ISIM: Iterative Self-Improved Model for Weakly Supervised Segmentation

Weakly Supervised Semantic Segmentation (WSSS) is a challenging task aiming to learn the segmentation labels from class-level labels. In the literature, exploiting the information obtained from Class Activation Maps (CAMs) is widely used for WSSS studies. However, as CAMs are obtained from a classification network, they are interested in the most discriminative parts of the objects, producing non-complete prior information for segmentation tasks. In this study, to obtain more coherent CAMs with segmentation labels, we propose a framework that employs an iterative approach in a modified encoder-decoder-based segmentation model, which simultaneously supports classification and segmentation tasks. As no ground-truth segmentation labels are given, the same model also generates the pseudo-segmentation labels with the help of dense Conditional Random Fields (dCRF). As a result, the proposed framework becomes an iterative self-improved model. The experiments performed with DeepLabv3 and UNet models show a significant gain on the Pascal VOC12 dataset, and the DeepLabv3 application increases the current state-of-the-art metric by %2.5. The implementation associated with the experiments can be found: https://github.com/cenkbircanoglu/isim.Comment: This paper was submitted to IJCV on 15 Nov 2021. The reviewers decided to reject it. After getting the reviews, we wanted to study more. Unfortunately, one of the authors had severe issues (COVID-19 vaccination). After one year, the study was outdated and similar studies had been published. So, we leave the study by putting it in an archive in case it might have some effect on the literatur

Comparison of 3D Versus 4D Path Planning for Unmanned Aerial Vehicles

This research compares 3D versus 4D (three spatial dimensions and the time dimension) multi-objective and multi-criteria path-planning for unmanned aerial vehicles in complex dynamic environments. In this study, we empirically analyse the performances of 3D and 4D path planning approaches. Using the empirical data, we show that the 4D approach is superior over the 3D approach especially in complex dynamic environments. The research model consisting of flight objectives and criteria is developed based on interviews with an experienced military UAV pilot and mission planner to establish realism and relevancy in  unmanned aerial vehicle flight planning. Furthermore, this study incorporates one of the most comprehensive set of criteria identified during our literature search. The simulation results clearly show that the 4D path planning approach is able to provide solutions in complex dynamic environments in which the 3D approach could not find a solution

Unmanned Aerial Vehicle Domain: Areas of Research

Unmanned aerial vehicles (UAVs) domain has seen rapid developments in recent years. As the number of UAVs increases and as the missions involving UAVs vary, new research issues surface. An overview of the existing research areas in the UAV domain has been presented including the nature of the work categorised under different groups. These research areas are divided into two main streams: Technological and operational research areas. The research areas in technology are divided into onboard and ground technologies. The research areas in operations are divided into organization level, brigade level, user level, standards and certifications, regulations and legal, moral, and ethical issues. This overview is intended to serve as a starting point for fellow researchers new to the domain, to help researchers in positioning their research, identifying related research areas, and focusing on the right issues.Defence Science Journal, Vol. 65, No. 4, July 2015, pp. 319-329, DOI: http://dx.doi.org/10.14429/dsj.65.863

İMGE KARELERİ KULLANAN YENİ BİR STEGANOGRAFİ YÖNTEMİ

Bu çalışmada imge karelerini kullanan yeni bir steganografi yöntemi önerilmektedir. Önerilen yöntem, örtü imgesini karelere bölerek her kareye kare boyutuna bağlı uzunlukta mesaj bitini saklar. Kare içinde değişik yönlerdeki satır/sütunlardaki piksellerin en önemsiz bitlerinde (EÖB) arama yaparak mesaj bit dizisine en yakın satır/sütun bulunur. Bulunan satır/sütunun en önemsiz bitleri mesaj bit dizisiyle değiştirildikten sonra mesajın geri elde edilebilmesi amacıyla değiştirilen satır/sütunun çerçeve biti işaretlenir. Önerilen yöntemin steganografi analizlerine karşı performansı imge bozulma oranlarıyla ölçülmüş ve rastgele seçilen 100 imge ve gizli mesaj bitlerinde yapılan deneylerde Ortalama Karesel Hata ve Sinyal/Gürültü Oranı kullanılmıştır. Yöntemin literatürde yayınlanan imge uzayı tabanlı yöntemlerden daha düşük bozulma oranlarına sahip olduğu tespit edilmiştir

Abstract BAS: A Perceptual Shape Descriptor Based on the Beam Angle Statistics

The proposed shape descriptor is based on the beams originated from a boundary point, which are defined as lines connecting that point with the rest of the points on the boundary. At each point, the angle between a pair of beams is calculated to extract the topological structure of the boundary. Then, a shape descriptor is defined by using the third order statistics of all the beam angles in a set of neighborhood systems. It is shown that Beam Angle Statistics (BAS) is invariant to translation, rotation, scale and is insensitive to distortions. Experiments are done on the dataset of MPEG 7 Core Experiments Shape-1. It is observed that BAS outperforms the MPEG 7 shape descriptors