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pro vyhledávání: '"Chandran, Sharat"'
Screening for any of the Autism Spectrum Disorders is a complicated process often involving a hybrid of behavioural observations and questionnaire based tests. Typically carried out in a controlled setting, this process requires trained clinicians or
Externí odkaz:
http://arxiv.org/abs/2111.04064
Low-dose tomography is highly preferred in medical procedures for its reduced radiation risk when compared to standard-dose Computed Tomography (CT). However, the lower the intensity of X-rays, the higher the acquisition noise and hence the reconstru
Externí odkaz:
http://arxiv.org/abs/1912.11022
The need for tomographic reconstruction from sparse measurements arises when the measurement process is potentially harmful, needs to be rapid, or is uneconomical. In such cases, information from previous longitudinal scans of the same object helps t
Externí odkaz:
http://arxiv.org/abs/1909.05686
Autor:
Baswana, Surender, Chakrabarti, Partha Pratim, Kanoria, Yashodhan, Patange, Utkarsh, Chandran, Sharat
Until 2014, admissions to the Indian Institutes of Technology (IITs) were conducted under one umbrella, whereas the admissions to the non-IIT Centrally Funded Government Institutes (CFTIs) were conducted under a different umbrella, the Central Seat A
Externí odkaz:
http://arxiv.org/abs/1904.06698
The need for tomographic reconstruction from sparse measurements arises when the measurement process is potentially harmful, needs to be rapid, or is uneconomical. In such cases, prior information from previous longitudinal scans of the same or simil
Externí odkaz:
http://arxiv.org/abs/1812.10998
Autor:
Mitra, Rahul, Doiphode, Nehal, Gautam, Utkarsh, Narayan, Sanath, Ahmed, Shuaib, Chandran, Sharat, Jain, Arjun
We propose a new dataset for learning local image descriptors which can be used for significantly improved patch matching. Our proposed dataset consists of an order of magnitude more number of scenes, images, and positive and negative correspondences
Externí odkaz:
http://arxiv.org/abs/1801.01466
Recent research in tomographic reconstruction is motivated by the need to efficiently recover detailed anatomy from limited measurements. One of the ways to compensate for the increasingly sparse sets of measurements is to exploit the information fro
Externí odkaz:
http://arxiv.org/abs/1712.02423
We propose a convolutional neural network (ConvNet) based approach for learning local image descriptors which can be used for significantly improved patch matching and 3D reconstructions. A multi-resolution ConvNet is used for learning keypoint descr
Externí odkaz:
http://arxiv.org/abs/1701.06854
Akademický článek
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Publikováno v:
In Signal Processing October 2020 175
