ORCID Profile
0000-0001-5943-1989
Current Organisation
CSIRO
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Artificial Intelligence and Image Processing | Pattern Recognition and Data Mining | Computer Vision | Microelectromechanical Systems (MEMS) | Library and Information Studies | Environmental Monitoring | Information Retrieval and Web Search | Image Processing
ANZSRC Socio-Economic Objective (SEO)
Expanding Knowledge in Technology | Control of Pests, Diseases and Exotic Species at Regional or Larger Scales | Plant Production and Plant Primary Products not elsewhere classified | Aquaculture Rock Lobster | Fisheries - Aquaculture not elsewhere classified | Electronic Information Storage and Retrieval Services |
Related Links
Publications
Infrared ship target segmentation through integration of multiple feature maps
Publisher: Elsevier BV
Date: 04-2016
EOCSA: Predicting prognosis of Epithelial ovarian cancer with whole slide histopathological images
Publisher: Elsevier BV
Date: 11-2022
Circular shortest path in images
Publisher: Elsevier BV
Date: 03-2003
An End-to-End TextSpotter with Explicit Alignment and Attention
Publisher: IEEE
Date: 06-2018
A Modified Distance Regularized Level Set Model for Liver Segmentation from CT Images
Publisher: Academy and Industry Research Collaboration Center (AIRCC)
Date: 28-02-2015
Robust Blood Cell Image Segmentation Method Based on Neural Ordinary Differential Equations
Publisher: Hindawi Limited
Date: 07-08-2021
DOI: 10.1155/2021/5590180
Abstract: For the analysis of medical images, one of the most basic methods is to diagnose diseases by examining blood smears through a microscope to check the morphology, number, and ratio of red blood cells and white blood cells. Therefore, accurate segmentation of blood cell images is essential for cell counting and identification. The aim of this paper is to perform blood smear image segmentation by combining neural ordinary differential equations (NODEs) with U-Net networks to improve the accuracy of image segmentation. In order to study the effect of ODE-solve on the speed and accuracy of the network, the ODE-block module was added to the nine convolutional layers in the U-Net network. Firstly, blood cell images are preprocessed to enhance the contrast between the regions to be segmented secondly, the same dataset was used for the training set and testing set to test segmentation results. According to the experimental results, we select the location where the ordinary differential equation block (ODE-block) module is added, select the appropriate error tolerance, and balance the calculation time and the segmentation accuracy, in order to exert the best performance finally, the error tolerance of the ODE-block is adjusted to increase the network depth, and the training NODEs-UNet network model is used for cell image segmentation. Using our proposed network model to segment blood cell images in the testing set, it can achieve 95.3% pixel accuracy and 90.61% mean intersection over union. By comparing the U-Net and ResNet networks, the pixel accuracy of our network model is increased by 0.88% and 0.46%, respectively, and the mean intersection over union is increased by 2.18% and 1.13%, respectively. Our proposed network model improves the accuracy of blood cell image segmentation and reduces the computational cost of the network.
Clustered nuclei splitting via curvature information and gray-scale distance transform
Publisher: Wiley
Date: 09-04-2015
DOI: 10.1111/JMI.12246
Abstract: Clusters or clumps of cells or nuclei are frequently observed in two dimensional images of thick tissue sections. Correct and accurate segmentation of overlapping cells and nuclei is important for many biological and biomedical applications. Many existing algorithms split clumps through the binarization of the input images therefore, the intensity information of the original image is lost during this process. In this paper, we present a curvature information, gray scale distance transform, and shortest path splitting line-based algorithm which can make full use of the concavity and image intensity information to find out markers, each of which represents an in idual object, and detect accurate splitting lines between objects using shortest path and junction adjustment. The proposed algorithm is tested on both synthetic and real nuclei images. Experiment results show that the performance of the proposed method is better than that of marker-controlled watershed method and ellipse fitting method.
Trinocular stereo image rectification in closed-form just using fundamental matrices
Publisher: IEEE
Date: 09-2013
Fusing convolutional neural network features with hand-crafted features for osteoporosis diagnoses
Publisher: Elsevier BV
Date: 04-2020
Robust sparse learning based on kernel non-second order minimization
Publisher: IEEE
Date: 09-2019
Supervised brain tumor segmentation based on gradient and context-sensitive features
Publisher: Frontiers Media SA
Date: 14-03-2019
Semi-automated infrared simulation on real urban scenes based on multi-view images
Publisher: The Optical Society
Date: 17-05-2016
DOI: 10.1364/OE.24.011345
Automating the quantification of membrane proteins under confocal microscopy
Publisher: IEEE
Date: 05-2012
Image Feature Information Extraction for Interest Point Detection: A Comprehensive Review
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2023
Multi-modal ship target image smoothing based on adaptive mean shift
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Instance-Aware Embedding for Point Cloud Instance Segmentation
Publisher: Springer International Publishing
Date: 2020
Skew and slant correction for document images using gradient direction
Publisher: IEEE Comput. Soc
Touching cells splitting by using concave points and ellipse fitting
Publisher: IEEE
Date: 2008
Iterated dynamic programming and quadtree subregioning for fast stereo matching
Publisher: Elsevier BV
Date: 10-2008
RELATIONSHIP BETWEEN HOVERING DEPTH AND VIEWING POSITION
Publisher: SPIE
Date: 27-08-1992
DOI: 10.1117/12.135994
Cascade knowledge diffusion network for skin lesion diagnosis and segmentation
Publisher: Elsevier BV
Date: 02-2021
Measuring the distance of vegetation from powerlines using stereo vision
Publisher: Elsevier BV
Date: 06-2006
Mosaicing of microscope images in the presence of large areas with insufficient information content
Publisher: SPIE
Date: 04-03-2010
DOI: 10.1117/12.844141
Deep learning based stereo cost aggregation on a small dataset
Publisher: IEEE
Date: 11-2021
Feature matching in stereo images encouraging uniform spatial distribution
Publisher: Elsevier BV
Date: 08-2015
Efficient corner detection based on corner enhancement filters
Publisher: Elsevier BV
Date: 04-2022
Automated feature weighting in fuzzy declustering-based vector quantization
Publisher: IEEE
Date: 08-2010
A robust matching pursuit algorithm using information theoretic learning
Publisher: Elsevier BV
Date: 11-2020
Discrete Curvature Representations for Noise Robust Image Corner Detection
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2019
STEREO VISION TECHNOLOGIES FOR CHINA’S LUNAR ROVER EXPLORATION MISSION
Publisher: ACTA Press
Date: 2016
Cell Segmentation Based on FOPSO Combined With Shape Information Improved Intuitionistic FCM
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Multipoint Filtering with Local Polynomial Approximation and Range Guidance
Publisher: IEEE
Date: 06-2014
Global threshold and region-based active contour model for accurate image segmentation
Publisher: Academy and Industry Research Collaboration Center (AIRCC)
Date: 30-06-2014
Early Yield Estimation in Viticulture Based on Grapevine Inflorescence Detection and Counting in Videos
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Refinement of LiDAR point clouds using a super voxel based approach
Publisher: Elsevier BV
Date: 09-2018
Matching Pursuit based on Kernel Non-second Order Minimization
Publisher: IEEE
Date: 10-2018
Edge-aware Filtering with Local Polynomial Approximation and Rectangle-based Weighting
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2016
DroplIT, an improved image analysis method for droplet identification in high-throughput crystallization trials
Publisher: International Union of Crystallography (IUCr)
Date: 29-10-2010
DOI: 10.1107/S0021889810040963
Abstract: The application of robotics to protein crystallization trials has resulted in the production of millions of images. Manual inspection of these images to find crystals and other interesting outcomes is a major rate-limiting step. As a result there has been intense activity in developing automated algorithms to analyse these images. The very first step for most systems that have been described in the literature is to delineate each droplet. Here, a novel approach that reaches over 97% success rate and subsecond processing times is presented. This will form the seed of a new high-throughput system to scrutinize massive crystallization c aigns automatically.
Deep learning based HEp-2 image classification: A comprehensive review
Publisher: Elsevier BV
Date: 10-2020
Stereoscopic flatbed scanner
Publisher: SPIE-Intl Soc Optical Eng
Date: 2009
DOI: 10.1117/1.3059582
Automated detection of nucleoplasmic bridges for DNA damage scoring in binucleated cells
Publisher: IEEE
Date: 08-2010
Fast Corner Detection Using Approximate Form of Second-Order Gaussian Directional Derivative
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
A local scale selection scheme for multiscale area integral invariants
Publisher: IEEE
Date: 11-2016
Tubule detection in testis images using boundary weighting and circular shortest path
Publisher: IEEE
Date: 07-2013
Learning a Compact Vein Discrimination Model With GANerated Samples
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Robust Multi-Frame Adaptive Optics Image Restoration Algorithm Using Maximum Likelihood Estimation with Poisson Statistics
Publisher: MDPI AG
Date: 06-04-2017
DOI: 10.3390/S17040785
3D symmetry detection using the extended Gaussian image
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1997
DOI: 10.1109/34.574800
Splitting touching cells based on concave points and ellipse fitting
Publisher: Elsevier BV
Date: 11-2009
Junction detection for linear structures based on Hessian, correlation and shape information
Publisher: Elsevier BV
Date: 10-2012
Detection of Tubule Boundaries based on Circular Shortest Path and Polar-Transformation of Arbitrary Shape
Publisher: Wiley
Date: 12-05-2016
DOI: 10.1111/JMI.12421
Abstract: In studies of germ cell transplantation, counting cells and measuring tubule diameters from different populations using labelled antibodies are important measurement processes. However, it is slow and sanity grinding to do these tasks manually. This paper proposes a way to accelerate these processes using a new image analysis framework based on several novel algorithms: centre points detection of tubules, tubule shape classification, skeleton-based polar-transformation, boundary weighting of polar-transformed image, and circular shortest path smoothing. The framework has been tested on a dataset consisting of 27 images which contain a total of 989 tubules. Experiments show that the detection results of our algorithm are very close to the results obtained manually and the novel approach can achieve a better performance than two existing methods.
ECFRNet: Effective corner feature representations network for image corner detection
Publisher: Elsevier BV
Date: 2023
Fast stereo matching using rectangular subregioning and 3D maximum-surface techniques
Publisher: Springer Science and Business Media LLC
Date: 2002
Mutual Guidance-based Saliency Propagation for Infrared Pedestrian Images
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
High Throughput Detection of Linear Features: Selected Applications in Biological Imaging
Publisher: Springer New York
Date: 2011
Robust Tensor Factorization Using Maximum Correntropy Criterion
Publisher: IEEE
Date: 12-2016
A robust method for high-precision quantification of the complex three-dimensional vasculatures acquired by X-ray microtomography
Publisher: International Union of Crystallography (IUCr)
Date: 11-08-2016
DOI: 10.1107/S1600577516011498
Abstract: The quantification of micro-vasculatures is important for the analysis of angiogenesis on which the detection of tumor growth or hepatic fibrosis depends. Synchrotron-based X-ray computed micro-tomography (SR-µCT) allows rapid acquisition of micro-vasculature images at micrometer-scale spatial resolution. Through skeletonization, the statistical features of the micro-vasculature can be extracted from the skeleton of the micro-vasculatures. Thinning is a widely used algorithm to produce the vascular skeleton in medical research. Existing three-dimensional thinning methods normally emphasize the preservation of topological structure rather than geometrical features in generating the skeleton of a volumetric object. This results in three problems and limits the accuracy of the quantitative results related to the geometrical structure of the vasculature. The problems include the excessively shortened length of elongated objects, eliminated branches of blood vessel tree structure, and numerous noisy spurious branches. The inaccuracy of the skeleton directly introduces errors in the quantitative analysis, especially on the parameters concerning the vascular length and the counts of vessel segments and branching points. In this paper, a robust method using a consolidated end-point constraint for thinning, which generates geometry-preserving skeletons in addition to maintaining the topology of the vasculature, is presented. The improved skeleton can be used to produce more accurate quantitative results. Experimental results from high-resolution SR-µCT images show that the end-point constraint produced by the proposed method can significantly improve the accuracy of the skeleton obtained using the existing ITK three-dimensional thinning filter. The produced skeleton has laid the groundwork for accurate quantification of the angiogenesis. This is critical for the early detection of tumors and assessing anti-angiogenesis treatments.
Multi-Scale Anisotropic Gaussian Kernels for Image Edge Detection
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Fast recovery of rotational symmetry parameters using gradient orientation
Publisher: SPIE-Intl Soc Optical Eng
Date: 04-1997
DOI: 10.1117/1.601290
Recent advances on image edge detection: A comprehensive review
Publisher: Elsevier BV
Date: 09-2022
Identifying weak linear features with the coalescing shortest path image transform
Publisher: Oxford University Press (OUP)
Date: 09-11-2011
DOI: 10.1017/S1431927611012128
Abstract: The detection of line-like features in images finds many applications in microanalysis. Actin fibers, microtubules, neurites, pilis, DNA, and other biological structures all come up as tenuous curved lines in microscopy images. A reliable tracing method that preserves the integrity and details of these structures is particularly important for quantitative analyses. We have developed a new image transform called the “Coalescing Shortest Path Image Transform” with very encouraging properties. Our scheme efficiently combines information from an extensive collection of shortest paths in the image to delineate even very weak linear features.
Iterative infrared ship target segmentation based on multiple features
Publisher: Elsevier BV
Date: 09-2014
Disparity-based Error Concealment for Stereoscopic Images with Superpixel Segmentation
Publisher: Korean Society for Internet Information (KSII)
Date: 30-09-2018
Kernel Mean p Power Error Loss for Robust Two-Dimensional Singular Value Decomposition
Publisher: IEEE
Date: 09-2019
Super-Resolution Phase Retrieval Network for Single-Pattern Structured Light 3D Imaging
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Image Dehazing Using Adaptive Bi-Channel Priors on Superpixels
Publisher: Elsevier BV
Date: 12-2017
Attentive Feature Refinement Network for Single Rainy Image Restoration
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Cross image inference scheme for stereo matching
Publisher: Springer Berlin Heidelberg
Date: 2013
Knowledge Translation and Adaptation for Efficient Semantic Segmentation
Publisher: IEEE
Date: 06-2019
Can Walking and Measuring along Chord Bunches Better Describe Leaf Shapes?
Publisher: IEEE
Date: 07-2017
Automated Image Analysis on Insect Soups
Publisher: IEEE
Date: 11-2016
Automated vein detection for drill core analysis by fusion of hyperspectral and visible image data
Publisher: IEEE
Date: 11-2016
Clustered nuclei splitting using curvature information
Publisher: IEEE
Date: 12-2011
Descent image based landing area terrain reconstruction technology for lunar landing mission
Publisher: Informa UK Limited
Date: 28-08-2015
Inter- and intra-uncertainty based feature aggregation model for semi-supervised histopathology image segmentation
Publisher: Elsevier BV
Date: 03-2024
Corner detection using multi-directional structure tensor with multiple scales
Publisher: Springer Science and Business Media LLC
Date: 29-10-2020
Robust Cell Image Segmentation via Improved Markov Random Field based on a Chinese Restaurant Process Model
Publisher: Fuji Technology Press Ltd.
Date: 20-12-2020
DOI: 10.20965/JACIII.2020.P0963
Abstract: In this paper, a segmentation method for cell images using Markov random field (MRF) based on a Chinese restaurant process model (CRPM) is proposed. Firstly, we carry out the preprocessing on the cell images, and then we focus on cell image segmentation using MRF based on a CRPM under a maximum a posteriori (MAP) criterion. The CRPM can be used to estimate the number of clusters in advance, adjusting the number of clusters automatically according to the size of the data. Finally, the conditional iteration mode (CIM) method is used to implement the MRF based cell image segmentation process. To validate our proposed method, segmentation experiments are performed on oral mucosal cell images. The segmentation results were compared with other methods, using precision, Dice, and mean square error (MSE) as the objective evaluation criteria. The experimental results show that our method produces accurate cell image segmentation results, and our method can effectively improve segmentation for the nucleus, binuclear cell, and micronucleus cell. This work will play an important role in cell image recognition and analysis.
Robust Retinal Image Enhancement via Dual-tree Complex Wavelet Transform and Morphology-based Method
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
High-Throughput Detection of Linear Features: Selected Applications in Biological Imaging
Publisher: Springer New York
Date: 2011
Segmentation of clustered nuclei based on concave curve expansion
Publisher: Wiley
Date: 20-05-2013
DOI: 10.1111/JMI.12043
Abstract: Segmentation of nuclei from images of tissue sections is important for many biological and biomedical studies. Many existing image segmentation algorithms may lead to oversegmentation or undersegmentation for clustered nuclei images. In this paper, we proposed a new image segmentation algorithm based on concave curve expansion to correctly and accurately extract markers from the original images. Marker-controlled watershed is then used to segment the clustered nuclei. The algorithm was tested on both synthetic and real images and better results are achieved compared with some other state-of-the-art methods.
Robust Tensor Decomposition for Image Representation Based on Generalized Correntropy
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Intelligent Tuna Recognition for Fisheries Monitoring
Publisher: IEEE
Date: 12-2015
A Framework for Leveraging Inter-image Information in Stereo Images for Enhanced Semantic Segmentation in Autonomous Driving
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Evaluation and Comparison of Current Fetal Ultrasound Image Segmentation Methods for Biometric Measurements: A Grand Challenge
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2014
Improving Monocular Visual Odometry Using Learned Depth
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2022
Applications of heterogeneous computing in computational and simulation science
Publisher: Inderscience Publishers
Date: 2013
Fog Density Estimation and Image Defogging Based on Surrogate Modeling for Optical Depth
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2017
Soft Cost Aggregation with Multi-Resolution Fusion
Publisher: Springer International Publishing
Date: 2014
A Novel Decision Mechanism for Image Edge Detection
Publisher: Springer International Publishing
Date: 2021
Priority-based path growing for linear feature detection
Publisher: IEEE
Date: 12-2007
Domain adaptation based self-correction model for COVID-19 infection segmentation in CT scans
Publisher: Elsevier BV
Date: 08-2021
Rectangular subregioning and 3-D maximum-surface techniques for fast stereo matching
Publisher: IEEE Comput. Soc
Semi-supervised histological image segmentation via hierarchical consistency enforcement
Publisher: Springer Nature Switzerland
Date: 2022
ReDro: Efficiently Learning Large-Sized SPD Visual Representation
Publisher: Springer International Publishing
Date: 2020
A novel method for dendritic spines detection based on directional morphological filter and shortest path
Publisher: Elsevier BV
Date: 12-2014
DOI: 10.1016/J.COMPMEDIMAG.2014.07.006
Abstract: Dendritic spines are tiny membranous protrusions from neuron's dendrites. They play a very important role in the nervous system. A number of mental diseases such as Alzheimer's disease and mental retardation are revealed to have close relations with spine morphologies or spine number changes. Spines have various shapes, and spine images are often not of good quality hence it is very challenging to detect spines in neuron images. This paper presents a novel pipeline to detect dendritic spines in 2D maximum intensity projection (MIP) images and a new dendrite backbone extraction method is developed in the pipeline. The strategy for the backbone extraction approach is that it iteratively refines the extraction result based on directional morphological filtering and improved Hessian filtering until a satisfactory extraction result is obtained. A shortest path method is applied along a backbone to extract the boundary of the dendrites. Spines are then segmented from the dendrites outside the extracted boundary. Touching spines will be split using a marker-controlled watershed algorithm. We present the results of our algorithm on real images and compare our algorithm with two other spine detection methods. The results show that the proposed approach can detect dendrites and spines more accurately. Measurements and classification of spines are also made in this paper.
Crowdsourcing the creation of image segmentation algorithms for connectomics
Publisher: Frontiers Media SA
Date: 05-11-2015
Automated analysis of neurite branching in cultured cortical neurons using HCA-Vision
Publisher: Wiley
Date: 24-08-2007
DOI: 10.1002/CYTO.A.20462
Abstract: Manual neuron tracing is a very labor-intensive task. In the drug screening context, the sheer number of images to process means that this approach is unrealistic. Moreover, the lack of reproducibility, objectivity, and auditing capability of manual tracing is limiting even in the context of smaller studies. We have developed fast, sensitive, and reliable algorithms for the purpose of detecting and analyzing neurites in cell cultures, and we have integrated them in software called HCA-Vision, suitable for the research environment. We validate the software on images of cortical neurons by comparing results obtained using HCA-Vision with those obtained using an established semi-automated tracing solution (NeuronJ). The effect of the Sez-6 deletion was characterized in detail. Sez-6 null neurons exhibited a significant increase in neurite branching, although the neurite field area was unchanged due to a reduction in mean branch length. HCA-Vision delivered considerable speed benefits and reliable traces.
Membrane boundary extraction using a circular shortest path technique
Publisher: AIP
Date: 2007
DOI: 10.1063/1.2816642
Mosaicing of microscope images with global geometric and radiometric corrections
Publisher: Wiley
Date: 11-2006
DOI: 10.1111/J.1365-2818.2006.01687.X
Abstract: Image mosaicing has found a number of applications such as panoramic imaging, digital terrain mapping, ophthalmology and virtual microscopy. In this study, we present an automated mosaicing technique for generating virtual slides from microscope images. We carried out robust image feature matching and global geometric and radiometric parameter estimation. The input images were transformed using the estimated geometric and radiometric parameters and mosaiced together, producing accurate registration of overlapping regions without visible seams.
Image Intensity Variation Information for Interest Point Detection
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2023
Linear feature detection on GPUs
Publisher: IEEE
Date: 12-2010
Linear feature enhancement based on morphological operation and Gabor function
Publisher: ACM
Date: 26-11-2012
A novel global threshold-based active contour model
Publisher: Academy & Industry Research Collaboration Center (AIRCC)
Date: 21-02-2014
Applications of heterogeneous computing in computational and simulation science
Publisher: IEEE
Date: 12-2011
DOI: 10.1109/UCC.2011.64
Corner detection based on shearlet transform and multi-directional structure tensor
Publisher: Elsevier BV
Date: 07-2020
Fish Counting and Measurement: A Modular Framework and Implementation
Publisher: IGI Global
Date: 2016
DOI: 10.4018/978-1-4666-9435-4.CH003
Abstract: An approach is suggested for automating fish identification and measurement using stereo Baited Remote Underwater Video footage. Simple methods for identifying fish are not sufficient for measurement, since the snout and tail points must be found, and the stereo data should be incorporated to find a true measurement. We present a modular framework that ties together various approaches in order to develop a generalized system for automated fish detection and measurement. A method is also suggested for using machine learning to improve identification. Experimental results indicate the suitability of our approach.
An energy minimisation approach to stereo-temporal dense reconstruction
Publisher: IEEE
Date: 2004
Membrane boundary extraction using circular multiple paths
Publisher: Elsevier BV
Date: 04-2009
Junction detection for linear structures
Publisher: AIP
Date: 2011
DOI: 10.1063/1.3596624
A Novel Color-Texture Descriptor Based on Local Histograms for Image Segmentation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Corner Detection Using Second-Order Generalized Gaussian Directional Derivative Representations
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Embedded voxel colouring with adaptive threshold selection using globally minimal surfaces
Publisher: Springer Science and Business Media LLC
Date: 28-03-2012
Segmentation of clustered nuclei based on curvature weighting
Publisher: ACM
Date: 26-11-2012
Multi-image 2D-PAGE feature detection
Publisher: IEEE
Date: 2005
Disentangled Representation Learning and Enhancement Network for Single Image De-Raining
Publisher: ACM
Date: 17-10-2021
Fast optical flow using 3D shortest path techniques
Publisher: Elsevier BV
Date: 12-2002
Arbitrarily Oriented Object Detection in Remote Sensing Images Based on Improved YOLOv4-CSP
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
2013 International Symposium on Computational Models for Life Sciences, 27-29 November, 2013, Sydney Australia
Publisher: AIP
Date: 2013
DOI: 10.1063/1.4824987
Orientation-guided geodesic weighting for PatchMatch-based stereo matching
Publisher: Elsevier BV
Date: 03-2016
Free-form tumor synthesis in computed tomography images via richer generative adversarial network
Publisher: Elsevier BV
Date: 04-2021
De-interlacing of video images using a shortest path technique
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2001
DOI: 10.1109/30.964101
Multi-Weighted Co-Occurrence Descriptor Encoding for Vein Recognition
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Knowledge-based method for segmentation and analysis of lung boundaries in chest X-ray images
Publisher: Elsevier BV
Date: 11-1998
DOI: 10.1016/S0895-6111(98)00051-2
Abstract: We present a knowledge-based approach to segmentation and analysis of the lung boundaries in chest X-rays. Image edges are matched to an anatomical model of the lung boundary using parametric features. A modular system architecture was developed which incorporates the model, image processing routines, an inference engine and a blackboard. Edges associated with the lung boundary are automatically identified and abnormal features are reported. In preliminary testing on 14 images for a set of 18 detectable abnormalities, the system showed a sensitivity of 88% and a specificity of 95% when compared with assessment by an experienced radiologist.
Closed-form stereo image rectification
Publisher: ACM
Date: 26-11-2012
Automatic Fish Recognition and Counting in the Video Footage of Fishery Operations
Publisher: IEEE
Date: 12-2015
DOI: 10.1109/CICN.2015.66
Circular shortest paths by branch and bound
Publisher: Elsevier BV
Date: 11-2003
Moving average algorithms for diamond, hexagon, and general polygonal shaped window operations
Publisher: Elsevier BV
Date: 04-2006
Fast Panoramic Stereo Matching Using Cylindrical Maximum Surfaces
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2004
DOI: 10.1109/TSMCB.2003.816997
Abstract: This paper presents a fast panoramic stereo matching algorithm using a cylindrical maximum surface technique. The disparity for a pair of panoramic images is found in a cylindrical shaped correlation coefficient volume by obtaining the maximum surface rather than simply choosing a position that gives the maximum correlation coefficient value. The use of our cylindrical maximum surface technique ensures that the disparities obtained at the left and the right columns of the panoramic stereo images are properly constrained. Typical running time for a pair of 1324 x 120 images is about 0.33 s on a 1.7-GHz PC. A variety of real images have been tested, and good results have been obtained.
A new method for linear feature and junction enhancement in 2D images based on morphological operation, oriented anisotropic Gaussian function and Hessian information
Publisher: Elsevier BV
Date: 10-2014
Design of a Clinical Decision Support System for Predicting Erectile Dysfunction in Men Using NHIRD Dataset
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2019
Thickness measurement and crease detection of wheat grains using stereo vision
Publisher: Elsevier BV
Date: 09-2007
Detection of nuclear buds based on ellipse fitting
Publisher: IEEE
Date: 12-2010
Stereo Matching based on Multi-Direction Polynomial Model
Publisher: Elsevier BV
Date: 05-2016
RA-UNet: a hybrid deep attention-aware network to extract liver and tumor in CT scans
Publisher: Frontiers Media SA
Date: 23-12-2020
DOI: 10.3389/FBIOE.2020.605132
Abstract: Automatic extraction of liver and tumor from CT volumes is a challenging task due to their heterogeneous and diffusive shapes. Recently, 2D deep convolutional neural networks have become popular in medical image segmentation tasks because of the utilization of large labeled datasets to learn hierarchical features. However, few studies investigate 3D networks for liver tumor segmentation. In this paper, we propose a 3D hybrid residual attention-aware segmentation method, i.e., RA-UNet, to precisely extract the liver region and segment tumors from the liver. The proposed network has a basic architecture as U-Net which extracts contextual information combining low-level feature maps with high-level ones. Attention residual modules are integrated so that the attention-aware features change adaptively. This is the first work that an attention residual mechanism is used to segment tumors from 3D medical volumetric images. We evaluated our framework on the public MICCAI 2017 Liver Tumor Segmentation dataset and tested the generalization on the 3DIRCADb dataset. The experiments show that our architecture obtains competitive results.
Finding splitting lines for touching cell nuclei with a shortest path algorithm
Publisher: Elsevier BV
Date: 08-2015
DOI: 10.1016/J.COMPBIOMED.2014.10.008
Abstract: A shortest path-based algorithm is proposed in this paper to find splitting lines for touching cell nuclei. First, an initial splitting line is obtained through the distance transform of a marker image and the watershed algorithm. The initial splitting line is then separated into different line segments as necessary, and the endpoint positions of these line segments are adjusted to the concave points on the contour. Finally, a shortest path algorithm is used to find the accurate splitting line between the starting-point and the end-point, and the final split can be achieved by the contour of the touching cell nuclei and the splitting lines. Comparisons of experimental results show that the proposed algorithm is effective for segmentation of different types of touching cell nuclei.
Efficient Message Passing Methods with Fully Connected Models for Early Vision
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2017
ERL-Net: Entangled Representation Learning for single image de-raining
Publisher: IEEE
Date: 10-2019
Shortest Path Based Splitting Line Finding for Touching Cells
Publisher: AIP
Date: 2013
DOI: 10.1063/1.4824997
Automatic nuclear bud detection using ellipse fitting, moving sticks or top-hat transformation
Publisher: Wiley
Date: 20-08-2013
DOI: 10.1111/JMI.12076
Abstract: Micronucleus assays are extensively used by biologists to assess genotoxicity and to monitor human exposure to genotoxic materials. As recent studies suggested that nuclear buds can be a new source of micronuclei formed in interphase, the quantification of nuclear buds, which are micronucleus like objects that are attached to the nuclei in interphase, in normal and control group is needed. Three automatic nuclear bud detection algorithms fit for different situations are proposed in this paper. One is based on ellipse fitting, one is based on a stick model and the other is based on the top-hat transform. Comparison of the three methods is also given in this paper. Experimental results showed that the proposed algorithms are all effective and efficient for nuclear bud detection.
Segmentation and tracking individual pseudomonas aeruginosa bacteria in dense populations of motile cells
Publisher: IEEE
Date: 11-2009
Context-Enhanced Representation Learning for Single Image Deraining
Publisher: Springer Science and Business Media LLC
Date: 02-03-2021
Chord Bunch Walks for Recognizing Naturally Self-Overlapped and Compound Leaves
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2019
Multiple paths extraction in images using a constrained expanded trellis
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2005
ASRSNet: Automatic Salient Region Selection Network for Few-Shot Fine-Grained Image Classification
Publisher: Springer International Publishing
Date: 2022
Foreground Object Image Masking via EPI and Edge Detection for Photogrammetry with Static Background
Publisher: Springer International Publishing
Date: 2019
2-Point linear algorithm for camera translation vector estimation with known rotation
Publisher: Cambridge University Press (CUP)
Date: 09-2000
DOI: 10.1017/S0263574799002520
Abstract: This paper introduces a simple method for obtaining the pure 3-D translational motion parameter (without rotation) and the translation parameter with a known rotation of a camera. Once the translation vector is available, the 3-D structure of an object can also be obtained. The minimum number of matching points in the two images can be as few as two, and the equations to be solved are linear. Synthetic and real image tests has been performed, and good results have been obtained.
Guided Image Completion by Confidence Propagation
Publisher: Elsevier BV
Date: 02-2016
Dendritic spines detection based on directional morphological filter and shortest path
Publisher: IEEE
Date: 08-2012
Feature correspondence with even distribution
Publisher: IEEE
Date: 12-2012
Complex shearlets and rotary phase congruence tensor for corner detection
Publisher: Elsevier BV
Date: 08-2022
Fast linear feature detection using multiple directional non-maximum suppression
Publisher: Wiley
Date: 22-04-2009
DOI: 10.1111/J.1365-2818.2009.03156.X
Abstract: The capacity to detect linear features is central to image analysis, computer vision and pattern recognition and has practical applications in areas such as neurite outgrowth detection, retinal vessel extraction, skin hair removal, plant root analysis and road detection. Linear feature detection often represents the starting point for image segmentation and image interpretation. In this paper, we present a new algorithm for linear feature detection using multiple directional non-maximum suppression with symmetry checking and gap linking. Given its low computational complexity, the algorithm is very fast. We show in several ex les that it performs very well in terms of both sensitivity and continuity of detected linear features.
Cascaded Attention Guidance Network for Single Rainy Image Restoration
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Fast linear feature detection using multiple directional non-maximum suppression
Publisher: IEEE
Date: 2006
Stereo matching using cost volume watershed and region merging
Publisher: Elsevier BV
Date: 11-2014
Boundary extraction of linear features using dual paths through gradient profiles
Publisher: Elsevier BV
Date: 09-2008
Uncalibrated three-view image rectification
Publisher: Elsevier BV
Date: 03-2003
Towards Automated Quantitative Vasculature Understanding via Ultra High-Resolution Imagery
Publisher: Springer International Publishing
Date: 13-10-2015
DOI: 10.1007/978-3-319-10984-8_10
Abstract: This chapter presents an approach to processing ultra high-resolution, large-size biomedical imaging data for the purposes of detecting and quantifying vasculature and microvasculature . Capturing early signs of any changes in vasculature may have significant values for early-diagnosis and treatment assessment due to the well understood observation that vascular changes precede cancerous growth and metastasis metastasis . With the advent of key enabling technologies for extremely high-resolution imaging, such as synchrotron radiation synchrotron radiation based computed tomography (CT) computed tomography , the required levels of detail have become accessible. However, these technologies also present challenges in data analysis. This chapter aims to offer some insights as to how these changes might be best dealt with. We argue that the necessary steps in quantitative understanding of vasculatures include targeted data enhancement enhancement , information reduction aimed at characterizing the linear structure of vessels vessels , and quantitatively describing the vessel hierarchy. We present results on cerebral and liver vasculatures of a mouse captured at the Shanghai Synchrotron Radiation Facility (SSRF). These results were achieved with a processing pipeline comprising of our empirically selected component for each of the above steps. Towards the end, we discuss how alternative and additional components may be incorporated for improved speed and robustness.
Tree structural watershed for stereo matching
Publisher: ACM
Date: 26-11-2012
Automatic segmentation of fiducial marks using attribute-based mathematical morphology
Publisher: SPIE-Intl Soc Optical Eng
Date: 04-2001
DOI: 10.1117/1.1344186
Signal and Image Analysis for Biomedical and Life Sciences
Publisher: Springer International Publishing
Date: 2014
HCA-Vision: Automated neurite outgrowth analysis
Publisher: Elsevier BV
Date: 10-2010
Abstract: Automating the analysis of neurons in culture represents a key aspect of the search for neuroactive compounds. A number of commercial neurite analysis software packages tend to measure some basic features such as total neurite length and number of branching points. However, with only these measurements, some differences between neurite morphologies that are clear to a human observer cannot be identified. The authors have developed a suite of image analysis tools that will allow researchers to produce quality analyses at primary screening rates. The suite provides sensitive and information-rich measurements of neurons and neurites. It can discriminate subtle changes in complex neurite arborization even when neurons and neurites are dense. This allows users to selectively screen for compounds triggering different types of neurite outgrowth behavior. In mixed cell populations, neurons can be filtered and separated from other brain cell types so that neurite analysis can be performed only on neurons. It supports batch processing with a built-in database to store the batch-processing results, a batch result viewer, and an ad hoc query builder for users to retrieve features of interest. The suite of tools has been deployed into a software package called HCA-Vision. The free version of the software package is available at www.hca-vision.com.
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