4 publications
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Identifying Number Plates using Feature ExtractionBy SAURABH ALLAWADHI
Abstract: A number plate can be recognized completely only when the number plate on the vehicle is neither broken nor deliberately mutilated. This pose a hinderance in recognizing these types of plates. In order to overcome this problem, one has to either compare the characters recognized from the broken plate or directly compare the features of the given plate to the database. This helps in reducing time and space complexity. Various …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 5, Issue 2, 2019 · pp. 20–26 Read article →
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Feature Extraction: Comparing Feature ExtractorsBy Nitin Kumar, SAURABH ALLAWADHI, Aastha Sharma, Abhilash Shokeen
Abstract: Machine learning is getting fuzz nowadays a lot. In order to train the computer to recognize the set of images is the key factor. For this neural networks and classifiers are used to do the identification and recognition. Now do the matching feature points or the interest points are required which makes it to our area of research i.e. feature extractors. In our paper we compared various feature extractors Fast, …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 4, Issue 1, 2018 · pp. 12–17 Read article →
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Computing Effect of Gaussian Noise on Pixels: Testing on Red Color TriangleBy SAURABH ALLAWADHI, Priti Gangania
Abstract: Gaussian noise is the noise that effects the pixels of the image that are in the center i.e. its effect is due to change in light conditions. This paper tests the effect of gaussian noise on the red color image by graphically depicting the matrix representation of the image using mesh diagram in MATLAB. This paper proves that pixels in the center of the image are more affected when these …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 4, Issue 1, 2018 · pp. 7–11 Read article →
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Compare Effect of Gaussian Noise on RBG LineBy SAURABH ALLAWADHI, Priti Gangania
Abstract: Gaussian noise tends to come in digital images. It enters into images when there is low light condition or high light conditions. This paper deals with comparison of gaussian noise effect on red, green, blue line image by accounting Entropy, Signal to Noise Ratio, Peak Signal to Noise Ratio and Root Mean Square Error as performance parameters. This paper proves that Blue color is the least affected and green being …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 4, Issue 1, 2018 · pp. 1–6 Read article →