Infrared thermal image segmentation using expectation-maximization-based clustering

Ramirez-Rozo, T. J.; Garcia-Alvarez, J. C.; Castellanos-Domínguez, G.

doi:10.1109/stsiva.2012.6340586

Cited by 18 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…En el trabajo de Ramirez, García y Castellanos [36] se implementa un algoritmo de segmentación en imágenes IR de un motor eléctrico, basado en el agrupamiento de información a partir de maximizar los valores esperados (esperanza), y en donde el desempeño se realiza a partir de su comparación con otro método de segmentación basado en fronteras usando como parámetro de evaluación el coeficiente de Dice.…”

Section: A Técnicas De Procesamientounclassified

El ensayo no destructivo usando termografía infrarroja en el mundo y en América Latina: Una revisión

2022

View full text Add to dashboard Cite

En este artículo se sintetiza una revisión de los principales trabajos relacionados con ensayo no destructivo usando termografía infrarroja incluyendo aportes realizados por investigadores de América Latina. Se encontró en este campo una tendencia creciente de la producción académica en los últimos años, lo cual ratifica que es un área de interés y desarrollo continuo. El resultado de la revisión se clasificó en cuatro líneas temáticas cubriendo principalmente: i) trabajos que recopilan el estado del arte, ii) desarrollos en técnicas de procesamiento de información térmica, iii) indicadores de desempeño y iv) simulación, modelos térmicos y campos de aplicación. Adicionalmente, se registraron los países tradicionales y emergentes que están generando contribuciones en el sector del ensayo no destructivo usando termografía infrarroja. Finalmente, se identificó cómo el enfoque actual del procesamiento de información térmica está orientado al procesamiento de secuencias de imágenes, donde el número de técnicas es cada vez mayor y se evidenció una reducida cantidad de métricas para la evaluación objetiva de su rendimiento

show abstract

Section: A Técnicas De Procesamientounclassified

El ensayo no destructivo usando termografía infrarroja en el mundo y en América Latina: Una revisión

2022

View full text Add to dashboard Cite

show abstract

“…T. J. Ram´irez-Rozo, J.C.Garc´ia,Alvarez,C. G. Castellanos-Dom´inguez, [33] where the Expectation Maximization Clustering (EM-Clustering) segmentation is evaluated for IR images, using as reference watershed transform-based segmentation. A major challenge in segmentation [35] evaluation comes from the fundamental conflict between generality and objectivity.…”

Section: Noteworthy Contributions In the Field Of Proposed Workmentioning

confidence: 99%

A Survey of Image Segmentation based on Artificial Intelligence and Evolutionary Approach

Jaiswal¹

2013

IOSR-JCE

View full text Add to dashboard Cite

In image analysis, segmentation is the partitioning of a digital image into multiple regions (sets of pixels), according to some homogeneity criterion. The problem of segmentation is a well-studied one in literature and there are a wide variety of approaches that are used. Different approaches are suited to different types of images and the quality of output of a particular algorithm is difficult to measure quantitatively due to the fact that there may be much correct segmentation for a single image. Image segmentation denotes a process by which a raw input image is partitioned into nonoverlapping regions such that each region is homogeneous and the union of any two adjacent regions is heterogeneous. A segmented image is considered to be the highest domain-independent abstraction of an input image. Image segmentation is an important processing step in many image, video and computer vision applications. Extensive research has been done in creating many different approaches and algorithms for image segmentation, but it is still difficult to assess whether one algorithm produces more accurate segmentations than another, whether it be for a particular image or set of images, or more generally, for a whole class of images. In this paper, The Survey of Image Segmentation using Artificial Intelligence and Evolutionary Approach methods that have been proposed in the literature. The rest of the paper is organized as follows. 1. Introduction, 2.Literature review, 3.Noteworthy contributions in the field of proposed work, 4.Proposed Methodology, 5.Expected outcome of the proposed research work, 6.Conclusion.

show abstract

“…There are two main categories of existing semantic segmentation methods: traditional methods and deep learning-based methods. Traditional image segmentation methods frequently utilize machine learning algorithms, such as thresholding [4], edge detection [5], and clustering [6], and often depend on handcrafted features, such as histograms of oriented gradient [7] and scale-invariant feature transform [8]. With the rapid development of deep learning technology, researchers have increasingly applied it to image segmentation tasks, including semantic segmentation [9][10][11] and instance segmentation.…”

Section: Introductionmentioning

confidence: 99%

Accurate segmentation of infrared images for circuit board diagnosis using an improved Deeplabv3+ network

et al. 2023

View full text Add to dashboard Cite

An effective infrared image segmentation algorithm is essential for non-contact fault diagnosis of circuit boards. However, the uneven grayscale of the infrared images, multiple target regions, and large radiation noise pose challenges to achieving accurate segmentation and efficient data extraction for the interested regions. In this paper, we propose a segmentation algorithm based on the Deeplabv3+ network, using the lightweight MobileNetV2 as a replacement for the original Xception backbone network to improve computational efficiency and reduce overfitting. We also employ a composite loss function and cosine annealing learning rate to balance foreground-background segmentation and avoid local optima. Furthermore, we integrate the Convolutional Block Attention Module (CBAM) to extract and combine important spatial and channel features, allowing the algorithm to focus on identifying elements of the circuit board instead of background pixels, thereby improving segmentation accuracy. Experimental results demonstrate that our proposed algorithm achieves state-of-the-art performance in terms of both segmentation accuracy and computational efficiency on our self-built infrared circuit board dataset, with a MIoU of 90.34%, MPA of 95.26%, and processing speed of 25.19 fps. Overall, our proposed segmentation algorithm can effectively identify the key regions of interest in infrared images of circuit boards, providing technical support for non-contact diagnosis.

show abstract

Infrared thermal image segmentation using expectation-maximization-based clustering

Cited by 18 publications

References 9 publications

El ensayo no destructivo usando termografía infrarroja en el mundo y en América Latina: Una revisión

El ensayo no destructivo usando termografía infrarroja en el mundo y en América Latina: Una revisión

A Survey of Image Segmentation based on Artificial Intelligence and Evolutionary Approach

Accurate segmentation of infrared images for circuit board diagnosis using an improved Deeplabv3+ network

Contact Info

Product

Resources

About