8,086 research outputs found
Event-Based Motion Segmentation by Motion Compensation
In contrast to traditional cameras, whose pixels have a common exposure time,
event-based cameras are novel bio-inspired sensors whose pixels work
independently and asynchronously output intensity changes (called "events"),
with microsecond resolution. Since events are caused by the apparent motion of
objects, event-based cameras sample visual information based on the scene
dynamics and are, therefore, a more natural fit than traditional cameras to
acquire motion, especially at high speeds, where traditional cameras suffer
from motion blur. However, distinguishing between events caused by different
moving objects and by the camera's ego-motion is a challenging task. We present
the first per-event segmentation method for splitting a scene into
independently moving objects. Our method jointly estimates the event-object
associations (i.e., segmentation) and the motion parameters of the objects (or
the background) by maximization of an objective function, which builds upon
recent results on event-based motion-compensation. We provide a thorough
evaluation of our method on a public dataset, outperforming the
state-of-the-art by as much as 10%. We also show the first quantitative
evaluation of a segmentation algorithm for event cameras, yielding around 90%
accuracy at 4 pixels relative displacement.Comment: When viewed in Acrobat Reader, several of the figures animate. Video:
https://youtu.be/0q6ap_OSBA
Accelerated hardware video object segmentation: From foreground detection to connected components labelling
This is the preprint version of the Article - Copyright @ 2010 ElsevierThis paper demonstrates the use of a single-chip FPGA for the segmentation of moving objects in a video sequence. The system maintains highly accurate background models, and integrates the detection of foreground pixels with the labelling of objects using a connected components algorithm. The background models are based on 24-bit RGB values and 8-bit gray scale intensity values. A multimodal background differencing algorithm is presented, using a single FPGA chip and four blocks of RAM. The real-time connected component labelling algorithm, also designed for FPGA implementation, run-length encodes the output of the background subtraction, and performs connected component analysis on this representation. The run-length encoding, together with other parts of the algorithm, is performed in parallel; sequential operations are minimized as the number of run-lengths are typically less than the number of pixels. The two algorithms are pipelined together for maximum efficiency
Visual Importance-Biased Image Synthesis Animation
Present ray tracing algorithms are computationally intensive, requiring hours of computing time for complex scenes. Our previous work has dealt with the development of an overall approach to the application of visual attention to progressive and adaptive ray-tracing techniques. The approach facilitates large computational savings by modulating the supersampling rates in an image by the visual importance of the region being rendered. This paper extends the approach by incorporating temporal changes into the models and techniques developed, as it is expected that further efficiency savings can be reaped for animated scenes. Applications for this approach include entertainment, visualisation and simulation
Hierarchical fuzzy logic based approach for object tracking
In this paper a novel tracking approach based on fuzzy concepts is introduced. A methodology for both single and multiple object tracking is presented. The aim of this methodology is to use these concepts as a tool to, while maintaining the needed accuracy, reduce the complexity usually involved in object tracking problems. Several dynamic fuzzy sets are constructed according to both kinematic and non-kinematic properties that distinguish the object to be tracked. Meanwhile kinematic related fuzzy sets model the object's motion pattern, the non-kinematic fuzzy sets model the object's appearance. The tracking task is performed through the fusion of these fuzzy models by means of an inference engine. This way, object detection and matching steps are performed exclusively using inference rules on fuzzy sets. In the multiple object methodology, each object is associated with a confidence degree and a hierarchical implementation is performed based on that confidence degree.info:eu-repo/semantics/publishedVersio
Interaction between high-level and low-level image analysis for semantic video object extraction
Authors of articles published in EURASIP Journal on Advances in Signal Processing are the copyright holders of their articles and have granted to any third party, in advance and in perpetuity, the right to use, reproduce or disseminate the article, according to the SpringerOpen copyright and license agreement (http://www.springeropen.com/authors/license)
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