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Published: 14 December 2021
by MDPI
Remote Sensing, Volume 13; https://doi.org/10.3390/rs13245066

Abstract:
This paper proposes a unique Graph SLAM framework to generate precise 2.5D LIDAR maps in an XYZ plane. A node strategy was invented to divide the road into a set of nodes. The LIDAR point clouds are smoothly accumulated in intensity and elevation images in each node. The optimization process is decomposed into applying Graph SLAM on nodes’ intensity images for eliminating the ghosting effects of the road surface in the XY plane. This step ensures true loop-closure events between nodes and precise common area estimations in the real world. Accordingly, another Graph SLAM framework was designed to bring the nodes’ elevation images into the same Z-level by making the altitudinal errors in the common areas as small as possible. A robust cost function is detailed to properly constitute the relationships between nodes and generate the map in the Absolute Coordinate System. The framework is tested against an accurate GNSS/INS-RTK system in a very challenging environment of high buildings, dense trees and longitudinal railway bridges. The experimental results verified the robustness, reliability and efficiency of the proposed framework to generate accurate 2.5D maps with eliminating the relative and global position errors in XY and Z planes. Therefore, the generated maps significantly contribute to increasing the safety of autonomous driving regardless of the road structures and environmental factors.
Diego Aliaga, , , , Samara Carbone, Evgeny Kadantsev, Paolo Laj, Alfred Wiedensohler, ,
Published: 10 November 2021
Atmospheric Chemistry and Physics, Volume 21, pp 16453-16477; https://doi.org/10.5194/acp-21-16453-2021

Abstract:
Observations of aerosol and trace gases in the remote troposphere are vital to quantify background concentrations and identify long-term trends in atmospheric composition on large spatial scales. Measurements made at high altitude are often used to study free-tropospheric air; however such high-altitude sites can be influenced by boundary layer air masses. Thus, accurate information on air mass origin and transport pathways to high-altitude sites is required. Here we present a new method, based on the source–receptor relationship (SRR) obtained from backwards WRF-FLEXPART simulations and a k-means clustering approach, to identify source regions of air masses arriving at measurement sites. Our method is tailored to areas of complex terrain and to stations influenced by both local and long-range sources. We have applied this method to the Chacaltaya (CHC) GAW station (5240 m a.s.l.; 16.35∘ S, 68.13∘ W) for the 6-month duration of the “Southern Hemisphere high-altitude experiment on particle nucleation and growth” (SALTENA) to identify where sampled air masses originate and to quantify the influence of the surface and the free troposphere. A key aspect of our method is that it is probabilistic, and for each observation time, more than one air mass (cluster) can influence the station, and the percentage influence of each air mass can be quantified. This is in contrast to binary methods, which label each observation time as influenced by either boundary layer or free-troposphere air masses. Air sampled at CHC is a mix of different provenance. We find that on average 9 % of the air, at any given observation time, has been in contact with the surface within 4 d prior to arriving at CHC. Furthermore, 24 % of the air has been located within the first 1.5 km above ground level (surface included). Consequently, 76 % of the air sampled at CHC originates from the free troposphere. However, pure free-tropospheric influences are rare, and often samples are concurrently influenced by both boundary layer and free-tropospheric air masses. A clear diurnal cycle is present, with very few air masses that have been in contact with the surface being detected at night. The 6-month analysis also shows that the most dominant air mass (cluster) originates in the Amazon and is responsible for 29 % of the sampled air. Furthermore, short-range clusters (origins within 100 km of CHC) have high temporal frequency modulated by local meteorology driven by the diurnal cycle, whereas the mid- and long-range clusters' (>200 km) variability occurs on timescales governed by synoptic-scale dynamics. To verify the reliability of our method, in situ sulfate observations from CHC are combined with the SRR clusters to correctly identify the (pre-known) source of the sulfate: the Sabancaya volcano located 400 km north-west from the station.
, Slim Smaoui, Mohamed Ali Triki
Circular Economy and Sustainability, Volume 1, pp 1423-1437; https://doi.org/10.1007/s43615-021-00035-y

The publisher has not yet granted permission to display this abstract.
Published: 23 February 2021
by MDPI
Sensors, Volume 21; https://doi.org/10.3390/s21041529

Abstract:
We introduce an integrated method for processing depth maps measured by a laser profile sensor. It serves for the recognition and alignment of an object given by a single example. Firstly, we look for potential object contours, mainly using the Retinex filter. Then, we select the actual object boundary via shape comparison based on Triangle Area Representation (TAR). We overcome the limitations of the TAR method by extension of its shape descriptor. That is helpful mainly for objects with symmetric shapes but other asymmetric aspects like squares with asymmetric holes. Finally, we use point-to-point pairing, provided by the extended TAR method, to calculate the 3D rigid affine transform that aligns the scanned object to the given example position. For the transform calculation, we design an algorithm that overcomes the Kabsch point-to-point algorithm’s accuracy and accommodates it for a precise contour-to-contour alignment. In this way, we have implemented a pipeline with features convenient for industrial use, namely production inspection.
Published: 14 November 2020
by MDPI
Entropy, Volume 22; https://doi.org/10.3390/e22111299

Abstract:
A new solution to overcome the constraints of multimodality medical intra-subject image registration is proposed, using the mutual information (MI) of image histogram-oriented gradients as a new matching criterion. We present a rigid, multi-modal image registration algorithm based on linear transformation and oriented gradients for the alignment of T2-weighted (T2w) images (as a fixed reference) and diffusion tensor imaging (DTI) (b-values of 500 and 1250 s/mm2) as floating images of three patients to compensate for the motion during the acquisition process. Diffusion MRI is very sensitive to motion, especially when the intensity and duration of the gradient pulses (characterized by the b-value) increases. The proposed method relies on the whole brain surface and addresses the variability of anatomical features into an image stack. The sparse features refer to corners detected using the Harris corner detector operator, while dense features use all image pixels through the image histogram of oriented gradients (HOG) as a measure of the degree of statistical dependence between a pair of registered images. HOG as a dense feature is focused on the structure and extracts the oriented gradient image in the x and y directions. MI is used as an objective function for the optimization process. The entropy functions and joint entropy function are determined using the HOGs data. To determine the best image transformation, the fiducial registration error (FRE) measure is used. We compare the results against the MI-based intensities results computed using a statistical intensity relationship between corresponding pixels in source and target images. Our approach, which is devoted to the whole brain, shows improved registration accuracy, robustness, and computational cost compared with the registration algorithms, which use anatomical features or regions of interest areas with specific neuroanatomy. Despite the supplementary HOG computation task, the computation time is comparable for MI-based intensities and MI-based HOG methods.
Published: 2 May 2020
by MDPI
Sensors, Volume 20; https://doi.org/10.3390/s20092592

Abstract:
In this study, we propose a method for training convolutional neural networks to make them identify and classify images with higher classification accuracy. By combining the Cartesian and polar coordinate systems when describing the images, the method of recognition and classification for plankton images is discussed. The optimized classification and recognition networks are constructed. They are available for in situ plankton images, exploiting the advantages of both coordinate systems in the network training process. Fusing the two types of vectors and using them as the input for conventional machine learning models for classification, support vector machines (SVMs) are selected as the classifiers to combine these two features of vectors, coming from different image coordinate descriptions. The accuracy of the proposed model was markedly higher than those of the initial classical convolutional neural networks when using the in situ plankton image data, with the increases in classification accuracy and recall rate being 5.3% and 5.1% respectively. In addition, the proposed training method can improve the classification performance considerably when used on the public CIFAR-10 dataset.
Dan Anh Do, Giap Nguyen Vu, Minh Quang Bui, Huong Ninh, Hai Tran Tien
Proceedings of the 2020 6th International Conference on Computer and Technology Applications; https://doi.org/10.1145/3397125.3397136

Abstract:
Kernelized correlation filter (KCF) based trackers have drawn great attention for their superiority in terms of accuracy and speed in visual tracking problem. However, these methods are not robust under scale changes, rotation and occlusion due to their having a fixed size filter. In this work, we take advantage of the KCF tracker to propose a novel algorithm for long-term visual object tracking which handle scale variation using Log-Polar Transformation and Phase Correlation. To detect exactly loss tracker moment when object partly for fully occlusion, this paper propose an effective technique combining PRS ratio and histogram distance. We also learn an online SVM classifier on consecutive and reliable samples to redetect objects in case of tracking failure due to heavy occlusion or out of view movement. Experimental results in several challenging tracking datasets from camera UAV show that our tracker achieves remarkable speed in real-time application at 40FPS while handling scale changes and occlusion better than many state-of-the art tracking algorithms.
, Dan Xiong, Junhao Xiao, Zhiqiang Zheng
International Journal of Advanced Robotic Systems, Volume 17; https://doi.org/10.1177/1729881420909736

Abstract:
In this article, a robust long-term object tracking algorithm is proposed. It can tackle the challenges of scale and rotation changes during the long-term object tracking for security robots. Firstly, a robust scale and rotation estimation method is proposed to deal with scale changes and rotation motion of the object. It is based on the Fourier–Mellin transform and the kernelized correlation filter. The object’s scale and rotation can be estimated in the continuous space, and the kernelized correlation filter is used to improve the estimation accuracy and robustness. Then a weighted object searching method based on the histogram and the variance is introduced to handle the problem that trackers may fail in the long-term object tracking (due to semi-occlusion or full occlusion). When the tracked object is lost, the object can be relocated in the whole image using the searching method, so the tracker can be recovered from failures. Moreover, two other kernelized correlation filters are learned to estimate the object’s translation and the confidence of tracking results, respectively. The estimated confidence is more accurate and robust using the dedicatedly designed kernelized correlation filter, which is employed to activate the weighted object searching module, and helps to determine whether the searching windows contain objects. We compare the proposed algorithm with state-of-the-art tracking algorithms on the online object tracking benchmark. The experimental results validate the effectiveness and superiority of our tracking algorithm.
Hongsheng Wang, Qun Hao, Jie Cao, Chongdao Wang, Heng Zhang
Published: 2 December 2019
Applied Optics, Volume 58, pp 9532-9539; https://doi.org/10.1364/ao.58.009532

Abstract:
A target-recognition method for retina-like laser detection and range images is proposed. A log-polar (LP) frequency descriptor (LPHFM) is constructed using Fourier–Mellin transformation combined with a high-pass filter for LP range images. The target-recognition result can be obtained via the maximum energy from the phase correlation spectrum. The LPHFM feature with LP images is explored, and examples are used to demonstrate the validity and capability of the proposed method. Finally, important conclusions are drawn as follows: (I) Applying frequency features with the LP process is valid; (II) LPHFM feature extraction is easily accomplished when the technique is directly applied to LP images after high-pass filtering; (III) The accuracy of the proposed solution agrees well with theoretical values when angle and scale variants are used. Thus, the proposed recognition solution may be used in various applications involving space-variant image processing.
Jan Novotny, Ludmila Nováková
38TH MEETING OF DEPARTMENTS OF FLUID MECHANICS AND THERMODYNAMICS, Volume 2118; https://doi.org/10.1063/1.5114761

Abstract:
The presented work shows results of study of influence of recorded data quality when measuring by mean of Particle Image Velocimetry method on accuracy of evaluation of signal displacement. In the last ten years, special consideration has been given to the measurement accuracy of the Particle Image Velocimetry method. The work explores main aspects affecting the measurement accuracy of the Particle Image Velocimetry method with a focus on Standard Cross Correlation and Robust Phase Correlation method. The aim of this work is the verify possibility of using Hartley transformation instead of classical FFT transformation. The whole procedure measurement uncertainty is based on the analysis of synthetic data and the use of a Uniform Flow Test.
Published: 20 March 2019
by MDPI
Sensors, Volume 19; https://doi.org/10.3390/s19061380

Abstract:
The capability of landing on previously unvisited areas is a fundamental challenge for an unmanned aerial vehicle (UAV). In this paper, we developed a vision-based motion estimation as an aid to improve landing performance. As an alternative to the common scenarios accompanying by external infrastructures or well-defined marker, the proposed hybrid framework can successfully land on a new area without any prior information about guiding marks. The implementation was based on the optical flow technique associated with a multi-scale strategy to overcome the decreasing field-of-view during the UAV descending. Compared with a commercial Global Positioning System (GPS) through a sequence of flight trials, the vision-aided scheme can effectively minimize the possible sensing error, thus, leading to a more accurate result. Moreover, this work has potential to integrate the fast-growing image learning process and yields more practical versatility for UAV applications in the future.
, , Xiao Ling, Xiang Wang
International Journal of Remote Sensing, Volume 40, pp 5429-5453; https://doi.org/10.1080/01431161.2019.1579941

Abstract:
Automatic registration of multimodal remote sensing images, which is a critical prerequisite in a range of applications (e.g. image fusion, image mosaic, and image analysis), continues to be a fundamental and challenging problem. In this paper, we propose a novel extended phase correlation algorithm based on Log-Gabor filtering (LGEPC) for the registration of images with nonlinear radiometric differences and geometric differences (e.g. rotation, scale, and translation). Our algorithm focuses on two problems that the traditional extended phase correlation algorithms cannot well handle: 1) significant nonlinear radiometric differences and 2) large-scale differences between image pairs. After an over-complete multi-scale atlas space of the original image is built based on the filtered magnitudes obtained by using Log-Gabor filters with different central frequencies, the phase correlation of the single scale images is extended by LGEPC to atlases phase correlation, which is conducive to solving the problem of large scale and rotation differences between the image pairs. Subsequently, LGEPC eliminates the interface of the significant nonlinear radiometric differences by superimposing multi-scale geometric structural spectra and carrying out the phase correlation module, so that the translation can be well determined. Our experiments on synthetic images demonstrated the rationality and effectiveness of LGEPC, and the experiments on a variety of multimodal images confirmed that LGEPC can ideally achieve pixel-wise registration accuracy for multimodal image pairs that conform to the similarity transformation model.
Published: 30 December 2018
by MDPI
Journal of Imaging, Volume 5; https://doi.org/10.3390/jimaging5010005

Abstract:
Multi-modal image registration is the primary step in integrating information stored in two or more images, which are captured using multiple imaging modalities. In addition to intensity variations and structural differences between images, they may have partial or full overlap, which adds an extra hurdle to the success of registration process. In this contribution, we propose a multi-modal to mono-modal transformation method that facilitates direct application of well-founded mono-modal registration methods in order to obtain accurate alignment of multi-modal images in both cases, with complete (full) and incomplete (partial) overlap. The proposed transformation facilitates recovering strong scales, rotations, and translations. We explain the method thoroughly and discuss the choice of parameters. For evaluation purposes, the effectiveness of the proposed method is examined and compared with widely used information theory-based techniques using simulated and clinical human brain images with full data. Using RIRE dataset, mean absolute error of 1.37, 1.00, and 1.41 mm are obtained for registering CT images with PD-, T1-, and T2-MRIs, respectively. In the end, we empirically investigate the efficacy of the proposed transformation in registering multi-modal partially overlapped images.
Boyuan Ma, , , WanBo Liu, Chuni Liu, Di Wu, Yonghong Zhi
Published: 9 November 2018
Computational Materials Science, Volume 158, pp 1-13; https://doi.org/10.1016/j.commatsci.2018.10.044

The publisher has not yet granted permission to display this abstract.
Pattern Recognition and Image Analysis, Volume 28, pp 261-272; https://doi.org/10.1134/s1054661818020050

Abstract:
Automatic mosaicing is an important image processing application and we propose several improvements and simplifications to the image registration pipeline used in microscopy to automatically construct large images of whole specimen samples from a series of images. First of all we propose a feature descriptor based on the amplitude of a few elements of the Fourier transform, which makes it fast to compute and that can be used for any image matching and registration applications where scale and rotation invariance is not needed. Secondly, we propose a cascade matching approach that will reduce the time for the nearest neighbour search considerably, making it almost independent on feature vector length. Moreover, several improvements are proposed that will speed up the whole matching process. These are: faster interest point detection, a regular sampling strategy and a deterministic false positive removal procedure that finds the transformation. All steps of the improved pipeline are explained and the results comparative experiments are presented.
T Aksoy, Ž Špiclin, F Pernuš, G Unal
Published: 21 November 2017
Physics in Medicine and Biology, Volume 62, pp 9377-9394; https://doi.org/10.1088/1361-6560/aa9474

Abstract:
Registration of 3D pre-interventional to 2D intra-interventional medical images has an increasingly important role in surgical planning, navigation and treatment, because it enables the physician to co-locate depth information given by pre-interventional 3D images with the live information in intra-interventional 2D images such as x-ray. Most tasks during image-guided interventions are carried out under a monoplane x-ray, which is a highly ill-posed problem for state-of-the-art 3D to 2D registration methods. To address the problem of rigid 3D–2D monoplane registration we propose a novel multi-objective stratified parameter optimization, wherein a small set of high-magnitude intensity gradients are matched between the 3D and 2D images. The stratified parameter optimization matches rotation templates to depth templates, first sampled from projected 3D gradients and second from the 2D image gradients, so as to recover 3D rigid-body rotations and out-of-plane translation. The objective for matching was the gradient magnitude correlation coefficient, which is invariant to in-plane translation. The in-plane translations are then found by locating the maximum of the gradient phase correlation between the best matching pair of rotation and depth templates. On twenty pairs of 3D and 2D images of ten patients undergoing cerebral endovascular image-guided intervention the 3D to monoplane 2D registration experiments were setup with a rather high range of initial mean target registration error from 0 to 100 mm. The proposed method effectively reduced the registration error to below 2 mm, which was further refined by a fast iterative method and resulted in a high final registration accuracy (0.40 mm) and high success rate (96%). Taking into account a fast execution time below 10 s, the observed performance of the proposed method shows a high potential for application into clinical image-guidance systems.
Wei-Jun Chen
2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) pp 1-8; https://doi.org/10.1109/dicta.2017.8227491

Abstract:
This paper suggests a new method for image registration, based on a new similarity measure, the standard deviation normalized summed squared difference. Such a similarity measure is explicitly defined on the effective overlap between two images, and the image registration is fulfilled by searching for a global minimum peak of this measure over the entire parameter space. Conceptually the suggested method differs from traditional ones in requiring "zero" image pre- processing like image content dependent or optical system specified spatial windowing, frequency filtering, salient feature detection, or intensity normalization. Without any prior knowledge and image pre-processing, experimental results on a dataset of 90 image pairs with various application backgrounds as well as imaging conditions show a 100% success ratio by our suggestion, which is superior to widely recommended methods of SIFT- stitching (44.4%), linear phase cross-correlation (60%), normalized cross-correlation (54.4%), and some recently top-ranked market products (≈74%).
Wei-Jun Chen
2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA) pp 1-6; https://doi.org/10.1109/ipta.2016.7820989

Abstract:
This paper suggests a new method for detecting 2D translation between two images based on calculating three independent cross-correlations (CCs) on them. Such a method is conceptually different from other area based methods which generally perform only one CC or its variants for phase shift detection. The principle of traditional area based methods could be interpreted as a fast but simplified implementation of least squares (LS), by ignoring two summed squares of given images while keeping one CC component between them. It is argued by us that such an ignorance often inevitably results in the requirement of data pre-processing for robustness and accuracy. Keeping all the source information but calculating the whole LS by three CCs, the computation performance is kept as O(N log N). Without any data pre-processing, experiments on a dataset with rich application backgrounds and comparisons with widely recommended methods including both the area based and the feature based methods, show that our suggestion is very promising for general-purpose 2D translation detection.
, , Jerome L. Obermark
Review of Scientific Instruments, Volume 87; https://doi.org/10.1063/1.4954730

Abstract:
Flat-field image processing is an essential step in producing high-quality and radiometrically calibrated images. Flat-fielding corrects for variations in the gain of focal plane array electronics and unequal illumination from the system optics. Typically, a flat-field image is captured by imaging a radiometrically uniform surface. The flat-field image is normalized and removed from the images. There are circumstances, such as with remote sensing, where a flat-field image cannot be acquired in this manner. For these cases, we developed a phase-correlation method that allows the extraction of an effective flat-field image from a sequence of scene-based displaced images. The method uses sub-pixel phase correlation image registration to align the sequence to estimate the static scene. The scene is removed from sequence producing a sequence of misaligned flat-field images. An average flat-field image is derived from the realigned flat-field sequence.
Fan Yang, , Zhen Sheng Deng, Ang Yan
Published: 5 February 2016
Computers in Biology and Medicine, Volume 71, pp 108-114; https://doi.org/10.1016/j.compbiomed.2016.01.026

The publisher has not yet granted permission to display this abstract.
, Mohamed Ibrahim, Quan D. Nguyen, Ahmed S. Fahmy
IET Image Processing, Volume 9, pp 486-495; https://doi.org/10.1049/iet-ipr.2013.0672

Abstract:
Fundus fluorescein angiography (FFA) is a standard screening and diagnosis technique for several retinal diseases. The analysis of FFA images is performed qualitatively by skilled observers, and thus is vulnerable to inter- and intra-observer variability. In this study, the authors present a method for computer-aided analysis of FFA images. The method is based on generating quantitative colour fluorescein leakage maps (FLM) that mimic the thickness maps generated by the optical coherence tomography (OCT). Results from 64 patients show strong correlation between the FLM and OCT thickness maps (r = 0.8). The method was found to be reproducible and robust to variability in the image acquisition times.
Amr Yousef, Jiang Li, Mohammad Karim
IEEE Signal Processing Letters, Volume 22, pp 1796-1800; https://doi.org/10.1109/lsp.2015.2437881

Abstract:
A new, fast and computationally efficient lateral subpixel shift registration algorithm is presented. It is limited to register images that differ by small subpixel shifts otherwise its performance degrades. This algorithm significantly improves the performance of the single-step discrete Fourier transform approach proposed by Guizar-Sicairos and can be applied efficiently on large dimension images. It reduces the dimension of Fourier transform of the cross correlation matrix and reduces the discrete Fourier transform (DFT) matrix multiplications to speed up the registration process. Simulations show that our algorithm reduces computation time and memory requirements without sacricing the accuracy associated with the usual FFT approach accuracy.
Kui Zhang, Xiao-Long Zhang, Xin Xu, Xiao-Wei Fu
Journal of Shanghai Jiaotong University (Science), Volume 20, pp 61-67; https://doi.org/10.1007/s12204-015-1589-8

The publisher has not yet granted permission to display this abstract.
Anders Hast
2014 22nd International Conference on Pattern Recognition pp 809-814; https://doi.org/10.1109/icpr.2014.149

Abstract:
Any feature matching algorithm needs to be robust, producing few false positives but also needs to be invariant to changes in rotation, illumination and scale. Several improvements are proposed to a previously published Phase Correlation based algorithm, which operates on local disc areas, using the Log Polar Transform to sample the disc neighborhood and the FFT to obtain the phase. It will be shown that the matching can be done in the frequency domain directly, using the Chi-squared distance, instead of computing the cross power spectrum. Moreover, it will be shown how combining these methods yields an algorithm that sorts out a majority of the false positives. The need for a peak to sub lobe ratio computation in order to cope with sub pixel accuracy will be discussed as well as how the FFT of the periodic component can enhance the matching. The result is a robust local feature matcher that is able to cope with rotational, illumination and scale differences to a certain degree.
Jorge Herrera-Ramírez, , Jaume Pujol
Published: 9 May 2014
Applied Optics, Volume 53, pp 3131-3141; https://doi.org/10.1364/ao.53.003131

Abstract:
To expand and investigate the potential of spectral imaging, we developed a portable multispectral system using light-emitting diodes. This system recovers spectral information from the UV to the near IR over a large area using two different image sensors synchronized with 23 bands of illumination. The system was assessed for spectral reconstruction through simulations and experimental measurements by means of two methods of spectral reconstruction and three different evaluation metrics. The results over a Macbeth ColorChecker chart and other samples, including pigments usually employed in art paintings, are compared and discussed. The portable multispectral system using LEDs constitutes a cost-effective and versatile method for spectral imaging.
Allen George, G. Karthick, R. Harikumar
2014 International Conference on Intelligent Computing Applications pp 249-253; https://doi.org/10.1109/icica.2014.60

Abstract:
Palm print recognition is widely studied in the past few years and many efforts are done to use it as a biometric modality for various applications. Existing research on palm prints is based on low resolution images [1]and hence matching is based on the creases present on the palm prints[2]. Recently it was analyzed that ridges on palm prints can be used for matching since it is unique and persistent for humans and can be used for large forensic applications. Since ridges are insensitive to distortion and discrimination power, they are very reliable and hence used for palm print matching. The issues in existing systems are that those algorithms for palm print matching followed the fingerprint algorithms and hence not much speed and matching accuracy and was inefficient. The palm print databases are not in same coordinate system and hence the computational complexity in matching is more and is not able to handle noise and distortion. The aim of this project is to design an efficient authentication system using ridge features for palm print recognition with reduced computational complexity and hence to increase the matching speed and accuracy. Here, the proposed system is extracting the features such as orientation field and region mask[3]. For matching minutiae extraction and cascade filtering is done to increase the speed of matching. Hence palm print matching is being done efficiently with an accurate speed and good performance.
Anders Hast,
2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA) pp 107-112; https://doi.org/10.1109/ispa.2013.6703723

Abstract:
Rotation invariance is an important property for any feature matching method and it has been implemented in different ways for different methods. The Log Polar Transform has primarily been used for image registration where it is applied after phase correlation, which in its turn is applied on the whole images or in the case of template matching, applied on major parts of them followed by an exhaustive search. We investigate how this transform can be used on local neighborhoods of features and how phase correlation as well as normalized cross correlation can be applied on the result. Thus, the order is reversed and we argue why it is important to do so. We demonstrate a common problem with the log polar transform and that many implementations of it are not suitable for local feature detectors. We propose an implementation of it based on Gaussian filtering. We also show that phase correlation generally will perform better than normalized cross correlation. Both handles illumination differences well, but changes in scale is handled better by the phase correlation approach.
Joseph French, William Turri, Joseph Fernando, Eric Balster, James French, Jude Fernando
2013 IEEE High Performance Extreme Computing Conference (HPEC) pp 1-6; https://doi.org/10.1109/hpec.2013.6670323

Abstract:
This paper proposes a lower latency implementation of the georegistration algorithm proposed by [5]. The algorithm has been modified to mitigate the registration errors and has been parallelized to map to a Graphical Processor Unit (GPU). Also, the target image offset and the painting value computations have been combined to a single loop to eliminate the use of shared memory. Modifications to a current widely used algorithm are proposed. The proposed modified algorithm has been implemented in compute unified device (CUDA) architecture to reduce latency. A fixed coordinate system is used to represent the image, focal, and projection planes. Experimental results show that the proposed algorithm is capable of generating accurate georegistered images for high flying airborne vehicles. While this method has been tested using aerial photographs, it can be extended to Satellite images as well as other image data. A speedup of over 10x has been achieved over the CPU version.
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