Color mixing and random search for optimal illumination in machine vision

Kim, Hyung-Tae; Cho, KyeongYong; Kim, SeungTaek; Kim, Jong-Seok

doi:10.1109/sii.2013.6776736

Cited by 4 publications

(4 citation statements)

References 23 publications

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“…In order to increase the reliability of machine vision systems, [4] developed a colour model for simulating the behaviour of shadows. Furthermore [5] developed a method optimizing the sharpness of an image by adapting the illumination conditions.…”

Section: State Of the Artmentioning

confidence: 99%

Identification of Influence Parameters and Dependencies on the Illumination of Machine Vision Systems for Robots

Berger

Biehle

Villarroel

et al. 2016

AMR

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Machine vision systems need to cope with harsh environmental conditions, when used for guiding robots. To increase the reliability of these systems, they are either shielded from their environment or highly advanced, using sophisticated mathematical algorithms. In this paper the effects of external illumination on machine vision systems are investigated. For this the pose and the brightness of the external illumination and the internal illumination of the camera and its exposure time are varied. In a next step the correlation between the parameters of the external illumination and reliability of the machine vision system is analysed. Finally requirements are derived for further development of machine vision systems.

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Section: State Of the Artmentioning

confidence: 99%

Identification of Influence Parameters and Dependencies on the Illumination of Machine Vision Systems for Robots

Berger

Biehle

Villarroel

et al. 2016

AMR

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show abstract

“…Proper lighting is a key factor in traditional industrial vision systems since it is difficult to maintain a constant light due to a constantly changing environment. Traditional solutions require the use of specific lighting systems suited for specific applications [15,16,17]. Event-based cameras minimize light effects since only pixel intensity differences are considered and no specific light intensity is required, independently of light conditions.…”

Section: Introductionmentioning

confidence: 99%

Less Data Same Information for Event-Based Sensors: A Bioinspired Filtering and Data Reduction Algorithm

Barrios-Aviles

Muñoz

Medus

et al. 2018

Sensors

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Sensors provide data which need to be processed after acquisition to remove noise and extract relevant information. When the sensor is a network node and acquired data are to be transmitted to other nodes (e.g., through Ethernet), the amount of generated data from multiple nodes can overload the communication channel. The reduction of generated data implies the possibility of lower hardware requirements and less power consumption for the hardware devices. This work proposes a filtering algorithm (LDSI—Less Data Same Information) which reduces the generated data from event-based sensors without loss of relevant information. It is a bioinspired filter, i.e., event data are processed using a structure resembling biological neuronal information processing. The filter is fully configurable, from a “transparent mode” to a very restrictive mode. Based on an analysis of configuration parameters, three main configurations are given: weak, medium and restrictive. Using data from a DVS event camera, results for a similarity detection algorithm show that event data can be reduced up to 30% while maintaining the same similarity index when compared to unfiltered data. Data reduction can reach 85% with a penalty of 15% in similarity index compared to the original data. An object tracking algorithm was also used to compare results of the proposed filter with other existing filter. The LDSI filter provides less error (4.86 ± 1.87) when compared to the background activity filter (5.01 ± 1.93). The algorithm was tested under a PC using pre-recorded datasets, and its FPGA implementation was also carried out. A Xilinx Virtex6 FPGA received data from a 128 × 128 DVS camera, applied the LDSI algorithm, created a AER dataflow and sent the data to the PC for data analysis and visualization. The FPGA could run at 177 MHz clock speed with a low resource usage (671 LUT and 40 Block RAM for the whole system), showing real time operation capabilities and very low resource usage. The results show that, using an adequate filter parameter tuning, the relevant information from the scene is kept while fewer events are generated (i.e., fewer generated data).

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“…Proper lighting is a key factor in traditional industrial vision systems since it is difficult to maintain a constant light due to a constantly changing environment. Traditional solutions required the use of specific lighting systems suited for specific applications [9][10][11]. Event-based cameras minimize light effects since only pixel intensity differences are considered and no need of specific light intensity is required, independently of light conditions.…”

Section: Introductionmentioning

confidence: 99%

Movement Detection with Event-Based Cameras: Comparison with Frame-Based Cameras in Robot Object Tracking Using Powerlink Communication

et al. 2018

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Event-based cameras are not common in industrial applications despite the fact that they can add multiple advantages for applications with moving objects. In comparison with frame-based cameras, the amount of generated data is very low while keeping the main information in the scene. For an industrial environment with interconnected systems, data reduction becomes very important to avoid network congestion and provide faster response time. However, the use of new sensors as event-based cameras is not common since they do not usually provide connectivity to industrial buses. This work develops a network node based on a Field Programmable Gate Array (FPGA), including data acquisition and tracking position for an event-based camera. It also includes spurious reduction and filtering algorithms while keeping the main features at the scene. The FPGA node also includes the stack of the network protocol to provide standard communication among other nodes. The powerlink IEEE 61158 industrial network is used to communicate the FPGA with a controller connected to a self-developed two-axis servo-controlled robot. The inverse kinematics model for the robot is included in the controller. To complete the system and provide a comparison, a traditional frame-based camera is also connected to the controller. Response time and robustness to lighting conditions are tested. Results show that, using the event-based camera, the robot can follow the object using fast image recognition achieving up to 85% percent data reduction providing an average of 99 ms faster position detection and less dispersion in position detection (4.96 mm vs. 17.74 mm in the Y-axis position, and 2.18 mm vs. 8.26 mm in the X-axis position) than the frame-based camera, showing that event-based cameras are more stable under light changes. Additionally, event-based cameras offer intrinsic advantages due to the low computational complexity required: small size, low power, reduced data and low cost. Thus, it is demonstrated how the development of new equipment and algorithms can be efficiently integrated into an industrial system, merging commercial industrial equipment with new devices.

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Color mixing and random search for optimal illumination in machine vision

Cited by 4 publications

References 23 publications

Identification of Influence Parameters and Dependencies on the Illumination of Machine Vision Systems for Robots

Identification of Influence Parameters and Dependencies on the Illumination of Machine Vision Systems for Robots

Less Data Same Information for Event-Based Sensors: A Bioinspired Filtering and Data Reduction Algorithm

Movement Detection with Event-Based Cameras: Comparison with Frame-Based Cameras in Robot Object Tracking Using Powerlink Communication

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