Making image and vision effortless: Learning methodology through the quick and easy design of short case studies

Sanguino, T. de J. Mateo; Webber, Pedro A. Castilla

doi:10.1002/cae.22003

Cited by 10 publications

(7 citation statements)

References 24 publications

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“…In a similar context, Mateo Sanguino and Castilla Webber [34] presented the Vision Picture tool to teach the basic principles of machine vision and image processing through the design of short case studies under a PBL approach. This tool was developed using the G language, LabVIEW 2011 and the IMAQ Vision toolbox to perform machine vision through image and video processing, particle analysis, and pattern matching.…”

Section: Related Workmentioning

confidence: 99%

An FPGA‐based tool for supporting the design, modeling, and evaluation of hybrid object recognition systems on computer engineering courses

Guzmán‐Ramírez,

Garcia,

Pacheco

et al. 2024

Comp Applic In Engineering

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The field of computer vision is characterized by computationally intensive algorithms and techniques with strict real‐time requirements. Field programmable gate arrays (FPGAs) are based on a concurrent paradigm which allows the design of efficient hardware architectures and has positioned FPGAs as an ideal device for implementing compute‐intensive applications. For this reason, FPGA technology has had a great impact in areas such as computer vision, where one of the main objectives for researchers working in this field is to create efficient automatic object recognition systems. Therefore, the need to provide undergraduates with the necessary skills to design FPGA‐based object recognition systems is evident. With this aim in mind, it is essential that specialization courses related to the design of these systems include the required resources for the student to apply the theoretical knowledge in solving practical problems. In this article, we present a development tool designed to help students, teachers, and researchers during the design‐modeling‐implementation process of object recognition systems based on FPGAs. The proposed tool operates under a modular approach as this facilitates the working on any of the phases of a recognition system and it is considered as a hybrid because the other phases can be developed using a software language. An empirical evaluation involving undergraduates enrolled in a Computer Engineering program was conducted to create a hardware architecture for the DAISY descriptor that uses the homogeneous features of objects immersed in images to produce an efficient representation. By considering similar descriptors such as Scale‐Invariant Feature Transform (SIFT) and Histogram of Oriented Gradients (HOG), DAISY is computed by convolving orientation maps instead of using weighted sums of gradient norms, which offers the same kind of invariance at a lower computational cost for the dense case. The results obtained during such an evaluation indicated that students consider this FPGA‐based tool to be an alternative to receiving practical training on designing systems for solving problems related to the area of object recognition.

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Section: Related Workmentioning

confidence: 99%

An FPGA‐based tool for supporting the design, modeling, and evaluation of hybrid object recognition systems on computer engineering courses

Guzmán‐Ramírez,

Garcia,

Pacheco

et al. 2024

Comp Applic In Engineering

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“…In this regard, different software‐based and hardware‐based approaches designed to strengthen undergraduate teaching of object recognition can be found in the literature. Research by Mateo Sanguino and Castilla Webber [12], for example, led to the development of an educational software tool to teach the basic principles of machine vision and image processing. This tool was used by undergraduates for designing short case studies by following the principles of the Project‐based Learning (PBL) approach.…”

Section: Object Recognition Training In Related Workmentioning

confidence: 99%

HS‐IDEOR—A tool for conceptual modeling and designing of hardware architectures focused on object recognition

Guzmán‐Ramírez

García

Arroyo‐Fernández

et al. 2022

Comp Applic In Engineering

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Considering the increasing demand for object recognition systems, it is crucial that modern engineering courses include tools and methods to ensure that students acquire the theoretical and practical skills necessary to become specialists in designing such systems. This paper introduces a hardwaresoftware integrated development environment to support and accelerate the teaching/learning process of conceptual modeling and hardware architectures design focused on object recognition. In addition, our proposal facilitates the implementation of modular and hybrid object recognition schemes, offering great flexibility in the study of such systems. An empirical evaluation was conducted with 30 fourth-year undergraduate students who designed and implemented an object recognition system in the Computer Vision course of the Bachelor's Degree in Computer Science and Engineering at the

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“…Based on the Vision Assistant of LabVIEW, a basic vision processing program [13][14][15][16] was compiled to detect and extract tomato contours from the images. The basic processes include image segmentation, image graying, image binarization, particle filtering, and the IMAQ particle analysis for solving pixel area.…”

Section: Image Processingmentioning

confidence: 99%

A Novel Tomato Volume Measurement Method based on Machine Vision

Sun

Liu

et al. 2021

Teh. vjesn.

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Density is one of the auxiliary indicators for judging the internal quality of tomatoes. However, in the density measurement process, it is often difficult to measure the volume of the tomatoes accurately. To solve this problem, first, this study proposed a novel tomato volume measurement method based on machine vision. The proposed method uses machine vision to measure the geometric feature parameters of tomatoes, and inputs them into the LabVIEW software to convert the calculation of irregular tomato volume into a BP neural network (BPNN) model that calculates the plane pixel area and pixel volume, thereby realizing the modeling, analysis, design and simulation of tomato volume; then, an experimental platform was constructed to compare the results of the proposed method with the results predicted by the 3D wireframe model. When the number of photos taken was n = 5, the average error of the tomato volume prediction results of the 3D wireframe model was 8.22%, and the highest accuracy was 92.93%; while the average error of the tomato volume prediction results of the BPNN was 4.60%, and the highest accuracy was 95.60%. Increasing the number of orthographic projections can improve the accuracy of the model, but when the number of photos was more than 7, the accuracy improvement was not significant. Also, increasing the number of nodes in the hidden layer can improve the accuracy of the model, however, considering that increasing the number of nodes will increase the host operating cost, it is suggested to choose a node number of 12 for the tomato volume measurement. In the end, the final experimental results showed that the proposed method achieved better measurement results. However, the volume measured by the two models is larger than the real volume of tomatoes. For this reason, we added a correction coefficient to the BPNN model, and its highest accuracy has increased by 1.3%.

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Making image and vision effortless: Learning methodology through the quick and easy design of short case studies

Cited by 10 publications

References 24 publications

An FPGA‐based tool for supporting the design, modeling, and evaluation of hybrid object recognition systems on computer engineering courses

An FPGA‐based tool for supporting the design, modeling, and evaluation of hybrid object recognition systems on computer engineering courses

HS‐IDEOR—A tool for conceptual modeling and designing of hardware architectures focused on object recognition

A Novel Tomato Volume Measurement Method based on Machine Vision

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