A software system for automated identification and retrieval of moth images based on wing attributes

Feng, Linan; Bhanu, Bir; Heraty, John M.

doi:10.1016/j.patcog.2015.09.012

Cited by 33 publications

(22 citation statements)

References 82 publications

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“…Finally some studies base their segmentation pipelines on image apriori. [42] assumes the object in the image yields the longest outline while [40] searches for lines on wing images and [54] uses the symmetry of lepidoptera as a detection criterion.…”

Section: Foreground/background Detectionmentioning

confidence: 99%

A survey on image-based insect classification

Martineau

Conte

Raveaux

et al. 2017

Pattern Recognition

177

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International audienceEntomology has had many applications in many biological domains (i.e insect counting as a biodiversity index). To meet a growing biological demand and to compensate a decreasing workforce amount, automated entomology has been around for decades. This challenge has been tackled by computer scientists as well as by biologists themselves. This survey investigates fourty-four studies on this topic and tries to give a global picture on what are the scientific locks and how the problem was addressed. Views are adopted on image capture, feature extraction, classification methods and the tested datasets. A general discussion is finally given on the questions that might still remain unsolved such as: the image capture conditions mandatory to good recognition performance, the definition of the problem and whether computer scientist should consider it as a problem in its own or just as an instance of a wider image recognition problem

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Section: Foreground/background Detectionmentioning

confidence: 99%

A survey on image-based insect classification

Martineau

Conte

Raveaux

et al. 2017

Pattern Recognition

177

View full text Add to dashboard Cite

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“…Automation of species identification systems proved that these tedious tasks could be accomplished more feasible and efficient while minimising sources of errors [48]. Examples of such systems are Automated Leafhopper Identification System (ALIS) [21], Digital Automated Identification System (DAISY) [73], Automatic Identification and characterisation of Microbial Populations (AIMS) [44], Automated Bee Identification System (ABIS) [6], Bug-Visux [32], automated identification of bacteria using statistical methods [97], an automated identification system which estimates whiteflies, aphids and thrips densities in a greenhouse [15], Species Identification Automated (SPIDA) [84], But2fly [58], Automated Insect Identification through Concatenated Histograms of Local Appearance (AIICHLA) [53], an automated identification system for algae [17], automatic recognition of biological particles in microscopic images [81], automatic species identification of live moths [67] automated image-based phenotypic analysis in zebrafish embryos [100], automatic recognition system for some cyanobacteria using image processing techniques and ANN approach [65], automatic detection of malaria parasites for estimating parasitaemia [89], automated weed classification with local pattern-based texture descriptors [2], automated processing of imaging data through multi-tiered classification of biological structures illustrated using caenorhabditis elegans [111], automated identification of copepods using digital image processing and artificial neural network [55], automatic plant species identification using sparse representation of leaf tooth features [42], automated system for malaria parasite identification [88], a software system for automated identification and retrieval of moth images based on wing attributes [24], automatic wild animal monitoring by identification of animal species in camera-trap images using very deep convolutional neural networks [29], automated identification of anastrepha fruit flies in the fraterculus group [76] and automated identification of fish species based on otolith contour, using Short-Time Fourier Transform and Discriminant Analysis (STFT-DA) [85]. Auto...…”

Section: What Has Been Done?mentioning

confidence: 99%

“…Most of the classification methods are mentioned in [60,89,112], including structural, fuzzy, transform, neural network-based methods and many more. Some automated identification systems [55] employ neural networks or learning algorithms when there are many classes and small Automatic insect classification 10 SVM > 90 [56] Automated identification and retrieval of moth images 50 SRV 85 [25] Automatic identification of species 740 ANN 91-93 [35] Water monitoring -automated and real time identification and classification of algae 23 ANN: SOM 98 [17] Automatic identification of butterfly species 5 ANN 98 [49] Automated system for malaria parasite identification 2 SVM 80 [88] Automatic plant species identification 8 Sparse representation 76-79 [42] Automated identification of copepods 8 ANN 93.13 [55] Automated identification and retrieval of moth images 50 SRV attributes 34-70 [24] Automatic wild animal identification 26…”

Section: Classificationmentioning

confidence: 99%

How automated image analysis techniques help scientists in species identification and classification?

2018

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Identification of taxonomy at a specific level is time consuming and reliant upon expert ecologists. Hence the demand for automated species identification incre-ased over the last two decades. Automation of data classification is primarily focussed on images while incorporating and analysing image data has recently become easier due to developments in computational technology. Research ef-forts on identification of species include specimens' image processing, extraction of identical features, followed by classifying them into correct categories. In this paper, we discuss recent automated species identification systems, mainly for categorising and evaluating their methods. We reviewed and compared different methods in step by step scheme of automated identification and classification systems of species images. The selection of methods is influenced by many variables such as level of classification, number of training data and complexity of images. The aim of writing this paper is to provide researchers and scientists an extensive background study on work related to automated species identification, focusing on pattern recognition techniques in building such systems for biodiversity studies. (Folia Morphol 2018; 77, 2: 179-193).

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“…It is crucial to detect background and foreground of an image because the background usually contains disturbing visual information that can affect the performance of models (Feng, Bhanu, & Heraty, 2016). Therefore, multi-threshold methods, as described by (Xiao-bo, Jiewen, Yanxiao, & Holmes, 2010) were applied for background removal using the Image Processing Toolbox of MATLAB ® (the Mathworks Inc., Natick, MA, USA).…”

Section: Image Acquisitionmentioning

confidence: 99%