Adaptive Shape Kernel-Based Mean Shift Tracker in Robot Vision System

Liu, Chunmei; Wang, Yirui; Gao, Shangce

doi:10.1155/2016/6040232

Cited by 5 publications

(3 citation statements)

References 15 publications

(19 reference statements)

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“…They combined the contour evolution technology with the mean shift and proposed an enhanced mean shift tracking algorithm based on evolutive asymmetric kernel. Liu et al [ 145 ] presented an adaptive shape kernel-based mean shift tracker. Shape of the adaptive kernel is reconstructed from the low-dimensional shape space obtained by nonlinear manifold learning technique to the high-dimensional shape space, aiming to be adaptive to the object shape.…”

Section: Human Tracking Approachesmentioning

confidence: 99%

A Review on Human Activity Recognition Using Vision-Based Method

Zhang

Wei

Nie

et al. 2017

Journal of Healthcare Engineering

254

137

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Human activity recognition (HAR) aims to recognize activities from a series of observations on the actions of subjects and the environmental conditions. The vision-based HAR research is the basis of many applications including video surveillance, health care, and human-computer interaction (HCI). This review highlights the advances of state-of-the-art activity recognition approaches, especially for the activity representation and classification methods. For the representation methods, we sort out a chronological research trajectory from global representations to local representations, and recent depth-based representations. For the classification methods, we conform to the categorization of template-based methods, discriminative models, and generative models and review several prevalent methods. Next, representative and available datasets are introduced. Aiming to provide an overview of those methods and a convenient way of comparing them, we classify existing literatures with a detailed taxonomy including representation and classification methods, as well as the datasets they used. Finally, we investigate the directions for future research.

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Section: Human Tracking Approachesmentioning

confidence: 99%

A Review on Human Activity Recognition Using Vision-Based Method

Zhang

Wei

Nie

et al. 2017

Journal of Healthcare Engineering

254

137

View full text Add to dashboard Cite

show abstract

“…To tackle the problem of the large amount of spatial (non-linear) data, researchers have incorporated kernel estimation into analyses in a broad range of scientific disciplines, e.g. robotics (Liu et al, 2016b;Sugiyama et al, 2008) and geodesy (Grazzini and Soille, 2009;Yuan et al, 2019). From this perspective, analysis of brain gyrification can be compared and treated by analogy as, for example, an irregular, uneven terrain with cortical gyri as mountains and sulci as valleys.…”

Section: Introductionmentioning

confidence: 99%

Analysis of local gyrification index using a novel shape-adaptive kernel and the standard FreeSurfer spherical kernel – evidence from chronic schizophrenia outpatients

Płonka

Krześniak

Adamczyk

2020

Heliyon

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Schizophrenia can be considered a brain disconnectivity condition related to aberrant neurodevelopment that causes alterations in the brain structure, including gyrification of the cortex. Literature findings on cortical folding are incoherent: they report hypogyria in the frontal, superior-parietal and temporal cortices, but also frontal hypergyria. This discrepancy in local gyrification index (LGI) results could be due to the commonly used spherical kernel (Freesurfer), which is a method of analysis that is still not spatially precise enough. In this study we would like to test the spatial accuracy of a novel method based on a shape-adaptive kernel (Cmorph). The analysis of differences in gyrification between chronic schizophrenia outpatients (n ¼ 30) and healthy controls (n ¼ 30) was conducted with two methods: Freesurfer LGI and Cmorph LGI. Widespread differences in the LGI between schizophrenia outpatients and healthy controls were found using both methods. Freesurfer showed hypogyria in the superior temporal gyrus and the right temporal pole; it also showed hypergyria in the rostral-middle-frontal cortex in schizophrenia outpatients. In comparison, Cmorph revealed that hypergyria is equally represented as hypogyria in orbitofrontal and central brain regions. The clusters from Cmorph were smaller and distributed more broadly, covering all lobes of the brain. The presented evidence from disrupted cortical folding in schizophrenia indicates that the shape-adaptive kernel approach has a potential to improve the knowledge on the disrupted cortical folding in schizophrenia; therefore, it could be a valuable tool for further investigation on big sample size.

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“…Especially in the domains of video games and smart phones, the gesture-based interaction mode achieves great success by virtue of the favorable users' experiences. By means of the gestures, users can manipulate the virtual objects [1, 2], acquire the remote targets [3, 4], select the menus [5–7], type the text, etc. Therefore, the gesture interaction has attracted widespread research interests, such as biomechanical modeling, comfort evaluation, gesture design, gesture recognition and gesture-based HCI.…”

Section: Introductionmentioning

confidence: 99%

Mechanical Energy Expenditure-based Comfort Evaluation Model for Gesture Interaction

Wang

Qin

Chen

et al. 2018

Computational Intelligence and Neuroscience

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As an advanced interaction mode, the gesture has been widely used for the human-computer interaction (HCI). The paper proposes a comfort evaluation model based on the mechanical energy expenditure (MEE) and the mechanical efficiency (ME) to predict the comfort of gestures. The proposed comfort evaluation model takes nineteen muscles and seven degrees of freedom into consideration based on the data of muscles and joints and is capable of simulating the MEE and the ME of both static and dynamic gestures. The comfort scores (CSs) can be therefore calculated by normalizing and assigning different decision weights to the MEE and the ME. Compared with the traditional comfort prediction methods based on measurement, on the one hand, the proposed comfort evaluation model makes it possible for providing a quantitative value for the comfort of gestures without using electromyography (EMG) or other measuring devices; on the other hand, from the ergonomic perspective, the results provide an intuitive indicator to predict which act has the higher risk of fatigue or injury for joints and muscles. Experiments are conducted to validate the effectiveness of the proposed model. According to the comparison result among the proposed comfort evaluation model, the model based on the range of motion (ROM) and the model based on the method for movement and gesture assessment (MMGA), a slight difference can be found due to the ignorance of dynamic gestures and the relative kinematic characteristics during the movements of dynamic gestures. Therefore, considering the feedback of perceived effects and gesture recognition rate in HCI, designers can achieve a better optimization for the gesture design by making use of the proposed comfort evaluation model.

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Adaptive Shape Kernel-Based Mean Shift Tracker in Robot Vision System

Cited by 5 publications

References 15 publications

A Review on Human Activity Recognition Using Vision-Based Method

A Review on Human Activity Recognition Using Vision-Based Method

Analysis of local gyrification index using a novel shape-adaptive kernel and the standard FreeSurfer spherical kernel – evidence from chronic schizophrenia outpatients

Mechanical Energy Expenditure-based Comfort Evaluation Model for Gesture Interaction

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