To address the technical shortcomings of conventional histogram equalization (HE), such as over-enhancement and artifacts, we propose a histogram-constrained contrast-tunable HE technique for digital image enhancement. Firstly, input is partitioned into two parts, main constrained histogram, by cumulative probability density threshold; second, redistributed equally in whole grayscale range; fin...

many environmental and physiological factors affect plant temperature. the objective of this study was to use thermal imagery to investigate robust features for early diagnosis of botrytis cinerea infection in cut rose flowers under the postharvest application of methyl jasmonate (meja). three cases treated with different concentrations of meja (0.1, 0.2, and 0.3 µl.l−1), a control (0 µl.l−1 me...

in this paper, the performance of 11 different distances for image retrieval and classification, based on color, shape and texture, is evaluated. the precision-recall measure and the correct classification rate of the k-nn classifier are used to evaluate retrieval and classification performances, respectively. the experimental results for a database of 1000 images from 10 different semantic gro...

– Histogram equalization – Preserving the original brightness to avoid annoying artifacts ‹ Bright preserving Bi-Histogram Equalization – Not handling well to avoid annoying artifacts ‹ Recursive Mean-Separate Histogram Equalization – Separating each new histogram further based on respective mean Abstract 3 / 28 ‹ Histogram equalization – Base on the probability distribution of the input gray l...

Anomaly detection is the task of detecting samples that behave differently from rest data or include abnormal values. Unsupervised anomaly most common scenario, which implies algorithms cannot train with a labeled input and do not know behavior beforehand. Histogram-based methods are one approaches in unsupervised detection, remarking good performance low runtime. Despite performance, histogram...