Image Reassembly Combining Deep Learning and Shortest Path Problem

This paper addresses the problem of reassembling images from disjointed fragments. More specifically, given an unordered set of fragments, we aim at reassembling one or several possibly incomplete images. The main contributions of this work are: 1) several deep neural architectures to predict the relative position of image fragments that outperform the previous state of the art; 2) casting the reassembly problem into the shortest path in a graph problem for which we provide several construction algorithms depending on available information; 3) a new dataset of images taken from the Metropolitan Museum of Art (MET) dedicated to image reassembly for which we provide a clear setup and a strong baseline.Comment: ECCV 201

Entomopathogenic nematodes for the control of Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae) pupae.

Helicoverpa armigera (Hübner) is a polyphagous insect of difficult control and maize is an important host crop of this insect. Entomopathogenic nematodes (EPNs) are control agents of soil pests. This study aimed to verify the action of EPNs for the control of H. armigera pupae. Laboratory and greenhouse bioassays were conducted to select the concentration of nematode application and subsequently field test were conducted. It was obtained that Heterorhabditis amazonensis MC01 at the concentration of 400 infective juveniles (IJs) ·pupa-1 caused the highest mortality in a lower concentration, whereas for H. amazonensis JPM4, concentrations of both 200 and 400 IJs ·pupa-1 were similar causing pupae mortality. In the greenhouse, H. amazonensis MC01 caused mortality reached values of 80% after 10 days, at concentrations of 600 and 800 IJs ·pupa-1. The highest mortality caused by Steinernema carpocapsae was observed at eight days after the juvenile application, at a concentration of 600 IJs ·pupa-1, also reaching 80% mortality. In the field test, both forms of application were considered appropriate for H. amazonensis MC01, causing mortality rates of up to 80%

A Novel Hybrid Scheme Using Genetic Algorithms and Deep Learning for the Reconstruction of Portuguese Tile Panels

This paper presents a novel scheme, based on a unique combination of genetic algorithms (GAs) and deep learning (DL), for the automatic reconstruction of Portuguese tile panels, a challenging real-world variant of the jigsaw puzzle problem (JPP) with important national heritage implications. Specifically, we introduce an enhanced GA-based puzzle solver, whose integration with a novel DL-based compatibility measure (DLCM) yields state-of-the-art performance, regarding the above application. Current compatibility measures consider typically (the chromatic information of) edge pixels (between adjacent tiles), and help achieve high accuracy for the synthetic JPP variant. However, such measures exhibit rather poor performance when applied to the Portuguese tile panels, which are susceptible to various real-world effects, e.g., monochromatic panels, non-squared tiles, edge degradation, etc. To overcome such difficulties, we have developed a novel DLCM to extract high-level texture/color statistics from the entire tile information. Integrating this measure with our enhanced GA-based puzzle solver, we have demonstrated, for the first time, how to deal most effectively with large-scale real-world problems, such as the Portuguese tile problem. Specifically, we have achieved 82% accuracy for the reconstruction of Portuguese tile panels with unknown piece rotation and puzzle dimension (compared to merely 3.5% average accuracy achieved by the best method known for solving this problem variant). The proposed method outperforms even human experts in several cases, correcting their mistakes in the manual tile assembly

Efeito da temperatura e concentração na sobrevivência de nematóides entomopatogênicos em condições de armazenamento, visando seu uso no controle microbiano de pragas.

Fatores como temperatura e concentração de juvenis infectivos, podem influenciar no armazenamento de nematóides entomopatogênicos, afetando a sobrevivência e a infectividade. Neste bioensaio o objetivo foi avaliar a influência da temperatura e concentração na sobrevivência desses nematóides, a fim de melhorar o armazenamento obtendo juvenis infectivos mais virulentos. Foram avaliadas variáveis como viabilidade e infectividade (sobre larvas de Galleria mellonella) de Steinernema carpocapsae, S. riobrave, Heterorhabditis sp. CCA e Heterorhabditis sp. JPM 4, nas concentrações de 100, 1.000, 5.000 e 10.000 JI/mL em seis temperaturas (8, 12, 16, 20, 24 e 28°C). As avaliações foram feitas aos 15, 30, 60, 120, 150 e 180 dias de armazenamento. Observou-se que todas espécies apresentaram viabilidade e infectividade semelhantes nas concentrações testadas, com variação nas diferentes temperaturas. Assim, tanto Heterorhabditis sp. CCA como Heterorhabditis sp. JPM4 tiveram viabilidade reduzidas nas temperaturas de 8, 12, 24 e 28°C. S. carpocapsae e S. riobrave apresentaram redução de viabilidade a partir de 60 dias a 24 e 28°C. Heterorhabditis sp. CCA e Heterorhabditis sp. JPM4 tiveram redução de infectividade a partir de 15 dias para 8°C e a partir dos 60 dias a 24 e 28°C. S. carpocapsae e S. riobrave tiveram redução de infectividade a partir de 60 dias a 24 e 28°C. As diferenças encontradas entre nematóides evidenciam a importância do estudo de fatores favoráveis para a manutenção de suas características em diferentes condições de armazenamento a fim de prolongar o tempo de sobrevivência e infectividade