Contour Detection-based Discovery of Mid-level Discriminative Patches for Scene Classification

Feature extraction and representation is a key step in scene classification. In this paper, a contour detection-based mid-level features learning method is proposed for scene classification. First, a sketch tokens-based contour detection scheme is proposed to initialize seed blocks for learning mid-level patches and the patches with more contour pixels are selected as seed blocks. The procedure is demonstrated to be helpful for scene classification. Next, the seed blocks are employed to train an exemplar SVM to discover other similar occurrences and an entropy-rank criterion is utilized to mine the discriminative patches. Finally, scene categories are identified by matching the discriminative patches and testing images. Extensive experiments on the MIT Indoor-67 dataset, the 15-scene dataset and the UIUC-sports dataset show that the proposed approach yields better performance than other state-of-the-art counterparts

Semantic Map Building Based on Object Detection for Indoor Navigation

Building a map of the environment is a prerequisite for mobile robot navigation. In this paper, we present a semantic map building method for indoor navigation of a robot using only the image sequence acquired by a mon‐ ocular camera installed on the robot. First, a topological map of the environment is created, where each key frame forms a node of the map represented as visual words (VWs). The edges between two adjacent nodes are built from relative poses obtained by performing a novel pose estimation approach, called one-point RANSAC camera pose estimation (ORPE). Then, taking advantage of an improved deformable part model (iDPM) for object detection, the topological map is extended by assigning semantic attributes to the nodes. Extensive experimental evaluations demonstrate the effectiveness of the proposed monocular SLAM method

Multi-channel and multi-scale mid-level image representation for scene classification

Convolutional neural network (CNN)-based approaches have received state-of-the-art results in scene classification. Features from the output of fully connected (FC) layers express one-dimensional semantic information but lose the detailed information of objects and the spatial information of scene categories. On the contrary, deep convolutional features have been proved to be more suitable for describing an object itself and the spatial relations among objects in an image. In addition, the feature map from each layer is max-pooled within local neighborhoods, which weakens the invariance of global consistency and is unfavorable to scenes with highly complicated variation. To cope with the above issues, an orderless multi-channel mid-level image representation on pre-trained CNN features is proposed to improve the classification performance. The mid-level image representation of two channels from the FC layer and the deep convolutional layer are integrated at multi-scale levels. A sum pooling approach is also employed to aggregate multi-scale mid-level image representation to highlight the importance of the descriptors beneficial for scene classification. Extensive experiments on SUN397 and MIT 67 indoor datasets demonstrate that the proposed method achieves promising classification performance

In silico identification and characterization of a diverse subset of conserved microRNAs in bioenergy crop Arundo donax L.

MicroRNAs (miRNAs) are small non-coding RNA molecules involved in the post-transcriptional regulation of gene expression in plants. Arundo donax L. is a perennial C-3 grass considered one of the most promising bioenergy crops. Despite its relevance, many fundamental aspects of its biology still remain to be elucidated. In the present study we carried out the first in silico mining and tissue-specific characterization of microRNAs and their putative targets in A. donax. We identified a total of 141 miRNAs belonging to 14 families along with the corresponding primary miRNAs, precursor miRNAs and a total of 462 high-confidence predicted targets and novel target sites were validated by 5'-race. Gene Ontology functional annotation showed that miRNA targets are constituted mainly by transcription factors, but three of the newly validated targets are enzymes involved in novel functions like RNA editing, acyl lipid metabolism and post-Golgi trafficking. Folding variability of pre-miRNA loops and phylogenetic analyses indicate variable selective pressure acting on the different miRNA families. The set of miRNAs identified in this study will pave the road to further miRNA research in Arundo donax and contribute towards a better understanding of miRNA-mediated gene regulatory processes in other bioenergy crops.Peer reviewe

A novel isoprene synthase from the monocot tree Copernicia prunifera (Arecaceae) confers enhanced drought tolerance in transgenic Arabidopsis

The capacity to emit isoprene, among other stresses, protects plants from drought, but the molecular mechanisms underlying this trait are only partly understood. The Arecaceae (palms) constitute a very interesting model system to test the involvement of isoprene in enhancing drought tolerance, as their high isoprene emissions may have contributed to make them hyperdominant in neotropical dry forests, characterized by recurrent and extended periods of drought stress. In this study we isolated and functionally characterized a novel isoprene synthase, the gene responsible for isoprene biosynthesis, from Copernicia prunifera, a palm from seasonally dry tropical forests. When overexpressed in the non-emitter Arabidopsis thaliana, CprISPS conferred significant levels of isoprene emission, together with enhanced tolerance to water limitation throughout plant growth and development, from germination to maturity. CprISPS overexpressors displayed higher germination, cotyledon/leaf greening, water usage efficiency, and survival than WT Arabidopsis under various types of water limitation. This increased drought tolerance was accompanied by a marked transcriptional up-regulation of both ABA-dependent and ABA-independent key drought response genes. Taken together, these results demonstrate the capacity of CprISPS to enhance drought tolerance in Arabidopsis and suggest that isoprene emission could have evolved in Arecaceae as an adaptive mechanism against drough

Metal detoxification in land plants: from bryophytes to vascular plants: STATE of the art and opportunities

5openInternationalItalian coauthor/editorPotentially toxic elements are a widespread concern due to their increasing diffusion into the environment. To counteract this problem, the relationship between plants and metal(loid)s has been investigated in the last 30 years. In this field, research has mainly dealt with angiosperms, whereas plant clades that are lower in the evolutive scale have been somewhat overlooked. However, recent studies have revealed the potential of bryophytes, pteridophytes and gymnosperms in environmental sciences, either as suitable indicators of habitat health and elemental pollution or as efficient tools for the reclamation of degraded soils and waters. In this review, we summarize recent research on the interaction between plants and potentially toxic elements, considering all land plant clades. The focus is on plant applicability in the identification and restoration of polluted environments, as well as on the characterization of molecular mechanisms with a potential outlet in the engineering of element tolerance and accumulation.openFasani, Elisa; Li, Mingai; Varotto, Claudio; Furini, Antonella; DalCorso, GiovanniFasani, E.; Li, M.; Varotto, C.; Furini, A.; Dalcorso, G

Interspecific and intraspecific phenotypic diversity for drought adaptation in bioenergy Arundo species

Biomass crops are commonly grown in low-grade land and selection of drought tolerant accessions is of major importance to sustain productivity. In this work, we assess phenotypic variation under different environmental scenarios in a series of accessions of Arundo donax, and contrast it with two closely related species, Arundo donaciformis and Arundo plinii. Gas-exchange and stomatal anatomy analysis showed an elevated photosynthetic capacity in A. plinii compared to A. donax and A. donaciformis with a significant intraspecific variation in A. donax. The three species showed significantly contrasting behavior of transpiration under developing water stress and increasing vapour pressure deficit (VPD), with A. donax being the most conservative while A. plinii showed an elevated degree of insensitivity to environmental cues. Under optimal conditions, A. donax had the highest estimated leaf area (PLA) and plant dry weight although a significant reduction under water stress was observed for A. donax and A. donaciformis accessions, while no differences were recorded for A. plinii between optimal growing conditions (WW) and reduced soil water availability (WS). A. donax displayed a markedly conservative WU behavior but elevated sensitivity of biomass accumulation under stress conditions. By contrast, in A. plinii biomass and transpiration were largely insensitive to WS and increasing VPD, though biomass dry weight under optimal conditions was significantly lower than A. donax. We provide evidence of interspecific phenotypic variation within the Arundo genus while the intraspecific phenotypic plasticity may be exploited for further selection of superior clones under disadvantageous environmental conditions. The extensive trade-off between water use and biomass accumulation present in the three species under stress conditions provides a series of novel traits to be exploited in the selection of superior clones adapted to different environmental scenarios. Non-destructive approaches are provided to screen large populations for water stress tolerant A. donax clones