An Advert Creation System for 3D Product Placements

Bacher, Ivan; Javidnia, Hossein; Dev, Soumyabrata; Agrahari, Rahul; Hossari, Murhaf; Nicholson, Matthew; Conran, Clare; Tang, Jian; Song, Peng; Corrigan, Desmond; Pitié, François

doi:10.1007/978-3-030-67667-4_14

Cited by 5 publications

(1 citation statement)

References 34 publications

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“…With the rapid development of Artificial Intelligence (AI), computer vision as an interdisciplinary subject has widely been used in advertising [1], [2], product placement [3], [4], remote sensing [5], [6], atmospheric study [7], [8], quality control [9], [10], biometrics [11], and amongst others. Eyes are the main source of capturing external information and information conveyed by vision is most abundant and intuitive.…”

Section: Introductionmentioning

confidence: 99%

Stereo Matching Based on Visual Sensitive Information

Wang

Pathan

Dev

2021

2021 6th International Conference on Image, Vision and Computing (ICIVC)

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The area of computer vision is one of the most discussed topics amongst many scholars, and stereo matching is its most important sub fields. After the parallax map is transformed into a depth map, it can be applied to many intelligent fields. In this paper, a stereo matching algorithm based on visual sensitive information is proposed by using standard images from Middlebury dataset. Aiming at the limitation of traditional stereo matching algorithms regarding the cost window, a cost aggregation algorithm based on the dynamic window is proposed, and the disparity image is optimized by using left and right consistency detection to further reduce the error matching rate. The experimental results show that the proposed algorithm can effectively enhance the stereo matching effect of the image providing significant improvement in accuracy as compared with the classical census algorithm. The proposed model code, dataset, and experimental results are available at https://github.com/WangHewei16/Stereo-Matching.

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Section: Introductionmentioning

confidence: 99%

Stereo Matching Based on Visual Sensitive Information

Wang

Pathan

Dev

2021

2021 6th International Conference on Image, Vision and Computing (ICIVC)

Self Cite

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AdSegNet: a deep network to localize billboard in outdoor scenes

Dhang,

Zhang,

Dev

2024

SIViP

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Over the past decade, there has been a remarkable expansion in the popularity of online videos, due to the variety of content that has become accessible on the internet. This expansion presents an opportunity for advertising and marketing agencies to take advantage of targeted advertisements. Targeted advertisements can be accomplished by replacing an existing advertisement within an image frame with a new one. There is a limited amount of research on the general task of localizing billboard or advertisement board in outdoor scenes. Therefore, in this study, we proposed a deep neural network that uses a fusion of VGG16 and SegNet architecture to accurately identify the location of an advertisement in an image frame. To evaluate the effectiveness of our proposed method, we compare our proposed method to other semantic segmentation algorithms using a publicly available dataset of outdoor scenes annotated with binary maps of billboards. Our experimental results show that the proposed method achieves 98.58% training accuracy for billboard localization, while testing results gave 96.43% testing accuracy. Additionally, the low RMSE score of our AdSegNet model suggests that it can accurately determine the four corners of the billboard. Therefore, our approach could be beneficial to advertising and marketing agencies that seek to utilize targeted advertisements

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AMDCNet: An attentional multi-directional convolutional network for stereo matching

Wang

et al. 2022

Displays

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An Advert Creation System for 3D Product Placements

Cited by 5 publications

References 34 publications

Stereo Matching Based on Visual Sensitive Information

Stereo Matching Based on Visual Sensitive Information

AdSegNet: a deep network to localize billboard in outdoor scenes

AMDCNet: An attentional multi-directional convolutional network for stereo matching

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