Multi-channel wafer defect detection using diffusion maps

Mishne, Gal; Cohen, Israel

doi:10.1109/eeei.2014.7005897

Cited by 5 publications

(3 citation statements)

References 22 publications

(24 reference statements)

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“…Saliency models are applied for defect detection on a wide variety of applications such as the semiconductor manufacturing and electronic production [14], metallic surfaces [15] or wafer defects [16], etc.…”

Section: Machine Vision: Defect Detectionmentioning

confidence: 99%

Applications of Saliency Models

Mancaş¹,

Meur²

2016

From Human Attention to Computational Attention

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International audienceThis chapter proposes a taxonomy to classify the real-life applications which can benefit from the use of attention models. There are numerous applications and we try here to remain as exhaustive as possible to provide a picture of all the applications of saliency models, but also to detect where future developments might be of interest. The applications are grouped into three categories. The first one uses the detection of the most important regions in an image and contains applications such as video surveillance, audio surveillance, defect detection, pathology detection, expressive and social gestures, computer graphics and quality metrics. The second category uses saliency maps to detect the regions which are the less interesting in an image. Here one can found applications like texture metrics, compression, retargeting, summarization, watermarking and attention-based ad insertion. Finally, a third category uses the most interesting areas in an image with further processing like comparisons between those areas. In this category one can find image registration and landmarks, object recognition, action guidance in robotics or avatars, web sites optimization, images memorability, best viewpoint, symmetries and automatic focus on images

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Section: Machine Vision: Defect Detectionmentioning

confidence: 99%

Applications of Saliency Models

Mancaş¹,

Meur²

2016

From Human Attention to Computational Attention

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show abstract

“…LP based out-of-sample extension for target detection was presented in [13]. Extensions of the anomaly detection algorithm [13], which utilizes LP for extension, include anomaly detection in side-scan sonar images of sea-mines [14] and detection of defects in wafers [15]. LP was utilized for function extension in problems related to voice activity detection.…”

Section: Introductionmentioning

confidence: 99%

Auto-adaptive multi-scale Laplacian Pyramids for modeling non-uniform data

Fernández

Rabin

Fishelov

et al. 2020

Engineering Applications of Artificial Intelligence

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“…In a fruit grading application presented in [125], saliency models were used for defect detection. Saliency models were used for detecting defects in semiconductor manufacturing [9], metallic surfaces [21] and wafers [134].…”

Section: Applications Based On Abnormality Detectionmentioning

confidence: 99%

Epistemic guidance of visual attention for robotic agents in dynamic visual scenes

Bhakta¹

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<p>Humans and many animals can selectively sample important parts of their visual surroundings to carry out their daily activities like foraging or finding prey or mates. Selective attention allows them to efficiently use the limited resources of the brain by deploying sensory apparatus to collect data believed to be pertinent to the organism's current task in hand. Robots or other computational agents operating in dynamic environments are similarly exposed to a wide variety of stimuli, which they must process with limited sensory and computational resources. Developing computational models of visual attention has long been of interest as such models enable artificial systems to select necessary information from complex and cluttered visual environments, hence reducing the data-processing burden. Biologically inspired computational saliency models have previously been used in selectively sampling a visual scene, but these have limited capacity to deal with dynamic environments and have no capacity to reason about uncertainty when planning their visual scene sampling strategy. These models typically select contrast in colour, shape or orientation as salient and sample locations of a visual scene in descending order of salience. After each observation, the area around the sampled location is blocked using inhibition of return mechanism to keep it from being re-visited. This thesis generalises the traditional model of saliency by using an adaptive Kalman filter estimator to model an agent's understanding of the world and uses a utility function based approach to describe what the agent cares about in the visual scene. This allows the agents to adopt a richer set of perceptual strategies than is possible with the classical winner-take-all mechanism of the traditional saliency model. In contrast with the traditional approach, inhibition of return is achieved without implementing an extra mechanism on top of the underlying structure. This thesis demonstrates the use of five utility functions that are used to encapsulate the perceptual state that is valued by the agent. Each utility function thereby produces a distinct perceptual behaviour that is matched to particular scenarios. The resulting visual attention distribution of the five proposed utility functions is demonstrated on five real-life videos. In most of the experiments, pixel intensity has been used as the source of the saliency map. As the proposed approach is independent of the saliency map used, it can be used with other existing more complex saliency map building models. Moreover, the underlying structure of the model is sufficiently general and flexible, hence it can be used as the base of a new range of more sophisticated gaze control systems.</p>

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Multi-channel wafer defect detection using diffusion maps

Cited by 5 publications

References 22 publications

Applications of Saliency Models

Applications of Saliency Models

Auto-adaptive multi-scale Laplacian Pyramids for modeling non-uniform data

Epistemic guidance of visual attention for robotic agents in dynamic visual scenes

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