The Correction Algorithm Research of Inclination Paper Currency Image

2013 ◽  
Vol 756-759 ◽  
pp. 1464-1468
Author(s):  
Hong Hai Liu ◽  
Xiang Hua Hou
Keyword(s):  
Image Recognition ◽  
Inclination Angle ◽  
Experimental Results ◽  
Correction Algorithm ◽  
Recognition Algorithms ◽  
Paper Currency ◽  
Rotation Transformation ◽  
Straight Lines

There exist low recognition speed and non-ideal recognition effect in some recognition algorithms of paper currency value. One of very important reasons is that the shooting angle makes the image inclined. This paper firstly analyses the binarization processing of RMB 100-Yuan image and then the method of acquiring straight lines in image is discussed. Thus, the inclination angle of image is calculated by using the obtained straight lines. Finally, through rotation transformation, the inclination image is corrected. The experimental results show that this algorithm has a good corrected effect to the inclination image of paper currency and improves the image recognition effect.

Fast and accurate image recognition algorithms for fresh produce food safety sensing

2011 ◽  
Author(s):  
Chun-Chieh Yang ◽  
Moon S. Kim ◽  
Kuanglin Chao ◽  
Sukwon Kang ◽  
Alan M. Lefcourt
Keyword(s):  
Food Safety ◽  
Image Recognition ◽  
Fresh Produce ◽  
Recognition Algorithms

scholarly journals Synthetic heparan sulfate standards and machine learning facilitate the development of solid-state nanopore analysis

2021 ◽  
Vol 118 (11) ◽  
pp. e2022806118
Author(s):  
Ke Xia ◽  
James T. Hagan ◽  
Li Fu ◽  
Brian S. Sheetz ◽  
Somdatta Bhattacharya ◽  
...  
Keyword(s):  
Machine Learning ◽  
Solid State ◽  
Nucleic Acid ◽  
Heparan Sulfate ◽  
Image Recognition ◽  
Single Molecule ◽  
Monosaccharide Composition ◽  
Minimal Representation ◽  
Recognition Algorithms ◽  
Potential Use

The application of solid-state (SS) nanopore devices to single-molecule nucleic acid sequencing has been challenging. Thus, the early successes in applying SS nanopore devices to the more difficult class of biopolymer, glycosaminoglycans (GAGs), have been surprising, motivating us to examine the potential use of an SS nanopore to analyze synthetic heparan sulfate GAG chains of controlled composition and sequence prepared through a promising, recently developed chemoenzymatic route. A minimal representation of the nanopore data, using only signal magnitude and duration, revealed, by eye and image recognition algorithms, clear differences between the signals generated by four synthetic GAGs. By subsequent machine learning, it was possible to determine disaccharide and even monosaccharide composition of these four synthetic GAGs using as few as 500 events, corresponding to a zeptomole of sample. These data suggest that ultrasensitive GAG analysis may be possible using SS nanopore detection and well-characterized molecular training sets.

scholarly journals Hidden Worlds

IDEA JOURNAL ◽  
2020 ◽  
Vol 17 (02) ◽  
pp. 275-288
Author(s):  
J Rosenbaum
Keyword(s):  
Machine Learning ◽  
Augmented Reality ◽  
Image Recognition ◽  
Learning Systems ◽  
Transgender Identity ◽  
Masculinity And Femininity ◽  
Recognition Algorithms ◽  
Difficult Time ◽  
Gender Minorities ◽  
The Way

This art project examines non-binary and transgender identity through training machines to generate art based on Greek and Roman statuary. The statuary is binary in nature and appeals to the concept of pinnacles of masculinity and femininity but what of those of us who fall between, what of transgender bodies, gender non-conforming and non-binary bodies and intersex bodies?  Image recognition algorithms have a difficult time classifying people who fall outside the binary, those who don’t pass as cisgender and those who present in neutral or subversive ways. As image recognition becomes more prevalent, we need to have a past and a future for everyone who doesn’t fit neatly into one of the only two boxes on offer. We need to open up the categories, allow people to self-identify or to scrap the concept of gendering people mechanically all together. As a spatial installation, Hidden Worlds also explores the embodiment of interactive augmented reality bodies in the space between physical and digital worlds. I have worked with a classifier and some deliberately abstract figure works, generated by machine, to explore where gender is assigned in the process and what it looks like when you aren’t neatly classified, and the disconnect that is felt when misgendered. The generated captions have flipped around gender and as the figure resolves and each section is submitted to the narrative writer you see a different set of pronouns, a disconnection between what you see and what you hear. I will explore the assumptions we make about classical art; the way it can inform how we represent gender minorities going forward and how art can illustrate the gaps that exist in the training of these important machine learning systems.

scholarly journals Sharing taxonomic expertise between natural history collections using image recognition

2021 ◽  
Author(s):  
Michael Greeff ◽  
Max Caspers ◽  
Vincent Kalkman ◽  
Luc Willemse ◽  
Barry Sunderland ◽  
...  
Keyword(s):  
Natural History ◽  
Image Recognition ◽  
Historical Data ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Biodiversity Research ◽  
The Past ◽  
Recognition Algorithms ◽  
Natural History Collections ◽  
Sheer Number

Natural history collections play a vital role in biodiversity research and conservation by providing a window to the past. The usefulness of the vast amount of historical data depends on their quality, with correct taxonomic identifications being the most critical. The identification of many of the objects of natural history collections, however, is wanting, doubtful or outdated. Providing correct identifications is difficult given the sheer number of objects and the scarcity of expertise. Here we outline the construction of an ecosystem for the collaborative development and exchange of image recognition algorithms designed to support the identification of objects. Such an ecosystem will facilitate sharing taxonomic expertise among institutions by offering image datasets that are correctly identified by their in-house taxonomic experts. Together with openly accessible machine learning algorithms and easy to use workbenches, this will allow other institutes to train image recognition algorithms and thereby compensate for the lacking expertise.

PATTERN RECOGNITION ALGORITHMS FOR THE NAVIGATION OF MOBILE PLATFORM

2014 ◽  
pp. 117-137
Author(s):  
Rafał Jachowicz ◽  
Sylwester Błaszczyk ◽  
Piotr Duch ◽  
Maciej Łaski ◽  
Adam Wulkiewicz ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Mobile Platform ◽  
Recognition Algorithms

Construction of a Dataset of Stored-grain Insects Images for Intelligent Monitoring

2019 ◽  
Vol 35 (4) ◽  
pp. 647-655 ◽  
Author(s):  
Jiangtao Li ◽  
Huiling Zhou ◽  
Digvir S. Jayas ◽  
Qingxuan Jia
Keyword(s):  
Image Recognition ◽  
Common Species ◽  
Stored Grain ◽  
Intelligent Monitoring ◽  
Fine Grained ◽  
Detection Algorithms ◽  
Recognition Algorithms ◽  
Image Dataset ◽  
Bounding Boxes ◽  
Stored Grain Insects

Abstract. We constructed an image dataset of adults of 10 common species of stored-grain insects. This dataset is very significant for the research on image recognition algorithms for stored-grain insects, in order to implement intelligent monitoring for insects in warehouses. Images were collected using two kinds of devices: a developed automatic insect image acquisition device that can be fitted with different traps in warehouses and the commonly used smart phones. The images in this dataset contained 10 species of insect instances with various sizes, poses, and orientations. Each image corresponded to an xml file to store the species names and bounding boxes of insect instances in images. In total, 3,757 images were collected, and 159,238 insect instances were marked. The fine-grained classification algorithm based on Bilinear CNN and the object detection algorithms based on Faster R-CNN were adopted as baseline algorithms for benchmark experiments. Experiment results indicated that this dataset could support the research of image recognition algorithms of stored-grain insects, but it is a challenging task to detect small, adhesive and overlapped insect instances in images of this dataset. Currently, this dataset can be accessed at rgbinsect.cn. Keywords: Bilinear CNN, Faster R-CNN, Image dataset, Image recognition, Monitoring, Stored-grain Insects.

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