Computer-assisted algorithms have become a mainstay of biomedical applications to improve accuracy and reproducibility of repetitive tasks like manual segmentation and annotation. We propose a novel pipeline for red blood cell detection and counting in thin blood smear microscopy images, named RBCNet, using a dual deep learning architecture. RBCNet consists of a U-Net first stage for cell-cluster segmentation, followed by a second stage Faster R-CNN for detecting small cell objects within clusters, identified as connected components from the U-Net stage. RBCNet uses cell clustering instead of region proposals, which is robust to cell fragmentation, is highly scalable for detecting small objects or fine scale morphological structures in very large images, can be trained using non-overlapping tiles, and during inference is adaptive to the scale of cell-clusters with a low memory footprint. We tested our method on an archived collection of human malaria smears with nearly 200,000 labeled cells across 965 images from 193 patients, acquired in Bangladesh, with each patient contributing five images. Cell detection accuracy using RBCNet was higher than 97%. The novel dual cascade RBCNet architecture provides more accurate cell detections because the foreground cell-cluster masks from U-Net adaptively guide the detection stage, resulting in a notably higher true positive and lower false alarm rates, compared to traditional and other deep learning methods. The RBCNet pipeline implements a crucial step towards automated malaria diagnosis.

Current level-set based approaches for segmenting a large number of objects are computationally expensive since they require a unique level set per object (the N-level set paradigm), or \(\lceil\text{log}_2 N\rceil\) level sets when using a multiphase interface tracking formulation. Incorporating energy-based coupling constraints to control the topological interactions between level sets further increases the computational cost to O(N ²). We propose a new approach, with dramatic computational savings, that requires only four, or fewer, level sets for an arbitrary number of similar objects (like cells) using the Delaunay graph to capture spatial relationships. Even more significantly, the coupling constraints (energy-based and topological) are incorporated using just constant O(1) complexity. The explicit topological coupling constraint, based on predicting contour collisions between adjacent level sets, is developed to further prevent false merging or absorption of neighboring cells, and also reduce fragmentation during level set evolution. The proposed four-color level set algorithm is used to efficiently and accurately segment hundreds of individual epithelial cells within a moving monolayer sheet from time-lapse images of in vitro wound healing without any false merging of cells.

The rapid development of the Internet of Things (IoT) has fueled the widespread adoption of Unmanned Aerial Vehicles (UAVs) or drones across various fields, including their use in applications such as surveillance and monitoring. UAVs flight capabilities allow it to effortlessly access previously inaccessible locations, providing real-time, high-resolution data—images and videos—of any desired area or target. Furthermore, the growth of Artificial Intelligence (AI), and edge computing technologies has empowered UAVs with high computational capabilities, making them suitable for diverse applications such as agriculture, transportation and border security. These technology advancements also equip UAVs with powerful on-board processing for sophisticated decision-making that enhances UAV activeness and intelligence. This survey explores the promising areas of UAVs for intelligent active surveillance and monitoring across diverse applications. First, the various levels of UAV activeness within applications are discussed; second, prior research is examined to identify the key technologies and architectures that power intelligent UAV systems; and third, several UAV applications in surveillance and monitoring, ranging from basic tasks to highly intelligent operations are explored. Finally, the survey concludes by discussing emerging research challenges and outlines a guiding road map for future research of highly interdisciplinary and emerging areas in UAV-based systems for surveillance and monitoring.

Most existing image privacy protection works focus mainly on the privacy of photo owners and their friends, but lack the consideration of other people who are in the background of the photos and the related location privacy issues. When a person is in the background of someone else's photos, he/she may be unintentionally exposed to the public when the photo owner shares the photo online. Not only a single visited place could be exposed, attackers may also be able to piece together a person's travel route from images. In this paper, we propose a novel image privacy protection system, called LAMP, which aims to light up the location awareness for people during online image sharing. The LAMP system is based on a newly designed location-aware multi-party image access control model. Unlike previous works on small scales, the LAMP system is highly efficient and scalable as it can enforce privacy protection for billions of users on social networks in real time. The system automatically detects the user's occurrences on photos and replace the user's face with a synthetic face if he/she has location privacy concern at the photo location. A prototype was implemented to demonstrate its applicability in the real world.