Editorial Computational Intelligence in Image Processing 2016downloads.hindawi.com/journals/mpe/2016/5680246.pdf · 2019-07-30 · Editorial Computational Intelligence in Image Processing

EditorialComputational Intelligence in Image Processing 2016

Erik Cuevas,1 Daniel Zaldívar,1 Gonzalo Pajares,2 Marco Perez-Cisneros,1 and Raúl Rojas3

1Departamento de Electronica, Universidad de Guadalajara, CUCEI, Avenida Revolucion 1500, Guadalajara, JAL, Mexico2Departamento de Ingenierıa de Software e Inteligencia Artificial, Facultad Informatica, Universidad Complutense,28040 Madrid, Spain3Institut fur Informatik, Freie Universitat Berlin, Arnimallee 7, 14195 Berlin, Germany

Correspondence should be addressed to Erik Cuevas; [email protected]

Received 4 August 2016; Accepted 4 August 2016

Copyright © 2016 Erik Cuevas et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Computational intelligence (CI) has emerged as a power-ful tool for information processing, decision-making, andknowledge management. CI approaches, in general, are use-ful for designing advanced computerized systems that possessuseful characteristics mimicking human behaviors and capa-bilities in solving complex tasks, for example, learning, adap-tation, and evolution. Examples of some popular CI modelsinclude fuzzy systems, artificial neural networks, evolution-ary algorithms, multiagent systems, decision trees, roughset theory, knowledge-based systems, and hybrid of thesemodels.

On the other hand, images have always played an essentialrole in human life. In the past theywere, today they are, and inthe future they will continue to be one of our most importantinformation carriers. Recent advances in digital imaging andcomputer hardware technology have led to an explosion inthe use of digital images in a variety of scientific and engineer-ing applications. Therefore, each new approach that is devel-oped by engineers, mathematicians, and computer scientistsis quickly identified, understood, and assimilated in order tobe applied to image processing problems.

Classical image processing methods often face great diffi-culties while dealingwith images containing noise and distor-tions.Under such conditions, the use of computational intelli-gence approaches has been recently extended to address chal-lenging real-world image processing problems. The intereston the subject among researchers anddevelopers is increasingday by day as it is branded by huge volumes of researchworks that get published in leading international journals andinternational conference proceedings.

The main objective of this special issue is to bridgethe gap between computational intelligence techniques andchallenging image processing applications. Since this ideawas first conceived, the goal has aimed at exposing the readersto the cutting-edge research and applications that are goingon across the domain of image processing, particularly thosewhose contemporary computational intelligence techniquescan be or have been successfully employed.

The special issue received several high-quality submis-sions from different countries all over the world. All sub-mitted papers have followed the same standard of peer-reviewing by at least three independent reviewers, just asit is applied to regular submissions to the MathematicalProblems in Engineering journal. Due to the limited space, avery short number of papers have been finally included. Theprimary guideline has been to demonstrate the wide scope ofcomputational intelligence algorithms and their applicationsto image processing problems.

The paper authored by T. Wu and L. Zhang presents anuncertainty algorithm based on cloud model for the genera-tion of image-guided Voronoi aesthetic patterns. As a com-putational intelligence tool, cloud model handles the uncer-tainty more completely and more freely, and it cannot beconsidered as randomness compensated by fuzziness, fuzzi-ness compensated by randomness, second-order fuzziness,or second-order randomness. To obtain the default parame-ters, authors conduct seven groups of experiments to testthe proposed method. Using both visual and quantitativecomparisons, T. Wu and L. Zhang prove the efficacy of theproposed method using two groups of experiments. Com-pared with the related methods, experimental results show

Hindawi Publishing CorporationMathematical Problems in EngineeringVolume 2016, Article ID 5680246, 3 pageshttp://dx.doi.org/10.1155/2016/5680246

2 Mathematical Problems in Engineering

that the Voronoi-based aesthetic patterns with soft borderscan be successfully generated by using the new technique.

K. Zeng et al. introduced a rankingmodel by understand-ing the complex relations within product visual and textualinformation in visual search systems. To understand theircomplex relations, authors focused on using graph-basedparadigms to model the relations among product images,product category labels, and product names and descriptions.K. Zeng et al. developed a unified probabilistic hypergraphranking algorithm, which, modeling the correlations amongproduct visual features and textual features, extensivelyenriches the description of the image.The authors conductedexperiments on the proposed ranking algorithm on a dataset collected from a real e-commerce website. The results oftheir comparison demonstrate that the proposed algorithmextensively improves the retrieval performance over thevisual distance based ranking.

N. R. Soora and P. S. Deshpande present a novel LicensePlate (LP) detection method using different clustering tech-niques, based on geometrical properties of the LP characters.In the paper, authors also propose a new character extractionmethod, for noisy/missed character components of the LPdue to the presence of noise between LP characters and LPborder. The proposed method detects the LP of any type ofvehicle (including vans, cars, trucks, and motorcycles), hav-ing different plate variations, under different environmentalandweather conditions because of the geometrical propertiesof the set of characters in the LP. The proposed methodis independent of color, rotation, size, and scale variancesof the LP. The concept is tested using standard media-laband Application Oriented License Plate (AOLP) benchmarkLP recognition databases. The success rate of the proposedapproach for LP detection using media-lab database is 97.3%and using AOLP database is 93.7%. Results clearly indicatethat the proposed approach is comparable to the previouslypublished papers, which evaluated their performance onpublicly available benchmark LP databases.

The paper authored byC.Nyirarugira et al. presents a ges-ture recognition method derived from particle swarm move-ment for free-air hand gesture recognition. Under such con-ditions, authors suggest an automated process of segmentingmeaningful gesture trajectories based on particle swarmmovement. A subgesture detection and reasoning method isincorporated in the proposed recognizer to avoid prematuregesture spotting. Evaluation of the proposed method showspromising recognition results: 97.6% on preisolated gestures,94.9% on stream gestures with assistive boundary indicators,and 94.2% for blind gesture spotting on digit gesture vocab-ulary. The proposed recognizer requires fewer computationresources; thus it is a good candidate for real-time applica-tions.

R. Al Shehhi et al. present a hierarchical graph-basedsegmentation for blood vessel detection in digital retinalimages. This segmentation method employs some of per-ceptual Gestalt principles: similarity, closure, continuity, andproximity tomerge segments into coherent connected vessel-like patterns. The integration of Gestalt principles is basedon object-based features (e.g., color, black top-hat (BTH)morphology, and context) and graph-analysis algorithms

(e.g., Dijkstra path). The segmentation framework consistsof two main steps: preprocessing and multiscale graph-basedsegmentation. Preprocessing is to enhance lighting condition,due to low illumination contrast, and to construct necessaryfeatures to enhance vessel structure due to sensitivity ofvessel-patterns to multiscale/orientation structure. Graph-based segmentation is to decrease computational processingrequired for region of interest into most semantic objects.The segmentation was evaluated on three publicly avail-able datasets. Experimental results show that preprocessingstage achieves better results compared to the state-of-the-art enhancement methods.The performance of the proposedgraph-based segmentation is found to be consistent and com-parable to other existing methods, with improved capabilityin detecting small/thin vessels.

The paper authored by G. Niu et al. proposes amultikernel-like learning algorithmbased on data probabilitydistribution (MKDPD) for classification proposes. In theapproach, the parameters of a kernel function are locallyadjusted according to the data probability distribution andthus produce different kernel functions. These differentkernel functions will generate different Reproducing KernelHilbert Spaces (RKHS). The direct sum of the subspaces ofthese RKHS constitutes the solution space of the learningproblem. Furthermore, based on the proposed MKDPDalgorithm, an algorithm for labeling new coming data isalso introduced, in which the basic functions are retrainedaccording to the new coming data, while the coefficients ofthe retrained basic functions remainedunchanged to label thenew coming data. The experimental results presented in thispaper show the effectiveness of the proposed algorithms.

H. Yang et al. introduce a new general TV regular-izer, namely, generalized TV regularization, to study imagedenoising and nonblind image deblurring problems. Inorder to discuss the generalized TV image restoration withsolution-driven adaptivity, authors consider the existenceand uniqueness of the solution for mixed quasivariationalinequality. Moreover, the convergence of a modified projec-tion algorithm for solvingmixed quasivariational inequalitiesis also shown. The corresponding experimental results sup-port our theoretical findings.

C.-L. Cocianu and A. Stan propose a new method thatcombines the decorrelation and shrinkage techniques to neu-ral network-based approaches for noise removal purposes.The images are represented as sequences of equal sized blocks,each block being distorted by a stationary statistical corre-lated noise. Some significant amount of the induced noise inthe blocks is removed in a preprocessing step, using a decor-relation method combined with a standard shrinkage-basedtechnique. The preprocessing step provides for each initialimage a sequence of blocks that are further compressed at acertain rate, each component of the resulted sequence beingsupplied as inputs to a feed-forward neural architecture. Thelocal memories of the neurons of the layers are generatedthrough a supervised learning process based on the com-pressed versions of blocks of the same index value suppliedas inputs and the compressed versions of them resultedas the mean of their preprocessed versions. Finally, usingthe standard decompression technique, the sequence of the

Mathematical Problems in Engineering 3

decompressed blocks is the cleaned representation of theinitial image. The performance of the proposed method isevaluated by a long series of tests, the results being veryencouraging as compared to similar developments for noiseremoval purposes.

The paper authored by L. Chang et al. introduces amethod to solve the problems which basic Vibe algorithmcannot effectively eliminate such as the influence of back-ground noise, follower shadow, and ghost under complexbackground.Therefore, considering the basic Vibe algorithm,this paper puts forward some improvement measures inthreshold setting, shadow eliminating, and ghost suppres-sion. Firstly, judgment threshold takes adjustment with thechanges of background. Secondly, a fast eliminating ghostalgorithm depending on adaptive threshold is introduced.Finally, follower shadow is detected and inhibited effectivelythrough the gray properties and texture characteristics.Experiments show that the proposed algorithm works wellin complex environment without affecting computing speedand has stronger robustness and better adaptability than thebasic algorithm. Meanwhile, the ghost and follower shadowcan be absorbed quickly as well. Therefore, the accuracy oftarget detection is effectively improved.

L. Zeng et al. propose an image enhancement algorithmto solve the well-known problems that involve the detectionmethods for 3D nondestructive testing of printed circuitboards (PCBs). Therefore, considering the characteristics of3D CT images of PCBs, the proposed algorithm uses grayand its distance double-weighting strategy to change theform of the original image histogram distribution, suppressesthe grayscale of a nonmetallic substrate, and expands thegrayscale of wires and other metals. The proposed algorithmalso enhances the gray difference between a substrate anda metal and highlights metallic materials. The proposedalgorithm can enhance the gray value of wires and othermetals in 3D CT images of PCBs. It applies enhancementstrategies of changing gray and its distance double-weightingmechanism to adapt to this particular purpose.The flexibilityand advantages of the proposed algorithm are confirmed byanalyses and experimental results.

The paper authored by H. Xiang et al. presents a pixel-value-ordering hybrid algorithm for error prediction inimages. The proposed method predicts pixel in both positiveandnegative orientation. Assisted by expansion bins selectiontechnique, this hybrid predictor presents an optimized pre-diction-error expansion strategy including bin 0. Further-more, a novel field-biased context pixel selection is alreadydeveloped, with which detailed correlations of aroundpixels are better exploited more than equalizing schememerely. Experiment results show that the proposed approachimproves embedding capacity and enhances marked imagefidelity. It also outperforms some other state-of-the-art meth-ods of reversible data hiding, especially for moderate andlarge payloads.

J. Jia et al. introduce a novel normal inverse Gaussianmodel-based method that uses a Bayesian estimator to carryout image denoising in the nonsubsampled contourlet trans-form (NSCT) domain. In the proposed method, the model isfirst used to describe the distributions of the image transform

coefficients of each subband in the NSCT domain. Then, thecorresponding threshold function is derived from the modelusing Bayesian maximum a posteriori probability estimationtheory. Finally, optimal linear interpolation thresholdingalgorithm (OLI-Shrink) is employed to guarantee a gentlerthresholding effect. The results of comparative experimentsconducted indicate that the denoising performance of ourproposed method in terms of peak signal-to-noise ratio issuperior to that of several state-of-the-art methods, includ-ing BLS-GSM, K-SVD, BivShrink, and BM3D. Further, theproposedmethod achieves structural similarity (SSIM) indexvalues that are comparable to those of the block-matching 3Dtransformation (BM3D) method.

Thepaper authored byB. Li et al. develops a new approachfor solving the problem of single image superresolution bygeneralizing this property. The main idea of this approachtakes advantage of a generic prior that assumes a randomlyselected patch in the underlying high resolution (HR) imageshould visually resemble asmuch as possible with some patchextracted from the input low resolution (LR) image. Undersuch conditions, this approach deploys a cost function andapplies an iterative scheme to estimate the optimal HR image.For solving the cost function, authors introduce Gaussianmixture model (GMM) to train upon a sampled data setfor approximating the joint probability density function(PDF) of input image with different scales.Through extensivecomparative experiments, this paper demonstrates that thevisual fidelity of our proposed method is often superior tothose generated by other state-of-the-art algorithms as deter-mined through both perceptual judgment and quantitativemeasures.

Acknowledgments

Finally, we would like to express our gratitude to all of theauthors for their contributions and the reviewers for theirefforts to provide valuable comments and feedback. We hopethis special issue offers a comprehensive and timely viewof the area of applications of computational intelligence inimage processing and that it will grant stimulation for furtherresearch.

Erik CuevasDaniel ZaldıvarGonzalo Pajares

Marco Perez-CisnerosRaul Rojas

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Editorial Computational Intelligence in Image Processing 2016downloads.hindawi.com/journals/mpe/2016/5680246.pdf · 2019-07-30 · Editorial Computational Intelligence in Image Processing