Robust vision system to illumination changes in a color-dependent task

Espínola, Andrés; Romay, Alberto; Baidyk, Tatiana; Kussul, Ernst

doi:10.1109/robio.2011.6181339

Cited by 4 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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).

show abstract

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

View full text Add to dashboard Cite

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 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

View full text Add to dashboard Cite

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.

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 required the use of specific lighting systems suited for specific applications [8], [9], [10]. 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%

An Event-based Fast Movement Detection Algorithm for a Positioning Robot Using POWERLINK Communication

Barrios-Aviles¹,

Iakymchuk²,

Samaniego³

et al. 2017

Preprint

View full text Add to dashboard Cite

Event-based cameras are not common in industrial applications despite 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 saturation 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 Ethernet buses. This work develops a tracking system based on an event-based camera. A bioinspired filtering algorithm to reduce noise and transmitted data while keeping the main features at the scene is implemented in FPGA which also serves as a network node. 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 FPGA includes the network protocol to integrate the eventbased camera as any other existing network node. The inverse kinematics for the robot is included in the controller. In addition, another network node is used to control pneumatic valves blowing the ball at different speed and trajectories. To complete the system and provide a comparison, a traditional frame-based camera is also connected to the controller. The imaging data for the tracking system are obtained either from the eventbased or frame-based camera. The controller acts on the servos to be moved acting as a ball follower. Results show that the robot can accurately follow the ball using fast image recognition, with the intrinsic advantages of the event-based system (size, price, power). This works shows how the development of new equipment and algorithms can be efficiently integrated in an industrial system, merging commercial industrial equipment with the new devices so that new technologies can rapidly enter into the industrial field.

show abstract

Robust vision system to illumination changes in a color-dependent task

Cited by 4 publications

References 10 publications

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

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

An Event-based Fast Movement Detection Algorithm for a Positioning Robot Using POWERLINK Communication

Contact Info

Product

Resources

About