Revision of the Neotropical moth genera Mallodeta Butler and Erruca Walker, revalidated (Noctuidae, Arctiinae, Arctiini, Euchromiina)

Mallodeta Butler and Erruca Walker, revalidated, are redescribed and revised. Mallodeta henceforth includes only its type-species, Glaucopis (Lycorea) clavata Walker, and Erruca is resurrected with seven species: E. deyrolii Walker (type-species), E. consors (Walker), new combination, E. erythrarchos (Walker), new combination, E. cardinalis (Hampson), new combination, E. hanga (Herrich-Schaffer), new combination, E. cruenta (Perty), new combination and E. sanguipuncta (Druce), new combination. Six new synonyms are established, four specific and two generic (junior synonyms in parentheses): Zygaena capistrata Fabricius (=Mallodeta cubana Gaede), Glaucopis (Lycorea) clavata Walker (=M. simplex Rothschild), Erruca deyrolii Walker (=Laemocharis aecyra Herrich-Schaffer and Glaucopis (Hyda) sortita Walker), and Erruca Walker (=Aristodaema Wallengren and Rezia Kirby). Lectotypes are designated to the following species: Erruca deyrolii Walker, Laemocharis deyrollei Herrich-Schaffer, Laemocharis hanga Herrich-Schaffer, Laemocharis aecyra Herrich-Schaffer, Laemocharis norma Herrich-Schaffer, Cosmosoma cardinalis Hampson and Mallodeta sanguipuncta Druce. Illustrations of adults and male and female genitalia of Mallodeta and Erruca are provided, as well as a key to the species of the latter.

Texture descriptor based on partially self-avoiding deterministic walker on networks

Texture image analysis is an important field of investigation that has attracted the attention from computer vision community in the last decades. In this paper, a novel approach for texture image analysis is proposed by using a combination of graph theory and partially self-avoiding deterministic walks. From the image, we build a regular graph where each vertex represents a pixel and it is connected to neighboring pixels (pixels whose spatial distance is less than a given radius). Transformations on the regular graph are applied to emphasize different image features. To characterize the transformed graphs, partially self-avoiding deterministic walks are performed to compose the feature vector. Experimental results on three databases indicate that the proposed method significantly improves correct classification rate compared to the state-of-the-art, e.g. from 89.37% (original tourist walk) to 94.32% on the Brodatz database, from 84.86% (Gabor filter) to 85.07% on the Vistex database and from 92.60% (original tourist walk) to 98.00% on the plant leaves database. In view of these results, it is expected that this method could provide good results in other applications such as texture synthesis and texture segmentation. (C) 2012 Elsevier Ltd. All rights reserved.