184,550 research outputs found
Fast Detection of Curved Edges at Low SNR
Detecting edges is a fundamental problem in computer vision with many
applications, some involving very noisy images. While most edge detection
methods are fast, they perform well only on relatively clean images. Indeed,
edges in such images can be reliably detected using only local filters.
Detecting faint edges under high levels of noise cannot be done locally at the
individual pixel level, and requires more sophisticated global processing.
Unfortunately, existing methods that achieve this goal are quite slow. In this
paper we develop a novel multiscale method to detect curved edges in noisy
images. While our algorithm searches for edges over a huge set of candidate
curves, it does so in a practical runtime, nearly linear in the total number of
image pixels. As we demonstrate experimentally, our algorithm is orders of
magnitude faster than previous methods designed to deal with high noise levels.
Nevertheless, it obtains comparable, if not better, edge detection quality on a
variety of challenging noisy images.Comment: 9 pages, 11 figure
Outlier Edge Detection Using Random Graph Generation Models and Applications
Outliers are samples that are generated by different mechanisms from other
normal data samples. Graphs, in particular social network graphs, may contain
nodes and edges that are made by scammers, malicious programs or mistakenly by
normal users. Detecting outlier nodes and edges is important for data mining
and graph analytics. However, previous research in the field has merely focused
on detecting outlier nodes. In this article, we study the properties of edges
and propose outlier edge detection algorithms using two random graph generation
models. We found that the edge-ego-network, which can be defined as the induced
graph that contains two end nodes of an edge, their neighboring nodes and the
edges that link these nodes, contains critical information to detect outlier
edges. We evaluated the proposed algorithms by injecting outlier edges into
some real-world graph data. Experiment results show that the proposed
algorithms can effectively detect outlier edges. In particular, the algorithm
based on the Preferential Attachment Random Graph Generation model consistently
gives good performance regardless of the test graph data. Further more, the
proposed algorithms are not limited in the area of outlier edge detection. We
demonstrate three different applications that benefit from the proposed
algorithms: 1) a preprocessing tool that improves the performance of graph
clustering algorithms; 2) an outlier node detection algorithm; and 3) a novel
noisy data clustering algorithm. These applications show the great potential of
the proposed outlier edge detection techniques.Comment: 14 pages, 5 figures, journal pape
A framework for community detection in heterogeneous multi-relational networks
There has been a surge of interest in community detection in homogeneous
single-relational networks which contain only one type of nodes and edges.
However, many real-world systems are naturally described as heterogeneous
multi-relational networks which contain multiple types of nodes and edges. In
this paper, we propose a new method for detecting communities in such networks.
Our method is based on optimizing the composite modularity, which is a new
modularity proposed for evaluating partitions of a heterogeneous
multi-relational network into communities. Our method is parameter-free,
scalable, and suitable for various networks with general structure. We
demonstrate that it outperforms the state-of-the-art techniques in detecting
pre-planted communities in synthetic networks. Applied to a real-world Digg
network, it successfully detects meaningful communities.Comment: 27 pages, 10 figure
Detection of Airport Runway Edges using Line Detection Techniques
Airport runway detection is a vital aspect for both military and commercial applications. An algorithm to extract runway edges based on edge detection and line detection techniques is discussed. The runway images are initially enhanced by dilation, thresholding and edge detection. Based on some unique characteristics like the runway being gray with two white lines indicating the runway boundaries, long and continuous edges of the runway are considered to be straight lines. The straight lines are detected using Convolution operators pertaining to vertical, 45° or -45° lines. Hough Transform is then applied to fit only the pair of lines corresponding to the runway boundaries in certain orientations. The test results prove that combination of Convolution and Hough transform is very competent in detecting runway edges accurately
On Multistage Learning a Hidden Hypergraph
Learning a hidden hypergraph is a natural generalization of the classical
group testing problem that consists in detecting unknown hypergraph
by carrying out edge-detecting tests. In the given paper we
focus our attention only on a specific family of localized
hypergraphs for which the total number of vertices , the number of
edges , , and the cardinality of any edge ,
. Our goal is to identify all edges of by
using the minimal number of tests. We develop an adaptive algorithm that
matches the information theory bound, i.e., the total number of tests of the
algorithm in the worst case is at most . We also discuss
a probabilistic generalization of the problem.Comment: 5 pages, IEEE conferenc
A network for multiscale image segmentation
Detecting edges of objects in their images is a basic problem in computational vision. The scale-space technique introduced by Witkin [11] provides means of using local and global reasoning in locating edges. This approach has a major drawback: it is difficult to obtain accurately
the locations of the 'semantically meaningful' edges. We have refined the definition of scale-space, and introduced a class of algorithms for implementing it based on using anisotropic diffusion [9]. The algorithms involves simple, local operations replicated over the image making parallel
hardware implementation feasible. In this paper we present the
major ideas behind the use of scale space, and anisotropic diffusion for edge detection, we show that anisotropic diffusion can enhance edges, we suggest a network implementation of anisotropic diffusion, and provide
design criteria for obtaining networks performing scale space, and edge detection. The results of a software implementation are shown
Apparatus and process for microbial detection and enumeration
An apparatus and process for detecting and enumerating specific microorganisms from large volume samples containing small numbers of the microorganisms is presented. The large volume samples are filtered through a membrane filter to concentrate the microorganisms. The filter is positioned between two absorbent pads and previously moistened with a growth medium for the microorganisms. A pair of electrodes are disposed against the filter and the pad electrode filter assembly is retained within a petri dish by retainer ring. The cover is positioned on base of petri dish and sealed at the edges by a parafilm seal prior to being electrically connected via connectors to a strip chart recorder for detecting and enumerating the microorganisms collected on filter
Detecting Strong Ties Using Network Motifs
Detecting strong ties among users in social and information networks is a
fundamental operation that can improve performance on a multitude of
personalization and ranking tasks. Strong-tie edges are often readily obtained
from the social network as users often participate in multiple overlapping
networks via features such as following and messaging. These networks may vary
greatly in size, density and the information they carry. This setting leads to
a natural strong tie detection task: given a small set of labeled strong tie
edges, how well can one detect unlabeled strong ties in the remainder of the
network?
This task becomes particularly daunting for the Twitter network due to scant
availability of pairwise relationship attribute data, and sparsity of strong
tie networks such as phone contacts. Given these challenges, a natural approach
is to instead use structural network features for the task, produced by {\em
combining} the strong and "weak" edges. In this work, we demonstrate via
experiments on Twitter data that using only such structural network features is
sufficient for detecting strong ties with high precision. These structural
network features are obtained from the presence and frequency of small network
motifs on combined strong and weak ties. We observe that using motifs larger
than triads alleviate sparsity problems that arise for smaller motifs, both due
to increased combinatorial possibilities as well as benefiting strongly from
searching beyond the ego network. Empirically, we observe that not all motifs
are equally useful, and need to be carefully constructed from the combined
edges in order to be effective for strong tie detection. Finally, we reinforce
our experimental findings with providing theoretical justification that
suggests why incorporating these larger sized motifs as features could lead to
increased performance in planted graph models.Comment: To appear in Proceedings of WWW 2017 (Web-science track
Using the discrete hadamard transform to detect moving objects in surveillance video
In this paper we present an approach to object detection in surveillance video based on detecting moving edges
using the Hadamard transform. The proposed method is characterized by robustness to illumination changes
and ghosting effects and provides high speed detection, making it particularly suitable for surveillance applications.
In addition to presenting an approach to moving edge detection using the Hadamard transform, we
introduce two measures to track edge history, Pixel Bit Mask Difference (PBMD) and History Update Value
(H UV ) that help reduce the false detections commonly experienced by approaches based on moving edges.
Experimental results show that the proposed algorithm overcomes the traditional drawbacks of frame differencing
and outperforms existing edge-based approaches in terms of both detection results and computational
complexity
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