4,251 research outputs found
Planogram Compliance Checking Based on Detection of Recurring Patterns
In this paper, a novel method for automatic planogram compliance checking in
retail chains is proposed without requiring product template images for
training. Product layout is extracted from an input image by means of
unsupervised recurring pattern detection and matched via graph matching with
the expected product layout specified by a planogram to measure the level of
compliance. A divide and conquer strategy is employed to improve the speed.
Specifically, the input image is divided into several regions based on the
planogram. Recurring patterns are detected in each region respectively and then
merged together to estimate the product layout. Experimental results on real
data have verified the efficacy of the proposed method. Compared with a
template-based method, higher accuracies are achieved by the proposed method
over a wide range of products.Comment: Accepted by MM (IEEE Multimedia Magazine) 201
Detection of Dispersed Radio Pulses: A machine learning approach to candidate identification and classification
Searching for extraterrestrial, transient signals in astronomical data sets
is an active area of current research. However, machine learning techniques are
lacking in the literature concerning single-pulse detection. This paper
presents a new, two-stage approach for identifying and classifying dispersed
pulse groups (DPGs) in single-pulse search output. The first stage identified
DPGs and extracted features to characterize them using a new peak
identification algorithm which tracks sloping tendencies around local maxima in
plots of signal-to-noise ratio vs. dispersion measure. The second stage used
supervised machine learning to classify DPGs. We created four benchmark data
sets: one unbalanced and three balanced versions using three different
imbalance treatments.We empirically evaluated 48 classifiers by training and
testing binary and multiclass versions of six machine learning algorithms on
each of the four benchmark versions. While each classifier had advantages and
disadvantages, all classifiers with imbalance treatments had higher recall
values than those with unbalanced data, regardless of the machine learning
algorithm used. Based on the benchmarking results, we selected a subset of
classifiers to classify the full, unlabelled data set of over 1.5 million DPGs
identified in 42,405 observations made by the Green Bank Telescope. Overall,
the classifiers using a multiclass ensemble tree learner in combination with
two oversampling imbalance treatments were the most efficient; they identified
additional known pulsars not in the benchmark data set and provided six
potential discoveries, with significantly less false positives than the other
classifiers.Comment: 13 pages, accepted for publication in MNRAS, ref. MN-15-1713-MJ.R
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