Eva Ramirez-Llorda

Bio: Eva Ramirez-Llorda is an academic researcher from Spanish National Research Council. The author has an hindex of 1, co-authored 1 publications receiving 56 citations.

A Novel Morphometry-Based Protocol of Automated Video-Image Analysis for Species Recognition and Activity Rhythms Monitoring in Deep-Sea Fauna

Abstract: The understanding of ecosystem dynamics in deep-sea areas is to date limited by technical constraints on sampling repetition. We have elaborated a morphometry-based protocol for automated video-image analysis where animal movement tracking (by frame subtraction) is accompanied by species identification from animals' outlines by Fourier Descriptors and Standard K-Nearest Neighbours methods. One-week footage from a permanent video-station located at 1,100 m depth in Sagami Bay (Central Japan) was analysed. Out of 150,000 frames (1 per 4 s), a subset of 10.000 was analyzed by a trained operator to increase the efficiency of the automated procedure. Error estimation of the automated and trained operator procedure was computed as a measure of protocol performance. Three displacing species were identified as the most recurrent: Zoarcid fishes (eelpouts), red crabs (Paralomis multispina), and snails (Buccinum soyomaruae). Species identification with KNN thresholding produced better results in automated motion detection. Results were discussed assuming that the technological bottleneck is to date deeply conditioning the exploration of the deep-sea.

Shape Analysis of Agricultural Products: A Review of Recent Research Advances and Potential Application to Computer Vision

Abstract: The appearance of agricultural products deeply conditions their marketing. Appearance is normally evaluated by considering size, shape, form, colour, freshness condition and finally the absence of visual defects. Among these features, the shape plays a crucial role. Description of agricultural product shape is often necessary in research fields for a range of different purposes, including the investigation of shape traits heritability for cultivar descriptions, plant variety or cultivar patents and evaluation of consumer decision performance. This review reports the main applications of shape analysis on agricultural products such as relationships between shape and: (1) genetic; (2) conformity and condition ratios; (3) products characterization; (4) product sorting and finally, (5) clone selection. Shape can be a protagonist of evaluation criteria only if an appreciable level of image shape processing and automation and data are treated with solid multivariate statistic. In this context, image-processing algorithms have been increasingly developed in the last decade in order to objectively measure the external features of agricultural products. Grading and sorting of agricultural products using machine vision in conjunction with pattern recognition techniques offers many advantages over the conventional optical or mechanical sorting devices. With this aims, we propose a new automated shape processing system which could be useful for both scientific and industrial purposes, forming the bases of a common language for the scientific community. We applied such a processing scheme to morphologically discriminate nuts fruit of different species. Operative Matlab codes for shape analysis are reported.

Changing coasts: marine aliens and artificial structures

Abstract: Marine aliens are non-native species that have been transported across major geographical barriers by human activities, involving vectors that move propagules along pathways. Species may also be newly observed in a geographical area due to range shifts, generally in association with climate change. Artificial structures are considered to be either man-made materials or natural materials shaped or displaced to serve a specific function for human activities. All types of artificial structures are currently increasing dramatically in coastal zones due to increasing human populations on coastlines. Most of the significant marine vectors and pathways involve mobile artificial structures and are reviewed here. These include shipping (ballast water and hull fouling) and aquaculture, including stock transfer and unintentional introductions, all of which can move species into new biogeographical provinces. Some types of structures frequently move long distances but have low fouling loads (e.g., commercial shipping), whereas others (e.g., barges and pontoons) can be hyperfouled due to long stationary periods such that when moved they transport mature fouling communities. We also examine the presence of alien marine species on static (immobile) artificial structures, which support different communities from those on natural hard substrata. We consider the role of these structures, such as coastal defences, artificial reefs, and offshore platforms, in the dispersal and abundance of alien species. Marinas include both mobile and immobile structures and are apparently particularly favourable habitats for many aliens. For example, in coastal North America approximately 90% of the alien species inhabiting hard substrata have been reported from docks and marinas. Detailed case studies of alien marine species (two seaweeds and four invertebrates) are provided, with an analysis of their origin, vectors of transport, habitat in the introduced range, and potential impact. Although there are exceptions, a large majority of marine alien species seem to be associated, at least for some of the time, with artificial structures. It is clear that artificial structures can pave the way and act as stepping stones or even corridors for some marine aliens, as do urban areas, roads and riparian environments in terrestrial ecosystems. The observed acceleration of spread rates for marine invasions over the course of the last two centuries may partly be a result of the increase of artificial structures in coastal environments coupled with greater activity of vectors.

Automated identification of animal species in camera trap images

Abstract: Image sensors are increasingly being used in biodiversity monitoring, with each study generating many thousands or millions of pictures. Efficiently identifying the species captured by each image is a critical challenge for the advancement of this field. Here, we present an automated species identification method for wildlife pictures captured by remote camera traps. Our process starts with images that are cropped out of the background. We then use improved sparse coding spatial pyramid matching (ScSPM), which extracts dense SIFT descriptor and cell-structured LBP (cLBP) as the local features, that generates global feature via weighted sparse coding and max pooling using multi-scale pyramid kernel, and classifies the images by a linear support vector machine algorithm. Weighted sparse coding is used to enforce both sparsity and locality of encoding in feature space. We tested the method on a dataset with over 7,000 camera trap images of 18 species from two different field cites, and achieved an average classification accuracy of 82%. Our analysis demonstrates that the combination of SIFT and cLBP can serve as a useful technique for animal species recognition in real, complex scenarios.

On plant detection of intact tomato fruits using image analysis and machine learning methods.

Abstract: Fully automated yield estimation of intact fruits prior to harvesting provides various benefits to farmers. Until now, several studies have been conducted to estimate fruit yield using image-processing technologies. However, most of these techniques require thresholds for features such as color, shape and size. In addition, their performance strongly depends on the thresholds used, although optimal thresholds tend to vary with images. Furthermore, most of these techniques have attempted to detect only mature and immature fruits, although the number of young fruits is more important for the prediction of long-term fluctuations in yield. In this study, we aimed to develop a method to accurately detect individual intact tomato fruits including mature, immature and young fruits on a plant using a conventional RGB digital camera in conjunction with machine learning approaches. The developed method did not require an adjustment of threshold values for fruit detection from each image because image segmentation was conducted based on classification models generated in accordance with the color, shape, texture and size of the images. The results of fruit detection in the test images showed that the developed method achieved a recall of 0.80, while the precision was 0.88. The recall values of mature, immature and young fruits were 1.00, 0.80 and 0.78, respectively.