Assessment of natural regeneration status: the case of Durgapur hill forest, Netrokona, Bangladesh

Enumeration of regeneration status is an authentic tool to know the actual condition of forest ecosystem. The study was conducted to assess the regeneration status of Durgapur hill forest following stratified random sampling method (2 m × 2 m quadrate) from October 2017 to May 2018. A total of 27 species under 18 families were recorded from the study area. The study revealed maximum (37.78) family importance value (FIV) index was recorded for Euphorbiaceae followed by Moraceae (16.09). Importance value index (IVI) of Grewia nervosa was maximum (23.97 out of 300) followed by Shorea robusta (21.02), and Aporosa wallichii (20.19). Conservation status showed highest (77.78%) plant species were in least concerned (LC) where only one species (Dillenia pentagyna) was found as data deficient (DD) category. Seedlings of different height classes showed maximum (33.2%) seedling were within the height range of 50–<100 cm. However, different biological diversity indices, i.e., Shannon–Winner index (H) (4.27), species evenness index (E) (1.30), Simpson index (D) (0.15), and Margalef’s species richness index (4.24) were enumerated to know the complete diversity condition of the forest area. Hierarchical cluster of the recorded species also showed that Grewia nervosa is the most dominant species in that area

DTCTH: a discriminative local pattern descriptor for image classification

Abstract Despite lots of effort being exerted in designing feature descriptors, it is still challenging to find generalized feature descriptors, with acceptable discrimination ability, which are able to capture prominent features in various image processing applications. To address this issue, we propose a computationally feasible discriminative ternary census transform histogram (DTCTH) for image representation which uses dynamic thresholds to perceive the key properties of a feature descriptor. The code produced by DTCTH is more stable against intensity fluctuation, and it mainly captures the discriminative structural properties of an image by suppressing unnecessary background information. Thus, DTCTH becomes more generalized to be used in different applications with reasonable accuracies. To validate the generalizability of DTCTH, we have conducted rigorous experiments on five different applications considering nine benchmark datasets. The experimental results demonstrate that DTCTH performs as high as 28.08% better than the existing state-of-the-art feature descriptors such as GIST, SIFT, HOG, LBP, CLBP, OC-LBP, LGP, LTP, LAID, and CENTRIST