Pill Recognition Using Imprint Information by Two-Step Sampling Distance Sets

Yu, Jingyi; Chen, Zhiyuan; Kamata, Sei‐ichiro

doi:10.1109/icpr.2014.544

Cited by 22 publications

(12 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A preliminary version of our system is described in [17]. Here are the expansion parts in this work: The algorithm of TSDS has been improved.…”

Section: Automatic Pill Recognition Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Accurate system for automatic pill recognition using imprint information

Chen

Kamata

et al. 2015

IET Image Processing

Self Cite

View full text Add to dashboard Cite

“…A preliminary version of our system is described in [17]. Here are the expansion parts in this work: The algorithm of TSDS has been improved.…”

Section: Automatic Pill Recognition Methodsmentioning

confidence: 99%

“…Comparing with [17], imprint extraction part in this work has been improved by accumulated gradient Calculation and LBP image segmentation step. In addition, two-step sampling strategy in TSDS has also been modified.…”

Section: Comparison With Other Recognition Algorithmsmentioning

confidence: 99%

Accurate system for automatic pill recognition using imprint information

Chen

Kamata

et al. 2015

IET Image Processing

Self Cite

View full text Add to dashboard Cite

“…For instance, the Hu moment [20] was applied [11,31], because of its rotationinvariant properties. Other methods [7,47,61,62] were proposed to generate imprint features, recognizing the importance of imprints for pill identification. The past methods achieved remarkable success; however, the lack of benchmarks prevents us from developing methods for realworld low-shot settings.…”

Section: Related Workmentioning

confidence: 99%

“…created a 5284image dataset of 400 pill types with a QR-like board to rectify geometric and color distortions. Yu et al[61] collected 12,500 images of 2500 pill categories. Unfortunately, nei-…”

mentioning

confidence: 99%

ePillID Dataset: A Low-Shot Fine-Grained Benchmark for Pill Identification

Usuyama¹,

Delgado²,

Hall³

et al. 2020

Preprint

View full text Add to dashboard Cite

Identifying prescription medications is a frequent task for patients and medical professionals; however, this is an error-prone task as many pills have similar appearances (e.g. white round pills), which increases the risk of medication errors. In this paper, we introduce ePillID, the largest public benchmark on pill image recognition, composed of 13k images representing 8184 appearance classes (two sides for 4092 pill types). For most of the appearance classes, there exists only one reference image, making it a challenging low-shot recognition setting. We present our experimental setup and evaluation results of various baseline models on the benchmark. The best baseline using a multi-head metric-learning approach with bilinear features performed remarkably well; however, our error analysis suggests that they still fail to distinguish particularly confusing classes. The code and data are available at https: //github.com/usuyama/ePillID-benchmark.

show abstract

“…11,13,16,18], and (iii) Web application [5,6,9,14,17]. The key principle that most of these applications used for distinguishing pills is the use of physical characteristics of the pills such as color, shape, dimension, texture, score, and/or imprint, provided by the user or extracted by some image processing techniques.…”

Section: Introductionmentioning

confidence: 99%

iPill: An iOS Mobile Application for Automatic Pill Identification Based on Image Processing Technique and Eigenface Algorithm

2017

IJCCIE

View full text Add to dashboard Cite

Abstract-Consumable medical drugs are currently available in a variety of forms such as liquid, capsule, and tablet. For the tablet/pill form, many of them are manufactured in very similar appearances, which could lead to incorrect medical consumption, particularly for elderly and visually-impaired people. For the past several years, a number of software applications have been developed by both academic and business sectors to facilitate pill identification. These tools are ranged from Web-based applications, desktop applications, and to mobile applications. The key approach used by most of these tools is using image processing techniques to extract pill characteristics from a pill's image, converting these features into some numerical forms such as a vector model, and comparing the features with those stored in the database using some similarity or distance measures to determine a matched pill. Rather than using direct pixel-based characteristics and vector model for pill comparisons, an investigation of bringing in a face recognition algorithm to assist pill identification is presented in this paper. The proposed method exploits a commonly-known face recognition algorithm, known as Eigenface algorithm, to mathematically derive features from pills' images. These acquired features are used for determining the pill's shape, which are then combined with pill's color as criteria for the overall process of pill identification. In addition, the proposed technique is implemented as an iOS mobile application, named iPill, for an automatic pill identification. A preliminary result using 320 pill images from 20 types of pills with 9 different shapes and 10 dissimilar colors shows that iPill mobile application gives a satisfactory performance on the accuracy for automatic pill identifications.

show abstract

Pill Recognition Using Imprint Information by Two-Step Sampling Distance Sets

Cited by 22 publications

References 14 publications

Accurate system for automatic pill recognition using imprint information

Accurate system for automatic pill recognition using imprint information

ePillID Dataset: A Low-Shot Fine-Grained Benchmark for Pill Identification

iPill: An iOS Mobile Application for Automatic Pill Identification Based on Image Processing Technique and Eigenface Algorithm

Contact Info

Product

Resources

About