The Experts below are selected from a list of 138 Experts worldwide ranked by ideXlab platform

Oskar Liset Pryds Hansen - One of the best experts on this subject based on the ideXlab platform.

  • species level image classification with convolutional neural network enables Insect Identification from habitus images
    Ecology and Evolution, 2020
    Co-Authors: Oskar Liset Pryds Hansen, Jenschristian Svenning, Kent Olsen, Steen Dupont, Beulah H Garner, Alexandros Iosifidis, Benjamin W Price, Toke T Hoye
    Abstract:

    Changes in Insect biomass, abundance, and diversity are challenging to track at sufficient spatial, temporal, and taxonomic resolution. Camera traps can capture habitus images of ground-dwelling Insects. However, currently sampling involves manually detecting and identifying specimens. Here, we test whether a convolutional neural network (CNN) can classify habitus images of ground beetles to species level, and estimate how correct classification relates to body size, number of species inside genera, and species identity.We created an image database of 65,841 museum specimens comprising 361 carabid beetle species from the British Isles and fine-tuned the parameters of a pretrained CNN from a training dataset. By summing up class confidence values within genus, tribe, and subfamily and setting a confidence threshold, we trade-off between classification accuracy, precision, and recall and taxonomic resolution.The CNN classified 51.9% of 19,164 test images correctly to species level and 74.9% to genus level. Average classification recall on species level was 50.7%. Applying a threshold of 0.5 increased the average classification recall to 74.6% at the expense of taxonomic resolution. Higher top value from the output layer and larger sized species were more often classified correctly, as were images of species in genera with few species.Fine-tuning enabled us to classify images with a high mean recall for the whole test dataset to species or higher taxonomic levels, however, with high variability. This indicates that some species are more difficult to identify because of properties such as their body size or the number of related species.Together, species-level image classification of arthropods from museum collections and ecological monitoring can substantially increase the amount of occurrence data that can feasibly be collected. These tools thus provide new opportunities in understanding and predicting ecological responses to environmental change.

Toke T Hoye - One of the best experts on this subject based on the ideXlab platform.

  • species level image classification with convolutional neural network enables Insect Identification from habitus images
    Ecology and Evolution, 2020
    Co-Authors: Oskar Liset Pryds Hansen, Jenschristian Svenning, Kent Olsen, Steen Dupont, Beulah H Garner, Alexandros Iosifidis, Benjamin W Price, Toke T Hoye
    Abstract:

    Changes in Insect biomass, abundance, and diversity are challenging to track at sufficient spatial, temporal, and taxonomic resolution. Camera traps can capture habitus images of ground-dwelling Insects. However, currently sampling involves manually detecting and identifying specimens. Here, we test whether a convolutional neural network (CNN) can classify habitus images of ground beetles to species level, and estimate how correct classification relates to body size, number of species inside genera, and species identity.We created an image database of 65,841 museum specimens comprising 361 carabid beetle species from the British Isles and fine-tuned the parameters of a pretrained CNN from a training dataset. By summing up class confidence values within genus, tribe, and subfamily and setting a confidence threshold, we trade-off between classification accuracy, precision, and recall and taxonomic resolution.The CNN classified 51.9% of 19,164 test images correctly to species level and 74.9% to genus level. Average classification recall on species level was 50.7%. Applying a threshold of 0.5 increased the average classification recall to 74.6% at the expense of taxonomic resolution. Higher top value from the output layer and larger sized species were more often classified correctly, as were images of species in genera with few species.Fine-tuning enabled us to classify images with a high mean recall for the whole test dataset to species or higher taxonomic levels, however, with high variability. This indicates that some species are more difficult to identify because of properties such as their body size or the number of related species.Together, species-level image classification of arthropods from museum collections and ecological monitoring can substantially increase the amount of occurrence data that can feasibly be collected. These tools thus provide new opportunities in understanding and predicting ecological responses to environmental change.

Eric N. Mortensen - One of the best experts on this subject based on the ideXlab platform.

  • Automated Insect Identification through concatenated histograms of local appearance features: feature vector generation and region detection for deformable objects
    Machine Vision and Applications, 2008
    Co-Authors: Natalia Larios, Hongli Deng, Matt Sarpola, Jenny Yuen, Andrew Moldenke, David A. Lytle, Salvador Ruiz Correa, Robert Paasch, Wei Zhang, Eric N. Mortensen
    Abstract:

    This paper describes a computer vision approach to automated rapid-throughput taxonomic Identification of stonefly larvae. The long-term objective of this research is to develop a cost-effective method for environmental monitoring based on automated Identification of indicator species. Recognition of stonefly larvae is challenging because they are highly articulated, they exhibit a high degree of intraspecies variation in size and color, and some species are difficult to distinguish visually, despite prominent dorsal patterning. The stoneflies are imaged via an apparatus that manipulates the specimens into the field of view of a microscope so that images are obtained under highly repeatable conditions. The images are then classified through a process that involves (a) Identification of regions of interest, (b) representation of those regions as SIFT vectors (Lowe, in Int J Comput Vis 60(2):91–110, 2004) (c) classification of the SIFT vectors into learned “features” to form a histogram of detected features, and (d) classification of the feature histogram via state-of-the-art ensemble classification algorithms. The steps (a) to (c) compose the concatenated feature histogram (CFH) method. We apply three region detectors for part (a) above, including a newly developed principal curvature-based region (PCBR) detector. This detector finds stable regions of high curvature via a watershed segmentation algorithm. We compute a separate dictionary of learned features for each region detector, and then concatenate the histograms prior to the final classification step. We evaluate this classification methodology on a task of discriminating among four stonefly taxa, two of which, Calineuria and Doroneuria , are difficult even for experts to discriminate. The results show that the combination of all three detectors gives four-class accuracy of 82% and three-class accuracy (pooling Calineuria and Doro-neuria ) of 95%. Each region detector makes a valuable contribution. In particular, our new PCBR detector is able to discriminate Calineuria and Doroneuria much better than the other detectors.

  • automated Insect Identification through concatenated histograms of local appearance features
    Workshop on Applications of Computer Vision, 2007
    Co-Authors: Natalia Larios, Hongli Deng, Matt Sarpola, Jenny Yuen, Andrew Moldenke, David A. Lytle, Salvador Ruiz Correa, Robert Paasch, Wei Zhang, Eric N. Mortensen
    Abstract:

    This paper describes a fully automated stone fly-larvae classification system using a local features approach. It compares the three region detectors employed by the system: the Hessian-affine detector, the Kadir entropy detector and a new detector we have developed called the principal curvature based region detector (PCBR). It introduces a concatenated feature histogram (CFH) methodology that uses histograms of local region descriptors as feature vectors for classification and compares the results using this methodology to that of Opelt [Opelt, A, et.al., 2006.] on three stonefly Identification tasks. Our results indicate that the PCBR detector outperforms the other two detectors on the most difficult discrimination task and that the use of all three detectors outperforms any other configuration. The CFH methodology also outperforms the Opelt methodology in these tasks

Jamie R. Stevens - One of the best experts on this subject based on the ideXlab platform.

  • species Identification of hypoderma affecting domestic and wild ruminants by morphological and molecular characterization
    Medical and Veterinary Entomology, 2003
    Co-Authors: Domenico Otranto, Donato Traversa, Douglas D. Colwell, Jamie R. Stevens
    Abstract:

    .  Cuticular structures and the sequence of the cytochrome oxidase I gene were compared for Hypoderma bovis (Linnaeus), Hypoderma lineatum (De Villers), Hypoderma actaeon Brauer, Hypoderma diana Brauer and Hypoderma tarandi (Linnaeus) (Diptera, Oestridae). Third-stage larvae of each species were examined by scanning electron microscopy revealing differences among species in the pattern and morphology of spines on the cephalic and thoracic segments, by spine patterns on the tenth abdominal segment, and by morphology of the spiracular plates. The morphological approach was supported by the molecular characterization of the most variable region of the cytochrome oxidase I (COI) gene of these species, which was amplified by polymerase chain reaction and analysed. Amplicons were digested with the unique restriction enzyme, BfaI, providing diagnostic profiles able to simultaneously differentiate all Hypoderma species examined. These findings confirm the utility of morphological characters for differentiating the most common Hypoderma larvae and reconfirm the power of the COI gene for studying Insect Identification and systematics.

Natalia Larios - One of the best experts on this subject based on the ideXlab platform.

  • Automated Insect Identification through concatenated histograms of local appearance features: feature vector generation and region detection for deformable objects
    Machine Vision and Applications, 2008
    Co-Authors: Natalia Larios, Hongli Deng, Matt Sarpola, Jenny Yuen, Andrew Moldenke, David A. Lytle, Salvador Ruiz Correa, Robert Paasch, Wei Zhang, Eric N. Mortensen
    Abstract:

    This paper describes a computer vision approach to automated rapid-throughput taxonomic Identification of stonefly larvae. The long-term objective of this research is to develop a cost-effective method for environmental monitoring based on automated Identification of indicator species. Recognition of stonefly larvae is challenging because they are highly articulated, they exhibit a high degree of intraspecies variation in size and color, and some species are difficult to distinguish visually, despite prominent dorsal patterning. The stoneflies are imaged via an apparatus that manipulates the specimens into the field of view of a microscope so that images are obtained under highly repeatable conditions. The images are then classified through a process that involves (a) Identification of regions of interest, (b) representation of those regions as SIFT vectors (Lowe, in Int J Comput Vis 60(2):91–110, 2004) (c) classification of the SIFT vectors into learned “features” to form a histogram of detected features, and (d) classification of the feature histogram via state-of-the-art ensemble classification algorithms. The steps (a) to (c) compose the concatenated feature histogram (CFH) method. We apply three region detectors for part (a) above, including a newly developed principal curvature-based region (PCBR) detector. This detector finds stable regions of high curvature via a watershed segmentation algorithm. We compute a separate dictionary of learned features for each region detector, and then concatenate the histograms prior to the final classification step. We evaluate this classification methodology on a task of discriminating among four stonefly taxa, two of which, Calineuria and Doroneuria , are difficult even for experts to discriminate. The results show that the combination of all three detectors gives four-class accuracy of 82% and three-class accuracy (pooling Calineuria and Doro-neuria ) of 95%. Each region detector makes a valuable contribution. In particular, our new PCBR detector is able to discriminate Calineuria and Doroneuria much better than the other detectors.

  • automated Insect Identification through concatenated histograms of local appearance features
    Workshop on Applications of Computer Vision, 2007
    Co-Authors: Natalia Larios, Hongli Deng, Matt Sarpola, Jenny Yuen, Andrew Moldenke, David A. Lytle, Salvador Ruiz Correa, Robert Paasch, Wei Zhang, Eric N. Mortensen
    Abstract:

    This paper describes a fully automated stone fly-larvae classification system using a local features approach. It compares the three region detectors employed by the system: the Hessian-affine detector, the Kadir entropy detector and a new detector we have developed called the principal curvature based region detector (PCBR). It introduces a concatenated feature histogram (CFH) methodology that uses histograms of local region descriptors as feature vectors for classification and compares the results using this methodology to that of Opelt [Opelt, A, et.al., 2006.] on three stonefly Identification tasks. Our results indicate that the PCBR detector outperforms the other two detectors on the most difficult discrimination task and that the use of all three detectors outperforms any other configuration. The CFH methodology also outperforms the Opelt methodology in these tasks