KEYWORDS: Video, Image segmentation, Detection and tracking algorithms, Video processing, 3D modeling, RGB color model, Image processing, Neural networks, Hough transforms, Data modeling
Advertising in TV shows and movies is expensive and has one of the largest market shares in the entire advertising industry. We address the task of adding a given advertising banner in a given video. In this paper, we propose a new algorithm for processing and replacing advertising banners in videos, which preserves the quality of the original video content. This algorithm allows the given posters to be inserted into a video in a fully automated mode. In order to replace a banner, the algorithm requires only a video and an image of the banner to be inserted. Our algorithm uses computer vision methods for localizing banners on the scene, analyzing, and transforming them. We suggest the approach to create a synthetic dataset for fine-tuning advertising banners detection models. We implement three various methods for the banner localization task and compared the approaches with each other and existing methods. The source code and examples of the algorithm performance are publicly available https://github.com/leonfed/ReAds.
When taking photos of historical buildings and landmarks, one can often encounter more modern and less attractive objects blocking the view (poles, electricity cables, road signs, and others). Photographers strive to take the unobstructed shot, without random objects popping up in the image. However, they avoid deleting unwanted objects with inpainting or exemplar-based methods, because it can introduce notable artefacts. We propose a new algorithm for removing large static objects such as road signs, sitting people, and parked cars from a photograph keeping its originality and unique details. We do not use artificial patterns to fill covered regions, only two shots taken from different angles. Usually, removing long thin objects such as road sign poles cause image deformations, but our approach avoids this. For comparison, we created a dataset from “Caltech Buildings” and “Kaggle Architecture” Datasets and added road signs, cars, and other objects to photos. We compared our approach with state-of-the-art methods such as Deep Image Prior, Gated Convolution, and the Region Filling by block sampling. The real photographs of historical building demonstrate the effectiveness of our algorithm. Code and example images are available on GitHub1.
Automated text recognition is used in autonomous driving systems, search engines, document analysis, and many other applications. There are many techniques to extract text information from scanned documents, but text recognition from arbitrary images is a much harder task. Recently suggested deep learning approaches have demonstrated highquality results, but they require a huge amount of data to achieve them. The process of collecting and labelling training data to train a deep learning network is costly. In this paper, we suggest an approach for automatic dataset generation for text recognition for arbitrary languages. We use a generative adversarial network structure, which is adapted to generate readable and clear text looking naturally on the image background. We evaluate our approach using SegLink and Textboxes++ text localization models, which were trained on examples generated by SynthText and by variations of our method. The comparison showed the superiority of our method on a subset of the ICDAR 2017 dataset for English and Arabic languages.
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