Optimization of decision-timing for early termination of SSDA-based block matching

Hatabu, Atsushi; Miyazaki, Takashi; Kuroda, I.

doi:10.1109/icme.2003.1221743

Cited by 3 publications

(11 citation statements)

References 8 publications

(9 reference statements)

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“…We have also compared the processing time of the proposed algorithm with the classical correlation scheme [10], NCC [13], successive similarity detection algorithm (SSDA) [15], Chamfer image matching algorithm [18], RIMA [21] and FDIM [24]. Results of this comparison are provided in Fig.…”

Section: Speed Analysismentioning

confidence: 99%

“…Thus, the adaptive prediction mechanism supervises the subset extraction process and provides a tight bound in the search area boundaries. The proposed algorithm is compared with six different algorithms, classical correlation scheme [10], NCC [13], SSDA [15], Chamfer image matching [18], Robust image matching algorithm (RIMA) [21] and Fourier Descriptors Image Matching (FDIM) [24]. The classical correlation scheme [10] accumulates pixel errors on the basis of their gray value difference.…”

Section: Adaptive Convergencementioning

confidence: 99%

“…NCC [13] utilizes an advance form of the correlation. SSDA is a block matching algorithm used with early truncation approach [15]. Chamfer image matching [18] and RIMA [21] are edge matching algorithms using distance transforms of the images.…”

Section: Adaptive Convergencementioning

confidence: 99%

“…Normalize cross correlation (NCC) with interest point search mechanism has also been effectively presented for image matching [13]. Block matching approach gives another attractive way of computation reduction by parallel processing of blocks [14], [15]. However, all these techniques are a tradeoff between robustness and computational load.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Visual Aerial Navigation through Adaptive Prediction and Hyper-Space Image Matching

Manzar

Cheema

Jalil

et al. 2009

IEICE Trans. Inf. & Syst.

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SUMMARYImage matching is an important area of research in the field of artificial intelligence, machine vision and visual navigation. This paper presents a new image matching scheme suitable for visual navigation. In this scheme, gray scale images are sliced and quantized to form sub-band binary images. The information in the binary images is then signaturized to form a vector space and the signatures are sorted as per significance. These sorted signatures are then normalized to transform the represented image pictorial features in a rotation and scale invariant form. For the image matching these two vector spaces from both the images are compared in the transformed domain. This comparison yields efficient results directly in the image spatial domain avoiding the need of image inverse transformation. As compared to the conventional correlation, this comparison avoids the wide range of square error calculations all over the image. In fact, it directly guides the solution to converge towards the estimate given by the adaptive prediction for a high speed performance in an aerial video sequence. A four dimensional solution population scheme has also been presented with a matching confidence factor. This factor helps in terminating the iterations when the essential matching conditions have been achieved. The proposed scheme gives robust and fast results for normal, scaled and rotated templates. Speed comparison with older techniques shows the computational viability of this new technique and its much lesser dependence on image size. The method also shows noise immunity at 30 dB AWGN and impulsive noise.

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Section: Speed Analysismentioning

confidence: 99%

Section: Adaptive Convergencementioning

confidence: 99%

Section: Adaptive Convergencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Visual Aerial Navigation through Adaptive Prediction and Hyper-Space Image Matching

Manzar

Cheema

Jalil

et al. 2009

IEICE Trans. Inf. & Syst.

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“…In [55], models to describe the probability distribution of the total distortion given a measured partial distortion are introduced. In [56], analysis-based method for optimizing the timing of decisions regarding early termination was proposed for developing PDE algorithms on different platforms.…”

Section: Fast Full Searchmentioning

confidence: 99%

Survey on Block Matching Motion Estimation Algorithms and Architectures with New Results

Huang

Chen

Tsai

et al. 2006

J VLSI Sign Process Syst Sign Image Video Technol

138

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Abstract. Block matching motion estimation is the heart of video coding systems. During the last two decades, hundreds of fast algorithms and VLSI architectures have been proposed. In this paper, we try to provide an extensive exploration of motion estimation with our new developments. The main concepts of fast algorithms can be classified into six categories: reduction in search positions, simplification of matching criterion, bitwidth reduction, predictive search, hierarchical search, and fast full search. Comparisons of various algorithms in terms of video quality and computational complexity are given as useful guidelines for software applications. As for hardware implementations, full search architectures derived from systolic mapping are first introduced. The systolic arrays can be divided into inter-type and intra-type with 1-D, 2-D, and tree structures. Hexagonal plots are presented for system designers to clearly evaluate the architectures in six aspects including gate count, required frequency, hardware utilization, memory bandwidth, memory bitwidth, and latency. Next, architectures supporting fast algorithms are also reviewed. Finally, we propose our algorithmic and architectural co-development. The main idea is quick checking of the entire search range with simplified matching criterion to globally eliminate impossible candidates, followed by finer selection among potential best matched candidates. The operations of the two stages are mapped to the same hardware for resource sharing. Simulation results show that our design is ten times more area-speed efficient than full search architectures while the video quality is competitively the same.

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SSDA for Colored Target Based on Characteristic Differences

Sun

Zhang

et al. 2012

2012 International Conference on Computer Science and Electronics Engineering

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Optimization of decision-timing for early termination of SSDA-based block matching

Cited by 3 publications

References 8 publications

Visual Aerial Navigation through Adaptive Prediction and Hyper-Space Image Matching

Visual Aerial Navigation through Adaptive Prediction and Hyper-Space Image Matching

Survey on Block Matching Motion Estimation Algorithms and Architectures with New Results

SSDA for Colored Target Based on Characteristic Differences

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