Hardware Accelerators for Iris Localization

Kumar, Vineet; Asati, Abhijit; Gupta, Anu

doi:10.1007/s11265-017-1282-2

Cited by 10 publications

(11 citation statements)

References 15 publications

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“…Our FPGA based architecture for a real-time implementation of the 1D Hough transform multi-circles detection is focused on the acceleration of the construction of histograms V x and V y, [1 -7] and [8][9][10][11][12][13][14] respectively to the algorithm 1. Our solution significantly reduces the use of memory and the latency execution.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Our solution significantly reduces the use of memory and the latency execution. First, we propose an equivalent algorithm, algorithm 2, which gives the same results, that is to say the same x-coordinate and y-coordinate histograms as in steps [1 -7] and [8][9][10][11][12][13][14] of the algorithm 1.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Here, we present the highlights found, a survey of Hough transform methods can be read in [2] and [13]. We can notice some facts: 1) all the FPGA implementations [1], [3], [19], [16], [8], [11] use embedded internal memories or BRAMs (Block RAMs), 2) all the works consider only the acceleration of the voting process of the Hough transform. In this process the goal is to accumulate intersection points in the Hough parameter space, corresponding to the number of possible circles.…”

Section: State Of the Artmentioning

confidence: 99%

“…Then a vote is done to find the local maximum that are considered as real circle. This process is memory and time consuming, two different approaches emerged, the first approach uses the original Hough transform algorithm were the parametric equations of a circle are used to find the center and the radius of the circles [1], [3], [16], [8], [11] and the second approach uses a modified version of Hough transform called One dimensional Hough transform algorithm [19]. We describe below the works related to both approaches.…”

Section: State Of the Artmentioning

confidence: 99%

“…Specificity of this work is that it detects only one circle owning of the target application, an iris detection. The same approach is used in [11], where a FPGAbased hardware accelerator for iris localization is introduced.…”

Section: A Original Hough Transform Implementationsmentioning

confidence: 99%

See 4 more Smart Citations

FPGA-Based Real Time Embedded Hough Transform Architecture for Circles Detection

Chuquimia¹,

Pinna²,

Christophel³

et al. 2018

2018 Conference on Design and Architectures for Signal and Image Processing (DASIP)

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Hough Transform is a widely used shapebased algorithm for object detection and localization [6], this technique can be generalized to parametric curves as circles. For a real time execution and embedded integration, several optimizations are necessary due to the large memory and computational requirements. This paper presents an efficient real-time pipelined architecture with a FPGA implementation of our Hough Transform for multi-circles detection. The computation of center candidates was improved. A three stages pipeline architecture was designed in order to reduce the processing latency and cadence. The architecture has been integrated into a Xilinx Zynq-7000 XC7Z020 containing a FPGA Artix-7. The global system uses 78.5 BRAMs, 153 DSP slices, 21638 LUTs. Our global system can support a maximum clock frequency of 128.89 MHz. We validate our architecture using a 125MHz clock frequency and we obtain a latency of 33.214 ms and an interval between two images of 16,607 ms for a 1920x1080 pixels image. According to our results, our architecture offer a throughput more than 4 times better than the faster state of the art architecture.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Proposed Methodsmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Section: A Original Hough Transform Implementationsmentioning

confidence: 99%

See 3 more Smart Citations

FPGA-Based Real Time Embedded Hough Transform Architecture for Circles Detection

Chuquimia¹,

Pinna²,

Christophel³

et al. 2018

2018 Conference on Design and Architectures for Signal and Image Processing (DASIP)

View full text Add to dashboard Cite

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A comprehensive review on iris image-based biometric system

Winston

Hemanth

2018

Soft Comput

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Anomaly Detection Techniques in the Gaia Space Mission Data

Roberti

Druetto

Busonero

et al. 2021

J Sign Process Syst

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In this paper we deal with classification of anomalous data detected by the data reduction system of the Gaia space mission, in operation since 2013. Given the size and complexity of intermediate data and plots for diagnostics, beyond practical possibility of full human evaluation, the need for automated signal processing tools is becoming more and more relevant. Our classification task consists in discriminating among "normal" data and data affected by anomalies, which at present are grouped into four different classes. We investigate the use of some clever pre-processing approaches that allow the application of a tailored technique based on the Hough transform, and of some machine learning tools, evidencing that the task can be exactly solved in the former case. In the latter case, random forests and support vector machine provide less than satisfactory performance, while convolutional neural networks achieve very good classification accuracy, up to 91.22%. Statistics show satisfactory results also in terms of precision and recall of each class.

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Hardware Accelerators for Iris Localization

Cited by 10 publications

References 15 publications

FPGA-Based Real Time Embedded Hough Transform Architecture for Circles Detection

FPGA-Based Real Time Embedded Hough Transform Architecture for Circles Detection

A comprehensive review on iris image-based biometric system

Anomaly Detection Techniques in the Gaia Space Mission Data

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