Segmentation of FLIR Images by Pixel Classification

Panda, Durga P.

doi:10.21236/ada045334

Cited by 10 publications

(9 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The filter can make a good compromise between the blurring and the sharpening when compared with other filters. The WLS filter tries to seek an output image I that is a smooth version of original image S and is as similar as possible to original image S. The filtering image I can be defined as I =arg min {||I-S|| 2 2 + λ(α x ||D x I|| 2 2 + α y ||D y I|| 2 2 )} (1) The data term ||I-S|| 2 2 is for generating output image as similar as possible to original image S, while the rest term i.e., the regularization term tries to generate a smooth version of the original image by minimizing the partial derivatives of the output image.…”

Section: A Image Enhancementmentioning

confidence: 99%

“…Therefore, IR images are widely used in surveillance, industry, military and other fields. Infrared thermography is used in various fields, from clinical diagnostics toindustrial preventive and in agriculture also in predictive evaluation [2][3][4]. In these cases, it is important toreveal oddities and irregularities in the distribution of the analyzed bodies'temperature as these are indicators of anomalous, and therefore undesired,situations.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Score Level Fusion Based Approach for Water Leak Detection in Infrared Images

Rao¹

2017

IJRASET

View full text Add to dashboard Cite

Identification of water region plays an important role in many of the industrial and agricultural applications. Detection of water and non-water region will be tedious if manual methods are adapted. To tackle this, this article presents a detailed image processing based method to detect water and non-water region using infrared Tau 2 640 camera.An algorithm is developed to identify the water and non-water regions in a remote area using Tau 2 640 camera mounted quad copter. A serious of experiments is carried to detect the water regions. Results of the experiments present the effectiveness of the proposed algorithm. Keywords: water regions, infrared, water detection, Tau 2 640INTRODUCTION With the development of technology for infrared (IR)sensors in last decades, many image processing applications based on IR images has emerged. IR image is generated by detecting radiation in the long-wave electromagnetic spectrum. The intensity value of a pixel in an IR image represents the emitted IR radiation intensity ie., the temperature of an object. IR images can provide all weather information, which is not observed by the human with naked eye. Therefore, IR images are widely used in surveillance, industry, military and other fields. Infrared thermography is used in various fields, from clinical diagnostics toindustrial preventive and in agriculture also in predictive evaluation [2][3][4]. In these cases, it is important toreveal oddities and irregularities in the distribution of the analyzed bodies'temperature as these are indicators of anomalous, and therefore undesired,situations. For example, areas of higher temperature than the standard conditionsmay identify infections or muscle injuries in the medical field, or damaged isolatorsand overheated joint, signs of incipient failure, in the industrial field, or high temperature in the regions are the sign of absences of water level in that particular area.This research article is focuses towards developing algorithms for identifying water resources using Tau2 640 Flir infrared camera. Water is an essential element without which any living species goes extinct. This research work mainly focuses on providing an invention which helps in detecting/ identifying the underground water resource present which can be mapped and tapped to the reservoir. This saved water can be fed to the people during summer season or to the farmers for the agriculture purpose during drought period. The quad copter technology thus used will cover the maximum area autonomously whereas yielding a real-time detection of the water bodies using the infra-Red cameras and the image classification algorithms.

show abstract

Section: A Image Enhancementmentioning

confidence: 99%

mentioning

confidence: 99%

Score Level Fusion Based Approach for Water Leak Detection in Infrared Images

Rao¹

2017

IJRASET

View full text Add to dashboard Cite

show abstract

“…A comparative study of threshold selection schemes based on this approach has been conducted [5]. This work will be discussed further in Section 4.…”

Section: For Intermediate Edge Valuesmentioning

confidence: 99%

“…The approach above and several variations [5] can be viewed as methods of decision surface selection in (gray level, edge value)…”

Section: Threshold Selectionmentioning

confidence: 99%

See 1 more Smart Citation

Object detection in infrared images

Milgram¹,

Rosenfeld

1981

Digital Image Processing Systems

View full text Add to dashboard Cite

This paper describes algorithms for detecting and classifying objects such as tanks and trucks in forward-looking infrared (FLIR) imagery.It summarizes research conducted in the course of a two-year project in the areas of image modeling, pre-and post-processing, segmentation, feature extraction, and classification. Image modelsThe work on image modeling conducted under this project was concentrated in three main areas:i) Modeling of the joint (gray level, edge value) statistics of FLIR scenes, as a basis for defining threshold selection techniques.2) Modeling of thresholding and edge detection responses to background regions, as a basis for predicting false alarm

show abstract

Automatische Verarbeitung von Cranialen Computer-Tomogrammen

Stiehl

1978

Bildverarbeitung Und Mustererkennung

View full text Add to dashboard Cite

Segmentation of FLIR Images by Pixel Classification

Cited by 10 publications

References 0 publications

Score Level Fusion Based Approach for Water Leak Detection in Infrared Images

Score Level Fusion Based Approach for Water Leak Detection in Infrared Images

Object detection in infrared images

Automatische Verarbeitung von Cranialen Computer-Tomogrammen

Contact Info

Product

Resources

About