A Pipeline for Hand 2-D Keypoint Localization Using Unpaired Image to Image Translation

Farahanipad, Farnaz; Rezaei, Mohammad; Dillhoff, Alex; Kamangar, Farhad; Athitsos, Vassilis

doi:10.1145/3453892.3453904

Cited by 7 publications

(3 citation statements)

References 29 publications

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“…According to the results of the program implementation document (pid) on databases, the proposed technique can detect diabetes more accurately than the conventional xcs system, the Elman neural network, svm clustering, knn, c4.5, and ad tree. Farahanipad et al [ 29 ] developed a pipeline for the identification of hand 2D keypoints using unpaired image-to-image translation. In Shi et al's [ 30 ] study, they investigated the effect of ultrasonic intensity on the structure and characteristics of sago starch complexes and their implications for the quality of Chinese steamed bread.…”

Section: Literature Reviewmentioning

confidence: 99%

WTD-PSD: Presentation of Novel Feature Extraction Method Based on Discrete Wavelet Transformation and Time-Dependent Power Spectrum Descriptors for Diagnosis of Alzheimer’s Disease

Taghavirashidizadeh

Sharifi

Vahabi

et al. 2022

Computational Intelligence and Neuroscience

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Alzheimer’s disease (AD) is a type of dementia that affects the elderly population. A machine learning (ML) system has been trained to recognize particular patterns to diagnose AD using an algorithm in an ML system. As a result, developing a feature extraction approach is critical for reducing calculation time. The input image in this article is a Two-Dimensional Discrete Wavelet (2D-DWT). The Time-Dependent Power Spectrum Descriptors (TD-PSD) model is used to represent the subbanded wavelet coefficients. The principal property vector is made up of the characteristics of the TD-PSD model. Based on classification algorithms, the collected characteristics are applied independently to present AD classifications. The categorization is used to determine the kind of tumor. The TD-PSD method was used to extract wavelet subbands features from three sets of test samples: moderate cognitive impairment (MCI), AD, and healthy controls (HC). The outcomes of three modes of classic classification methods, including KNN, SVM, Decision Tree, and LDA approaches, are documented, as well as the final feature employed in each. Finally, we show the CNN architecture for AD patient classification. Output assessment is used to show the results. Other techniques are outperformed by the given CNN and DT.

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Section: Literature Reviewmentioning

confidence: 99%

WTD-PSD: Presentation of Novel Feature Extraction Method Based on Discrete Wavelet Transformation and Time-Dependent Power Spectrum Descriptors for Diagnosis of Alzheimer’s Disease

Taghavirashidizadeh

Sharifi

Vahabi

et al. 2022

Computational Intelligence and Neuroscience

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“…There has been a considerable research effort in this area in the last two decades (Duan et al, 2019). Many of these researches have been developed from data-driven 2D interfaces to 3D joint positions and have achieved accurate results (Farahanipad et al,2021). However, because of the complexity of hand and self-occlusion among other factors, hand pose estimation is still a challenging task.…”

Section: Introductionmentioning

confidence: 99%

“…Previous research results show that CNNbased methods perform better when estimating hand pose. The existing methods for 2D position estimation of hand joints have attained high accuracy (Farahanipad et al,2021). However, since the depth information in the original image cannot be fully utilized due to occlusion, HPE results are not ideal.…”

Section: Introductionmentioning

confidence: 99%

Occlusion Handler Density Networks for 3D Multimodal Joint Location of Hand Pose Hypothesis

Tito

Banzi

Mvuma

2022

TJET

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Predicting the pose parameters during the hand pose estimation (HPE) process is an ill-posed challenge. This is due to severe self-occluded joints of the hand. The existing approaches for predicting pose parameters of the hand, utilize a single-value mapping of an input image to generate final pose output. This way makes it difficult to handle occlusion especially when it comes from the multimodal pose hypothesis. This paper introduces an effective method of handling multimodal joint occlusion using the negative log-likelihood of a multimodal mixture-of-Gaussians through a hybrid hierarchical mixture density network (HHMDN). The proposed approach generates multiple feasible hypotheses of 3D poses with visibility, unimodal and multimodal distribution units to locate joint visibility. The visible features are extracted and fed into the Convolutional Neural Networks (CNN) layer of the HHMDN for feature learning. Finally, the effectiveness of the proposed method is proved on ICVL, NYU, and BigHand public hand pose datasets. The imperative results show that the proposed method in this paper is effective as it achieves a visibility error of 30.3mm, which is less error compared to many state-of-the-art approaches that use different distributions of visible and occluded joints.

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2D Fingertip Localization on Depth Videos Using Paired Video-to-Video Translation

Farahanipad

Nasr

Rezaei

et al. 2022

Advances in Visual Computing

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A Pipeline for Hand 2-D Keypoint Localization Using Unpaired Image to Image Translation

Cited by 7 publications

References 29 publications

WTD-PSD: Presentation of Novel Feature Extraction Method Based on Discrete Wavelet Transformation and Time-Dependent Power Spectrum Descriptors for Diagnosis of Alzheimer’s Disease

WTD-PSD: Presentation of Novel Feature Extraction Method Based on Discrete Wavelet Transformation and Time-Dependent Power Spectrum Descriptors for Diagnosis of Alzheimer’s Disease

Occlusion Handler Density Networks for 3D Multimodal Joint Location of Hand Pose Hypothesis

2D Fingertip Localization on Depth Videos Using Paired Video-to-Video Translation

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