Image Edge Detection Based on Swarm Intelligence Using Memristive Networks

Pajouhi, Zoha; Roy, Kaushik

doi:10.1109/tcad.2017.2775227

Cited by 41 publications

(10 citation statements)

References 21 publications

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“…For example, it was shown that implementing a swarm intelligence algorithm of image edge detection with circuits of memristors consumes less energy than conventional methods. 18 Exploring other swarm intelligence ideas and different substrates to implement them is an exciting road towards low energy cost systems that perform complex optimization tasks.…”

Section: Discussionmentioning

confidence: 99%

“…Pershin and Di Ventra proposed to solve the shortest path problem directly in hardware, using nanodevices called memristors, which have dynamics that provide reinforcement mechanisms similar to the ones at play in the ant colony optimization. [15][16][17][18] Memristors are defined by having conductances that change when subjected to electrical current. [19][20][21] As a voltage is applied to a network of memristors forming a graph, more current will flow through the shortest branch because that branch has the lowest resistance.…”

Section: Introductionmentioning

confidence: 99%

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Scalable Method to Find the Shortest Path in a Graph with Circuits of Memristors

et al. 2018

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Finding the shortest path in a graph has applications to a wide range of optimization problems. However, algorithmic methods scale with the size of the graph in terms of time and energy. We propose a method to solve the shortest path problem using circuits of nanodevices called memristors and validate it on graphs of different sizes and topologies. It is both valid for an experimentally derived memristor model and robust to device variability. The time and energy of the computation scale with the length of the shortest path rather than with the size of the graph, making this method particularly attractive for solving large graphs with small path lengths.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Scalable Method to Find the Shortest Path in a Graph with Circuits of Memristors

et al. 2018

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“…More bespoke implementations depart further from the conventional crossbar or neural network architectures and replace the dot product entirely. Fuzzy XOR gates implemented with memristors can determine pixel gradients (Merrikh-Bayat et al, 2014) and swarm computations, based on the behavior of ants, have been replicated with grids of memristors (Pajouhi and Roy, 2018).…”

Section: Introductionmentioning

confidence: 99%

Memristor-Based Edge Detection for Spike Encoded Pixels

Mannion

Mehonić

et al. 2020

Front. Neurosci.

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Memristors have many uses in machine learning and neuromorphic hardware. From memory elements in dot product engines to replicating both synapse and neuron wall behaviors, the memristor has proved a versatile component. Here we demonstrate an analog mode of operation observed in our silicon oxide memristors and apply this to the problem of edge detection. We demonstrate how a potential divider exploiting this analog behavior can prove a scalable solution to edge detection. We confirm its behavior experimentally and simulate its performance on a standard testbench. We show good performance comparable to existing memristor based work with a benchmark score of 0.465 on the BSDS500 dataset, while simultaneously maintaining a lower component count.

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“…2,3 A key element of any such algorithm is the detection of edge points in noisy images, which is an ongoing field of research. [4][5][6][7] We base our work on the idea of rotational difference kernel estimators (RDKE) from Qiu, 8,9 further improved by Garlipp and Müller. 10 Chu et al 11 combine these results with a kernel smoothing method to estimate jump location curves.…”

Section: Introductionmentioning

confidence: 99%

Detection of circlelike overlapping objects in thermal spray images

Kirchhoff

Kuhnt

Bloch

et al. 2020

Quality & Reliability Eng

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In this paper, we present a new algorithm for the detection of distorted and overlapping circlelike objects in noisy grayscale images. Its main step is an edge detection using rotated difference kernel estimators. To the resulting estimated edge points, circles are fitted in an iterative manner using a circular clustering algorithm. A new measure of similarity can assess the performance of algorithms for the detection of circlelike objects, even if the number of detected circles does not coincide with the number of true circles. We apply the algorithm to scanning electron microscope images of a high‐velocity oxygen fuel (HVOF) spray process, which is a popular coating technique. There, a metal powder is fed into a jet, gets accelerated and heated up by means of a mixture of oxygen and fuel, and finally deposits as coating upon a substrate. If the process is stopped before a continuous layer is formed, the molten metal powder solidifies in form of small, almost circular so‐called splats, which vary with regard to their shape, size, and structure and can overlap each other. As these properties are challenging for existing image processing algorithms, engineers analyze splat images manually up to now. We further compare our new algorithm with a baseline approach that uses the Laplacian of Gaussian blob detection. It turns out that our algorithm performs better on a set of test images of round, spattered, and overlapping circles.

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Image Edge Detection Based on Swarm Intelligence Using Memristive Networks

Cited by 41 publications

References 21 publications

Scalable Method to Find the Shortest Path in a Graph with Circuits of Memristors

Scalable Method to Find the Shortest Path in a Graph with Circuits of Memristors

Memristor-Based Edge Detection for Spike Encoded Pixels

Detection of circlelike overlapping objects in thermal spray images

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