Multi-Kernel Implicit Curve Evolution for Selected Texture Region Segmentation in VHR Satellite Images

Balla-Arabé, Souleymane; Gao, Xinbo; Wang, Bin; Yang, Fan; Brost, Vincent

doi:10.1109/tgrs.2013.2287239

Cited by 18 publications

(8 citation statements)

References 39 publications

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“…Finally, the generated code is classically synthesized via control and data-flow extraction and RTL generation processes. Two applications were tested: Kubelka-Munk genetic algorithm (KMGA) [39] for the multispectral image-based skin lesion assessments and level set method (LSM) [40] for very high-resolution satellite image segmentation. e experimental results showed that the design complexity for VHLS version is 50% less than its equivalent in C code.…”

Section: Design Space Exploration All Design Variations Listed Inmentioning

confidence: 99%

ViPar: High-Level Design Space Exploration for Parallel Video Processing Architectures

Ali

Atitallah

Cadi

et al. 2019

International Journal of Reconfigurable Computing

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Embedded video applications are now involved in sophisticated transportation systems like autonomous vehicles and driver assistance systems. As silicon capacity increases, the design productivity gap grows up for the current available design tools. Hence, high-level synthesis (HLS) tools emerged in order to reduce that gap by shifting the design efforts to higher abstraction levels. In this paper, we present ViPar as a tool for exploring different video processing architectures at higher design level. First, we proposed a parametrizable parallel architectural model dedicated for video applications. Second, targeting this architectural model, we developed ViPar tool with two main features: (1) An empirical model was introduced to estimate the power consumption based on hardware utilization and operating frequency. In addition to that, we derived the equations for estimating the hardware utilization and execution time for each design point during the space exploration process. (2) By defining the main characteristics of the parallel video architecture like parallelism level, the number of input/output ports, the pixel distribution pattern, and so on, ViPar tool can automatically generate the dedicated architecture for hardware implementation. In the experimental validation, we used ViPar tool to generate automatically an efficient hardware implementation for a Multiwindow Sum of Absolute Difference stereo matching algorithm on Xilinx Zynq ZC706 board. We succeeded to increase the design productivity by converging rapidly to the appropriate designs that fit with our system constraints in terms of power consumption, hardware utilization, and frame execution time.

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Section: Design Space Exploration All Design Variations Listed Inmentioning

confidence: 99%

ViPar: High-Level Design Space Exploration for Parallel Video Processing Architectures

Ali

Atitallah

Cadi

et al. 2019

International Journal of Reconfigurable Computing

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“…τ is the relaxation coefficient that is used to control the curvature, and we fix it to 2.75. α and β control the impact of the texture information on the segmentation results. In order to segment the text region of interest well, we find the suitable values of α according to observing the segmentation results in a test image [14]. In the experiment result analysis of Figure 6, we can see that a too low value of α (α = 0.5) leads to an over-segmentation, while a too high value (α = 10) decreases the precision of the result (under-segmentation).…”

Section: Parameter Configurationmentioning

confidence: 99%

“…(1) a highly parallel image segmentation algorithm dedicated to very high resolution satellite images [14,15] is prototyped and validated, (2) the implementation process of this algorithm into the register-transfer level is described, (3) a high-level design flow is developed by using the recent high-level synthesis tools to improve the development productivity and maintainability of the design, (4) a series of optimizations are sequentially made in the routine level to reduce the running-cost of the design; the experiments demonstrate that a significant performance improvement is achieved.…”

Section: Introductionmentioning

confidence: 99%

Embedded Implementation of VHR Satellite Image Segmentation

2017

Architecture‐Aware Optimization Strategies in Real‐time Image Processing

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Processing and analysis of Very High Resolution (VHR) satellite images provide a mass of crucial information, which can be used for urban planning, security issues or environmental monitoring. However, they are computationally expensive and, thus, time consuming, while some of the applications, such as natural disaster monitoring and prevention, require high efficiency performance. Fortunately, parallel computing techniques and embedded systems have made great progress in recent years, and a series of massively parallel image processing devices, such as digital signal processors or Field Programmable Gate Arrays (FPGAs), have been made available to engineers at a very convenient price and demonstrate significant advantages in terms of running-cost, embeddability, power consumption flexibility, etc. In this work, we designed a texture region segmentation method for very high resolution satellite images by using the level set algorithm and the multi-kernel theory in a high-abstraction C environment and realize its register-transfer level implementation with the help of a new proposed high-level synthesis-based design flow. The evaluation experiments demonstrate that the proposed design can produce high quality image segmentation with a significant running-cost advantage.

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“…DOI: 10.1109/TCYB.2015.2482365 are being made toward satisfying those needs by exploiting dedicated hardware, such as Graphics Processing Units (GPUs) [2], [3], [4]. Some areas where GPUs are emerging to improve Human Computer Interaction (HCI) include cybernetics (for example, in facial [5] or object [6] recognition, classification using Support Vector Machines [7] and genetic algorithms for clustering [8]), and image processing (e.g., unsupervised image segmentation [3], [9], optical coherence tomography systems [4], efficient surface reconstruction from noisy data [10], remote sensing [11], real-time background subtraction [12], etc.). In such hardware-oriented designed algorithms, the computational efficiency of processing tasks is significantly improved by parallelizing the operations.…”

Section: Introductionmentioning

confidence: 99%

Optimal Piecewise Linear Function Approximation for GPU-Based Applications

Berjón

Gallego

Cuevas

et al. 2016

IEEE Trans. Cybern.

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Many computer vision and human-computer interaction applications developed in recent years need evaluating complex and continuous mathematical functions as an essential step toward proper operation. However, rigorous evaluation of these kind of functions often implies a very high computational cost, unacceptable in real-time applications. To alleviate this problem, functions are commonly approximated by simpler piecewise-polynomial representations. Following this idea, we propose a novel, efficient, and practical technique to evaluate complex and continuous functions using a nearly optimal design of two types of piecewise linear approximations in the case of a large budget of evaluation subintervals. To this end, we develop a thorough error analysis that yields asymptotically tight bounds to accurately quantify the approximation performance of both representations. It provides an improvement upon previous error estimates and allows the user to control the tradeoff between the approximation error and the number of evaluation subintervals. To guarantee real-time operation, the method is suitable for, but not limited to, an efficient implementation in modern graphics processing units, where it outperforms previous alternative approaches by exploiting the fixed-function interpolation routines present in their texture units. The proposed technique is a perfect match for any application requiring the evaluation of continuous functions; we have measured in detail its quality and efficiency on several functions, and, in particular, the Gaussian function because it is extensively used in many areas of computer vision and cybernetics, and it is expensive to evaluate.

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Multi-Kernel Implicit Curve Evolution for Selected Texture Region Segmentation in VHR Satellite Images

Cited by 18 publications

References 39 publications

ViPar: High-Level Design Space Exploration for Parallel Video Processing Architectures

ViPar: High-Level Design Space Exploration for Parallel Video Processing Architectures

Embedded Implementation of VHR Satellite Image Segmentation

Optimal Piecewise Linear Function Approximation for GPU-Based Applications

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