5,031 research outputs found
Improving Small Object Proposals for Company Logo Detection
Many modern approaches for object detection are two-staged pipelines. The
first stage identifies regions of interest which are then classified in the
second stage. Faster R-CNN is such an approach for object detection which
combines both stages into a single pipeline. In this paper we apply Faster
R-CNN to the task of company logo detection. Motivated by its weak performance
on small object instances, we examine in detail both the proposal and the
classification stage with respect to a wide range of object sizes. We
investigate the influence of feature map resolution on the performance of those
stages.
Based on theoretical considerations, we introduce an improved scheme for
generating anchor proposals and propose a modification to Faster R-CNN which
leverages higher-resolution feature maps for small objects. We evaluate our
approach on the FlickrLogos dataset improving the RPN performance from 0.52 to
0.71 (MABO) and the detection performance from 0.52 to 0.67 (mAP).Comment: 8 Pages, ICMR 201
Open Set Logo Detection and Retrieval
Current logo retrieval research focuses on closed set scenarios. We argue
that the logo domain is too large for this strategy and requires an open set
approach. To foster research in this direction, a large-scale logo dataset,
called Logos in the Wild, is collected and released to the public. A typical
open set logo retrieval application is, for example, assessing the
effectiveness of advertisement in sports event broadcasts. Given a query sample
in shape of a logo image, the task is to find all further occurrences of this
logo in a set of images or videos. Currently, common logo retrieval approaches
are unsuitable for this task because of their closed world assumption. Thus, an
open set logo retrieval method is proposed in this work which allows searching
for previously unseen logos by a single query sample. A two stage concept with
separate logo detection and comparison is proposed where both modules are based
on task specific CNNs. If trained with the Logos in the Wild data, significant
performance improvements are observed, especially compared with
state-of-the-art closed set approaches.Comment: accepted at VISAPP 201
Lake Mead Science Symposium, January 13 an 14, 2009, Las Vegas, Nevada: Program
Conference program for the 2009 Lake Mead Science Symposium. Includes abstracts of presentations, registration packet, exhibitor and sponsor information
The Scent of Change: A Case Study
Decisions about entering into a new business venture involve a variety of considerations, despite the level of experience an entrepreneur has. This case presents the story of a business owner Bennett Gage and his decisions concerning whether or not he should enter into a business where canines are used to detect bed bugs in hotels. This case study gives the reader an opportunity to wrestle with some of the many questions that are part of entering into the creation of a new service
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