Sparse representation and convolutional neural networks for 3D human pose estimation

Alikarami, Hassan; Fadaeieslam, Mohammad Javad

doi:10.1109/icspis.2017.8311614

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…To increase the efficiency of CBIR and image representation, one way is to use low-level features in fusion. On the other hand, researchers used deep learning methods to extract features from images and this approach can be useful in improving the performance of different areas such as the face recognition [19] and human pose estimation [20]. In these methods, they used CNNs for proper feature extraction and decision making.…”

Section: Introductionmentioning

confidence: 99%

Farsi handwritten phone number recognition using deep learning

Akhlaghi

Ghods

2020

SN Appl. Sci.

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An application of artificial intelligence in mobile phones which might be widely accepted by users of these phones is the intelligent system used to automatically detect, search and dial phone numbers using an image taken from a handwritten phone number. In this paper, a reliable method is presented for Farsi handwritten phone number recognition using deep neural networks. In order to recognize a Farsi handwritten digit string, the digit string is first converted to single digits using the proposed segmentation algorithm, and then each segment is classified using a single Farsi handwritten digit recognition algorithm. By classifying each segment, finally, the digit string of the Farsi handwritten phone number image is created. Since there is no database for Farsi handwritten phone numbers, this paper first collects a database of Farsi handwritten phone numbers. Accuracy of the proposed algorithm for Farsi handwritten phone number recognition is 94.6%. After recognizing digits of the phone number, the proposed algorithm is able to search in the phonebook.

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Section: Introductionmentioning

confidence: 99%

Farsi handwritten phone number recognition using deep learning

Akhlaghi

Ghods

2020

SN Appl. Sci.

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Recent Advances in 3D Human Pose Estimation: From Optimization to Implementation and Beyond

Yan

Zhou

Pan

et al. 2022

Int. J. Patt. Recogn. Artif. Intell.

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3D human pose estimation describes estimating 3D articulation structure of a person from an image or a video. The technology has massive potential because it can enable tracking people and analyzing motion in real time. Recently, much research has been conducted to optimize human pose estimation, but few works have focused on reviewing 3D human pose estimation. In this paper, we offer a comprehensive survey of the state-of-the-art methods for 3D human pose estimation, referred to as pose estimation solutions, implementations on images or videos that contain different numbers of people and advanced 3D human pose estimation techniques. Furthermore, different kinds of algorithms are further subdivided into sub-categories and compared in light of different methodologies. To the best of our knowledge, this is the first such comprehensive survey of the recent progress of 3D human pose estimation and will hopefully facilitate the completion, refinement and applications of 3D human pose estimation.

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Sparse representation and convolutional neural networks for 3D human pose estimation

Cited by 2 publications

References 28 publications

Farsi handwritten phone number recognition using deep learning

Farsi handwritten phone number recognition using deep learning

Recent Advances in 3D Human Pose Estimation: From Optimization to Implementation and Beyond

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