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A two-step approach for detecting Striga in a complex agroecological system using Sentinel-2 data
2020
Science of the Total Environment
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In this study, a two-step classification approach was used to detect Striga (Striga hermonthica) weed occurrence within croplands in Rongo, Kenya. ...
The remaining cropland area was then used in a subpixel multiple endmember spectral mixture analysis (MESMA) to detect Striga occurrence and infestation using endmembers (EMs) obtained from the in-situ ...
For each of the mapping approaches (i.e. cropland mapping in GEE and Striga occurrence mapping using MESMA), the error matrices that provided all the four metrics i.e. ...
doi:10.1016/j.scitotenv.2020.143151
pmid:33143922
fatcat:q43i4eznijd3hpqzd6jchrooem
A Review of Web Infodemic Analysis and Detection Trends across Multi-modalities using Deep Neural Networks
[article]
2021
arXiv
pre-print
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This review primarily deals with multi-modal fake news detection techniques that include images, videos, and their combinations with text. ...
Researchers are analyzing online data based on multiple modalities composed of text, image, video, speech, and other contributing factors. ...
[98] used RGB color components in the images to detect changes that occurred due to tampering. Nataraj et al. [99] detected GAN-generated images using co-occurrence matrices. ...
arXiv:2112.00803v1
fatcat:twppg5v37bdozcdloaa6zfk7s4
Context based object categorization: A critical survey
2010
Computer Vision and Image Understanding
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Recognizing an object in an image is difficult when images present occlusion, poor quality, noise or background clutter, and this task becomes even more challenging when many objects are present in the ...
Several models for object categorization use appearance and context information from objects to improve recognition accuracy. ...
The majority of the context-based models include at most two different types of context, semantic and spatial, since the complexity to determine scale context is still high for 2D images. ...
doi:10.1016/j.cviu.2010.02.004
fatcat:3ee2st4tffbnplewyrq5o4i5rm
Detection of Opinion Communities with the Help of Chance-Corrected Measures of Agreement
2020
SN Computer Science
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It is argued that with their help it is possible to increase the accuracy of users' assessment of various items (texts, but also potentially images, movies, music and goods). ...
Chance-corrected measures of similarity also allow for the detection of similarly minded users in a more accurate manner. Results of small-scale empirical tests inform the discussion. ...
In the case of the image, segments refer to picture elements (pixels of certain size); in the case of the movie or video, segments refer to scenes, etc. ...
doi:10.1007/s42979-020-00129-8
fatcat:uzeye4qxpzcnpbyjiuj36vl3my
Pipelining Localized Semantic Features for Fine-Grained Action Recognition
[chapter]
2014
Lecture Notes in Computer Science
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In fine-grained action (object manipulation) recognition, it is important to encode object semantic (contextual) information, i.e., which object is being manipulated and how it is being operated. ...
In the feature encoding stage, we develop a semantic-grouped locality-constrained linear coding (SG-LLC) method that captures the joint distributions between motion and object-in-use information. ...
This is because in fine-grained actions, local manipulation motion details (e.g., subtle movements of hand in operating an object) are much more important than global co-occurrence information. ...
doi:10.1007/978-3-319-10593-2_32
fatcat:zoakjrjhsjdenos4rnnbe5u4lq
GANprintR: Improved Fakes and Evaluation of the State of the Art in Face Manipulation Detection
[article]
2020
arXiv
pre-print
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of this type of facial manipulation, considering the state-of-the-art fake detection systems (based on holistic deep networks, steganalysis, and local artifacts), remarking how challenging is this task ...
detection systems while keeping the visual quality of the resulting images; ii) an in-depth analysis of the recent literature in facial manipulation detection; iii) a complete experimental assessment ...
In particular, this approach calculates the co-occurrence matrices directly from the image pixels on each channel (red, green and blue), and passes this information through a custom CNN, which allows the ...
arXiv:1911.05351v4
fatcat:vleylpyukfhuxbjl724imrgn6m
Context modeling in computer vision: techniques, implications, and applications
2010
Multimedia tools and applications
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The basic motivation behind these diverse efforts is generally the same-attempting to enhance current image analysis technologies by incorporating information from outside the target object, including ...
This review is intended to introduce researchers in computer vision and image analysis to this increasingly important field as well as provide a reference for those who may wish to incorporate context ...
It is straightforward to build context matrices to count co-occurrence of labels given a dataset where many objects are labeled. ...
doi:10.1007/s11042-010-0631-y
fatcat:cspmqzbtinexrghz2zxjklkyva
Application of Radiomics and Decision Support Systems for Breast MR Differential Diagnosis
2018
Computational and Mathematical Methods in Medicine
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Over the years, MR systems have evolved from imaging modalities to advanced computational systems producing a variety of numerical parameters that can be used for the noninvasive preoperative assessment ...
Furthermore, the combination with state-of-the-art image analysis methods provides a plethora of quantifiable imaging features, termed radiomics that increases diagnostic accuracy towards individualized ...
Second-order histograms such as graylevel co-occurrence matrices (GLCMs) [28, 48] and graylevel run-length matrices (GLRLMs) [29, 49] characterize spatial relationships between pixel intensities in ...
doi:10.1155/2018/7417126
fatcat:z25pzfuwnzfthdf762nchgcphy
Fusion of handcrafted and deep features for forgery detection in digital images
2021
IEEE Access
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The pixels of an image are used to determine the intrinsic changes in the images due to underlying manipulations. ...
Due to such manipulations, the intrinsic properties of the image, such as pixel correlations, chrominance, and luminance characteristics, become inconsistent. ...
doi:10.1109/access.2021.3096240
fatcat:momibd4a7vaixobzqzv5bg53sm
Exemplar-Based Recognition of Human–Object Interactions
2016
IEEE transactions on circuits and systems for video technology (Print)
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Human action can be recognised from a single still image by modelling human-object interactions (HOI), which infers the mutual spatial structure information between human and the manipulated object as ...
Existing approaches rely heavily on accurate detection of human and object and estimation of human pose; they are thus sensitive to large variations of human poses, occlusion and unsatisfactory detection ...
Existing approaches focus on modelling the co-occurrence or spatial relationship between human and the manipulated object. ...
doi:10.1109/tcsvt.2015.2397200
fatcat:v7cusm7hcfbj5aeqrdqwrb6cq4
Biocomplexity and Fractality in the Search of Biomarkers of Aging and Pathology: Focus on Mitochondrial DNA and Alzheimer's Disease
2017
Aging and Disease
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Fractal lacunarity analysis represents a useful tool to analyze mtDNA mutations. ...
Therefore, Chaos Game Representation method has been used to display DNA fractal properties after adapting the algorithm to visualize also heteroplasmic mutations. ...
They contributed to generate CGR matrices during their INRCA experience as stagers. ...
doi:10.14336/ad.2016.0629
pmid:28197358
pmcid:PMC5291006
fatcat:nf24hjam4rfyjeo7flkys7jdjm
A rational model of the effects of distributional information on feature learning
2011
Cognitive Psychology
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In particular, we investigate whether people are sensitive to how parts co-vary over objects they observe. ...
In a series of four behavioral experiments (three using visual stimuli, one using conceptual stimuli), we demonstrate that people infer different features to represent the same four objects depending on ...
structure of the stimuli: The co-occurrence of parts creates a pattern of correlation that is best explained by postulating the objects themselves as features, resulting in a holistic representation of ...
doi:10.1016/j.cogpsych.2011.08.002
pmid:21937008
fatcat:buoxuzp47zdg7k47ak6pbovtza
Comparative Performance Evaluation of Pixel-Level and Decision-Level Data Fusion of Landsat 8 OLI, Landsat 7 ETM+ and Sentinel-2 MSI for Crop Ensemble Classification
2018
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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This research implements parallel and concatenation approach to ensemble classify the images. ...
Objective of this study is to evaluate the comparative performance between decision-and pixel-levels data fusion ensemble classified maps using Landsat 8, Landsat 7, and Sentinel-2 data. ...
Cloud and cloud shadow mask from Landsat 8 imagery are used to mask out cloud-free pixels from either reference images. ...
doi:10.1109/jstars.2018.2870650
fatcat:flrvhffevvfklonp65qxgd7jbq
Author Index
2010
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
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Fuchs, Thomas
Neuron Geometry Extraction by Perceptual Grouping in ssTEM Images
Fujiyoshi, Hironobu
Workshop: Feature Co-occurrence Representation Based on Boosting for Object Detection
Furukawa ...
Co-clustering of Image Segments Using Convex Optimization Applied to EM Neuronal Reconstruction Vitaladevuni, Shiv Increasing Depth Resolution of Electron Microscopy of Neural Circuits using Sparse Tomographic ...
doi:10.1109/cvpr.2010.5539913
fatcat:y6m5knstrzfyfin6jzusc42p54
Invariant Deep Compressible Covariance Pooling for Aerial Scene Categorization
2020
IEEE Transactions on Geoscience and Remote Sensing
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2020 pre-print arXiv:2011.05702v1 fatcat:tctau4cjsvfrzjocgdei6kv4ra
We consider transforming the input image according to a finite transformation group that consists of multiple confounding orthogonal matrices, such as the D4 group. ...
In particular, with using ResNet architecture, our IDCCP model can reduce the dimension of the tensor representation by about 98% without sacrificing accuracy (i.e., <0.5%). ...
These methods can detect co-occurrences of features at any positions in a standard CNN architecture, and any preferred poses in a G-space, but the computational cost scales dramatically with the increasing ...
doi:10.1109/tgrs.2020.3026221
fatcat:254e2pou3fb3tlexrebsubtlia
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