A biologically inspired framework for contour detection

Mignotte, Maurice

doi:10.1007/s10044-015-0494-y

Cited by 6 publications

(5 citation statements)

References 79 publications

(132 reference statements)

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“…This procedure changes image representation. 57 The result of Google aerial image segmentation would be a set of homogenous segments that cover the entire area. Each cluster contains a set of similar pixels that are similar in terms of certain characteristics, such as color, texture, or intensity.…”

Section: Methodsmentioning

confidence: 99%

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Predicting Traffic-Related Air Pollution Using Feature Extraction from Built Environment Images

Ganji

Minet

Weichenthal

et al. 2020

Environ. Sci. Technol.

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This study develops a set of algorithms to extract built environment features from Google aerial and street view images, reflecting the microcharacteristics of an urban location as well as the different functions of buildings. These features were used to train a Bayesian regularized artificial neural network (BRANN) model to predict near-road air quality based on measurements of ultrafine particles (UFPs) and black carbon (BC) in Toronto, Canada. The resulting models [adjusted R 2 of 75.87 and 79.10% for UFP and BC and root mean squared error (RMSE) of 21,800 part/cm 3 and 1300 ng/m 3 for UFP and BC] were compared with similar ANN models developed using the same predictors, but extracted from traditional geographic information system (GIS) databases [adjusted R 2 of 58.74 and 64.21% for UFP and BC and RMSE values of 23,000 part/ cm 3 and 1600 ng/m 3 for UFP and BC]. The models based on feature extraction exhibited higher predictive power, thus highlighting the greater accuracy of the proposed methods compared to GIS layers that are solely based on aerial images. A comparison with other neural network approaches as well as with a traditional land-use regression model demonstrates the strength of the BRANN model for spatial interpolation of air quality.

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Section: Methodsmentioning

confidence: 99%

“…Image segmentation was originally developed to present an image using a set of categorized pixels and/or partition a digital image into multiple segments. This procedure changes image representation . The result of Google aerial image segmentation would be a set of homogenous segments that cover the entire area.…”

Section: Methodsmentioning

confidence: 99%

Predicting Traffic-Related Air Pollution Using Feature Extraction from Built Environment Images

Ganji

Minet

Weichenthal

et al. 2020

Environ. Sci. Technol.

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“…Contour, which is a curve joining all the continuous points along the boundary of the same color or intensity, is a useful tool for the spot detection and recognition [ 38 , 39 , 40 , 41 ]. In the findcontours function of the OpenCV library, we tried different finding modes and found that the “RETR-CCOMP” mode is the best choice.…”

Section: Methodsmentioning

confidence: 99%

Automatic Spot Identification Method for High Throughput Surface Plasmon Resonance Imaging Analysis

2018

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An automatic spot identification method is developed for high throughput surface plasmon resonance imaging (SPRi) analysis. As a combination of video accessing, image enhancement, image processing and parallel processing techniques, the method can identify the spots in SPRi images of the microarray from SPRi video data. In demonstrations of the method, SPRi video data of different protein microarrays were processed by the method. Results show that our method can locate spots in the microarray accurately regardless of the microarray pattern, spot-background contrast, light nonuniformity and spotting defects, but also can provide address information of the spots.

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“…Background segmentation was outside of the scope of this work; however, it is a necessary aspect of the system for correct deformation estimation. The development of generic target segmentation algorithms is a field of computer vision in and of itself, with several notable examples available in References [82][83][84][85].…”

Section: Background Segmentationmentioning

confidence: 99%

A Methodology for Multi-Camera Surface-Shape Estimation of Deformable Unknown Objects

Nuger

Benhabib

2018

Robotics

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A novel methodology is proposed herein to estimate the three-dimensional (3D) surface shape of unknown, markerless deforming objects through a modular multi-camera vision system. The methodology is a generalized formal approach to shape estimation for a priori unknown objects. Accurate shape estimation is accomplished through a robust, adaptive particle filtering process. The estimation process yields a set of surface meshes representing the expected deformation of the target object. The methodology is based on the use of a multi-camera system, with a variable number of cameras, and range of object motions. The numerous simulations and experiments presented herein demonstrate the proposed methodology's ability to accurately estimate the surface deformation of unknown objects, as well as its robustness to object loss under self-occlusion, and varying motion dynamics.Typical motion capture methods utilize an articulated object model (i.e., a skeleton model) that is fit to the recovered 3D data [34][35][36][37][38]. The deformation of articulated objects is defined as the change in pose and orientation of the articulated links in the object. The accuracy of these methods is quantified by the angular joint error between the recovered model and ground truth. Markered motion capture methods yield higher-resolution shape recovery compared to articulated-object based methods, but depend on engineered surface features [3,39]. Several markerless motion-capture methods depend on off-line user-assisted processing for model generation [40][41][42]. High-resolution motion-capture methods fit a known mesh model to the capture data to improve accuracy [10,43,44]. Similarly, the known object model and material properties can be used to further improve the shape recovery [45]. The visual hull technique can create a movie-strip motion capture sequence of objects [8,[46][47][48] or estimate the 3D background by removing the dynamic objects in the scene [49]. All these methods require some combination of off-line processing, a priori known models, and constrained workspaces to produce a collection of models at each demand instant resulting in a movie-strip representation. Deformation estimation, however, is absent.Multi-camera deformation-estimation methods commonly implement either a Kalman filter (KF) [50-53], particle filter [54], or particle swarm optimization (PSO) [55] to track an object in a motion-capture sequence. Articulated-motion deformation prediction methods rely on a skeleton model of the target object. KFs were successfully implemented to estimate joint deformation for consecutive demand instants [2,13]. PSO-base methods were also shown to be successful in deformation estimation for articulated objects [56,57]. Mesh models combined with a KF tracking process produce greater surface accuracy for deformation estimation [3,14]. Patch-based methods track independent surface patches through an extended Kalman filter (EKF) [58], and a particle filter [59], producing deformation estimations of each patch. Many active-vision ...

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A biologically inspired framework for contour detection

Cited by 6 publications

References 79 publications

Predicting Traffic-Related Air Pollution Using Feature Extraction from Built Environment Images

Predicting Traffic-Related Air Pollution Using Feature Extraction from Built Environment Images

Automatic Spot Identification Method for High Throughput Surface Plasmon Resonance Imaging Analysis

A Methodology for Multi-Camera Surface-Shape Estimation of Deformable Unknown Objects

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