A reconfigurable computing framework for multi-scale cellular image processing

Porter, Reid B.; Frigo, Janette; Conti, Al; Harvey, Neal R.; Kenyon, Garrett T.; Gokhale, Maya

doi:10.1016/j.micpro.2006.02.016

Cited by 23 publications

(6 citation statements)

References 12 publications

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“…FPGAbased computation engines appear to be very attractive for CA algorithms, since CA comprise a uniform structure composed of many finite state machines, thus matching the inherent design layout of FPGA HW. There are various recent CAbased applications that have been implemented on FPGAs and record significant performance evaluation compared to the corresponding software implementations [47]. Besides, tests based on double-precision floating point operations' (add/multiply) performance showed that FPGAs are competitive to multicore processors.…”

Section: Hardwarementioning

confidence: 99%

Human and Fire Detection from High Altitude UAV Images

Giitsidis

Karakasis

Γαστεράτος

et al. 2015

2015 23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing

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Illegal migration as well as wildfires constitute commonplace situations in southern European countries, where the mountainous terrain and thick forests make the surveillance and location of these incidents a tall task. This territory could benefit from Unmanned Aerial Vehicles (UAVs) equipped with optical and thermal sensors in conjunction with sophisticated image processing and computer vision algorithms, in order to detect suspicious activity or prevent the spreading of a fire. Taking into account that the flight height is about to two kilometers, human and fire detection algorithms are mainly based on blob detection. For both processes thermal imaging is used in order to improve the accuracy of the algorithms, while in the case of human recognition information like movement patterns as well as shadow size and shape are also considered. For fire detection a blob detector is utilized in conjunction with a color based descriptor, applied to thermal and optical images, respectively. Unlike fire, human detection is a more demanding process resulting in a more sophisticated and complex algorithm. The main difficulty of human detection originates from the high flight altitude. In images taken from high altitude where the ground sample distance is not small enough, people appear as small blobs occupying few pixels, leading corresponding research works to be based on blob detectors to detect humans. Their shadows as well as motion detection and object tracking can then be used to determine whether these regions of interest do depict humans. This work follows this motif as well; nevertheless, its main novelty lies in the fact that the human detection process is adapted for high altitude and vertical shooting images in contrast with the majority of other similar works where lower altitudes and different shooting angles are considered. Additionally, in the interest of making our algorithms as fast as possible in order for them to be used in real time during the UAV flights, parallel image processing with the help of a specialized hardware device based on Field Programmable Gate Array (FPGA) is being worked on.

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Section: Hardwarementioning

confidence: 99%

Human and Fire Detection from High Altitude UAV Images

Giitsidis

Karakasis

Γαστεράτος

et al. 2015

2015 23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing

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“…The systolic array architecture was implemented using pipelined, parameterized hardware library components from the Harpo framework [6]. The systolic array implementation obtained a speed-up of 102X on the AMS Wildstar II Pro board compared to a Xeon, 3.18GHz implementation written in C++.…”

Section: Case Studymentioning

confidence: 99%

A Programmable, Maximal Throughput Architecture for Neighborhood Image Processing

Porter

Frigo

Gokhale

et al. 2006

2006 14th Annual IEEE Symposium on Field-Programmable Custom Computing Machines

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We propose a run-time re-configurable architecture for local neighborhood image processing. We discuss how the new architecture can offer improved flexibility to the developer. We show that for a satellite image feature extraction application, our architecture, implemented on Stratix II and Virtex 2 Field Programmable Gate Arrays, achieves similar performance, hardware resource utilization, and throughput as a fully pipelined systolic array architecture.

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“…There are various recent CA-based applications that have been implemented on FPGA's and achieve significant performance evaluation compared to the corresponding soft-ware implementations [44][45][46]. Finally, FPGAs can be completely dedicated to a particular function, in several cases they are more energy efficient than general-purpose CPUs.…”

Section: Introductionmentioning

confidence: 99%

Hardware Acceleration of Cellular Automata Physarum polycephalum Model

Dourvas

Tsompanas

Sirakoulis

et al. 2015

Parallel Process. Lett.

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During the past decades, computer science experts were inspired from the study of biological organisms. Moreover, bio-inspired algorithms were produced that many times can give excellent solutions with low computational cost in complex engineering problems. In our case, the plasmodium of Physarum polycephalum is capable of finding the shortest path solution between two points in a labyrinth. In this study, we implement a Cellular Automata (CA) model in hardware, which attempts to describe and, moreover, mimic the behavior of the plasmodium in a maze. Beyond the successful implementation of the CA-based Physarum model in software, in order to take full advantage of the inherent parallelism of CA, we focus on a Field Programmable Gate Array (FPGA) implementation of the proposed model. Namely, two different implementations were considered here. Their difference is on the desired precision produced by the numerical representation of CA model parameters. Based on the corresponding results of the shortest path in the labyrinth,the modeling efficiency of both approaches was compared depending on the resulting error propagation. The presented FPGA implementations succeed to take advantage of the CA's inherit parallelism and improve the performance of the CA algorithm when compared with software in terms of computational speed and power consumption. As a result, the implementations presented here, can also be considered as a preliminary CA-based Physarum polycephalum IP core which produces a biological inspired solution to the shortest-path problem.

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A reconfigurable computing framework for multi-scale cellular image processing

Cited by 23 publications

References 12 publications

Human and Fire Detection from High Altitude UAV Images

Human and Fire Detection from High Altitude UAV Images

A Programmable, Maximal Throughput Architecture for Neighborhood Image Processing

Hardware Acceleration of Cellular Automata Physarum polycephalum Model

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