4 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
Planogram compliance checking based on detection of recurring patterns
© 1994-2012 IEEE. In this article, the authors propose a novel method for automatic planogram compliance checking in retail chains that doesn't require 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
Machine Learning in Sensors and Imaging
Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens