Feature-points nearest neighbor clustering on 3D face models

Gunanto, Samuel Gandang; Hariadi, Mochamad; Yuniarno, Eko Mulyanto

doi:10.1109/citsm.2016.7577570

Cited by 6 publications

(5 citation statements)

References 7 publications

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“…The main rule in the nearest neighbor clustering is to identify categories of unknown data using the already established nearest neighbor data group [71]. The nearest neighbor technique is divided into two categories: (1) structureless and (2) structure-based [72].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

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Framework for the Development of Data-Driven Mamdani-Type Fuzzy Clinical Decision Support Systems

et al. 2019

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Clinical decision support systems (CDSS) have been designed, implemented, and validated to help clinicians and practitioners for decision-making about diagnosing some diseases. Within the CDSSs, we can find Fuzzy inference systems. For the reasons above, the objective of this study was to design, to implement, and to validate a methodology for developing data-driven Mamdani-type fuzzy clinical decision support systems using clusters and pivot tables. For validating the proposed methodology, we applied our algorithms on five public datasets including Wisconsin, Coimbra breast cancer, wart treatment (Immunotherapy and cryotherapy), and caesarian section, and compared them with other related works (Literature). The results show that the Kappa Statistics and accuracies were close to 1.0% and 100%, respectively for each output variable, which shows better accuracy than some literature results. The proposed framework could be considered as a deep learning technique because it is composed of various processing layers to learn representations of data with multiple levels of abstraction.

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Section: Theoretical Backgroundmentioning

confidence: 99%

“…Distance calculation performed on the entire training data to the data sample if a point has the closest or minimum distance. Those points are expressed as the nearest neighbor [71].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Framework for the Development of Data-Driven Mamdani-Type Fuzzy Clinical Decision Support Systems

et al. 2019

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“…Once the matrix H (6) and the weight of each coordinate x, y, z (9) are obtained, the retargeting feature points can be calculated quickly for each marker position from the motion of source face using (10).…”

Section: Rbf Methodsmentioning

confidence: 99%

“…Marker-based means of facial animation is done by automation approach such as retargeting feature points. Automation should be able to minimize human intervention in the process [10]. While the markerless can mean automation facial animation by using the surface as a comparison, they can be interpreted facial animation is done manually by the animator.…”

Section: A Facial Animationmentioning

confidence: 99%

2D to 3D space transformation for facial animation based on marker data

Troy

Pranowo

Gunanto

2016

2016 6th International Annual Engineering Seminar (InAES)

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“…These methods also need intensive human interference for tracking the facial features. On the contrary, some methods [9][10][11][12][13][14][15] are no longer required to mark these features manually but instead use multi-view stereo methodologies, which require controlled lighting and calibration of cameras. Jo et al [16] proposed a method for 3D face reconstruction that is an example of these algorithms.…”

Section: Introductionmentioning

confidence: 99%

3D Face Reconstruction From Single 2D Image Using Distinctive Features

et al. 2020

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3D face reconstruction is considered to be a useful computer vision tool, though it is difficult to build. This paper proposes a 3D face reconstruction method, which is easy to implement and computationally efficient. It takes a single 2D image as input, and gives 3D reconstructed images as output. Our method primarily consists of three main steps: feature extraction, depth calculation, and creation of a 3D image from the processed image using a Basel face model (BFM). First, the features of a single 2D image are extracted using a two-step process. Before distinctive-features extraction, a face must be detected to confirm whether one is present in the input image or not. For this purpose, facial features like eyes, nose, and mouth are extracted. Then, distinctive features are mined by using scale-invariant feature transform (SIFT), which will be used for 3D face reconstruction at a later stage. Second step comprises of depth calculation, to assign the image a third dimension. Multivariate Gaussian distribution helps to find the third dimension, which is further tuned using shading cues that are obtained by the shape from shading (SFS) technique. Thirdly, the data obtained from the above two steps will be used to create a 3D image using BFM. The proposed method does not rely on multiple images, lightening the computation burden. Experiments were carried out on different 2D images to validate the proposed method and compared its performance to those of the latest approaches. Experiment results demonstrate that the proposed method is time efficient and robust in nature, and it outperformed all of the tested methods in terms of detail recovery and accuracy.

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Feature-points nearest neighbor clustering on 3D face models

Cited by 6 publications

References 7 publications

Framework for the Development of Data-Driven Mamdani-Type Fuzzy Clinical Decision Support Systems

Framework for the Development of Data-Driven Mamdani-Type Fuzzy Clinical Decision Support Systems

2D to 3D space transformation for facial animation based on marker data

3D Face Reconstruction From Single 2D Image Using Distinctive Features

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