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Since the Pre-Roman era, olive trees have a significant economic and cultural value. In 2019, the Al-Jouf region, in the north of the Kingdom of Saudi Arabia, gained a global presence by entering the Guinness World Records, with the largest number of olive trees in the world. Olive tree detecting and counting from a given satellite image are a significant and difficult computer vision problem. Because olive farms are spread out over a large area, manually counting the trees is impossible. Moreover, accurate automatic detection and counting of olive trees in satellite images have many challenges such as scale variations, weather changes, perspective distortions, and orientation changes. Another problem is the lack of a standard database of olive trees available for deep learning applications. To address these problems, we first build a large-scale olive dataset dedicated to deep learning research and applications. The dataset consists of 230 RGB images collected over the territory of Al-Jouf, KSA. We then propose an efficient deep learning model (SwinTUnet) for detecting and counting olive trees from satellite imagery. The proposed SwinTUnet is a Unet-like network which consists of an encoder, a decoder, and skip connections. Swin Transformer block is the fundamental unit of SwinTUnet to learn local and global semantic information. The results of an experimental study on the proposed dataset show that the SwinTUnet model outperforms the related studies in terms of overall detection with a 0.94% estimation error., Competing Interests: The authors declare that they have no conflicts of interest to report regarding the present study., (Copyright © 2022 Amr Abozeid et al.)

This study introduces a novel approach for integrating sensitive patient information within medical images with minimal impact on their diagnostic quality. Utilizing the mask region-based convolutional neural network for identifying regions of minimal medical significance, the method embeds information using discrete cosine transform-based steganography. The focus is on embedding within "insignificant areas", determined by deep learning models, to ensure image quality and confidentiality are maintained. The methodology comprises three main steps: neural network training for area identification, an embedding process for data concealment, and an extraction process for retrieving embedded information. Experimental evaluations on the CHAOS dataset demonstrate the method's effectiveness, with the model achieving an average intersection over union score of 0.9146, indicating accurate segmentation. Imperceptibility metrics, including peak signal-to-noise ratio, were employed to assess the quality of stego images, with results showing high capacity embedding with minimal distortion. Furthermore, the embedding capacity and payload analysis reveal the method's high capacity for data concealment. The proposed method outperforms existing techniques by offering superior image quality, as evidenced by higher peak signal-to-noise ratio values, and efficient concealment capacity, making it a promising solution for secure medical image handling. [ABSTRACT FROM AUTHOR]

Indoor localization methods can help many sectors, such as healthcare centers, smart homes, museums, warehouses, and retail malls, improve their service areas. As a result, it is crucial to look for low-cost methods that can provide exact localization in indoor locations. In this context, imagebased localization methods can play an important role in estimating both the position and the orientation of cameras regarding an object. Image-based localization faces many issues, such as image scale and rotation variance. Also, image-based localization's accuracy and speed (latency) are two critical factors. This paper proposes an efficient 6-DoF deep-learning model for image-based localization. This model incorporates the channel attention module and the Scale PyramidModule (SPM). It not only enhances accuracy but also ensures the model's real-time performance. In complex scenes, a channel attention module is employed to distinguish between the textures of the foregrounds and backgrounds. Our model adapted an SPM, a feature pyramid module for dealing with image scale and rotation variance issues. Furthermore, the proposed model employs two regressions (two fully connected layers), one for position and the other for orientation, which increases outcome accuracy. Experiments on standard indoor and outdoor datasets show that the proposed model has a significantly lower Mean Squared Error (MSE) for both position and orientation. On the indoor 7-Scenes dataset, the MSE for the position is reduced to 0.19 m and 6.25° for the orientation. Furthermore, on the outdoor Cambridge landmarks dataset, the MSE for the position is reduced to 0.63 m and 2.03° for the orientation. According to the findings, the proposed approach is superior and more successful than the baseline methods. [ABSTRACT FROM AUTHOR]