Moving object detection using a background modeling based on entropy theory and quad-tree decomposition

Elharrouss, Omar; Moujahid, Driss; Elkah, Samah; Tairi, Hamid

doi:10.1117/1.jei.25.6.061615

Cited by 16 publications

(8 citation statements)

References 23 publications

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“…The evaluation of the deep learning model performance computed in the testing phase was based on the segmentation metrics [ 34 , 35 ]. These metrics are defined as follows: Precision : This calculates how close the values are to each other and how close they are to the true values.…”

Section: Experimental Resultsmentioning

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: Experimental Resultsmentioning

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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“…The same combination is used in [47] for image inpainting and image-to-image transformation purposes. On the RBG-D images, Dhamo et al [48] use CNN and GAN model to generate the background of a scene by removing the object in the foreground image as performed by many methodsod of motion detection using background subtraction [49][50][51] . In order to complete the missing regions in the image, Vitoria et al [52] proposed an improved version of the Wasserstein GAN with the incorporation of Discriminator and Generator architecture.…”

Section: Gan-based Approachesmentioning

confidence: 99%

Image Inpainting: A Review

Elharrouss

Almaadeed

Al‐Maadeed

et al. 2019

Neural Process Lett

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268

100

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Although image inpainting, or the art of repairing the old and deteriorated images, has been around for many years, it has gained even more popularity because of the recent development in image processing techniques. With the improvement of image processing tools and the flexibility of digital image editing, automatic image inpainting has found important applications in computer vision and has also become an important and challenging topic of research in image processing. This paper is a brief review of the existing image inpainting approaches we first present a global vision on the existing methods for image inpainting. We attempt to collect most of the existing approaches and classify them into three categories, namely, sequential-based, CNN-based and GAN-based methods. In addition, for each category, a list of methods for the different types of distortion on the images is presented. Furthermore, collect a list of the available datasets and discuss these in our paper. This is a contribution for digital image inpainting researchers trying to look for the available datasets because there is a lack of datasets available for image inpainting. As the final step in this overview, we present the results of real evaluations of the three categories of image inpainting methods performed on the datasets used, for the different types of image distortion. In the end, we also present the evaluations metrics and discuss the performance of these methods in terms of these metrics. This overview can be used as a reference for image inpainting researchers, and it can also facilitate the comparison of the methods as well as the datasets used. The main contribution of this paper is the presentation of the three categories of image inpainting methods along with a list of available datasets that the researchers can use to evaluate their proposed methodology against.

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“…Low lighting, uneven illumination or any change of illumination in the observed scene are among the sources of degradation that strongly affect video quality and consequently the process of scene analysis and understanding and particularly object detection and visual tracking performance [65,66]. It is therefore useful to detect the illumination changes and apply the appropriate pre-processing before performing high-level vision tasks such as moving object detection and tracking.…”

Section: Illumination Change Detection and Video Enhancementmentioning

confidence: 99%

A combined multiple action recognition and summarization for surveillance video sequences

et al. 2020

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Human action recognition and video summarization represent challenging tasks for several computer vision applications including video surveillance, criminal investigations, and sports applications. For long videos, it is difficult to search within a video for a specific action and/or person. Usually, human action recognition approaches presented in the literature deal with videos that contain only a single person, and they are able to recognize his action. This paper proposes an effective approach to multiple human action detection, recognition, and summarization. The multiple action detection extracts human bodies' silhouette, then generates a specific sequence for each one of them using motion detection and tracking method. Each of the extracted sequences is then divided into shots that represent homogeneous actions in the sequence using the similarity between each pair frames. Using the histogram of the oriented gradient (HOG) of the Temporal Difference Map (TDMap) of the frames of each shot, we recognize the action by performing a comparison between the generated HOG and the existed HOGs in the training phase which represents all the HOGs of many actions using a set of videos for training. Also, using the TDMap images we recognize the action using a proposed CNN model. Action summarization is performed for each detected person. The efficiency of the proposed approach is shown through the obtained results for mainly multi-action detection and recognition.

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Moving object detection using a background modeling based on entropy theory and quad-tree decomposition

Cited by 16 publications

References 23 publications

Encoder-Decoder Architecture for Ultrasound IMC Segmentation and cIMT Measurement

Encoder-Decoder Architecture for Ultrasound IMC Segmentation and cIMT Measurement

Image Inpainting: A Review

A combined multiple action recognition and summarization for surveillance video sequences

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