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Efficiently tracking a moving object in two-dimensional image space

JOURNAL OF ELECTRONIC IMAGING 2001年第3期10卷 785-793页

作者： Barrett, SF Wright, CHG Zwick, H Wilcox, M Rockwell, BA Naess, E Univ Wyoming Dept Elect Engn Laramie WY 82071 USA USAF Acad Dept Elect Engn Colorado Springs CO 80840 USA USA Res Detachment Walter Reed Army Inst Res Brooks AFB TX 78235 USA USAF Acad Dept Biol Colorado Springs CO 80840 USA USAF Human Effectiveness Directorate Opt Radiat Branch Brooks AFB TX 78235 USA

A tracking algorithm has been developed to efficiently track a moving object in two-dimensional image space. The algorithm employs a limited exhaustive template matching scheme that combines the accuracy of an exhaustive search with the computational efficiency of a coarse-fine template matching scheme, The overall result is an accurate, time-efficient tracking algorithm. After providing a theoretical discussion of the algorithm, three separate biomedical applications of the algorithm are described. (1) stabilizing an irradiating laser on the retinal surface for photocoagulation treatment, (2) measuring target fixation eye movement to construct pattern densities at the retina, and (3) tracking a rat swimming in a Morris Water Maze for psychophysiological studies. Results for each application is provided, The paper concludes Vith a discussion of the relative merits of the tracking algorithm and recommendations for methods to improve the performance of the algorithm. (C) 2001 SPIE and IS&T.

关键词： Detection and tracking algorithms Retina algorithm development Video Eye Head Cameras Laser coagulation Scanning laser ophthalmoscopy Electrical engineering

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Color constancy measurements for synthetic image generation

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JOURNAL OF ELECTRONIC IMAGING 1999年第4期8卷 394-403页

作者： Marini, D Rizzi, A Rossi, M Univ Milan Dipartimento Sci Informaz Milan Italy Univ Brescia Dipartimento Elettron Automaz I-25123 Brescia Italy Politecn Milan DITEC I-20133 Milan Italy

Solving the color constancy problem in many applications implies the understanding of chromatic adaptation. The Refiner theory justifies chromatic adaptation, as well as other Color illusions, on visual perception principles. Based on the above theory, we have derived an algorithm to salve the color constancy problem and to simulate chromatic adaptation. The evaluation of the results depends an the kind of applications considered. Since our purpose is to contribute to the problem of color rendering for photorealistic image synthesis, we have devised a specific test approach. A virtual "Mondrian" patchwork has been created by applying a rendering algorithm with a photorealistic fight model to generate images under different light sources. Trichromatic values of the computer generated patches are the input data for the Retinex algorithm, computing new color corrected patches, The Euclidean and the Delta E-94* distances in the CIELAB space, between the original and Refiner color corrected trichromatic values, have been calculated. A preliminary analysis of the just noticeable difference has also been done on some colors compared to the closest MacAdam ellipses. Our work shows that the Refiner computational model is very well suited to solve the color constancy problem without any a priori information an the illuminant spectral distribution. (C) 1999 SPIE and IS&T.

关键词： Colorimetry Reflectivity Light sources Optical filters algorithm development Distance measurement Computer simulations Image filtering Visualization algorithms

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Entropy-constrained learning vector quantization algorithms and their application in image compression

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JOURNAL OF ELECTRONIC IMAGING 2000年第4期9卷 495-508页

作者： Karayiannis, NB Zervos, N Univ Houston Dept Elect & Comp Engn Houston TX 77204 USA Lucent Technol Bell Labs Holmdel NJ 07733 USA

This paper presents entropy-constrained learning vector quantization (ECLVQ) algorithms and their application in image compression. The development of these algorithms relies on reformulation, which is a powerful new methodology that essentially establishes a link between learning Vector quantization and clustering algorithms developed using alternating optimization. ECLVQ algorithms are developed in this paper by reformulating entropy-constrained fuzzy clustering (ECFC) algorithms, which were developed by minimizing an objective function incorporating the partition entropy and the average distortion between the feature vectors and their prototypes. The proposed algorithms allow the gradual transition from a maximally fuzzy partition to a nearly crisp partition of the feature vectors during the learning process. This paper presents two alternative implementations of the proposed algorithms, which differ in terms of the strategy employed for updating the prototypes during learning. The proposed algorithms are tested and evaluated on the design of codebooks used for image data compression. (C) 2000 SPIE and IS&T. [S1017-9909(00)00304-4].

关键词： Prototyping algorithm development Quantization Distortion Fuzzy logic Image compression Annealing Reconstruction algorithms Optimization (mathematics) Image quality

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Neural network estimation of chlorophyll a from MERIS full resolution data for the coastal waters of Galician rias (NW Spain)

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REMOTE SENSING OF ENVIRONMENT 2011年第2期115卷 524-535页

作者： Gonzalez Vilas, Luis Spyrakos, Evangelos Torres Palenzuela, Jesus M. Univ Vigo Dept Appl Phys Vigo 36310 Spain

In typical Case 2 waters, accurate remote sensing retrieval of chlorophyll a (chla) is still a challenging task. In this study, focusing on the Galician rias (NW Spain), algorithms based on neural network (NN) techniques were developed for the retrieval of chla concentration in optically complex waters, using Medium Resolution Imaging Spectrometer (MERIS) data. There is considerable interest in the accurate estimation of chla for the Galician rias, because of the economic and social importance of the extensive culture of mussels, and the high frequency of harmful algal events. Fifteen MERIS full resolution (FR) cloud-free images paired with in situ chla data (for 2002-2004 and 2006-2008) were used for the development and validation of the NN. The scope of NN was established from the clusters obtained using fuzzy c-mean (FCM) clustering techniques applied to the satellite-derived data. Three different NNs were developed: one including the whole data set, and two others using only points belonging to one of the clusters. The input data for these latter two NNs was chosen depending on the quality level, defined on the basis of quality flags given to each data set. The fitting results were fairly good and proved the capability of the tool to predict chla concentrations in the study area. The best prediction was given for the NN trained with high-quality data using the most abundant cluster data set. The performance parameters in the validation set of this NN were R-2 = 0.86, mean percentage error (MPE) = -0.14, root mean square error (RMSE) = 0.75 mg m(-3), and relative RMSE = 66%. The NN developed in this study detected accurately the peaks of chla, in both training and validation sets. The performance of the Case-2-Regional (C2R) algorithm, routinely used for MERIS data, was also tested and compared with our best performing NN and the sea-truthing data. Results showed that this NN outperformed the C2R, giving much higher R-2 and lower RMSE values. This study showed th

关键词： Chlorophyll a Galician rias Neural network MERIS Case 2 waters algorithm development

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Random Exploration and Attraction of the Best in Swarm Intelligence algorithms

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APPLIED SCIENCES-BASEL 2024年第23期14卷 11116页

作者： Vargas, Maria Cortes, Domingo Ramirez-Salinas, Marco Antonio Villa-Vargas, Luis Alfonso Lopez, Antonio Inst Politecn Nacl ESIME Culhuacan Ave St Ana 1000 Mexico City 04440 Mexico Inst Politecn Nacl Ctr Invest Comp Ave Juan Dios Batiz Mexico City 07738 Mexico

In this paper, it is revealed that random exploration and attraction of the best (REAB) are two underlying procedures in many swarm intelligence algorithms. This is particularly shown in two of the most known swarm algorithms: the particle swarm optimization (PSO) and gray wolf optimizer (GWO) algorithms. From this observation, it is here proposed that instead of building algorithms based on a narrative derived from observing some animal behavior, it is more convenient to focus on algorithms that perform REAB procedures;that is, to build algorithms to make a wide and efficient explorations of the search space and then gradually make that the best-evaluated search agent to attract the rest of the swarm. Following this general idea, two REAB-based algorithms are proposed;one derived from the PSO and one derived from the GWO, called REAB-PSO and REAB-GWO, respectively. To easily and succinctly express both algorithms, variable-sized open balls are employed. A comparison of proposed procedures in this paper and the original PSO and GWO using a controller tuning problem as a test bench show a significant improvement of the REAB-based algorithms over their original counterparts. Ideas here exposed can be used to derive new swarm intelligence algorithms.

关键词： metaheuristic optimization swarm algorithms algorithm development controller tuning optimization

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On the design of neuro-fuzzy hybrid multichannel filters to remove impulsive noise for color image restoration

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JOURNAL OF ELECTRONIC IMAGING 2000年第2期9卷 117-139页

作者： Tsai, HH Chen, SH Yu, PT Natl Chung Cheng Univ Dept Comp Sci & Informat Engn Chiayi 62107 Taiwan

This paper proposes a novel class of multichannel filters called neuro-fuzzy hybrid multichannel (NFHM) filters to simultaneously achieve three objectives: noise attenuation, chromaticity retention, and edges or details preservation. NFHM filters are characterized by a set or fuzzy rules (structure knowledge) such that they are capable of effectively fusing together the useful filtering merits from vector median, vector directional, and identity filters to further. improve the filtering performance of the conventional filters. Moreover, we adequately exploit the functional equivalence between fuzzy inference systems and radial-basis function networks on the optimal design of NFHM filters such that NFHM filters can be optimized by neuro-learning techniques based on the radial-basis function networks to obtain adaptive fuzzy rules for the different window contents. Finally, extensive simulation results demonstrate that the filtering performance of NFHM filters is superior to that of other proposed filters. (C) 2000 SPIE and IS&T. [S1017-9909(00)02102-4].

关键词： Image filtering Digital filtering Fuzzy logic Nonlinear filtering Neurons Visualization Fuzzy systems Optical filters Signal attenuation algorithm development

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Formulation and validation of simulated data for the Atmospheric Infrared Sounder (AIRS)

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2003年第2期41卷 314-329页

作者： Fishbein, E Farmer, CB Granger, SL Gregorich, DT Gunson, MR Hannon, SE Hofstadter, MD Lee, SY Leroy, SS Strow, LL CALTECH Jet Prop Lab Pasadena CA 91109 USA Univ Maryland Baltimore Cty Dept Phys Baltimore MD 21205 USA

Models for synthesizing radiance measurements by the Atmospheric Infrared Sounder (AIRS) are described. Synthetic radiances have been generated for developing and testing data processing algorithms. The radiances are calculated from geophysical states derived from weather forecasts and climatology using the AIRS rapid transmission algorithm. The data contain horizontal variability at the spatial resolution of AIRS from the surface and cloud fields. This is needed to test retrieval algorithms under partially cloudy conditions. The surface variability is added using vegetation and International Geosphere Biosphere Programme surface type maps, while cloud variability is added randomly. The radiances are spectrally averaged to create High Resolution Infrared Sounder (HIRS) data, and this is compared with actual HIRS2 data on the NOAA 14 satellite. The simulated data underrepresent high-altitude equatorial cirrus clouds and have too much local variability. They agree in the mean to within 1-4 K, and global standard deviation agrees to better than 2 K. Simulated data have been a valuable tool for developing retrieval algorithms and studying error characteristics and will continue to be so after launch.

关键词： algorithm development atmospheric retrieval cloud scene modeling data simulation High Resolution Infrared Sounder (HIRS) satellite remote sensing

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Chaotic gray-level image transformation

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JOURNAL OF ELECTRONIC IMAGING 2005年第4期14卷 043001.1-043001.9页

作者： Belkhouche, F Gokcen, I Qidwai, U Tulane Univ Dept Elect Engn & Comp Sci New Orleans LA 70118 USA Gen Elect Global Res Ctr Niskayuna NY USA

We deal with the applications of a class of nonlinear dynamic systems to image transformation and encoding whereby the nonlinear system presents a chaotic or hyperchaotic attractor. Several ways are possible to encode or transform the image using chaos. We develop algorithms for image encoding based on the permutation of the pixel value, position, or both. This approach enables the fast decorrelation of relations among pixels in the initial image in a random-like fashion. We illustrate the use of 1-D, 2-D, and 3-D maps for this purpose. We also use chaotic dynamical systems with a single or two outputs. A discussion of the sensitivity of the algorithms to the keys is followed by the illustration of the algorithms using example images. (c) 2005 SPIE and IS&T.

关键词： Computer programming Complex systems Image compression Dynamical systems Image encryption algorithm development Chaos Diffusion Fractal analysis Nonlinear dynamics

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Split and merge codebook design algorithms for image compression

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JOURNAL OF ELECTRONIC IMAGING 2000年第4期9卷 509-520页

作者： Karayiannis, NB Liu, ZY Univ Houston Dept Elect & Comp Engn Houston TX 77204 USA Toshiba Int Corp Houston TX 77041 USA

This paper introduces two new codebook design algorithms for image data compression applications. The proposed algorithms design codebooks by splitting and merging the prototypes of the training vectors, which play the role of code-vectors in the encoding/decoding process. The split-1 (S1) algorithm begins with a codebook containing a single prototype and designs a codebook of the desired size by splitting one prototype at each iteration. The split-1-merge-2 (S1M2) algorithm begins with a codebook of the desired size and improves its quality in an iterative fashion by splitting one prototype and merging two prototypes at each iteration. The proposed algorithms are used to design codebooks for image data compression based on vector quantization. The experimental results indicate that the proposed algorithms are strong competitors to the popular Linde-Buzo-Gray algorithm in terms of their computational requirements and the quality of the designed codebooks. (C) 2000 SPIE and IS&T. [S1017-9909(00)00204-X].

关键词： Prototyping Image compression Quantization Reconstruction algorithms Image quality Image filtering algorithm development Digital imaging Computer programming Optical filters

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Long-term evaluation of three satellite ocean color algorithms for identifying harmful algal blooms (Karenia brevis) along the west coast of Florida: A matchup assessment

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REMOTE SENSING OF ENVIRONMENT 2011年第1期115卷 1-18页

作者： Carvalho, Gustavo A. Minnett, Peter J. Banzon, Viva F. Baringer, Warner Heil, Cynthia A. Univ Miami Rosenstiel Sch Marine & Atmospher Sci Div Meteorol & Phys Oceanog Miami FL 33149 USA Univ Miami Rosenstiel Sch Marine & Atmospher Sci NSF NIEHS Oceans & Human Hlth Ctr Miami FL 33149 USA Fish & Wildlife Res Inst Florida Fish & Wildlife Conservat Commiss St Petersburg FL USA

We present a simple algorithm to identify Karenia brevis blooms in the Gulf of Mexico along the west coast of Florida in satellite imagery. It is based on an empirical analysis of collocated matchups of satellite and in situ measurements. The results of this Empirical Approach is compared to those of a Bio-optical Technique - taken from the published literature - and the Operational Method currently implemented by the NOAA Harmful Algal Bloom Forecasting System for K. brevis blooms. These three algorithms are evaluated using a multi-year MODIS data set (from July, 2002 to October, 2006) and a long-term in situ database. Matchup pairs, consisting of remotely-sensed ocean color parameters and near-coincident field measurements of K. brevis concentration, are used to assess the accuracy of the algorithms. Fair evaluation of the algorithms was only possible in the central west Florida shelf (i.e. between 25.75 degrees N and 28.25 degrees N) during the boreal Summer and Fall months (i.e. July to December) due to the availability of valid cloud-free matchups. Even though the predictive values of the three algorithms are similar, the statistical measure of success in red tide identification (defined as cell counts in excess of 1.5 x 10(4) cells L-1) varied considerably (sensitivity - Empirical: 86%;Bio-optical: 77%;Operational: 26%), as did their effectiveness in identifying non-bloom cases (specificity Empirical: 53%;Bio-optical: 65%;Operational: 84%). As the Operational Method had an elevated frequency of false-negative cases (i.e. presented low accuracy in detecting known red tides), and because of the considerable overlap between the optical characteristics of the red tide and non-bloom population, only the other two algorithms underwent a procedure for further inspecting possible detection improvements. Both optimized versions of the Empirical and Bio-optical algorithms performed similarly, being equally specific and sensitive (similar to 70% for both) and showing low

关键词： algorithm development Chlorophyll Detection Gulf of Mexico (west Florida shelf) Harmful algal bloom (HAB) Florida Red Tide (Karenia brevis) Ocean color (MODIS) Satellite remote sensing Water-leaving radiance

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