Robust localization and identification of African clawed frogs in digital images

We study the automatic localization and identification of African clawed frogs (Xenopus laevis sp.) in digital images taken in a laboratory environment. We propose a novel and stable frog body localization and skin pattern window extraction algorithm. We show that it compensates scale and rotation changes very well. Moreover, it is able to localize and extract highly overlapping regions (pattern windows) even in the cases of intense affine transformations, blurring, Gaussian noise, and intensity transformations. The frog skin pattern (i.e. texture) provides a unique feature for the identification of individual frogs. We investigate the suitability of five different feature descriptors (Gabor filters, area granulometry, HoG,1 dense SIFT,2 and raw pixel values) to represent frog skin patterns. We compare the robustness of the features based on their identification performance using a nearest neighbor classifier. Our experiments show that among five features that we tested, the best performing feature against rotation, scale, and blurring modifications was the raw pixel feature, whereas the SIFT feature was the best performing one against affine and intensity modifications

The Core Film Collection: 500 Titles Selected by the FLIC Board for a Medium-Sized Public Library

published or submitted for publicatio

Dissociating the effect of disruptive colouration on localisation and identification of camouflaged targets

Disruptive camouflage features contrasting areas of pigmentation across the animals’ surface that form false edges which disguise the shape of the body and impede detection. In many taxa these false edges feature local contrast enhancement or edge enhancement, light areas have lighter edges and dark areas have darker edges. This additional quality is often overlooked in existing research. Here we ask whether disruptive camouflage can have benefits above and beyond concealing location. Using a novel paradigm, we dissociate the time courses of localisation and identification of a target in a single experiment. We measured the display times required for a stimulus to be located or identified (the critical duration). Targets featured either uniform, disruptive or edge enhanced disruptive colouration. Critical durations were longer for identifying targets with edge enhanced disruptive colouration camouflage even when presented against a contrasting background, such that all target types were located equally quickly. For the first time, we establish empirically that disruptive camouflage not only conceals location, but also disguises identity. This shows that this form of camouflage can be useful even when animals are not hidden. Our findings offer insights into how edge enhanced disruptive colouration undermines visual perception by disrupting object recognition

Deep Neural Network Identification of Limnonectes Species and New Class Detection Using Image Data

As is true of many complex tasks, the work of discovering, describing, and understanding the diversity of life on Earth (viz., biological systematics and taxonomy) requires many tools. Some of this work can be accomplished as it has been done in the past, but some aspects present us with challenges which traditional knowledge and tools cannot adequately resolve. One such challenge is presented by species complexes in which the morphological similarities among the group members make it difficult to reliably identify known species and detect new ones. We address this challenge by developing new tools using the principles of machine learning to resolve two specific questions related to species complexes. The first question is formulated as a classification problem in statistics and machine learning and the second question is an out-of-distribution (OOD) detection problem. We apply these tools to a species complex comprising Southeast Asian stream frogs (Limnonectes kuhlii complex) and employ a morphological character (hind limb skin texture) traditionally treated qualitatively in a quantitative and objective manner. We demonstrate that deep neural networks can successfully automate the classification of an image into a known species group for which it has been trained. We further demonstrate that the algorithm can successfully classify an image into a new class if the image does not belong to the existing classes. Additionally, we use the larger MNIST dataset to test the performance of our OOD detection algorithm. We finish our paper with some concluding remarks regarding the application of these methods to species complexes and our efforts to document true biodiversity. This paper has online supplementary materials.Comment: 26 pages, 11 Figure

Animal coloration patterns: linking spatial vision to quantitative analysis

Animal coloration patterns, from zebra stripes to bird egg speckles, are remarkably varied. With research on the perception, function, and evolution of animal patterns growing rapidly, we require a convenient framework for quantifying their diversity, particularly in the contexts of camouflage, mimicry, mate choice, and individual recognition. Ideally, patterns should be defined by their locations in a low-dimensional pattern space that represents their appearance to their natural receivers, much as color is represented by color spaces. This synthesis explores the extent to which animal patterns, like colors, can be described by a few perceptual dimensions in a pattern space. We begin by reviewing biological spatial vision, focusing on early stages during which neurons act as spatial filters or detect simple features such as edges. We show how two methods from computational vision—spatial filtering and feature detection—offer qualitatively distinct measures of animal coloration patterns. Spatial filters provide a measure of the image statistics, captured by the spatial frequency power spectrum. Image statistics give a robust but incomplete representation of the appearance of patterns, whereas feature detectors are essential for sensing and recognizing physical objects, such as distinctive markings and animal bodies. Finally, we discuss how pattern space analyses can lead to new insights into signal design and macroevolution of animal phenotypes. Overall, pattern spaces open up new possibilities for exploring how receiver vision may shape the evolution of animal pattern signals

Biological Field Expedition to the Ashaninka Communities of Coriteni Tarso (Rio Tambo) & Camantavishi (Rio Ene)

The expedition to the Ashaninka communities of Coriteni Tarso, Camantavishi and Shirampari in the summer of 2004 carried out a number of studies; primarily herpetological surveys but also vegetation, soil and hunting surveys. The work was carried out in little known areas where no previous systematic studies had been completed. The expedition recorded frogs and a snake not previously found in the department of Junín1. The expedition discovered at least one new species (to be registered as Cochranella parijarensis)

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 128, May 1974

This special bibliography lists 282 reports, articles, and other documents introduced into the NASA scientific and technical information system in April 1974

Advances in Pattern Recognition Algorithms, Architectures, and Devices

Over the last decade, tremendous advances have been made in the general area of pattern recognition techniques, devices, and algorithms. We have had the distinct pleasure of witnessing this remarkable growth as evidenced through their dissemination in the previous Optical Engineering special sections we have jointly edited— January 1998, March 1998, May 2000, and January 2002. Twenty-six papers were finally accepted for this latest special section, encompassing the recent trends and advancements made in many different areas of pattern recognition techniques utilizing algorithms, architectures, implementations, and devices. These techniques include matched spatial filter based recognition, hit-miss transforms, invariant pattern recognition, joint transform correlator JTC based recognition, morphological processing based recognition, neural network based recognition, wavelet based recognition, fingerprint and face recognition, data fusion based recognition, and target tracking, as well as other techniques. These papers summarize the work of 70 researchers from eight countries