Zobrazeno 1 - 10
of 22
pro vyhledávání: '"RASHNO, ELYAS"'
Existing unsupervised keypoint detection methods apply artificial deformations to images such as masking a significant portion of images and using reconstruction of original image as a learning objective to detect keypoints. However, this approach la
Externí odkaz:
http://arxiv.org/abs/2410.14700
Transformers have made significant strides across various artificial intelligence domains, including natural language processing, computer vision, and audio processing. This success has naturally garnered considerable interest from both academic and
Externí odkaz:
http://arxiv.org/abs/2408.04723
Optical coherence tomography (OCT) as retina imaging technology is currently used by ophthalmologist as a non-invasive and non-contact method for diagnosis of agerelated degeneration (AMD) and diabetic macular edema (DME) diseases. Fluid regions in O
Externí odkaz:
http://arxiv.org/abs/1912.11540
Publikováno v:
5th Conference on Signal Processing and Intelligent Systems (ICSPIS2019)
Content-based image retrieval (CBIR) is a task of retrieving images from their contents. Since retrieval process is a time-consuming task in large image databases, acceleration methods can be very useful. This paper presents a novel method to speed u
Externí odkaz:
http://arxiv.org/abs/1911.11379
Autor:
Rashno, Abdolreza, Rashno, Elyas
Content-based image retrieval (CBIR) has become one of the most important research directions in the domain of digital data management. In this paper, a new feature extraction schema including the norm of low frequency components in wavelet transform
Externí odkaz:
http://arxiv.org/abs/1902.02059
Convolutional neural networks are sensitive to unknown noisy condition in the test phase and so their performance degrades for the noisy data classification task including noisy speech recognition. In this research, a new convolutional neural network
Externí odkaz:
http://arxiv.org/abs/1901.10629
Data certainty is one of the issues in the real-world applications which is caused by unwanted noise in data. Recently, more attentions have been paid to overcome this problem. We proposed a new method based on neutrosophic set (NS) theory to detect
Externí odkaz:
http://arxiv.org/abs/1812.11045
In this work, a new clustering algorithm is proposed based on neutrosophic set (NS) theory. The main contribution is to use NS to handle boundary and outlier points as challenging points of clustering methods. In the first step, a new definition of d
Externí odkaz:
http://arxiv.org/abs/1812.11034
Publikováno v:
In Engineering Applications of Artificial Intelligence March 2020 89
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