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(searched for: doi:10.13176/11.205)
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Ling Yang, Yun Wang, Zhongke Wang, Yang Qi, Yong Li, Zhipeng Yang, Wenle Chen
EURASIP Journal on Wireless Communications and Networking, Volume 2020, pp 1-15; https://doi.org/10.1186/s13638-020-01769-3

Abstract:
It is not denied that real-time monitoring of radar products is an important part in actual meteorological operations. But the weather radar often brings out abnormal radar echoes due to various factors, such as climate and hardware failure. So it is of great practical significance and research value to realize automatic identification of radar anomaly products. However, the traditional algorithms to identify anomalies of weather radar echo images are not the most accurate and efficient. In order to improve the efficiency of the anomaly identification, a novel method combining the theory of classical image processing and deep learning was proposed. The proposed method mainly includes three parts: coordinate transformation, integral projection, and classification using deep learning. Furthermore, extensive experiments have been done to validate the performance of the new algorithm. The results show that the recognition rate of the proposed method can reach up to more than 95%, which can successfully achieve the goal of screening abnormal radar echo images; also, the computation speed of it is fairly satisfactory.
Yuqing Xiao, Jie Cao, Zihan Wang, Qun Hao, Haoyong Yu, Qiang Luo
Tenth International Symposium on Precision Engineering Measurements and Instrumentation, Volume 11053; https://doi.org/10.1117/12.2511338

Abstract:
A novel super resolution reconstruction method is proposed to improve super resolution image performances. The proposed method uses bionic vision sampling model to obtain low resolution images and performs super resolution reconstruction in logarithmic polar coordinates. We carry out comparative experiments between the proposed method and the traditional method in terms of Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Mean Squared Error (MSE). The results show that the performances of proposed method are better than that of the traditional method. Especially the SSIM of global image (butterfly), the proposed method is 34.45% higher than the traditional method.
Ling Yang, Yun Wang, Zhongke Wang, Yang Qi, Yong Li, Zhipeng Yang, Wenle Chen
Lecture Notes in Electrical Engineering pp 2305-2312; https://doi.org/10.1007/978-981-10-6571-2_281

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S. Mohamed Mansoor Roomi, R. Raja, D. Kalaiyarasi
International Journal of Pattern Recognition and Artificial Intelligence, Volume 28; https://doi.org/10.1142/s0218001414540032

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, Chou-Chen Wang, Jiann-Shu Lee
Engineering Applications of Artificial Intelligence, Volume 26, pp 2215-2226; https://doi.org/10.1016/j.engappai.2013.06.019

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Youcef Brik, , Et-Tahir Zemouri,
2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA) pp 194-199; https://doi.org/10.1109/ispa.2013.6703738

Abstract:
In this paper we propose a system for word spotting in Arabic historical document using Ridgelet transform and Dynamic Time Warping (DTW). First, a preprocessing and segmentation processes are applied to all document pages to create a word image dataset. Keeping each word into its original size, Ridgelet descriptor is generated without applying the normalization criteria for Radon transform, where the rotation, translation and scaling invariance is achieved. Therefore, DTW algorithm is employed to match corresponding projection angle pairs from Ridgelet descriptor, while avoiding problems associated with dimensionality reduction of descriptor sets into one vector which cause a loss of useful information. Experiments were conducted on historical Arabic document from the National library. The obtained results showed the effectiveness of the proposed method.
Iqbal Quraishi, Arindam Das, Saikat Roy, Aruneema Das, Sandip Roy
2013 World Congress on Computer and Information Technology (WCCIT) pp 1-6; https://doi.org/10.1109/wccit.2013.6618680

Abstract:
This paper proposes an Artificial Neural Network based approach for implementing Automatic Signature verification and authentication system. In this era, with the rapid growth of Internet and the necessity of localized verification systems, handwritten signature has become an important biometric feature for the purpose of verification and authentication. The proposed method comprises spatial and frequency domain techniques for transformation. After extracting the Region of Interest Ripplet-II Transformation, Fractal Dimension and Log Polar Transformation are carried out to extract descriptors of the concerned signature to be verified as well as authenticated. In decision making stage Feed Forward Back Propagation Neural Network is used for verification and authentication purpose. This system has been tested with large sample of signatures to show its verification accuracy and the results have been found around 96.15%. Also forgery detection rate has been found 92% which is very encouraging. False Acceptance Rate and False Rejection rate of our system has been determined 5.28% and 2.56% respectively. This approach has been compared with some existing system and it has been observed that this system shows better performance.
, Hassan Dawood,
2012 6th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT) pp 380-385; https://doi.org/10.1109/setit.2012.6481945

Abstract:
Texture classification is an important problem in image analysis. A considerable amount of research work has been done for local or global rotation invariant feature extraction for texture classification. Local invariant features contain the spatial information, but usually do not have the contrast information. A new hybrid approach is proposed which considers the contrast information in spatial domain and the phase information in frequency domain of the image. It uses the joint histogram of the two complementary features, local phase quantization (LPQ) and the contrast of the image. Support vector machine is used for classification. The experimental results on standard benchmark datasets for texture classification Brodatz and KTH-TIPS2-a show that the proposed method can achieve significant improvement compared to the LPQ, Gabor filer or local Binary Pattern methods.
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