Trademark image retrieval using weighted combination of sift and HSV correlogram

Nigam, Akriti; Tripathi, Ramesh C.

doi:10.1504/ijcat.2016.077797

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…The experimental results reveal a 38  performance improvement over the first MATLAB implementation and a 1.33  performance improvement over the hardware implementation mistreatment the Xilinx System Generator tool. Nigam and Tripathi (2016) describe an efficient tool to automatise the method of trademark similarity checking at the time of registration. A mix of form and colour features has been illustrated here so pictures taken from completely different vantage points or levels make up an equivalent cluster.…”

Section: Related Workmentioning

confidence: 99%

Curvilinear tracing approach for recognition of Kannada sign language

Kagalkar

Gumaste

2019

IJCAT

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Sign languages are used as the main mode of communication for vocally disabled people; however, the diversity in sign symbol representation limits its usage to a particular region. There is a huge diversity in sign symbol representation from one country to another, one state to another. In India there are different sign languages observed for each state region. It is hence very difficult for one region individual to communicate to another using a significant symbol. This paper proposes a curvilinear tracing approach for shape representation and recognition of Kannada sign language to generate corresponding characters in Kannada language. To develop this approach, a dataset is created with all Swaragalu, Vyanjanagalu, and numbers in Kannada language. The dataset is formed by defining a vocabulary for different sign symbols used in common interfacing. In the representation of sign language for recognition, edge features of hand regions are considered to be an optimal feature representation of sign language.

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Section: Related Workmentioning

confidence: 99%

Curvilinear tracing approach for recognition of Kannada sign language

Kagalkar

Gumaste

2019

IJCAT

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“…Inspired by these works, this paper presents a new high-level feature for vibration signal analysis based on correlograms. The correlogram has been widely adopted in many fields, such as quality control [17] and image processing [18], and it has been demonstrated to be effective in the structural analysis/expression of data [19]. To the best of our knowledge scope, this is the first attempt using it to extract features in the analysis of machine vibration signals.…”

Section: Introductionmentioning

confidence: 99%

High-Level Feature Extraction based on Correlogram for State Monitoring of Rotating Machinery with Vibration Signals

Yang¹

2019

IJPE

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Vibration analysis is one of the most popular methods for state monitoring of rotating machines, and feature extraction is of much importance in the design of the monitoring system. In this paper, a new high-level feature extraction method based on correlograms for vibration signal analysis is proposed, and it includes two phases. Firstly, in the learning process, a codebook is created from training data using the k-means algorithm. Next, in the testing process, for a given data stream collected from a monitoring rotating machine, the correlogram in each cycle is obtained by comparing every data point with all codewords in the codebook at first; the entropy is then computed to form final high-level features to measure the state of the machine. A change decision can be made finally based on features extracted from null hypothesis testing. Based on an experimental setup used in our previous work, the proposed method is evaluated with application to the speed change monitoring of a rotating machine. Experimental results demonstrate the excellent performance and the priority of the method compared with ten typical features.

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