Heritage app

Panda, Jayaguru; Sharma, Shashank; Jawahar, C. V.

doi:10.1145/2425333.2425336

Cited by 12 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to confirm image similarity, we check whether the keypoints in the test image are in spatial consistency with the retrieved images. We use the popular method of geometric verification (GV) by fitting fundamental matrix (adopted from [16]) to find out the number of keypoints of the test image that are spatially consistent with those of the retrieved images.…”

Section: ) Spatial Re-rankingmentioning

confidence: 99%

Currency Recognition on Mobile Phones

Singh

Choudhury

Kumar³

et al. 2014

2014 22nd International Conference on Pattern Recognition

View full text Add to dashboard Cite

Abstract-In this paper, we present an application for recognizing currency bills using computer vision techniques, that can run on a low-end smartphone. The application runs on the device without the need for any remote server. It is intended for robust, practical use by the visually impaired. Though we use the paper bills of Indian National Rupee (|) as a working example, our method is generic and scalable to multiple domains including those beyond the currency bills. Our solution uses a visual Bag of Words (BoW) based method for recognition. To enable robust recognition in a cluttered environment, we first segment the bill from the background using an algorithm based on iterative graph cuts. We formulate the recognition problem as an instance retrieval task. This is an example of fine-grained instance retrieval that can run on mobile devices. We evaluate the performance on a set of images captured in diverse natural environments, and report an accuracy of 96.7% on 2584 images.

show abstract

Section: ) Spatial Re-rankingmentioning

confidence: 99%