Automatic Gain Control for Image-Intensified Camera

Fowler, Kim

doi:10.1109/tim.2004.831494

Cited by 25 publications

(15 citation statements)

References 13 publications

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“…Both types of changes might occur at the same time in an experimental setup in a way that is not trivial to control. For example, a robot might use consumer camera sensors with a Automatic Gain Control (AGC) that influence the resulting image in a non-linear manner when the surrounding illumination is changed (Fowler, 2004). Due to the integration of current image sensors themselves, it can be impossible to deactivate these assistance systems.…”

Section: Datasets and Methodsmentioning

confidence: 99%

On the Illumination Influence for Object Learning on Robot Companions

Keller

Lohan

2020

Front. Robot. AI

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Most collaborative tasks require interaction with everyday objects (e.g., utensils while cooking). Thus, robots must perceive everyday objects in an effective and efficient way. This highlights the necessity of understanding environmental factors and their impact on visual perception, such as illumination changes throughout the day on robotic systems in the real world. In object recognition, two of these factors are changes due to illumination of the scene and differences in the sensors capturing it. In this paper, we will present data augmentations for object recognition that enhance a deep learning architecture. We will show how simple linear and non-linear illumination models and feature concatenation can be used to improve deep learning-based approaches. The aim of this work is to allow for more realistic Human-Robot Interaction scenarios with a small amount of training data in combination with incremental interactive object learning. This will benefit the interaction with the robot to maximize object learning for long-term and location-independent learning in unshaped environments. With our model-based analysis, we showed that changes in illumination affect recognition approaches that use Deep Convolutional Neural Network to encode features for object recognition. Using data augmentation, we were able to show that such a system can be modified toward a more robust recognition without retraining the network. Additionally, we have shown that using simple brightness change models can help to improve the recognition across all training set sizes.

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Section: Datasets and Methodsmentioning

confidence: 99%

On the Illumination Influence for Object Learning on Robot Companions

Keller

Lohan

2020

Front. Robot. AI

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“…AEC/AGC-Auto Exposure Control [38], Auto Gain Control [39]. AEC/AGC block controls the exposure and gain of the image sensor.…”

Section: Overview Of Isp Architecturesmentioning

confidence: 99%

Overview and Empirical Analysis of ISP Parameter Tuning for Visual Perception in Autonomous Driving

et al. 2019

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Image quality is a well understood concept for human viewing applications, particularly in the multimedia space, but increasingly in an automotive context as well. The rise in prominence of autonomous driving and computer vision brings to the fore research in the area of the impact of image quality in camera perception for tasks such as recognition, localization and reconstruction. While the definition of “image quality” for computer vision may be ill-defined, what is clear is that the configuration of the image signal processing pipeline is the key factor in controlling the image quality for computer vision. This paper is partly review and partly positional with demonstration of several preliminary results promising for future research. As such, we give an overview of what is an

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“…In others, the exposure time is extended to compensate for low light flux. Another mechanism involves electron multiplication by image intensifiers [7,11]. We denote the effective amplifier gain in frame f as G f .…”

Section: Statement Of the Problemmentioning

confidence: 99%

“…The nonlinearity is usually caused by nonlinear electronic amplifiers. Alternatively, in image intensifiers it can be caused by the nonlinear response of the phosphors [7,11] of the intensifying tube. The graylevel value v in pixel x at frame f is:…”

Section: Statement Of the Problemmentioning

confidence: 99%