Abstract: All currencies around the world look very different from each other. For instance, the size, color, and pattern of the paper are different. With the development of modern banking services, automatic methods for paper currency recognition become important in many applications like vending machines. One of the currency recognition architecture’s phases is Feature detection and description. There are many algorithms that are used for this phase, but they still have some disadvantages. This paper proposes a feature detection algorithm, which merges the advantages given in the current SIFT and SURF algorithms, which we call, Speeded up Robust Scale-Invariant Feature Transform (SR-SIFT) algorithm. Our proposed SR-SIFT algorithm overcomes the problems of both the SIFT and SURF algorithms. The proposed algorithm aims to speed up the SIFT feature detection algorithm and keep it robust. Simulation results demonstrate that the proposed SR-SIFT algorithm decreases the average response time, especially in small and minimum number of best key points, increases the distribution of the number of best key points on the surface of the currency. Furthermore, the proposed algorithm increases the accuracy of the true best point distribution inside the currency edge than the other two algorithms.
Abstract: The demands of smart visual thing recognition in various devices have been increased rapidly for daily smart production, living and learning systems in recent years. This paper proposed a visual thing recognition system, which combines binary scale-invariant feature transform (SIFT), bag of words model (BoW), and support vector machine (SVM) by using color information. Since the traditional SIFT features and SVM classifiers only use the gray information, color information is still an important feature for visual thing recognition. With color-based SIFT features and SVM, we can discard unreliable matching pairs and increase the robustness of matching tasks. The experimental results show that the proposed object recognition system with color-assistant SIFT SVM classifier achieves higher recognition rate than that with the traditional gray SIFT and SVM classification in various situations.
Abstract: This paper presents the novel Rao-Blackwellised
particle filter (RBPF) for mobile robot simultaneous localization and
mapping (SLAM) using monocular vision. The particle filter is
combined with unscented Kalman filter (UKF) to extending the path
posterior by sampling new poses that integrate the current observation
which drastically reduces the uncertainty about the robot pose. The
landmark position estimation and update is also implemented through
UKF. Furthermore, the number of resampling steps is determined
adaptively, which seriously reduces the particle depletion problem,
and introducing the evolution strategies (ES) for avoiding particle
impoverishment. The 3D natural point landmarks are structured with
matching Scale Invariant Feature Transform (SIFT) feature pairs. The
matching for multi-dimension SIFT features is implemented with a
KD-Tree in the time cost of O(log2
N). Experiment results on real robot
in our indoor environment show the advantages of our methods over
previous approaches.
Abstract: Scale Invariant Feature Transform (SIFT) has been
widely applied, but extracting SIFT feature is complicated and
time-consuming. In this paper, to meet the demand of the real-time
applications, SIFT is parallelized and optimized on cluster system,
which is named pSIFT. Redundancy storage and communication are
used for boundary data to improve the performance, and before
representation of feature descriptor, data reallocation is adopted to
keep load balance in pSIFT. Experimental results show that pSIFT
achieves good speedup and scalability.
Abstract: In this paper, we present local image descriptor using
VQ-SIFT for more effective and efficient image retrieval. Instead of
SIFT's weighted orientation histograms, we apply vector quantization
(VQ) histogram as an alternate representation for SIFT features.
Experimental results show that SIFT features using VQ-based local
descriptors can achieve better image retrieval accuracy than the
conventional algorithm while the computational cost is significantly
reduced.