Some Novel Methods of Ordered Dither

Various authors have contributed their original works in the field of digital halftoning during past two to three decades. Still this field has not lost its glory. The goal of the study was to investigate novel methods in digital halftoning specially, in ordered dithering. This paper is concerned with two novel methods of ordered dither. In the first method dithering is done first by pre-embedding a pattern image generated from a matrix pattern with the original image. In the second method dithering is done by thresholding the original image with respect to a threshold matrix pattern constructed using a character writing pattern. The two methods may be applied in digital halftone reproduction and as special effect imaging

Binary operation based hard exudate detection and fuzzy based classification in diabetic retinal fundus images for real time diagnosis applications

Diabetic retinopathy (DR) is one of the most considerable reasons for visual impairment. The main objective of this paper is to automatically detect and recognize DR lesions like hard exudates, as it helps in diagnosing and screening of the disease. Here, binary operation based image processing for detecting lesions and fuzzy logic based extraction of hard exudates on diabetic retinal images are discused. In the initial stage, the binary operations are used to identify the exudates. Similarly, the RGB channel space of the DR image is used to create fuzzy sets and membership functions for extracting the exudates. The membership directives obtained from the fuzzy rule set are used to detect the grade of exudates. In order to evaluate the proposed approach, experiment tests are carriedout on various set of images and the results are verified. From the experiment results, the sensitivity obtained is 98.10%, specificity is 96.96% and accuracy is 98.2%.  These results suggest that the proposed method could be a diagnostic aid for ophthalmologists in the screening for DR

First report of Echthrogaleus denticulatus (Smith 1874) on the pelagic thresher shark (Alopias pelagicus Nakamura 1935) from Indian EEZ of Andaman Sea

The present study reports the occurrence of the pandarid parasite, Echthrogaleus denticulatus as an ectoparasite on the pelagic thresher shark (Alopias pelagicus) from the Indian EEZ of Andaman Sea. A total of 36 parasite specimens were found aggregated near the cloacal aperture of eight pelagic thresher sharks caught as bycatch by multifilament tuna longliner MFV Blue Marlin during July 2015 and February 2016 voyages in Andaman and Nicobar waters. This is the first report of ectoparasite from the Indian EEZ of the Andaman Sea

Improving Generalization via Meta-Learning on Hard Samples

Learned reweighting (LRW) approaches to supervised learning use an optimization criterion to assign weights for training instances, in order to maximize performance on a representative validation dataset. We pose and formalize the problem of optimized selection of the validation set used in LRW training, to improve classifier generalization. In particular, we show that using hard-to-classify instances in the validation set has both a theoretical connection to, and strong empirical evidence of generalization. We provide an efficient algorithm for training this meta-optimized model, as well as a simple train-twice heuristic for careful comparative study. We demonstrate that LRW with easy validation data performs consistently worse than LRW with hard validation data, establishing the validity of our meta-optimization problem. Our proposed algorithm outperforms a wide range of baselines on a range of datasets and domain shift challenges (Imagenet-1K, CIFAR-100, Clothing-1M, CAMELYON, WILDS, etc.), with ~1% gains using VIT-B on Imagenet. We also show that using naturally hard examples for validation (Imagenet-R / Imagenet-A) in LRW training for Imagenet improves performance on both clean and naturally hard test instances by 1-2%. Secondary analyses show that using hard validation data in an LRW framework improves margins on test data, hinting at the mechanism underlying our empirical gains. We believe this work opens up new research directions for the meta-optimization of meta-learning in a supervised learning context.Comment: Accepted at CVPR 202