History and Evolution of the Johnson Criteria.

Sjaardema, Tracy A.; Smith, Collin; Birch, Gabriel C.

doi:10.2172/1222446

Cited by 33 publications

(18 citation statements)

References 72 publications

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“…Since the baseline is directly proportional to the distance of objects, stereo systems with smaller baseline face this issue even at average distances, whereas systems with larger baseline face it at a greater distance. It's something similar along the lines of Johnson's criteria [11] that we are a little helpless for this kind of problem. Hence it is crucial to choose the stereo baseline suitable to one's use case.…”

Section: Disparity/stereo Matchingmentioning

confidence: 99%

Efficient Depth Estimation Using Sparse Stereo-Vision with Other Perception Techniques

Saravanan¹

2020

Coding Theory

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The stereo vision system is one of the popular computer vision techniques. The idea here is to use the parallax error to our advantage. A single scene is recorded from two different viewing angles, and depth is estimated from the measure of parallax error. This technique is more than a century old and has proven useful in many applications. This field has made a lot of researchers and mathematicians to devise novel algorithms for the accurate output of the stereo systems. This system is particularly useful in the field of robotics. It provides them with the 3D understanding of the scene by giving them estimated object depths. This chapter, along with a complete overview of the stereo system, talks about the efficient estimation of the depth of the object. It stresses on the fact that if coupled with other perception techniques, stereo depth estimation can be made a lot more efficient than the current techniques. The idea revolves around the fact that stereo depth estimation is not necessary for all the pixels of the image. This fact opens room for more complex and accurate depth estimation techniques for the fewer regions of interest in the image scene. Further details about this idea are discussed in the subtopics that follow.

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Section: Disparity/stereo Matchingmentioning

confidence: 99%

Efficient Depth Estimation Using Sparse Stereo-Vision with Other Perception Techniques

Saravanan¹

2020

Coding Theory

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“…Today, in the main known models, the required spatial resolution of SI is stated by a modified Johnson criterion, which was obtained experimentally and oftentimes was checked and updated [6].…”

Section: State-of-the-artmentioning

confidence: 99%

“…The primary advantage of the proposed rules (11) and (12) over known ones (5) and (6) is that the suitable SSOOs are selected not only by OIS resolution, but additionally taking into account pre-estimated contrast of GO and required recognition probability. It is significantly expands the range of suitable satellites.…”

Section: Satellite Systems Selection Model the Contrast оmentioning

confidence: 99%

Model for selecting the Earth observation satellite systems by object recognition probability

Артюшин

Kondratov

Andronov

et al. 2020

Fìz.-mat. model. ìnf. tehnol.

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The paper provides a model for selecting satellite systems for optical observation of the Earth by the probability of recognizing objects. The model is based on improved rules for selecting satellites by the spatial resolution of onboard imaging system. The model advantage over the known ones is the satellite systems selection not only due to the spatial resolution of image, but also taking into account the predictable contrast of objects and the required recognition level. The proposed model ensures a more correct pre-selection of satellite systems, in doing so reducing the cost of satellite imaging by preventing extra-requirements for spatial resolution of onboard imaging system. Also, the article proposes a method of forming a database of radiometric contrasts of typical objects and backgrounds for their consideration in the model for choosing satellite systems. This method does not depend on the specific types of on-board equipment, as well as on the availability of archival images at the time of planning satellite imagery.

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“…However, in most cases, being able to successfully detect a wildfire or hotspot based on the information given by a single pixel is not enough and can lead to significant errors. In this case, the Johnson criteria [ 17 ] determine that at least 1.5 pixels should be used to detect any feature, meaning that we need to effectively multiply our IFOV by a factor of 1.5. This affects the maximum operational distance of the thermographic camera, and using the same example as before, the camera needs to be located at a maximum distance of 976 m to be able to detect a hotspot of 1 m

based on Johnson’s criteria.…”

Section: Sources Of Error and Limitations Of An Automated Wildfirementioning

confidence: 99%

Challenges of an Autonomous Wildfire Geolocation System Based on Synthetic Vision Technology

Arana

Cabrera-Almeida

Perez-Mato

et al. 2018

Sensors

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Thermographic imaging has been the preferred technology for the detection and tracking of wildfires for many years. Thermographic cameras provide some very important advantages, such as the ability to remotely detect hotspots which could potentially turn into wildfires if the appropriate conditions are met. Also, they can serve as a key preventive method, especially when the 30-30-30 rule is met, which describes a situation where the ambient temperature is higher than 30 ∘C, the relative humidity is lower than 30%, and the wind speed is higher than 30 km/h. Under these circumstances, the likelihood of a wildfire outburst is quite high, and its effects can be catastrophic due to the high-speed winds and dry conditions. If this sort of scenario actually occurs, every possible technological advantage shall be used by firefighting teams to enable the rapid and efficient coordination of their response teams and to control the wildfire following a safe and well-planned strategy. However, most of the early detection methods for wildfires, such as the aforementioned thermographic cameras, lack a sufficient level of automation and usually rely on human interaction, imposing high degrees of subjectivity and latency. This is especially critical when a high volume of data is required in real time to correctly support decision-making scenarios during the wildfire suppression tasks. The present paper addresses this situation by analyzing the challenges faced by a fully autonomous wildfire detection and a tracking system containing a fully automated wildfire georeferencing system based on synthetic vision technology. Such a tool would provide firefighting teams with a solution capable of continuously surveilling a particular area and completely autonomously identifying and providing georeferenced information on current or potential wildfires in real time.

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History and Evolution of the Johnson Criteria.

Cited by 33 publications

References 72 publications

Efficient Depth Estimation Using Sparse Stereo-Vision with Other Perception Techniques

Efficient Depth Estimation Using Sparse Stereo-Vision with Other Perception Techniques

Model for selecting the Earth observation satellite systems by object recognition probability

Challenges of an Autonomous Wildfire Geolocation System Based on Synthetic Vision Technology

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