13,092 research outputs found
System Description for a Scalable, Fault-Tolerant, Distributed Garbage Collector
We describe an efficient and fault-tolerant algorithm for distributed cyclic
garbage collection. The algorithm imposes few requirements on the local
machines and allows for flexibility in the choice of local collector and
distributed acyclic garbage collector to use with it. We have emphasized
reducing the number and size of network messages without sacrificing the
promptness of collection throughout the algorithm. Our proposed collector is a
variant of back tracing to avoid extensive synchronization between machines. We
have added an explicit forward tracing stage to the standard back tracing stage
and designed a tuned heuristic to reduce the total amount of work done by the
collector. Of particular note is the development of fault-tolerant cooperation
between traces and a heuristic that aggressively reduces the set of suspect
objects.Comment: 47 pages, LaTe
The aceToolbox: low-level audiovisual feature extraction for retrieval and classification
In this paper we present an overview of a software platform
that has been developed within the aceMedia project,
termed the aceToolbox, that provides global and local lowlevel feature extraction from audio-visual content. The toolbox is based on the MPEG-7 eXperimental Model (XM),
with extensions to provide descriptor extraction from arbitrarily shaped image segments, thereby supporting local descriptors reflecting real image content. We describe the architecture of the toolbox as well as providing an overview of the descriptors supported to date. We also briefly describe the segmentation algorithm provided. We then demonstrate the usefulness of the toolbox in the context of two different content processing scenarios: similarity-based retrieval in large collections and scene-level classification of still images
Unfolding Shape Graphs
Shape graphs have been introduced in [Ren04a, Ren04b] as an abstraction to be used in model checking object oriented software, where states of the system are represented as graphs. Intuitively, the graphs modeling the states represent the structure of objects dynamically allocated in the heap. State transitions are then generated by applying graph transformation rules corresponding to the statements of the program. Since the state space of such systems is potentially unbounded, the graphs representing the states are abstracted by shape graphs. Graph transformation systems may be analyzed [BCK01, BK02] by constructing finite structures that approximate their behaviour with arbitrary accuracy, by using techniques developed in the context of Petri nets. The approach of [BK02] is to construct a chain of finite under-approximations of the Winskel’s style unfolding of a graph grammar, as well as a chain of finite over-approximations of the unfolding, where both chains converge to the full unfolding. The approximations may then be used to check properties of the underlying graph transformation system. We apply this technique to approximate the behaviour of systems represented by shape graphs and graph tranformation rules
Algorithmic Perception of Vertices in Sketched Drawings of Polyhedral Shapes
In this article, visual perception principles were used to build an artificial perception model aimed at developing an algorithm for detecting junctions in line drawings of polyhedral objects that are vectorized from hand-drawn sketches. The detection is performed in two dimensions (2D), before any 3D model is available and minimal information about the shape depicted by the sketch is used. The goal of this approach is to not only detect junctions in careful sketches created by skilled engineers and designers but also detect junctions when skilled people draw casually to quickly convey rough ideas. Current approaches for extracting junctions from digital images are mostly incomplete, as they simply merge endpoints that are near each other, thus ignoring the fact that different vertices may be represented by different (but close) junctions and that the endpoints of lines that depict edges that share a common vertex may not necessarily be close to each other, particularly in quickly sketched drawings. We describe and validate a new algorithm that uses these perceptual findings to merge tips of line segments into 2D junctions that are assumed to depict 3D vertices
Hierarchical Salient Object Detection for Assisted Grasping
Visual scene decomposition into semantic entities is one of the major
challenges when creating a reliable object grasping system. Recently, we
introduced a bottom-up hierarchical clustering approach which is able to
segment objects and parts in a scene. In this paper, we introduce a transform
from such a segmentation into a corresponding, hierarchical saliency function.
In comprehensive experiments we demonstrate its ability to detect salient
objects in a scene. Furthermore, this hierarchical saliency defines a most
salient corresponding region (scale) for every point in an image. Based on
this, an easy-to-use pick and place manipulation system was developed and
tested exemplarily.Comment: Accepted for ICRA 201
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