SIFT Based Feature Matching Algorithm for Cartoon Plagiarism Detection

Li, Dongxing; Jiazheng, Gong,; Li, De

doi:10.1007/978-981-16-8558-3_5

Cited by 2 publications

(1 citation statement)

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“…Once identified, SIFT computes a feature vector for each keypoint, encapsulating its distinctive visual information. These feature vectors are not only highly discriminative but also resistant to common image transformations, rendering them indispensable for tasks that demand robust and accurate feature extraction [24]. The concept of scale space involves applying a range of Gaussian filters with varying values for the sigma parameter to the target image.…”

Section: Extraction Of Sift Featuresmentioning

confidence: 99%

Diagnostic Tool for Diabetic Retinopathy using Thermal Eye Images

Rajagopalan,

Nirmala,

Vahini

et al. 2023

Proccedings of the 2023 International Conference on Quantitative InfraRed Thermography

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Diabetic Retinopathy (DR) occurs as an effect of Diabetes, affecting the retina causing loss of vision. Worldwide 27% of the diabetic individuals are estimated to have DR, leading to 0.4 million blindness. As the DR population increases every year, early diagnosis is the need of the hour to avoid further progression of the disease thereby preventing blindness. In this work, DR diagnosis is performed using thermal images of the eye. According to earlier research on the eye, the increase in ocular surface temperature caused by dilatation is less in individuals with diabetic retinopathy than in control participants, making it a possible parameter for DR diagnosis. This imaging modality is non-invasive and less time consuming making it an easier diagnostic tool for large scale screening. For this work real time images are acquired from both the eyes of 20 DR participants and 16 control participants, with a total of 62 thermal eye images. The significant features from the pre-processed thermal eye images are extracted using Scale invariant transform and Gabor Transform methods. The extracted features are processed using optimal Machine learning algorithms to classify DR images from normal images. The diagnostic accuracy of the proposed tools is 96%. The results indicate that the IR thermal imaging system could be an efficient method for the non-invasive and non-contact detection of thermal anomalies in the eyes of patients with diabetes.

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Section: Extraction Of Sift Featuresmentioning

confidence: 99%