scholarly journals High-NA optical edge detection via optimized multilayer films

2021 ◽  
Author(s):  
Wenjin Xue ◽  
Owen Miller
Keyword(s):  
Edge Detection ◽  
State Of The Art ◽  
Numerical Aperture ◽  
Spatial Filter ◽  
Multilayer Films ◽  
Linear Operators ◽  
Two Dimensional Image ◽  
Visible Frequency ◽  
Nanophotonic Structures ◽  
Significant Effort

Abstract There has been a significant effort to design nanophotonic structures that process images at the speed of light. A prototypical example is in edge detection, where photonic-crystal-, metasurface-, and plasmon-based designs have been proposed and in some cases experimentally demonstrated. In this work, we show that multilayer optical interference coatings can achieve visible-frequency edge detection in transmission with high numerical aperture, two-dimensional image formation, and straightforward fabrication techniques, unique among all nanophotonic approaches. We show that the conventional Laplacian-based transmission spectrum may not be ideal once the scattering physics of real designs is considered, and show that better performance can be attained with alternative spatial filter functions. Our designs, comprising alternating layers of Si and SiO2 with total thicknesses of only 1 µm, demonstrate the possibility for optimized multilayer films to achieve state-of-the-art edge detection, and, more broadly, analog optical implementations of linear operators.

scholarly journals Contour Detection for Fibre of Preserved Szechuan Pickle Based on Dilated Convolution

2019 ◽  
Vol 9 (13) ◽  
pp. 2684 ◽  
Author(s):  
Hongyang Li ◽  
Lizhuang Liu ◽  
Zhenqi Han ◽  
Dan Zhao
Keyword(s):  
Edge Detection ◽  
State Of The Art ◽  
Contour Detection ◽  
Experimental Results ◽  
Contour Method ◽  
Class Differences ◽  
Dilated Convolution ◽  
The Mean ◽  
Art Performance

Peeling fibre is an indispensable process in the production of preserved Szechuan pickle, the accuracy of which can significantly influence the quality of the products, and thus the contour method of fibre detection, as a core algorithm of the automatic peeling device, is studied. The fibre contour is a kind of non-salient contour, characterized by big intra-class differences and small inter-class differences, meaning that the feature of the contour is not discriminative. The method called dilated-holistically-nested edge detection (Dilated-HED) is proposed to detect the fibre contour, which is built based on the HED network and dilated convolution. The experimental results for our dataset show that the Pixel Accuracy (PA) is 99.52% and the Mean Intersection over Union (MIoU) is 49.99%, achieving state-of-the-art performance.

scholarly journals Bottom-up and Top-down: Bidirectional Additive Net for Edge Detection

2020 ◽  
Author(s):  
Lianli Gao ◽  
Zhilong Zhou ◽  
Heng Tao Shen ◽  
Jingkuan Song
Keyword(s):  
Edge Detection ◽  
Spatial Information ◽  
New Records ◽  
State Of The Art ◽  
Top Down ◽  
Bottom Up ◽  
Image Edge Detection ◽  
Universal Network ◽  
Image Edge ◽  
Hierarchical Representations

Image edge detection is considered as a cornerstone task in computer vision. Due to the nature of hierarchical representations learned in CNN, it is intuitive to design side networks utilizing the richer convolutional features to improve the edge detection. However, there is no consensus way to integrate the hierarchical information. In this paper, we propose an effective and end-to-end framework, named Bidirectional Additive Net (BAN), for image edge detection. In the proposed framework, we focus on two main problems: 1) how to design a universal network for incorporating hierarchical information sufficiently; and 2) how to achieve effective information flow between different stages and gradually improve the edge map stage by stage. To tackle these problems, we design a consecutive bottom-up and top-down architecture, where a bottom-up branch can gradually remove detailed or sharp boundaries to enable accurate edge detection and a top-down branch offers a chance of error-correcting by revisiting the low-level features that contain rich textual and spatial information. And attended additive module (AAM) is designed to cumulatively refine edges by selecting pivotal features in each stage. Experimental results show that our proposed methods can improve the edge detection performance to new records and achieve state-of-the-art results on two public benchmarks: BSDS500 and NYUDv2.

scholarly journals Fast horizon detection in maritime images using region-of-interest

2018 ◽  
Vol 14 (7) ◽  
pp. 155014771879075 ◽  
Author(s):  
Chi Yoon Jeong ◽  
Hyun S Yang ◽  
KyeongDeok Moon
Keyword(s):  
Edge Detection ◽  
State Of The Art ◽  
Region Of Interest ◽  
Least Square ◽  
Fast Method ◽  
Multi Scale ◽  
Art Methods ◽  
Horizon Line ◽  
Horizon Detection ◽  
Interest Detection

In this article, we propose a fast method for detecting the horizon line in maritime scenarios by combining a multi-scale approach and region-of-interest detection. Recently, several methods that adopt a multi-scale approach have been proposed, because edge detection at a single is insufficient to detect all edges of various sizes. However, these methods suffer from high processing times, requiring tens of seconds to complete horizon detection. Moreover, the resolution of images captured from cameras mounted on vessels is increasing, which reduces processing speed. Using the region-of-interest is an efficient way of reducing the amount of processing information required. Thus, we explore a way to efficiently use the region-of-interest for horizon detection. The proposed method first detects the region-of-interest using a property of maritime scenes and then multi-scale edge detection is performed for edge extraction at each scale. The results are then combined to produce a single edge map. Then, Hough transform and a least-square method are sequentially used to estimate the horizon line accurately. We compared the performance of the proposed method with state-of-the-art methods using two publicly available databases, namely, Singapore Marine Dataset and buoy dataset. Experimental results show that the proposed method for region-of-interest detection reduces the processing time of horizon detection, and the accuracy with which the proposed method can identify the horizon is superior to that of state-of-the-art methods.

Deadbands Tell No Tails: X-56A Dynamic Actuation Requirements

2020 ◽  
Author(s):  
Alexander M. Pankonien ◽  
Peter M. Suh ◽  
Jacob R. Schaefer ◽  
Robert M. Mitchell
Keyword(s):  
State Of The Art ◽  
Ride Quality ◽  
Flight Testing ◽  
Nonlinear Simulation ◽  
The Past ◽  
Actuation System ◽  
Testing Data ◽  
Aircraft Performance ◽  
Number Of Cycles ◽  
Significant Effort

Abstract Following significant effort over the past several years by AFRL and NASA, the X-56A flight vehicle has proven to be a useful platform for exploring controllers and distributed actuation on a flexible, swept flying-wing. The program sought to advance the state of the art in airworthiness for vehicles encountering flutter, leading to relaxed design constraints that could drastically decrease structural weight and improve aircraft performance. Specifically, the vehicle was designed to encounter different forms of flutter: body-freedom flutter, and wing-bending torsion flutter, making it an ideal candidate for identifying dynamic actuation challenges. Flight testing led to fundamental observations by controller designers about the actuation needs for such a vehicle. Namely, the small inherent actuator deadband led to significant constant-amplitude limit cycle oscillations of the system during post-flutter controlled flight. This work captures these observations by exploring theoretical changes in the actuators via a nonlinear simulation tuned with flight testing data and shows that a 60% reduction in actuator deadband can improve ride quality by nearly 50%. The results are combined into a set of actuation challenges for the adaptive structures community at large, including precise actuation for a large number of cycles over multiple timescales, with a relevant baseline described by original actuation system.

scholarly journals Two-stream fusion edge detection network

2021 ◽  
Author(s):  
Hasan W. Almawi
Keyword(s):  
Neural Network ◽  
Edge Detection ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Dynamic Features ◽  
Object Boundary ◽  
Specific Output ◽  
Decoder Architecture ◽  
Motion Boundaries ◽  
Motion Boundary

This thesis introduces a method to combine static and dynamic features in a convolutional neural network (CNN) to produce a motion and object boundary prediction map. This approach provides the CNN with dynamic and static cues and information, thus improving its predictions. The spatial stream of the CNN learns to compute an object boundary prediction map from a single RGB frame, while the temporal stream learns to compute a motion boundary prediction map from the corresponding optical ow map. The streams are then combined through an encoder-decoder architecture, where the decoder learns to fuse the features from both streams to obtain a task specific output. The proposed method yields state-of-the-art results on a motion boundaries benchmark, and systematic improvements in object boundaries benchmarks over methods that solely rely on static features extracted from a single RGB frame.

Study and Analysis of User Desired Image Retrieval

2020 ◽  
Vol 13 ◽  
Author(s):  
John Bosco P ◽  
S Janakiraman
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Image Retrieval ◽  
State Of The Art ◽  
The State ◽  
Content Based Image Retrieval ◽  
Processing Application ◽  
Detection Techniques ◽  
Digital World ◽  
Color Texture

Background: In the present digital world, Content Based Image Retrieval (CBIR) has gained significant importance. In this context, the image processing technology has become the most sought one, as a result its demand has increased to a large extend. The complex growth concerning computer technology offers a platform to apply the image processing application. Well-known image retrieval techniques suitable for application zone are 1.Text Based Image Retrieval (TBIR) 2. Content Based Image Retrieval (CBIR) and 3.Semantic Based Image Retrieval (SBIR) etc. In recent past, many researchers have conducted extensive research in the field of content-based image retrieval (CBIR). However, many related research studies on image retrieval and characterization have exemplified to be an immense issue and it should be progressively developed in its techniques. Hence, by putting altogether the research conducted in the recent years, this survey study makes a comprehensive attempt to review the state-of –the art in the field. Aims: This paper aims to retrieve similar images according to visual properties, which defined as Shape, color, Texture and edge detection. Objective: To investigate the CBIR to achieve the task because of the essential and fundamentals problems. The present and future trends are addressed to show come contributions and directions and it can inspire more research in the CBIR methods. Result: we present a deep analysis of the state of the art on CBIR methods; we explain the methods based on Color, Texture, and shape, and edge detection with performance evaluation metrics. In addition, we have discussed some significant future research directions reviewed. Methods: This paper has quickly anticipated the noteworthiness of CBIR and its related improvement, which incorporates Edge Detection Techniques, Various sorts of Distance Metric (DM), Performance measurements and various kinds of Datasets. This paper shows the conceivable outcomes to overcome the difficulties concerning re-positioning strategies with an exceptional spotlight on the improvement of accuracy and execution. Discussion: At last, we have proposed another technique for consolidating different highlights in a CBIR framework that can give preferred outcomes over the current strategies.

scholarly journals Correlation Plenoptic Imaging: An Overview

2018 ◽  
Vol 8 (10) ◽  
pp. 1958 ◽  
Author(s):  
Francesco Di Lena ◽  
Francesco Pepe ◽  
Augusto Garuccio ◽  
Milena D’Angelo
Keyword(s):  
State Of The Art ◽  
3D Visualization ◽  
Numerical Aperture ◽  
Image Resolution ◽  
Depth Of Field ◽  
Ghost Imaging ◽  
Temporal Correlations ◽  
Entangled Photon Pairs ◽  
Spatio Temporal ◽  
Plenoptic Imaging

Plenoptic imaging (PI) enables refocusing, depth-of-field (DOF) extension and 3D visualization, thanks to its ability to reconstruct the path of light rays from the lens to the image. However, in state-of-the-art plenoptic devices, these advantages come at the expenses of the image resolution, which is always well above the diffraction limit defined by the lens numerical aperture (NA). To overcome this limitation, we have proposed exploiting the spatio-temporal correlations of light, and to modify the ghost imaging scheme by endowing it with plenoptic properties. This approach, named Correlation Plenoptic Imaging (CPI), enables pushing both resolution and DOF to the fundamental limit imposed by wave-optics. In this paper, we review the methods to perform CPI both with chaotic light and with entangled photon pairs. Both simulations and a proof-of-principle experimental demonstration of CPI will be presented.

scholarly journals Quantum chemical approaches to [NiFe] hydrogenase

2017 ◽  
Vol 61 (2) ◽  
pp. 293-303 ◽  
Author(s):  
Valerie Vaissier ◽  
Troy Van Voorhis
Keyword(s):  
Quantum Chemical ◽  
Molecular Hydrogen ◽  
State Of The Art ◽  
Bioinorganic Chemistry ◽  
Kinetic Properties ◽  
Carbon Mitigation ◽  
Chemical Methods ◽  
Quantum Chemical Methods ◽  
Enzyme Functions ◽  
Significant Effort

The mechanism by which [NiFe] hydrogenase catalyses the oxidation of molecular hydrogen is a significant yet challenging topic in bioinorganic chemistry. With far-reaching applications in renewable energy and carbon mitigation, significant effort has been invested in the study of these complexes. In particular, computational approaches offer a unique perspective on how this enzyme functions at an electronic and atomistic level. In this article, we discuss state-of-the art quantum chemical methods and how they have helped deepen our comprehension of [NiFe] hydrogenase. We outline the key strategies that can be used to compute the (i) geometry, (ii) electronic structure, (iii) thermodynamics and (iv) kinetic properties associated with the enzymatic activity of [NiFe] hydrogenase and other bioinorganic complexes.

scholarly journals Hyperanalytic Wavelet-Based Robust Edge Detection

2021 ◽  
Vol 13 (15) ◽  
pp. 2888
Author(s):  
Alexandru Isar ◽  
Corina Nafornita ◽  
Georgiana Magu
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
State Of The Art ◽  
Speckle Noise ◽  
Transform Domain ◽  
Edge Detector ◽  
Edge Detectors ◽  
Gradient Based ◽  
New Type ◽  
Edge Detection Method

The imperfections of image acquisition systems produce noise. The majority of edge detectors, including gradient-based edge detectors, are sensitive to noise. To reduce this sensitivity, the first step of some edge detectors’ algorithms, such as the Canny’s edge detector, is the filtering of acquired images with a Gaussian filter. We show experimentally that this filtering is not sufficient in case of strong Additive White Gaussian or multiplicative speckle noise, because the remaining grains of noise produce false edges. The aim of this paper is to improve edge detection robustness against Gaussian and speckle noise by preceding the Canny’s edge detector with a new type of denoising system. We propose a two-stage denoising system acting in the Hyperanalytic Wavelet Transform Domain. The results obtained in applying the proposed edge detection method outperform state-of-the-art edge detection results from the literature.

scholarly journals Two-stream fusion edge detection network

2021 ◽  
Author(s):  
Hasan W. Almawi
Keyword(s):  
Neural Network ◽  
Edge Detection ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Dynamic Features ◽  
Object Boundary ◽  
Specific Output ◽  
Decoder Architecture ◽  
Motion Boundaries ◽  
Motion Boundary

This thesis introduces a method to combine static and dynamic features in a convolutional neural network (CNN) to produce a motion and object boundary prediction map. This approach provides the CNN with dynamic and static cues and information, thus improving its predictions. The spatial stream of the CNN learns to compute an object boundary prediction map from a single RGB frame, while the temporal stream learns to compute a motion boundary prediction map from the corresponding optical ow map. The streams are then combined through an encoder-decoder architecture, where the decoder learns to fuse the features from both streams to obtain a task specific output. The proposed method yields state-of-the-art results on a motion boundaries benchmark, and systematic improvements in object boundaries benchmarks over methods that solely rely on static features extracted from a single RGB frame.

Sign in / Sign up

Export Citation Format

Share Document