity3D dataset and the experimental results on these datasets proves the effectiveness of our approach.

Behavioral Factors Impact Impairment and Disability Evaluation

2011 ◽  
Vol 16 (5) ◽  
pp. 5-7
Author(s):  
Lee Ensalada
Keyword(s):  
Social History ◽  
Standardized Test ◽  
Somatoform Disorders ◽  
Disability Evaluation ◽  
Body Part ◽  
Emotional Reactions ◽  
Illness Behavior ◽  
Validity Testing ◽  
Symptom Validity Testing ◽  
Disease Pattern

Abstract Illness behavior refers to the ways in which symptoms are perceived, understood, acted upon, and communicated and include facial grimacing, holding or supporting the affected body part, limping, using a cane, and stooping while walking. Illness behavior can be unconscious or conscious: In the former, the person is unaware of the mental processes and content that are significant in determining behavior; conscious illness behavior may be voluntary and conscious (the two are not necessarily associated). The first broad category of inappropriate illness behavior is defensiveness, which is characterized by denial or minimization of symptoms. The second category includes somatoform disorders, factitious disorders, and malingering and is characterized by exaggerating, fabricating, or denying symptoms; minimizing capabilities or positive traits; or misattributing actual deficits to a false cause. Evaluators can detect the presence of inappropriate illness behaviors based on evidence of consistency in the history or examination; the likelihood that the reported symptoms make medical sense and fit a reasonable disease pattern; understanding of the patient's current situation, personal and social history, and emotional predispositions; emotional reactions to symptoms; evaluation of nonphysiological findings; results obtained using standardized test instruments; and tests of dissimulation, such as symptom validity testing. Unsupported and insupportable conclusions regarding inappropriate illness behavior represent substandard practice in view of the importance of these conclusions for the assessment of impairment or disability.

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