Human kind is exposed to large amounts of data. According to statistics, more than 80% of information received by humans comes from the visual system. Therefore, image information processing is not only an important research topic but also a challenging task. The unique information processing mechanism of the human visual system provides it with fast, accurate, and efficient image processing capabilities. At present, many advanced image analysis and processing techniques have been widely used in image communication, geographic information systems, medical image analysis, and virtual reality. However, there is still a large gap between these technologies and the human visual system. Therefore, building an image system research mechanism based on the biological vision system is an attractive but difficult target. Although it is a challenge, it can also be considered as an opportunity which utilizes biologically inspired ideas. Meanwhile, through the integration of neural biology, biological perception mechanisms, and computer science and mathematical science, related research can bridge biological vision and computer vision. Finally, the biologically inspired image analysis and processing system is expected to be built on the basis of further consideration of the learning mechanism of the human brain

Meng Q.

Murroni M.

Shao F.

Wang S.

Yang J.

English

Archivio istituzionale della ricerca - Università di Cagliari

SPECIAL SECTION ON BIOLOGICALLY INSPIRED IMAGEPROCESSING CHALLENGES AND FUTURE DIRECTIONSDate of current version August 21, 2020.Digital Object Identifier 10.1109/ACCESS.2020.3015372EDITORIALIEEE ACCESS SPECIAL SECTION EDITORIAL:BIOLOGICALLY INSPIRED IMAGE PROCESSINGCHALLENGES AND FUTURE DIRECTIONSHuman kind is exposed to large amounts of data. Accord-ing to statistics, more than 80% of information received byhumans comes from the visual system. Therefore, imageinformation processing is not only an important researchtopic but also a challenging task. The unique informationprocessing mechanism of the human visual system providesit with fast, accurate, and efficient image processing capa-bilities. At present, many advanced image analysis and pro-cessing techniques have been widely used in image com-munication, geographic information systems, medical imageanalysis, and virtual reality. However, there is still a largegap between these technologies and the human visual system.Therefore, building an image system research mechanismbased on the biological vision system is an attractive butdifficult target. Although it is a challenge, it can also be con-sidered as an opportunity which utilizes biologically inspiredideas. Meanwhile, through the integration of neural biology,biological perception mechanisms, and computer science andmathematical science, related research can bridge biologicalvision and computer vision. Finally, the biologically inspiredimage analysis and processing system is expected to be builton the basis of further consideration of the learning mecha-nism of the human brain.The goal of this IEEE ACCESS Special Section on‘‘Biologically Inspired Image Processing Challenges andFuture Directions’’ is to explore biological vision mecha-nisms and characteristics to establish an objective image-processing model and algorithm that is closer to the humanvisual information-processing model.The Call for Papers drew wide attention from the academiccommunity and attracted more than 30 submissions. Eighteenhigh-quality articles were accepted as final publications in theSpecial Section after a rigorous review by at least two peerreviewers.In the article, ‘‘Deep multi-level semantic hashing forcross-modal retrieval,’’ by Ji et al., the authors propose anovel deep hashing framework based on multi-level semanticsupervision for multi-label image-text cross retrieval. Thebinary similarity and complex semantic structure of datain different forms can be simultaneously captured by theproposed framework to improve the accuracy of searchingresults.In the article, ‘‘Knowledge graph-based image classifica-tion refinement,’’ by Zhang et al., the authors propose animage classification scheme with the novel concept of theimage knowledge graph (IKG) to incorporate semantic asso-ciation and scene association to fully consider the relationsbetween objects (external and internal). The scheme over-comes the problems of background complexity and visualinconsistency and verifies its effectiveness on the ImageNetdataset.In the article, ‘‘Learning identity-aware face featuresacross poses based on deep Siamese networks,’’ byGao et al., the authors introduce deep Siamese networks tolearn identity-aware and pose-invariant features by addingcontrastive loss into face synthesis models, which preservesthe face identity while synthesizing the face. Face synthesismodels are trained with different target poses as supervisorysignals, and the learned pose-invariant features are incorpo-rated by another Siamese network to generate deeper pose-invariant and identity-aware features.In the article, ‘‘Moving target detection and tracking algo-rithm based on context information,’’ by Li et al., the authorspropose a moving target detection and tracking algorithmbased on context information and closed-loop learning toimprove the robustness of the algorithm in complex environ-ments in the long-term tracking task.In the article, ‘‘ESFNet: Efficient network for buildingextraction from high-resolution aerial images,’’ by Lin et al.,the authors introduce a network named ESFNet, based on sep-arable factorized residual block and the dilated convolutionsfor extracting building footprint from high-resolution aerialimages. The network preserves slight accuracy loss, with lowcomputational cost and memory consumption, and the ideacan be further extended to other areas.In the article, ‘‘Parallel K nearest neighbor matching for3D reconstruction,’’ by Cao et al., the authors propose anew feature tacking method for 3D reconstruction that worksin a parallel manner, involving keypoint detection, descrip-tor computing, descriptor matching by parallel k-nearestneighbor (Parallel-KNN) search, and outlier rejecting. Thismethod can rapidlymatch a large volume of keypoints, avoidsconsuming high computation time, and then yields correctcorrespondences.VOLUME 8, 2020 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ 147459IEEE ACCESS SPECIAL SECTION EDITORIALIn the article, ‘‘A human-inspired recognition system forpre-modern Japanese historical documents,’’ by Le et al.,the authors present a human-inspired document reading sys-tem to recognize multiple lines of pre-modern Japanese his-torical documents. The system, based on an attention-basedencoder-decoder, is successful in recognizing multiple linesand connected and cursive characters without performingcharacter/lines segmentation. The coverage model, whichstores the history of eye movement, improves the precisionof predicting the next movement.In the article, ‘‘Hide-CAM: Finding multiple discrimina-tive regions in weakly supervised location,’’ by Xu et al.,the authors propose a weakly supervised localization schemeby hide strategy with the most discriminative and comple-mentary object regions of the object to complete the locationtask.In the article, ‘‘Finger-vein image enhancement based onpulse coupled neural network,’’ by Lei et al., the authorsdescribe a new finger-vein image enhancement model basedon the pulse coupled neural network (PCNN). A new param-eter setting scheme is developed to automatically adjustthe parameters of PCNN without empirical correlation ortraining. The enhanced finger-vein images produced by thepresent model improve recognition accuracy significantly.In the article, ‘‘Extracting palmprint ROI from whole handimage using straight line clusters,’’ by Xiao et al., the authorsdescribe a novel method to extract a palmprint region ofinterest (ROI) from whole hand images using a straight linecluster without knowing approximate positions of finger jointareas in advance, setting a reference point in advance, andextracting contours of the hand.In the article, ‘‘Real-time video saliency prediction via3D residual convolutional neural network,’’ by Sun et al.,the authors introduce a novel end-to-end video saliency pre-diction model based on 3D-ResNet for data-driven spatio-temporal feature representation combining a multi-scalefeature representation scheme and a frame skipping strategy.Moreover, a new challenging eye-tracking database with220 video clips is established to facilitate the research ofvideo saliency prediction.The article, ‘‘Toward a quality predictor for stereoscopicimages via analysis of human binocular visual perception,’’by Liu et al., investigates a full-reference objective qualityevaluator for stereoscopic images by simulating binocularinteraction and depth perception based on a cyclopean imagefrom a novel binocular combination model, and a depthbinocular combination model from a depth synthesized pro-cedure. The predicted results of the evaluator achieve high-consistent alignment with subjective observations.In the article, ‘‘An approach to extraction midsagittal planeof skull from brain CT images for oral and maxillofacialsurgery,’’ by Tan et al., the authors propose a semi-automaticextracting method based on the region growing method, ori-ented bounding box (OBB) bounding box, and mutual infor-mation algorithm for the midsagittal plane of skull in braincomputed tomography (CT) images to ensure the accuracyand stability of extracting midsagittal plane while shorteningthe operation time.In the article, ‘‘Automatic kidney lesion detection for CTimages using morphological cascade convolutional neuralnetworks,’’ by Zhang et al., the authors exploit a lesion detec-tion tool using multi intersection over union (IOU) thresholdbased on morphological cascade convolutional neural net-works (CNNs) to detect the location and size of lesions inCT scan images.The article, ‘‘Multivariate pattern analysis of EEG-basedfunctional connectivity: A study on the identification ofdepression,’’ by Peng et al., identifies the altered electroen-cephalography (EEG) resting-state functional connectivitypatterns of depressed patients based on the phase lag index forconstructing function connectivity matrices and the alteredKendall rank correlation coefficient for identifying highlydiscriminative features. The study sheds new light on thepathological mechanism of depression and suggests that EEGresting state functional connectivity analysis may identifypotentially effective biomarkers for clinical diagnosis.In the article, ‘‘Multiple image features-based retinalimage registration using global and local geometric structureconstraints,’’ by Bi et al., the authors propose a robust non-rigid retinal image registration based on multi-image featuresand dual constraints (i.e., the global and local geometricstructure constraints) to overcome the problems of unreliablefeatures, inaccurate matching, and geometric distortion.In the article, ‘‘Bio-inspired stereo vision calibration fordynamic vision sensors,’’ by Domínguez-Morales et al.,the authors present a bio-inspired stereo system connectingwith a spike-based calibration mechanism. The novel cali-bration reduces the number and complexity of operations,allowing its integration into autonomous robots (providedwith FPGA) without the need for computers with high powerconsumption and computational resources.Finally, the invited article, ‘‘MFNN: Position and attitudemeasurement neural network based on multi-feature fusion,’’by Man et al., describes a multi-feature fusion position andattitude measurement neural network (MFNN) combiningwith the HOG features to measure the position and attitudeinformation of application engineeringIn conclusion, we express our gratitude to the authors whosubmitted their research articles to this Special Section andthe reviewers for their constructive comments. We are alsograteful to the Editor-in-Chief and staff members for theirguidance and support.JIACHEN YANG, Guest EditorSchool of Electrical and Information EngineeringTianjin UniversityTianjin 300072, ChinaQINGGANG MENG, Guest EditorDepartment of Computer ScienceLoughborough UniversityLoughborough LE11 3TU, U.K.147460 VOLUME 8, 2020IEEE ACCESS SPECIAL SECTION EDITORIALMAURIZIO MURRONI, Guest EditorDepartment of Electrical and Electronic EngineeringUniversity of Cagliari09123 Cagliari, ItalySHIQI WANG, Guest EditorDepartment of Computer ScienceCity University of Hong KongHong KongFENG SHAO, Guest EditorDepartment of Information Science and EngineeringNingbo UniversityNingbo 315211, ChinaJIACHEN YANG (Member, IEEE) received the M.S. and Ph.D. degrees in communication andinformation engineering from Tianjin University, Tianjin, China, in 2005 and 2009, respectively.He was a Visiting Scholar with the Department of Computer Science, School of Science,Loughborough University, U.K. He was also a Visiting Scholar with the Department of Elec-trical, Computer, Software, and Systems Engineering, Embry-Riddle Aeronautical University,Daytona Beach, FL, USA. He is currently a Professor with the School of Electrical andInformation Engineering, Tianjin University. His research interests include multimedia signalprocessing, mathematical optimization, and deep learning. He is an Associate Editor of IEEEACCESS.QINGGANG MENG (Senior Member, IEEE) received the B.S. and M.S. degrees in electronicengineering from Tianjin University, Tianjin, China, and the Ph.D. degree in intelligent roboticsfrom the Department of Computer Science, Aberystwyth University, U.K. He is currently a Pro-fessor with the Department of Computer Science, Loughborough University, U.K. His currentresearch interests include biologically inspired learning algorithms and developmental robotics,service robotics, robot learning and adaptation, multi-UAV cooperation, human motion analysisand activity recognition, activity pattern detection, pattern recognition, artificial intelligence,and computer vision. He is on the editorial boards of several journals including the IEEETRANSACTIONS ON CYBERNETICS.MAURIZIO MURRONI (Senior Member, IEEE) received the M.Sc. degree in electronicengineering and the Ph.D. degree in electronic engineering and computers from theUniversity ofCagliari, in 1998 and 2001, respectively. He is currently an Associate Professor with the Depart-ment of Electrical and Electronic Engineering (DIEE), University of Cagliari, and a member ofthe CNIT-National Inter-University Consortium for Telecommunications. He has coauthoredthe 1900.6-2011–IEEE Standard for Spectrum Sensing Interfaces and Data Structures forDynamic Spectrum Access and other Advanced Radio Communication Systems. He has alsocoauthored an extensive list of journal articles and peer-reviewed conference papers and receivedseveral best paper awards. His research interests include quality of experience, multimediadata transmission and processing, broadcasting, cognitive radio system, and signal processingfor radio communications. He has served as a chair for various international conferences andworkshops. He is a Senior Member of the IEEE Communications Society, the IEEE BroadcastTechnology Society, the IEEE Vehicular Technology Society, and the IEEE Signal ProcessingSociety. He was a guest editor for several journals. He is a Distinguished Lecturer for the IEEE Broadcast Technology Societyand an Associate Editor of the IEEE TRANSACTIONS ON BROADCASTING.VOLUME 8, 2020 147461IEEE ACCESS SPECIAL SECTION EDITORIALSHIQI WANG (Member, IEEE) received the B.S. degree in computer science from the HarbinInstitute of Technology, in 2008, and the Ph.D. degree in computer application technologyfrom Peking University, in 2014. From 2014 to 2016, he was a Postdoctoral Fellow withthe Department of Electrical and Computer Engineering, University of Waterloo, Waterloo,ON, Canada. From 2016 to 2017, he was a Research Fellow with the Rapid-Rich ObjectSearch Laboratory, Nanyang Technological University, Singapore. He is currently an AssistantProfessor with the Department of Computer Science, City University of Hong Kong. He hassubmitted over 40 technical proposals to ISO/MPEG, ITU-T, and AVS standards, and hasauthored/coauthored more than 150 refereed journal/conference articles. His research interestsinclude video compression, image/video quality assessment, and image/video search andanalysis. He received the Best Paper Award from the IEEE ICME 2019, the IEEE Multimedia2018, and PCM 2017. He is the coauthor of an article that received the Best Student PaperAward in the IEEE ICIP 2018.FENG SHAO (Member, IEEE) received the B.S. and Ph.D. degrees from Zhejiang University,Hangzhou, China, in 2002 and 2007, respectively, all in electronic science and technology.He was a Visiting Fellow with the School of Computer Engineering, Nanyang TechnologicalUniversity, Singapore, from February 2012 to August 2012. He is currently a Professor with theFaculty of Information Science and Engineering, Ningbo University, China. He has publishedover 100 technical articles in refereed journals and proceedings in the areas of 3-D video coding,3-D quality assessment, and image perception. He received the Excellent Young Scholar Awardby the NSF of China (NSFC) in 2016.147462 VOLUME 8, 2020

IEEE Access Special Section Editorial: Biologically Inspired Image Processing Challenges and Future Directions

https://iris.unica.it/bitstream/11584/297916/1/IEEE-Access-Special-Section-Editorial-Biologically-Inspired-Image-Processing-Challenges-and-Future-Directions2020IEEE-AccessOpen-Access.pdf

IEEE Access Special Section Editorial: Biologically Inspired Image Processing Challenges and Future Directions

Abstract

Similar works

Full text

Available Versions

Archivio istituzionale della ricerca - Università di Cagliari