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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.


Box filter calculation using the integral image; the shaded area indicates the filter to be computed whereas ‘X’ shows the integral image values required for computation of this box filter.
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sensors-15-16804-f014: Box filter calculation using the integral image; the shaded area indicates the filter to be computed whereas ‘X’ shows the integral image values required for computation of this box filter.

Mentions: As a box type filter can be computed quickly using three addition and subtraction operations when the integral image values on the four corners of that filter are known [2] (see Figure 14), the proposed method does not require any extra computation if the required four values are those which are stored in memory. In the worst case, all four integral image values needed for computing the box filter will not be available from memory. In that particular case, Equation (23) to Equation (26) can be utilized for computing the integral image values which were discarded earlier; they can then be used for calculating the required box filter. Although there is a speed-memory tradeoff involved, the method is still an efficient way of computing box type filters as it eliminates computation intensive multiplications.


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

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

Box filter calculation using the integral image; the shaded area indicates the filter to be computed whereas ‘X’ shows the integral image values required for computation of this box filter.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-16804-f014: Box filter calculation using the integral image; the shaded area indicates the filter to be computed whereas ‘X’ shows the integral image values required for computation of this box filter.
Mentions: As a box type filter can be computed quickly using three addition and subtraction operations when the integral image values on the four corners of that filter are known [2] (see Figure 14), the proposed method does not require any extra computation if the required four values are those which are stored in memory. In the worst case, all four integral image values needed for computing the box filter will not be available from memory. In that particular case, Equation (23) to Equation (26) can be utilized for computing the integral image values which were discarded earlier; they can then be used for calculating the required box filter. Although there is a speed-memory tradeoff involved, the method is still an efficient way of computing box type filters as it eliminates computation intensive multiplications.

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.