Gait recognition for person re-identification

Elharrouss, Omar; Almaadeed, Noor; Al‐Maadeed, Somaya; Bouridane, Ahmed

doi:10.1007/s11227-020-03409-5

Cited by 60 publications

(44 citation statements)

References 46 publications

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“…Data augmentation is mainly used when we have a small dataset and would like to increase the number of images in a given dataset [ 29 ]. Thus, it provides small operations that can give the ability to rotate, flip, shift, zoom, or translate a given image without changing its content.…”

Section: Proposed Methodsmentioning

confidence: 99%

Encoder-Decoder Architecture for Ultrasound IMC Segmentation and cIMT Measurement

Al-Mohannadi

Al-Maadeed

Elharrouss

et al. 2021

Sensors

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Cardiovascular diseases (CVDs) have shown a huge impact on the number of deaths in the world. Thus, common carotid artery (CCA) segmentation and intima-media thickness (IMT) measurements have been significantly implemented to perform early diagnosis of CVDs by analyzing IMT features. Using computer vision algorithms on CCA images is not widely used for this type of diagnosis, due to the complexity and the lack of dataset to do it. The advancement of deep learning techniques has made accurate early diagnosis from images possible. In this paper, a deep-learning-based approach is proposed to apply semantic segmentation for intima-media complex (IMC) and to calculate the cIMT measurement. In order to overcome the lack of large-scale datasets, an encoder-decoder-based model is proposed using multi-image inputs that can help achieve good learning for the model using different features. The obtained results were evaluated using different image segmentation metrics which demonstrate the effectiveness of the proposed architecture. In addition, IMT thickness is computed, and the experiment showed that the proposed model is robust and fully automated compared to the state-of-the-art work.

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Section: Proposed Methodsmentioning

confidence: 99%

Encoder-Decoder Architecture for Ultrasound IMC Segmentation and cIMT Measurement

Al-Mohannadi

Al-Maadeed

Elharrouss

et al. 2021

Sensors

Self Cite

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“…In [ 10 ], authors proposed a gait recognition approach for person re-identification. The proposed approach starts with estimating the angle of gait first, and this is then followed with the recognition process, which is performed using convolution neural networks.…”

Section: Background and Related Workmentioning

confidence: 99%

Analysis and best parameters selection for person recognition based on gait model using CNN algorithm and image augmentation

2021

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Person Recognition based on Gait Model (PRGM) and motion features is are indeed a challenging and novel task due to their usages and to the critical issues of human pose variation, human body occlusion, camera view variation, etc. In this project, a deep convolution neural network (CNN) was modified and adapted for person recognition with Image Augmentation (IA) technique depending on gait features. Adaptation aims to get best values for CNN parameters to get best CNN model. In Addition to the CNN parameters Adaptation, the design of CNN model itself was adapted to get best model structure; Adaptation in the design was affected the type, the number of layers in CNN and normalization between them. After choosing best parameters and best design, Image augmentation was used to increase the size of train dataset with many copies of the image to boost the number of different images that will be used to train Deep learning algorithms. The tests were achieved using known dataset (Market dataset). The dataset contains sequential pictures of people in different gait status. The image in CNN model as matrix is extracted to many images or matrices by the convolution, so dataset size may be bigger by hundred times to make the problem a big data issue. In this project, results show that adaptation has improved the accuracy of person recognition using gait model comparing to model without adaptation. In addition, dataset contains images of person carrying things. IA technique improved the model to be robust to some variations such as image dimensions (quality and resolution), rotations and carried things by persons. Results for 200 persons recognition, validation accuracy was about 82% without IA and 96.23 with IA. For 800 persons recognition, validation accuracy was 93.62% without IA.

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“…Recent advancements in computer vision [1][2][3][4][5], natural language processing [6][7][8][9][10][11] and deep learning [12][13][14][15][16] research have enabled enormous progress in many medical image interpretation technologies that support clinical decision making and improve patient engagement [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Goal-Driven Visual Question Generation from Radiology Images

2021

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Visual Question Generation (VQG) from images is a rising research topic in both fields of natural language processing and computer vision. Although there are some recent efforts towards generating questions from images in the open domain, the VQG task in the medical domain has not been well-studied so far due to the lack of labeled data. In this paper, we introduce a goal-driven VQG approach for radiology images called VQGRaD that generates questions targeting specific image aspects such as modality and abnormality. In particular, we study generating natural language questions based on the visual content of the image and on additional information such as the image caption and the question category. VQGRaD encodes the dense vectors of different inputs into two latent spaces, which allows generating, for a specific question category, relevant questions about the images, with or without their captions. We also explore the impact of domain knowledge incorporation (e.g., medical entities and semantic types) and data augmentation techniques on visual question generation in the medical domain. Experiments performed on the VQA-RAD dataset of clinical visual questions showed that VQGRaD achieves 61.86% BLEU score and outperforms strong baselines. We also performed a blinded human evaluation of the grammaticality, fluency, and relevance of the generated questions. The human evaluation demonstrated the better quality of VQGRaD outputs and showed that incorporating medical entities improves the quality of the generated questions. Using the test data and evaluation process of the ImageCLEF 2020 VQA-Med challenge, we found that relying on the proposed data augmentation technique to generate new training samples by applying different kinds of transformations, can mitigate the lack of data, avoid overfitting, and bring a substantial improvement in medical VQG.

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Gait recognition for person re-identification

Cited by 60 publications

References 46 publications

Encoder-Decoder Architecture for Ultrasound IMC Segmentation and cIMT Measurement

Encoder-Decoder Architecture for Ultrasound IMC Segmentation and cIMT Measurement

Analysis and best parameters selection for person recognition based on gait model using CNN algorithm and image augmentation

Goal-Driven Visual Question Generation from Radiology Images

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