1,844 research outputs found
ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking
Physical intuition is pivotal for intelligent agents to perform complex
tasks. In this paper we investigate the passive acquisition of an intuitive
understanding of physical principles as well as the active utilisation of this
intuition in the context of generalised object stacking. To this end, we
provide: a simulation-based dataset featuring 20,000 stack configurations
composed of a variety of elementary geometric primitives richly annotated
regarding semantics and structural stability. We train visual classifiers for
binary stability prediction on the ShapeStacks data and scrutinise their
learned physical intuition. Due to the richness of the training data our
approach also generalises favourably to real-world scenarios achieving
state-of-the-art stability prediction on a publicly available benchmark of
block towers. We then leverage the physical intuition learned by our model to
actively construct stable stacks and observe the emergence of an intuitive
notion of stackability - an inherent object affordance - induced by the active
stacking task. Our approach performs well even in challenging conditions where
it considerably exceeds the stack height observed during training or in cases
where initially unstable structures must be stabilised via counterbalancing.Comment: revised version to appear at ECCV 201
Extraction of Vehicle Groups in Airborne Lidar Point Clouds with Two-Level Point Processes
In this paper we present a new object based hierarchical model for joint probabilistic extraction of vehicles and groups of corresponding vehicles - called traffic segments - in airborne Lidar point clouds collected from dense urban areas. Firstly, the 3-D point set is classified into terrain, vehicle, roof, vegetation and clutter classes. Then the points with the corresponding class labels and echo strength (i.e. intensity) values are projected to the ground. In the obtained 2-D class and intensity maps we approximate the top view projections of vehicles by rectangles. Since our tasks are simultaneously the extraction of the rectangle population which describes the position, size and orientation of the vehicles and grouping the vehicles into the traffic segments, we propose a hierarchical, Two-Level Marked Point Process (L2MPP) model for the problem. The output vehicle and traffic segment configurations are extracted by an iterative stochastic optimization algorithm. We have tested the proposed method with real data of a discrete return Lidar sensor providing up to four range measurements for each laser pulse. Using manually annotated Ground Truth information on a data set containing 1009 vehicles, we provide quantitative evaluation results showing that the L2MPP model surpasses two earlier grid-based approaches, a 3-D point-cloud-based process and a single layer MPP solution. The accuracy of the proposed method measured in F-rate is 97% at object level, 83% at pixel level and 95% at group level
Approaches Used to Recognise and Decipher Ancient Inscriptions: A Review
Inscriptions play a vital role in historical studies. In order to boost tourism and academic necessities, archaeological experts, epigraphers and researchers recognised and deciphered a great number of inscriptions using numerous approaches. Due to the technological revolution and inefficiencies of manual methods, humans tend to use automated systems. Hence, computational archaeology plays an important role in the current era. Even though different types of research are conducted in this domain, it still poses a big challenge and needs more accurate and efficient methods. This paper presents a review of manual and computational approaches used to recognise and decipher ancient inscriptions.Keywords: ancient inscriptions, computational archaeology, decipher, script
Improved YOLOv8 Detection Algorithm in Security Inspection Image
Security inspection is the first line of defense to ensure the safety of
people's lives and property, and intelligent security inspection is an
inevitable trend in the future development of the security inspection industry.
Aiming at the problems of overlapping detection objects, false detection of
contraband, and missed detection in the process of X-ray image detection, an
improved X-ray contraband detection algorithm CSS-YOLO based on YOLOv8s is
proposed.Comment: 23 pages,23 figure
Cooperative localization by dual foot-mounted inertial sensors and inter-agent ranging
The implementation challenges of cooperative localization by dual
foot-mounted inertial sensors and inter-agent ranging are discussed and work on
the subject is reviewed. System architecture and sensor fusion are identified
as key challenges. A partially decentralized system architecture based on
step-wise inertial navigation and step-wise dead reckoning is presented. This
architecture is argued to reduce the computational cost and required
communication bandwidth by around two orders of magnitude while only giving
negligible information loss in comparison with a naive centralized
implementation. This makes a joint global state estimation feasible for up to a
platoon-sized group of agents. Furthermore, robust and low-cost sensor fusion
for the considered setup, based on state space transformation and
marginalization, is presented. The transformation and marginalization are used
to give the necessary flexibility for presented sampling based updates for the
inter-agent ranging and ranging free fusion of the two feet of an individual
agent. Finally, characteristics of the suggested implementation are
demonstrated with simulations and a real-time system implementation.Comment: 14 page
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