Wireless aquatic navigator for detection and analysis (WANDA)

The cost of monitoring and detecting pollutants in natural waters is of major concern. Current and forthcoming bodies of legislation will continue to drive demand for spatial and selective monitoring of our environment, as the focus increasingly moves towards effective enforcement of legislation through detection of events, and unambiguous identification of perpetrators. However, these monitoring demands are not being met due to the infrastructure and maintenance costs of conventional sensing models. Advanced autonomous platforms capable of performing complex analytical measurements at remote locations still require individual power, wireless communication, processor and electronic transducer units, along with regular maintenance visits. Hence the cost base for these systems is prohibitively high, and the spatial density and frequency of measurements are insufficient to meet requirements. In this paper we present a more cost effective approach for water quality monitoring using a low cost mobile sensing/communications platform together with very low cost stand-alone ‘satellite’ indicator stations that have an integrated colorimetric sensing material. The mobile platform is equipped with a wireless video camera that is used to interrogate each station to harvest information about the water quality. In simulation experiments, the first cycle of measurements is carried out to identify a ‘normal’ condition followed by a second cycle during which the platform successfully detected and communicated the presence of a chemical contaminant that had been localised at one of the satellite stations

Page boundary extraction of bound historical herbaria

When digitizing bound historical collections such as herbaria it is important to extract the main page region so that it could be used for automated processing. The thickness of the herbaria books also gives rise to deformations during imaging which reduces the efficiency of automatic detection tasks. In this work we address these problems by proposing an automatic page detection algorithm that estimates all the boundaries of the page and performs morphological corrections in order to reduce deformations. The algorithm extracts features from Hue, Saturation and Value transformations of an RGB image to detect the main page polygon. The algorithm was evaluated on multiple textual and herbaria type historical collections and obtains over 94% mean intersection over union on all these datasets. Additionally, the algorithm was also subjected to an ablation test to demonstrate the importance of morphological corrections

Analysis of variability and phylogeny in pisum (Pisum spp.) using digital phenotyping and morphological traits

Plant phenotyping links genomics with plant ecophysiology and agronomy. It is usually performed by non-destructive, automated and image-based technology and generates information for efficient and searchable digital characterization of crop that can be performed during routine, periodical regeneration of accessions in germplasm collections. In the present work, ninety-two accessions of Pisum from different species and subspecies were studied during 2015 and 2016. Size and colour traits were measured using digital images from a Samsung CLX 3300 scanner and analysed with appropriate software; also seed weight, plant height and days to flowering were measured. Highly significant differences between accessions and species and subspecies for all these traits were found. When distances among species and subspecies are calculated, P. sativum subsp. sativum showed the greatest distance with P. fulvum (8.02) followed by P. abyssinicum (7.13); while the smallest distance was found between P. fulvum and P. sativum subsp. transcaucasicum (3.16). A Neighbour-joining tree with a cofenetic r of 0.985 was obtained. Seed and pod characteristics as colour parameters and size, obtained by digital phenotyping, have proved to be suitable markers for genetic diversity evaluation and they are useful in evolutionary analysis, allowing the discrimination of the main wild and cultivated species in the genus Pisum.Fil: Gatti, Ileana. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Departamento de Producción Vegetal. Cátedra de Mejoramiento Vegetal y Producción de Semillas; ArgentinaFil: Guindón, María Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Investigaciones en Ciencias Agrarias de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Instituto de Investigaciones en Ciencias Agrarias de Rosario; ArgentinaFil: Bermejo, Carolina Julieta. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Investigaciones en Ciencias Agrarias de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Instituto de Investigaciones en Ciencias Agrarias de Rosario; ArgentinaFil: Cointry Peix, Enrique Luis. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Departamento de Producción Vegetal. Cátedra de Mejoramiento Vegetal y Producción de Semillas; Argentin

Real-time detection and tracking of multiple objects with partial decoding in H.264/AVC bitstream domain

In this paper, we show that we can apply probabilistic spatiotemporal macroblock filtering (PSMF) and partial decoding processes to effectively detect and track multiple objects in real time in H.264|AVC bitstreams with stationary background. Our contribution is that our method cannot only show fast processing time but also handle multiple moving objects that are articulated, changing in size or internally have monotonous color, even though they contain a chaotic set of non-homogeneous motion vectors inside. In addition, our partial decoding process for H.264|AVC bitstreams enables to improve the accuracy of object trajectories and overcome long occlusion by using extracted color information.Comment: SPIE Real-Time Image and Video Processing Conference 200

An algorithm for accurate taillight detection at night

Vehicle detection is an important process of many advance driver assistance system (ADAS) such as forward collision avoidance, Time to collision (TTC) and Intelligence headlight control (IHC). This paper presents a new algorithm to detect a vehicle ahead by using taillight pair. First, the proposed method extracts taillight candidate regions by filtering taillight colour regions and applying morphological operations. Second, pairing each candidates and pair symmetry analysis steps are implemented in order to have taillight positions. The aim of this work is to improve the accuracy of taillight detection at night with many bright spot candidates from streetlamps and other factors from complex scenes. Experiments on still images dataset show that the proposed algorithm can improve the taillight detection accuracy rate and robust under limited light images