Face recognition under pose and illumination variations using the combination of Information set and PLPP features

Hanmandlu, M.; Singhal, Soniya

doi:10.1016/j.asoc.2017.01.014

Cited by 8 publications

(2 citation statements)

References 19 publications

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“…Step 5: Compute the updated entropy function of all the elements of the ikth t-normed error using the possibilistic Shannon transform 40 as follows:…”

Section: Algorithm For Hstcmentioning

confidence: 99%

“…Step 5: Compute the updated entropy function of all the elements of the ik th t‐normed error using the possibilistic Shannon transform 40 as follows:

S_{ik} ((), l) = - \sum_{j = 1}^{n} {E_{ik, l}}^{γ} ((), j) ((), \log ((), μ_{italicik, l} ((), j) new \times E_{italicik, l} ((), j))),

where

γ > 0

is a constant chosen as

γ = 1.2 .

This is derived from the adaptive possibilistic Mamta–Hanman entropy function expressed in terms of

E_{italicik, l} ((), j)

S_{ik} ((), l) = \sum_{j = 1}^{n} {E_{ik, l}}^{γ} ((), j) e^{- {(c_{j} {E_{italicik, l}}^{α} (j) + d_{j})}^{β}}

…”

Section: Development Of Hstcmentioning

confidence: 99%

See 1 more Smart Citation

Classification of brain tumor from magnetic resonance images using probabilistic features and possibilistic Hanman–Shannon transform classifier

Asthana

Hanmandlu²,

Vashisth

2021

Int J Imaging Syst Tech

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A brain tumor is considered one of the deadliest forms among all types of cancer due to its aggressive nature leading to patients' low survival rate. Detection and classification of brain tumors have a significant impact on treatment planning and patient survival. The significance and importance of this work lie in the formulation of several probabilistic features that represent higher-level probabilistic uncertainty. To create these features, the gain function of probabilistic Hanman transform is replaced with the gain functions of Shannon, Renyi, and Tsallis entropy functions thus paving a way to the corresponding hybrid transforms, Hanman-Shannon, Hanman-Renyi, and Hanman-Tsallis transforms. The new features are extracted from brain MR images to detect and classify tumor by developing the possibilistic Hanman-Shannon transform classifier. This uses the t-normed errors between the training and testing features. The proposed system when evaluated on the two Brain MRI datasets yields the highest accuracy of around 99%.

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“…Step 5: Compute the updated entropy function of all the elements of the ikth t-normed error using the possibilistic Shannon transform 40 as follows:…”

Section: Algorithm For Hstcmentioning

confidence: 99%

“…Step 5: Compute the updated entropy function of all the elements of the ik th t‐normed error using the possibilistic Shannon transform 40 as follows: