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Integral Images: Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision Systems.

Ehsan S, Clark AF - Sensors (Basel) (2015)

Bottom Line: The integral image, an intermediate image representation, has found extensive use in multi-scale local feature detection algorithms, such as Speeded-Up Robust Features (SURF), allowing fast computation of rectangular features at constant speed, independent of filter size.An efficient design strategy is also proposed for a parallel integral image computation unit to reduce the size of the required internal memory (nearly 35% for common HD video).Addressing the storage problem of integral image in embedded vision systems, the paper presents two algorithms which allow substantial decrease (at least 44.44%) in the memory requirements.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK. sehsan@essex.ac.uk.

ABSTRACT
The integral image, an intermediate image representation, has found extensive use in multi-scale local feature detection algorithms, such as Speeded-Up Robust Features (SURF), allowing fast computation of rectangular features at constant speed, independent of filter size. For resource-constrained real-time embedded vision systems, computation and storage of integral image presents several design challenges due to strict timing and hardware limitations. Although calculation of the integral image only consists of simple addition operations, the total number of operations is large owing to the generally large size of image data. Recursive equations allow substantial decrease in the number of operations but require calculation in a serial fashion. This paper presents two new hardware algorithms that are based on the decomposition of these recursive equations, allowing calculation of up to four integral image values in a row-parallel way without significantly increasing the number of operations. An efficient design strategy is also proposed for a parallel integral image computation unit to reduce the size of the required internal memory (nearly 35% for common HD video). Addressing the storage problem of integral image in embedded vision systems, the paper presents two algorithms which allow substantial decrease (at least 44.44%) in the memory requirements. Finally, the paper provides a case study that highlights the utility of the proposed architectures in embedded vision systems.

No MeSH data available.


Block diagram of the proposed architecture for parallel computation of integral image for 2 rows.
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sensors-15-16804-f005: Block diagram of the proposed architecture for parallel computation of integral image for 2 rows.

Mentions: This set of equations requiresaddition operations for an input image of size×pixels. This is not a significant increase compared to theadditions required for the standard recursive equations, Equations (6) and (7). For all odd rows, two additions are required per pixel, as given by Equations (8) and (10). An extra addition is done for each pixel in the even rows in Equation (11) to allow simultaneous calculation of integral image values for even and odd rows without any delay. The block diagram for the proposed architecture is shown in Figure 5. A pipelined approach for this two-stage system reduces the critical data path from two adders to one. The proposed system computes the integral image inclock cycles. The execution time is governed by.


Integral Images: Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision Systems.

Ehsan S, Clark AF - Sensors (Basel) (2015)

Block diagram of the proposed architecture for parallel computation of integral image for 2 rows.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541907&req=5

sensors-15-16804-f005: Block diagram of the proposed architecture for parallel computation of integral image for 2 rows.
Mentions: This set of equations requiresaddition operations for an input image of size×pixels. This is not a significant increase compared to theadditions required for the standard recursive equations, Equations (6) and (7). For all odd rows, two additions are required per pixel, as given by Equations (8) and (10). An extra addition is done for each pixel in the even rows in Equation (11) to allow simultaneous calculation of integral image values for even and odd rows without any delay. The block diagram for the proposed architecture is shown in Figure 5. A pipelined approach for this two-stage system reduces the critical data path from two adders to one. The proposed system computes the integral image inclock cycles. The execution time is governed by.

Bottom Line: The integral image, an intermediate image representation, has found extensive use in multi-scale local feature detection algorithms, such as Speeded-Up Robust Features (SURF), allowing fast computation of rectangular features at constant speed, independent of filter size.An efficient design strategy is also proposed for a parallel integral image computation unit to reduce the size of the required internal memory (nearly 35% for common HD video).Addressing the storage problem of integral image in embedded vision systems, the paper presents two algorithms which allow substantial decrease (at least 44.44%) in the memory requirements.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK. sehsan@essex.ac.uk.

ABSTRACT
The integral image, an intermediate image representation, has found extensive use in multi-scale local feature detection algorithms, such as Speeded-Up Robust Features (SURF), allowing fast computation of rectangular features at constant speed, independent of filter size. For resource-constrained real-time embedded vision systems, computation and storage of integral image presents several design challenges due to strict timing and hardware limitations. Although calculation of the integral image only consists of simple addition operations, the total number of operations is large owing to the generally large size of image data. Recursive equations allow substantial decrease in the number of operations but require calculation in a serial fashion. This paper presents two new hardware algorithms that are based on the decomposition of these recursive equations, allowing calculation of up to four integral image values in a row-parallel way without significantly increasing the number of operations. An efficient design strategy is also proposed for a parallel integral image computation unit to reduce the size of the required internal memory (nearly 35% for common HD video). Addressing the storage problem of integral image in embedded vision systems, the paper presents two algorithms which allow substantial decrease (at least 44.44%) in the memory requirements. Finally, the paper provides a case study that highlights the utility of the proposed architectures in embedded vision systems.

No MeSH data available.