20.7.3.7.19 Insects, Other Pests, Detection, Identification

Chapter Contents (Back)
Insect Recognition. For the damage they do:
See also Forest Change Evaluation, Bark Beetle, Pine Shoot Beetle, Other Insects.

Larios, N.[Natalia], Deng, H.L.[Hong-Li], Zhang, W.[Wei], Sarpola, M.[Matt], Yuen, J.[Jenny], Paasch, R.[Robert], Moldenke, A.[Andrew], Lytle, D.A.[David A.], Correa, S.R.[Salvador Ruiz], Mortensen, E.N.[Eric N.], Shapiro, L.G.[Linda G.], Dietterich, T.G.[Thomas G.],
Automated insect identification through concatenated histograms of local appearance features: feature vector generation and region detection for deformable objects,
MVA(19), No. 2, March 2008, pp. 105-123.
Springer DOI 0802
BibRef
Earlier:
Automated Insect Identification through Concatenated Histograms of Local Appearance Features,
WACV07(26-26).
IEEE DOI 0702
BibRef

Larios, N., Soran, B., Shapiro, L.G., Martinez-Munoz, G., Lin, J., Dietterich, T.G.,
Haar Random Forest Features and SVM Spatial Matching Kernel for Stonefly Species Identification,
ICPR10(2624-2627).
IEEE DOI 1008
BibRef

Larios, N.[Natalia], Lin, J., Zhang, M., Lytle, D.A.[David A.], Moldenke, A.[Andrew], Shapiro, L.G., Dietterich, T.G.,
Stacked spatial-pyramid kernel: An object-class recognition method to combine scores from random trees,
WACV11(329-335).
IEEE DOI 1101
BibRef

Kaya, Y.[Yilmaz], Kayci, L.[Lokman],
Application of artificial neural network for automatic detection of butterfly species using color and texture features,
VC(30), No. 1, January 2014, pp. 71-79.
WWW Link. 1412
BibRef

Martineau, M.[Maxime], Conte, D.[Donatello], Raveaux, R.[Romain], Arnault, I.[Ingrid], Munier, D.[Damien], Venturini, G.[Gilles],
A survey on image-based insect classification,
PR(65), No. 1, 2017, pp. 273-284.
Elsevier DOI 1702
Survey, Insects. Image-based insect recognition BibRef

Makori, D.M.[David M.], Fombong, A.T.[Ayuka T.], Abdel-Rahman, E.M.[Elfatih M.], Nkoba, K.[Kiatoko], Ongus, J.[Juliette], Irungu, J.[Janet], Mosomtai, G.[Gladys], Makau, S.[Sospeter], Mutanga, O.[Onisimo], Odindi, J.[John], Raina, S.[Suresh], Landmann, T.[Tobias],
Predicting Spatial Distribution of Key Honeybee Pests in Kenya Using Remotely Sensed and Bioclimatic Variables: Key Honeybee Pests Distribution Models,
IJGI(6), No. 3, 2017, pp. xx-yy.
DOI Link 1704
BibRef

Bakkay, M.C.[Mohamed Chafik], Chambon, S.[Sylvie], Rashwan, H.A.[Hatem A.], Lubat, C.[Christian], Barsotti, S.[Sébastien],
Automatic detection of individual and touching moths from trap images by combining contour-based and region-based segmentation,
IET-CV(12), No. 2, March 2018, pp. 138-145.
DOI Link 1804
BibRef

Rodriguez, I.F., Megret, R., Acuna, E., Agosto-Rivera, J.L., Giray, T.,
Recognition of Pollen-Bearing Bees from Video Using Convolutional Neural Network,
WACV18(314-322)
IEEE DOI 1806
convolution, feedforward neural nets, image classification, baseline classifiers, Task analysis BibRef

López-Fernández, L.[Luis], Lagüela, S.[Susana], Rodríguez-Gonzálvez, P.[Pablo], Martín-Jiménez, J.A.[José Antonio], González-Aguilera, D.[Diego],
Close-Range Photogrammetry and Infrared Imaging for Non-Invasive Honeybee Hive Population Assessment,
IJGI(7), No. 9, 2018, pp. xx-yy.
DOI Link 1810
BibRef

Li, F.[Fan], Xiong, Y.[Yin],
Automatic identification of butterfly species based on HoMSC and GLCMoIB,
VC(34), No. 11, November 2018, pp. 1525-1533.
WWW Link. 1810
BibRef

Machraoui, A.N.[Ahmed Nejmedine], Diouani, M.F.[Mohamed Fethi], Mouelhi, A.[Aymen], Jaouadi, K.[Kaouther], Ghrab, J.[Jamila], Abdelmelek, H.[Hafedh], Sayadi, M.[Mounir],
Automatic identification and behavioral analysis of phlebotomine sand flies using trajectory features,
VC(35), No. 5, May 2019, pp. 721-738.
Springer DOI 1906
BibRef

Wang, R.[Rui], Hu, C.[Cheng], Liu, C.J.[Chang-Jiang], Long, T.[Teng], Kong, S.Y.[Shao-Yang], Lang, T.J.[Tian-Jiao], Gould, P.J.L.[Philip J. L.], Lim, J.[Jason], Wu, K.M.[Kong-Ming],
Migratory Insect Multifrequency Radar Cross Sections for Morphological Parameter Estimation,
GeoRS(57), No. 6, June 2019, pp. 3450-3461.
IEEE DOI 1906
Insects, Radar cross-sections, Radar detection, Frequency estimation, Feature extraction, migratory insect, radar cross section (RCS) BibRef

Hu, C.[Cheng], Kong, S.Y.[Shao-Yang], Wang, R.[Rui], Zhang, F.[Fan], Wang, L.J.[Lian-Jun],
Insect Mass Estimation Based on Radar Cross Section Parameters and Support Vector Regression Algorithm,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Hu, C., Li, W., Wang, R., Long, T., Liu, C., Drake, V.A.,
Insect Biological Parameter Estimation Based on the Invariant Target Parameters of the Scattering Matrix,
GeoRS(57), No. 8, August 2019, pp. 6212-6225.
IEEE DOI 1908
eigenvalues and eigenfunctions, length measurement, mass measurement, matrix algebra, parameter estimation, orientation BibRef

Hu, C.[Cheng], Kong, S.[Shaoyang], Wang, R.[Rui], Zhang, F.[Fan],
Radar Measurements of Morphological Parameters and Species Identification Analysis of Migratory Insects,
RS(11), No. 17, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Dittrich, A.[Andreas], Roilo, S.[Stephanie], Sonnenschein, R.[Ruth], Cerrato, C.[Cristiana], Ewald, M.[Michael], Viterbi, R.[Ramona], Cord, A.F.[Anna F.],
Modelling Distributions of Rove Beetles in Mountainous Areas Using Remote Sensing Data,
RS(12), No. 1, 2020, pp. xx-yy.
DOI Link 2001
BibRef

Hao, Z.H.[Zhen-Hua], Drake, V.A.[V. Alistair], Taylor, J.R.[John R.], Warrant, E.[Eric],
Insect Target Classes Discerned from Entomological Radar Data,
RS(12), No. 4, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Drake, V.A.[V. Alistair], Hatty, S.[Shane], Symons, C.[Colin], Wang, H.[Haikou],
Insect Monitoring Radar: Maximizing Performance and Utility,
RS(12), No. 4, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Cao, X.Y.[Xiao-Yan], Guo, S.H.[Shi-Hui], Lin, J.C.[Jun-Cong], Zhang, W.S.[Wen-Shu], Liao, M.H.[Ming-Hong],
Online tracking of ants based on deep association metrics: Method, dataset and evaluation,
PR(103), 2020, pp. 107233.
Elsevier DOI 2005
Ant tracking, ResNet model, Mahalanobis distance, Appearance descriptors BibRef

de Sousa, D.J.[Daniela Justiniano], Cardoso, M.A.[Maira Arruda], Bisch, P.M.[Paulo Mascarello], Lopes, F.J.P.[Francisco José Pereira], Travençolo, B.A.N.[Bruno Augusto Nassif],
Automated standardization of images of Drosophila embryos,
JVCIR(71), 2020, pp. 102758.
Elsevier DOI 2009
Embryo standardization, Anterior-posterior orientation, Dorsal-ventral orientation, Automatic embryo positioning, BibRef

Wang, R.[Rui], Cai, J.[Jiong], Hu, C.[Cheng], Zhou, C.[Chao], Zhang, T.R.[Tian-Ran],
A Novel Radar Detection Method for Sensing Tiny and Maneuvering Insect Migrants,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Chen, C.L.[Chao-Liang], Qian, J.[Jing], Chen, X.[Xi], Hu, Z.Y.[Zeng-Yun], Sun, J.[Jiayu], Wei, S.[Shujie], Xu, K.B.[Kai-Bin],
Geographic Distribution of Desert Locusts in Africa, Asia and Europe Using Multiple Sources of Remote-Sensing Data,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Wang, R.[Rui], Zhang, Y.M.[Yi-Ming], Tian, W.M.[Wei-Ming], Cai, J.[Jiong], Hu, C.[Cheng], Zhang, T.R.[Tian-Ran],
Fast Implementation of Insect Multi-Target Detection Based on Multimodal Optimization,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
Insects on radar. BibRef

Kong, S.Y.[Shao-Yang], Hu, C.[Cheng], Wang, R.[Rui], Zhang, F.[Fan], Wang, L.J.[Lian-Jun], Long, T.[Teng], Wu, K.M.[Kong-Ming],
Insect Multifrequency Polarimetric Radar Cross Section: Experimental Results and Analysis,
GeoRS(59), No. 8, August 2021, pp. 6573-6585.
IEEE DOI 2108
Insects, Radar cross-sections, Antenna measurements, Scattering, Frequency measurement, Metals, Entomological radar, insect, radar cross section (RCS) BibRef

Fang, L.L.[Lin-Lin], Tian, W.M.[Wei-Ming], Wang, R.[Rui], Zhou, C.[Chao], Hu, C.[Cheng],
Design of Insect Target Tracking Algorithm in Clutter Based on the Multidimensional Feature Fusion Strategy,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
Track masses of insects using radar. BibRef

Wang, R.[Rui], Kou, X.[Xiao], Cui, K.[Kai], Mao, H.F.[Hua-Feng], Wang, S.[Shuaihang], Sun, Z.[Zhuoran], Li, W.D.[Wei-Dong], Li, Y.L.[Yun-Long], Hu, C.[Cheng],
Insect-Equivalent Radar Cross-Section Model Based on Field Experimental Results of Body Length and Orientation Extraction,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Sun, R.Q.[Rui-Qi], Huang, W.J.[Wen-Jiang], Dong, Y.Y.[Ying-Ying], Zhao, L.L.[Long-Long], Zhang, B.[Biyao], Ma, H.Q.[Hui-Qin], Geng, Y.[Yun], Ruan, C.[Chao], Xing, N.C.[Nai-Chen], Chen, X.D.[Xi-Dong], Li, X.L.[Xue-Ling],
Dynamic Forecast of Desert Locust Presence Using Machine Learning with a Multivariate Time Lag Sliding Window Technique,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
Insects. BibRef

Addison, F.I.[Freya I.], Dally, T.[Thomas], Duncan, E.J.[Elizabeth J.], Rouse, J.[James], Evans, W.L.[William L.], Hassall, C.[Christopher], Neely, R.R.[Ryan R.],
Simulation of the Radar Cross Section of a Noctuid Moth,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204
BibRef

Yu, T.[Teng], Li, M.[Muyang], Li, W.D.[Wei-Dong], Cai, J.[Jiong], Wang, R.[Rui], Hu, C.[Cheng],
Insect Migration Flux Estimation Based on Statistical Hypothesis for Entomological Radar,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Wang, R.[Rui], Zhang, T.R.[Tian-Ran], Cui, K.[Kai], Yu, T.[Teng], Jiang, Q.[Qi], Zhang, R.J.[Rong-Jing], Li, J.Y.[Jia-Yi], Hu, C.[Cheng],
High-Resolution and Low Blind Range Waveform for Migratory Insects' Taking-Off and Landing Behavior Observation,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Huang, Y.[Yanru], Lv, H.[Hua], Dong, Y.Y.[Ying-Ying], Huang, W.J.[Wen-Jiang], Hu, G.[Gao], Liu, Y.[Yang], Chen, H.[Hui], Geng, Y.[Yun], Bai, J.[Jie], Guo, P.[Peng], Cui, Y.F.[Yi-Feng],
Mapping the Spatio-Temporal Distribution of Fall Armyworm in China by Coupling Multi-Factors,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Buchaillot, M.L.[Ma. Luisa], Cairns, J.[Jill], Hamadziripi, E.[Esnath], Wilson, K.[Kenneth], Hughes, D.[David], Chelal, J.[John], McCloskey, P.[Peter], Kehs, A.[Annalyse], Clinton, N.[Nicholas], Araus, J.L.[José Luis], Kefauver, S.C.[Shawn C.],
Regional Monitoring of Fall Armyworm (FAW) Using Early Warning Systems,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
BibRef

Diyap, M.[Murat], Zadeh, A.T.[Ashkan Taremi], Moll, J.[Jochen], Krozer, V.[Viktor],
Numerical and Experimental Studies on the Micro-Doppler Signatures of Freely Flying Insects at W-Band,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Klein, I.[Igor], Cocco, A.[Arturo], Uereyen, S.[Soner], Mannu, R.[Roberto], Floris, I.[Ignazio], Oppelt, N.[Natascha], Kuenzer, C.[Claudia],
Outbreak of Moroccan Locust in Sardinia (Italy): A Remote Sensing Perspective,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Zhang, T.[Ting], Waqas, M.[Muhammad], Fang, Y.[Yu], Liu, Z.Y.[Zhao-Ying], Halim, Z.[Zahid], Li, Y.J.[Yu-Jian], Chen, S.[Sheng],
Weakly-supervised butterfly detection based on saliency map,
PR(138), 2023, pp. 109313.
Elsevier DOI 2303
Butterfly detection, Saliency map, Class activation map, Weakly-supervised object detection BibRef

Peignier, S.[Sergio], Lacotte, V.[Virginie], Duport, M.G.[Marie-Gabrielle], Baa-Puyoulet, P.[Patrice], Simon, J.C.[Jean-Christophe], Calevro, F.[Federica], Heddi, A.[Abdelaziz], da Silva, P.[Pedro],
Detection of Aphids on Hyperspectral Images Using One-Class SVM and Laplacian of Gaussians,
RS(15), No. 8, 2023, pp. 2103.
DOI Link 2305
BibRef

Victoriano, M.[Margarida], Oliveira, L.[Lino], Oliveira, H.P.[Hélder P.],
Automated Detection and Identification of Olive Fruit Fly Using Yolov7 Algorithm,
IbPRIA23(211-222).
Springer DOI 2307
BibRef

Lee, J.H.[Jae-Hyeon], Son, C.H.[Chang-Hwan],
Trap-Based Pest Counting: Multiscale and Deformable Attention CenterNet Integrating Internal LR and HR Joint Feature Learning,
RS(15), No. 15, 2023, pp. xx-yy.
DOI Link 2308
BibRef

Rahman, R.[Raiyan], Indris, C.[Christopher], Zhang, T.X.[Tian-Xiao], Li, K.[Kaidong], McCornack, B.[Brian], Flippo, D.[Daniel], Sharda, A.[Ajay], Wang, G.H.[Guang-Hui],
On the Real-Time Semantic Segmentation of Aphid Clusters in the Wild,
AgriVision23(6299-6306)
IEEE DOI 2309
BibRef

Vannoy, T.C.[Trevor C.], Sweeney, N.B.[Nathaniel B.], Shaw, J.A.[Joseph A.], Whitaker, B.M.[Bradley M.],
Comparison of Supervised Learning and Changepoint Detection for Insect Detection in Lidar Data,
RS(15), No. 24, 2023, pp. 5634.
DOI Link 2401
BibRef

Shukla, K.K.[Karunesh K.], Nigam, R.[Rahul], Birah, A.[Ajanta], Kanojia, A.K., Kumar, A.[Anoop], Bhattacharya, B.K.[Bimal K.], Chander, S.[Subhash],
Detection of Aphid-Infested Mustard Crop Using Ground Spectroscopy,
RS(16), No. 1, 2024, pp. xx-yy.
DOI Link 2401
BibRef


Tausch, F.[Frederic], Wagner, J.[Jan], Klaus, S.[Simon],
Pollinators as Data Collectors: Estimating Floral Diversity with Bees and Computer Vision,
CVPPA23(643-650)
IEEE DOI 2401
BibRef

Leme, M.H.C.[Matheus H. C.], Simm, V.S.[Vinicius S.], Tanno, D.R.[Douglas Rorie], Costa, Y.M.G.[Yandre M. G.], Domingues, M.A.[Marcos Aurélio],
Stingless Bee Classification: A New Dataset and Baseline Results,
CIARP23(I:730-744).
Springer DOI 2312
BibRef

Fan, M.[Min], Lu, Y.[Ying], Xu, Q.[Quanyuan], Zhang, H.[Hanrui], Chang, J.[Jumei], Deng, W.J.[Wei-Jie],
Identification of Papilionidae Species in Yunnan Province Based on Deep Learning,
ICIVC22(611-614)
IEEE DOI 2301
Deep learning, Visualization, Image recognition, Biological system modeling, Insects, Transfer learning, Adamax BibRef

Picek, L.[Lukas], Novozamsky, A.[Adam], Frydrychova, R.C.[Radmila C.], Zitova, B.[Barbara], Mach, P.[Pavel],
Monitoring of Varroa Infestation Rate in Beehives: A Simple AI Approach,
ICIP22(3341-3345)
IEEE DOI 2211
Machine learning algorithms, Costs, Machine learning, Hardware, Frequency measurement, Complexity theory, Apiculture, Bee, Varroa BibRef

Schurischuster, S.[Stefan], Kampel, M.[Martin],
Image-based Classification of Honeybees,
IPTA20(1-6)
IEEE DOI 2206
Image segmentation, Annotations, Semantics, Tools, Real-time systems, Task analysis, Testing BibRef

Shams, T.[Tooba], Desbarats, P.[Pascal],
Detection of asian hornet's nest on drone acquired FLIR and color images using deep learning methods,
IPTA20(1-6)
IEEE DOI 2206
Biological system modeling, Computational modeling, Neural networks, Color, Streaming media, Task analysis, Drones, Deep Neural Networks BibRef

Chen, X.[Xin], Wang, B.[Bin], Gao, Y.S.[Yong-Sheng],
Gaussian Convolution Angles: Invariant Vein and Texture Descriptors for Butterfly Species Identification,
ICPR21(5798-5803)
IEEE DOI 2105
Image recognition, Art, Convolution, Shape, Image databases, Veins, Lighting, butterfly species identification, image analysis, texture features BibRef

Tusubira, J.F., Nsumba, S., Ninsiima, F., Akera, B., Acellam, G., Nakatumba, J., Mwebaze, E., Quinn, J., Oyana, T.,
Improving In-field Cassava Whitefly Pest Surveillance with Machine Learning,
AgriVision20(303-309)
IEEE DOI 2008
Machine learning, Training, Task analysis, Object detection, Diseases BibRef

Tausch, F., Stock, S., Fricke, J., Klein, O.,
Bumblebee Re-Identification Dataset,
WACVWS20(35-37)
IEEE DOI 2006
Insects, Risk management, Visualization, Monitoring, Sociology, Statistics BibRef

Marstaller, J., Tausch, F., Stock, S.,
DeepBees - Building and Scaling Convolutional Neuronal Nets For Fast and Large-Scale Visual Monitoring of Bee Hives,
CVWC19(271-278)
IEEE DOI 2004
biology computing, cloud computing, computerised monitoring, data acquisition, beehive monitoring BibRef

Wu, X.P.[Xiao-Ping], Zhan, C.[Chi], Lai, Y.K.[Yu-Kun], Cheng, M.M.[Ming-Ming], Yang, J.F.[Ju-Feng],
IP102: A Large-Scale Benchmark Dataset for Insect Pest Recognition,
CVPR19(8779-8788).
IEEE DOI 2002
BibRef

Park, Y.J., Tuxworth, G., Zhou, J.,
Insect Classification Using Squeeze-and-Excitation and Attention Modules: a Benchmark Study,
ICIP19(3437-3441)
IEEE DOI 1910
Insect image classification, fine-grained image recognition, gating and attention enhanced networks BibRef

García, Z.[Zaira], Yanai, K.[Keiji], Nakano, M.[Mariko], Arista, A.[Antonio], Sanchez, L.C.[Laura Cleofas], Perez, H.[Hector],
Mosquito Larvae Image Classification Based on DenseNet and Guided Grad-CAM,
IbPRIA19(II:239-246).
Springer DOI 1910
BibRef

Murali, N., Schneider, J., Levine, J., Taylor, G.,
Classification and Re-Identification of Fruit Fly Individuals Across Days With Convolutional Neural Networks,
WACV19(570-578)
IEEE DOI 1904
biology computing, convolutional neural nets, genetics, image classification, learning (artificial intelligence), Training BibRef

Martineau, M.[Maxime], Raveaux, R.[Romain], Chatelain, C.[Clément], Conte, D.[Donatello], Venturini, G.[Gilles],
Effective Training of Convolutional Neural Networks for Insect Image Recognition,
ACIVS18(426-437).
Springer DOI 1810
BibRef

Schurischuster, S.[Stefan], Remeseiro, B.[Beatriz], Radeva, P.[Petia], Kampel, M.[Martin],
A Preliminary Study of Image Analysis for Parasite Detection on Honey Bees,
ICIAR18(465-473).
Springer DOI 1807
BibRef

Nie, L., Wang, K., Fan, X., Gao, Y.,
Fine-Grained Butterfly Recognition with Deep Residual Networks: A New Baseline and Benchmark,
DICTA17(1-7)
IEEE DOI 1804
biology computing, image classification, image recognition, learning (artificial intelligence), ResNet, Training BibRef

Gerund, S., Ogawa, T., Haseyama, M.,
Image retrieval based on LRGA algorithm and relevance feedback for insect identification,
ICIP17(3978-3982)
IEEE DOI 1803
Feature extraction, Image retrieval, Insects, Radio frequency, Visual databases, Visualization, Image retrieval, LRGA, relevance feedback BibRef

Duan, L., Shen, M., Gao, W., Cui, S., Deussen, O.,
Bee pose estimation from single images with convolutional neural network,
ICIP17(2836-2840)
IEEE DOI 1803
Antennas, Feature extraction, Insects, Pose estimation, Sugar, Tongue, Training, ConvNet, Insect pose estimation BibRef

Nguyen, N.R., Shin, M.C.,
Detecting Social Insects in Videos Using Spatiotemporal Regularization,
WACV17(493-500)
IEEE DOI 1609
Insects, Optical imaging, Spatiotemporal phenomena, Training, Training data, Videos BibRef

Carvajal, J.A.[Juan A.], Romero, D.G.[Dennis G.], Sappa, A.D.[Angel D.],
Fine-Tuning Based Deep Convolutional Networks for Lepidopterous Genus Recognition,
CIARP16(467-475).
Springer DOI 1703
BibRef

Souza, V.M.A.[Vinicius M. A.],
Identifying Aedes aegypti Mosquitoes by Sensors and One-Class Classifiers,
CIARP16(10-18).
Springer DOI 1703
BibRef

Shen, M.[Minmin], Duan, L.[Le], Deussen, O.[Oliver],
Single-Image Insect Pose Estimation by Graph Based Geometric Models and Random Forests,
BioImage16(I: 217-230).
Springer DOI 1611
BibRef

Lu, A.[An], Hou, X.W.[Xin-Wen], Liu, C.L.[Cheng-Lin], Chen, X.L.[Xiao-Lin],
Insect species recognition using discriminative local soft coding,
ICPR12(1221-1224).
WWW Link. 1302
BibRef

Sun, C., Flemons, P., Gao, Y., Wang, D., Fisher, N., La Salle, J.,
Automated Image Analysis on Insect Soups,
DICTA16(1-6)
IEEE DOI 1701
Australia BibRef

Nguyen, C.[Chuong], Lovell, D., Oberprieler, R., Jennings, D., Adcock, M., Gates-Stuart, E., La Salle, J.[John],
Virtual 3D Models of Insects for Accelerated Quarantine Control,
AccBio13(161-167)
IEEE DOI 1403
agriculture BibRef

Mele, K.,
Insect Soup Challenge: Segmentation, Counting, and Simple Classification,
AccBio13(168-171)
IEEE DOI 1403
cameras BibRef

Takahashi, A.[Akihiro], Ogawa, T.[Takahiro], Haseyama, M.[Miki],
Insect classification using Scanning Electron Microphotographs considering magnifications,
ICIP13(3269-3273)
IEEE DOI 1412
Insect classification BibRef

Shin, B.S.[Bok-Suk], Russell, J.[James], Klette, R.[Reinhard],
Feature Extraction and Classification for Insect Footprint Recognition,
CIARP12(196-203).
Springer DOI 1209
BibRef

Geng, H.[Haokun], Russell, J.[James], Shin, B.S.[Bok-Suk], Nicolescu, R.[Radu], Klette, R.[Reinhard],
A Flexible Method for Localisation and Classification of Footprints of Small Species,
PSIVT11(II: 274-286).
Springer DOI 1111
BibRef

Shin, B.S.[Bok-Suk], Cha, E.Y.[Eui-Young], Woo, Y.W.[Young Woon], Klette, R.[Reinhard],
Segmentation of Scanned Insect Footprints Using ART2 for Threshold Selection,
PSIVT07(311-320).
Springer DOI 0712
BibRef

Lu, A.[An], Hou, X.[Xin], Lin, C.[Chen], Liu, C.L.[Cheng-Lin],
Insect Species Recognition using Sparse Representation,
BMVC10(xx-yy).
HTML Version. 1009
BibRef

Zhang, X.Z.[Xiao-Zheng], Gao, Y.S.[Yong-Sheng], Caelli, T.,
Primitive-based 3D structure inference from a single 2D image for insect modeling: Towards an electronic field guide for insect identification,
ICARCV10(866-871).
IEEE DOI 1109
BibRef

Huang, S.G.[Shi-Guo], Zhou, M.Q.[Ming-Quan], Geng, G.H.[Guo-Hua], Wang, X.L.[Xiu-Li],
Ontology-based insect recognition,
IASP09(176-178).
IEEE DOI 0904
BibRef

Bechar, I.[Ikhlef], Moisan, S.[Sabine], Thonnat, M.[Monique], Bremond, F.[Francois],
On-Line Video Recognition and Counting of Harmful Insects,
ICPR10(4068-4071).
IEEE DOI 1008
BibRef

Roth, V.[Volker], Steinhage, V.[Volker], Schröder, S.[Stefan], Cremers, A.B.[Armin B.], Wittmann, D.[Dieter],
Pattern Recognition Combining De-noising and Linear Discriminant Analysis within a Real World Application,
CAIP99(251-258).
Springer DOI 9909
Classify bees based on forewings. BibRef

Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Inspection -- Lumber, Logs, Wood .


Last update:Mar 16, 2024 at 20:36:19