Fuzzy ARTMAP-Based Fast Object Recognition for Robots Using FPGA

Lomas-Barrié, Víctor; Peña‐Cabrera, Mario; López-Juárez, Ismael; Navarro-Gonzalez, Jose Luis

doi:10.3390/electronics10030361

Cited by 3 publications

(4 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors in [16] described how BOF elements are extracted. First, these values are stored in a BRAM.…”

Section: Bof Stage Implementationmentioning

confidence: 99%

“…The boundary object function (BOF) [15] descriptor vector, which is a formalism that characterizes an object by extracting some attributes, as detailed in the following sections, demonstrated potential in terms of being invariant to rotation, scale, and displacement, and being able to condense the information coming from a 2D image to a one-dimensional array. In [16], the BOF and a classifier based on the fuzzy ARTMAP neural network were implemented on an FPGA [17] with very favorable results. However, complications are detected when the object angle on the z-axis exceeds 15 degrees.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Multiview Recognition Method of Predefined Objects for Robot Assembly Using Deep Learning and Its Implementation on an FPGA

et al. 2022

Self Cite

View full text Add to dashboard Cite

The process of recognizing manufacturing parts in real time requires fast, accurate, small, and low-power-consumption sensors. Here, we describe a method to extract descriptors from several objects observed from a wide range of angles in a three-dimensional space. These descriptors define the dataset, which allows for the training and further validation of a convolutional neural network. The classification is implemented in reconfigurable hardware in an embedded system with an RGB sensor and the processing unit. The system achieved an accuracy of 96.67% and a speed 2.25× faster than the results reported for state-of-the-art solutions. Our proposal is 655 times faster than implementation on a PC. The presented embedded system meets the criteria of real-time video processing and it is suitable as an enhancement for the hand of a robotic arm in an intelligent manufacturing cell.

show abstract

“…The authors in [16] described how BOF elements are extracted. First, these values are stored in a BRAM.…”

Section: Bof Stage Implementationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multiview Recognition Method of Predefined Objects for Robot Assembly Using Deep Learning and Its Implementation on an FPGA

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Victor et al reported "Fuzzy ARTMAP-Based Fast Object Recognition for Robots Using FPGA" [3]. This article shows the design and implementation of an invariant recognition machine vision system to compute a descriptive vector called the Boundary Object Function (BOF) using the Fuzzy ARTMAP (FAM) Neural Network.…”

Section: The Topics Of Intelligent Electronic Devices and Its Applica...mentioning

confidence: 99%

Special Issue on Application of Electronic Devices on Intelligent System

Meen

Kuo

2021

Electronics

View full text Add to dashboard Cite

show abstract

“…Quantize the contour into n points, where n is the size of the descriptor. With n = 180, the test guarantees a good balance between accuracy and computer performance [13].…”

mentioning

confidence: 99%

A New Method for Classifying Scenes for Simultaneous Localization and Mapping Using the Boundary Object Function Descriptor on RGB-D Points

Lomas-Barrie,

Suarez-Espinoza,

Hernandez-Chavez

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

Scene classification in autonomous navigation is a highly complex task due to variations, such as light conditions and dynamic objects, in the inspected scenes; it is also a challenge for small-factor computers to run modern and highly demanding algorithms. In this contribution, we introduce a novel method for classifying scenes in simultaneous localization and mapping (SLAM) using the boundary object function (BOF) descriptor on RGB-D points. Our method aims to reduce complexity with almost no performance cost. All the BOF-based descriptors from each object in a scene are combined to define the scene class. Instead of traditional image classification methods such as ORB or SIFT, we use the BOF descriptor to classify scenes. Through an RGB-D camera, we capture points and adjust them onto layers than are perpendicular to the camera plane. From each plane, we extract the boundaries of objects such as furniture, ceilings, walls, or doors. The extracted features compose a bag of visual words classified by a support vector machine. The proposed method achieves almost the same accuracy in scene classification as a SIFT-based algorithm and is 2.38× faster. The experimental results demonstrate the effectiveness of the proposed method in terms of accuracy and robustness for the 7-Scenes and SUNRGBD datasets.

show abstract

Fuzzy ARTMAP-Based Fast Object Recognition for Robots Using FPGA

Cited by 3 publications

References 26 publications

A Multiview Recognition Method of Predefined Objects for Robot Assembly Using Deep Learning and Its Implementation on an FPGA

A Multiview Recognition Method of Predefined Objects for Robot Assembly Using Deep Learning and Its Implementation on an FPGA

Special Issue on Application of Electronic Devices on Intelligent System

A New Method for Classifying Scenes for Simultaneous Localization and Mapping Using the Boundary Object Function Descriptor on RGB-D Points

Contact Info

Product

Resources

About