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Relevant bibliographies by topics / Contour extraction

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Academic literature on the topic 'Contour extraction'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 September 2023

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Journal articles on the topic "Contour extraction"

1

Chen chen and Daohui Bi. "A Motion Image Pose Contour Extraction Method Based on B-Spline Wavelet." International Journal of Antennas and Propagation 2021 (October 26, 2021): 1–8. http://dx.doi.org/10.1155/2021/4553143.

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In order to improve the accuracy of traditional motion image pose contour extraction and shorten the extraction time, a motion image pose contour extraction method based on B-spline wavelet is proposed. Moving images are acquired through the visual system, the information fusion process is used to perform statistical analysis on the images containing motion information, the location of the motion area is determined, convolutional neural network technology is used to preprocess the initial motion image pose contour, and B-spline wavelet theory is used. The preprocessed motion image pose contour is detected, combined with the heuristic search method to obtain the pose contour points, and the motion image pose contour extraction is completed. The simulation results show that the proposed method has higher accuracy and shorter extraction time in extracting motion image pose contours.

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Liu, Guang Shuai, and Bai Lin Li. "Extraction of Optimal Contour Dominant Points Based on ICT Images in Reverse Engineering." Applied Mechanics and Materials 423-426 (September 2013): 2570–75. http://dx.doi.org/10.4028/www.scientific.net/amm.423-426.2570.

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How to effectively extract contour dominant points is one of key problems in process of industrial CT image, second extraction method was put forward. Second extraction method included two steps: rough extraction and accurate extraction. Firstly, discrete circular curvatures of contour points are calculated. Secondly, through rough extraction step, bad points and points which arent correlated with contour features were removed. At last, through accurate extraction step, contour dominant points were extracted by levels of detail. Experimental results show that contour dominant points can describe contours shape and redundant data are removed, the proposed method is simple and efficient.

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Fang, Fang, Kaishun Wu, Yuanyuan Liu, Shengwen Li, Bo Wan, Yanling Chen, and Daoyuan Zheng. "A Coarse-to-Fine Contour Optimization Network for Extracting Building Instances from High-Resolution Remote Sensing Imagery." Remote Sensing 13, no. 19 (September 23, 2021): 3814. http://dx.doi.org/10.3390/rs13193814.

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Building instances extraction is an essential task for surveying and mapping. Challenges still exist in extracting building instances from high-resolution remote sensing imagery mainly because of complex structures, variety of scales, and interconnected buildings. This study proposes a coarse-to-fine contour optimization network to improve the performance of building instance extraction. Specifically, the network contains two special sub-networks: attention-based feature pyramid sub-network (AFPN) and coarse-to-fine contour sub-network. The former sub-network introduces channel attention into each layer of the original feature pyramid network (FPN) to improve the identification of small buildings, and the latter is designed to accurately extract building contours via two cascaded contour optimization learning. Furthermore, the whole network is jointly optimized by multiple losses, that is, a contour loss, a classification loss, a box regression loss and a general mask loss. Experimental results on three challenging building extraction datasets demonstrated that the proposed method outperformed the state-of-the-art methods’ accuracy and quality of building contours.

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ZHAO, JIAN, and JIAN AN. "AN ITERATIVE CONVEX HULL APPROACH FOR IMAGE SEGMENTATION AND CONTOUR EXTRACTION." International Journal of Pattern Recognition and Artificial Intelligence 26, no. 07 (November 2012): 1255013. http://dx.doi.org/10.1142/s0218001412550130.

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The contours and segments of objects in digital images have many important applications. Contour extractions of gray images can be converted into contour extractions of binary images. This paper presents a novel contour-extraction algorithm for binary images and provides a deduction theory for this algorithm. First, we discuss the method used to construct convex hulls of regions of objects. The contour of an object evolves from a convex polygon until the exact boundary is obtained. Second, the projection methods from lines to objects are studied, in which, a polygon iteration method is presented using linear projection. The result of the iteration is the contour of the object region. Lastly, addressing the problem that direct projections probably cannot find correct projection points, an effective discrete ray-projection method is presented. Comparisons with other contour deformation algorithms show that the algorithm in the present paper is very robust with respect to the shapes of the object regions. Numerical tests show that time consumption is primarily concentrated on convex hull computation, and the implementation efficiency of the program can satisfy the requirement of interactive operations.

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Wu, Shaofei. "A Traffic Motion Object Extraction Algorithm." International Journal of Bifurcation and Chaos 25, no. 14 (December 30, 2015): 1540039. http://dx.doi.org/10.1142/s0218127415400398.

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A motion object extraction algorithm based on the active contour model is proposed. Firstly, moving areas involving shadows are segmented with the classical background difference algorithm. Secondly, performing shadow detection and coarse removal, then a grid method is used to extract initial contours. Finally, the active contour model approach is adopted to compute the contour of the real object by iteratively tuning the parameter of the model. Experiments show the algorithm can remove the shadow and keep the integrity of a moving object.

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Yang, Shudi, Jiaxiong Wu, and Zhipeng Feng. "Dual-Fusion Active Contour Model with Semantic Information for Saliency Target Extraction of Underwater Images." Applied Sciences 12, no. 5 (February 28, 2022): 2515. http://dx.doi.org/10.3390/app12052515.

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Underwater vision research is the foundation of marine-related disciplines. The target contour extraction is significant for target tracking and visual information mining. Aiming to resolve the problem that conventional active contour models cannot effectively extract the contours of salient targets in underwater images, we propose a dual-fusion active contour model with semantic information. First, the saliency images are introduced as semantic information and salient target contours are extracted by fusing Chan–Vese and local binary fitting models. Then, the original underwater images are used to supplement the missing contour information by using the local image fitting. Compared with state-of-the-art contour extraction methods, our dual-fusion active contour model can effectively filter out background information and accurately extract salient target contours. Moreover, the proposed model achieves the best results in the quantitative comparison of MAE (mean absolute error), ER (error rate), and DR (detection rate) indicators and provides reliable prior knowledge for target tracking and visual information mining.

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Matsumoto, Sumiaki, Reinin Asato, Tomohisa Okada, and Junji Konishi. "Intracranial contour extraction with active contour models." Journal of Magnetic Resonance Imaging 7, no. 2 (March 1997): 353–60. http://dx.doi.org/10.1002/jmri.1880070216.

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Tsuji, Hiroyuki, Shinji Tokumasu, Hiroki Takahashi, and Masayuki Nakajima. "Extracting Objects Using Contour Evolutions in Edge-Based Object Tracking." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 3 (May 20, 2006): 362–71. http://dx.doi.org/10.20965/jaciii.2006.p0362.

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We propose edge-based object extraction targeting automatic video object plane (VOP) generation in MPEG-4 content-based video coding. In an edge-based VOP generation framework proposed by Meier, the object is represented as a binary edge image that does not generally form a closed contour and that also contains many extra edges, making extracting the object contour accurately less straightforward in such situations. To solve this problem, we adopt a PDE-based contour evolution approach to evolve initial multiple contours contained inside the object toward its boundary based on evolution equations, and to finally merge them into a single contour that accurately represents the object’s shape. Our experimental results using an MPEG standard image sequence show that object contours obtained as we propose appear subjectively more natural in shape compared with those obtained by two conventional methods, especially when the binary object model is not in good condition.

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Lederman, Susan J., Roberta L. Klatzky, and J. D. Balakrishnan. "Task-Driven Extraction of Object Contour by Human Haptics: Part 2." Robotica 9, no. 2 (April 1991): 179–88. http://dx.doi.org/10.1017/s0263574700010225.

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SUMMARYThe extraction of contour information from subjects is essential for purposes of grasping and manipulation. We proposed that human haptic exploration of contours, in the absence of vision, would reveal specialized patterns, or “contour exploration procedures,” that are directly related to task goals and intrinsic system capacities. Our general assumptions, method, and initial results were described in Part 1. Part 2 provides an analysis of the relation between contour extraction procedures and processing constraints. These theoretical assumptions are supported by empirical findings, and implications are discussed for issues of importance to robotic exploration and manipulation.

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Wang, Bei, and Jin Guo He. "Contour Reconstruction Based on Non-Closed Contours." Applied Mechanics and Materials 220-223 (November 2012): 2313–18. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.2313.

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There are a lot of developments focused on 3D surface reconstruction based on 2D contours in recent decades, mostly based on closed contours. Due to the limitation of imperfect technology on material classification and edge extraction, it’s difficult to extract closed contours automatically, and this situation limits the application of 3D surface reconstruction based on contour. This paper designs a reconstruction algorithm based on non-closed contour, which not only provides a new viewpoint for research on 3D reconstruction, but also make 3D reconstruction based on contour more applicable.

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Dissertations / Theses on the topic "Contour extraction"

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Wang, Jiankang. "Actively searching contour models for contour extraction." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0014/NQ59691.pdf.

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Ananta, Soujanya Siddavaram. "Contour Extraction of Drosophila Embryos Using Active Contours in Scale Space." TopSCHOLAR®, 2012. http://digitalcommons.wku.edu/theses/1222.

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Contour extraction of Drosophila embryos is an important step to build a computational system for pattern matching of embryonic images which aids in the discovery of genes. Automatic contour extraction of embryos is challenging due to several image variations such as size, shape, orientation and neigh- boring embryos such as touching and non-touching embryos. In this thesis, we introduce a framework for contour extraction based on the connected components in the gaussian scale space of an embryonic image. The active contour model is applied on the images to refine embryo contours. Data cleaning methods are applied to smooth the jaggy contours caused by blurred embryo boundaries. The scale space theory is applied to improve the performance of the result. The active contour adjusts better to the object for finer scales. The proposed framework contains three components. In the first component, we find the connected components of the image. The second component is to find the largest component of the image. Finally, we analyze the largest component across scales by selecting the optimal scale corresponding to the largest component having largest area. The optimal scale at which maximum area is attained is assumed to give information about the feature being extracted. We tested the proposed framework on BDGP images, and the results achieved promising accuracy in extracting the targeting embryo.

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Gunn, Steve R. "Dual active contour models for image feature extraction." Thesis, University of Southampton, 1996. https://eprints.soton.ac.uk/250089/.

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Active contours are now a very popular technique for shape extraction, achieved by minimising a suitably formulated energy functional. Conventional active contour formulations suffer difficulty in appropriate choice of an initial contour and values of parameters. Recent approaches have aimed to resolve these problems, but can compromise other performance aspects. To relieve the problem in initialisation, an evolutionary dual active contour has been developed, which is combined with a local shape model to improve the parameterisation. One contour expands from inside the target feature, the other contracts from the outside. The two contours are inter-linked to provide a balanced technique with an ability to reject weak’local energy minima. Additionally a dual active contour configuration using dynamic programming has been developed to locate a global energy minimum and complements recent approaches via simulated annealing and genetic algorithms. These differ from conventional evolutionary approaches, where energy minimisation may not converge to extract the target shape, in contrast with the guaranteed convergence of a global approach. The new techniques are demonstrated to extract successfully target shapes in synthetic and real images, with superior performance to previous approaches. The new technique employing dynamic programming is deployed to extract the inner face boundary, along with a conventional normal-driven contour to extract the outer face boundary. Application to a database of 75 subjects showed that the outer contour was extracted successfully for 96% of the subjects and the inner contour was successful for 82%. This application highlights the advantages new dual active contour approaches for automatic shape extraction can confer.

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Kienel, Enrico, and Guido Brunnett. "GPU-Accelerated Contour Extraction on Large Images Using Snakes." Universitätsbibliothek Chemnitz, 2009. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-200900358.

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Active contours have been proven to be a powerful semiautomatic image segmentation approach, that seems to cope with many applications and different image modalities. However, they exhibit inherent drawbacks, including the sensibility to contour initialization due to the limited capture range of image edges and problems with concave boundary regions. The Gradient Vector Flow replaces the traditional image force and provides an enlarged capture range as well as enhanced concavity extraction capabilities, but it involves an expensive computational effort and considerably increased memory requirements at the time of computation. In this paper, we present an enhancement of the active contour model to facilitate semiautomatic contour detection in huge images. We propose a tile-based image decomposition accompanying an image force computation scheme on demand in order to minimize both computational and memory requirements. We show an efficient implementation of this approach on the basis of general purpose GPU processing providing for continuous active contour deformation without a considerable delay.

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Tejada, Pedro J. "A Computational Geometry Approach to Digital Image Contour Extraction." DigitalCommons@USU, 2009. https://digitalcommons.usu.edu/etd/422.

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We present a method for extracting contours from digital images, using techniques from computational geometry. Our approach is different from traditional pixel-based methods in image processing. Instead of working directly with pixels, we extract a set of oriented feature points from the input digital images, then apply classical geometric techniques, such as clustering, linking, and simplification, to find contours among these points. Experiments on synthetic and natural images show that our method can effectively extract contours, even from images with considerable noise; moreover, the extracted contours have a very compact representation.

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TANG, YINGJIE. "ON CLOSE CONTOUR PRESENTATION AND MATCHING PROBLEMS WITH BIOMEDICAL IMAGE APPLICATIONS." University of Cincinnati / OhioLINK, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin983567833.

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Gilson, Stuart J. "Deformable contour methods for shape extraction from binary edge-point images." Thesis, University of Southampton, 1999. https://eprints.soton.ac.uk/256268/.

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Nöjdh, Oscar. "Intelligent boundary extraction for area and volume measurement : Using LiveWire for 2D and 3D contour extraction in medical imaging." Thesis, Linköpings universitet, Programvara och system, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-136448.

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This thesis tries to answer if a semi-automatic tool can speed up the process of segmenting tumors to find the area of a slice in the tumor or the volume of the entire tumor. A few different 2D semi-automatic tools were considered. The final choice was to implement live-wire. The implemented live-wire was evaluated and improved upon with hands-on testing from developers. Two methods were found for extending live-wire to 3D bodies. The first method was to interpolate the seed points and create new contours using the new seed points. The second method was to let the user segment contours in two orthogonal projections. The intersections between those contours and planes in the third orthogonal projection were then used to create automatic contours in this third projection. Both tools were implemented and evaluated. The evaluation compared the two tools to manual segmentation on two cases posing different difficulties. Time-on-task and accuracy were measured during the evaluation. The evaluation revealed that the semi-automatic tools could indeed save the user time while maintaining acceptable (80%) accuracy. The significance of all results were analyzed using two-tailed t-tests.

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Niu, Xutong. "Highway extraction from high resolution aerial photography using a geometric active contour model." The Ohio State University, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=osu1101833084.

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Berjass, Hisham. "Hardware Implementation Of An Object Contour Detector Using Morphological Operators." Thesis, Linköpings universitet, Institutionen för systemteknik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-66353.

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The purpose of this study was the hardware implementation of a real time moving object contour extraction.Segmentation of image frames to isolate moving objects followed by contour extraction using digitalmorphology was carried out in this work. Segmentation using temporal difference with median thresholdingapproach was implemented, experimental methods were used to determine the suitable morphological operatorsalong with their structuring elements dimensions to provide the optimum contour extraction.The detector with image resolution of 1280 x1024 pixels and frame rate of 60 Hz was successfully implemented,the results indicate the effect of proper use of morphological operators for post processing and contourextraction on the overall efficiency of the system. An alternative segmentation method based on Stauffer & Grimson algorithm was investigated and proposed which promises better system performance at the expense ofimage resolution and frame rate

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Books on the topic "Contour extraction"

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Chayra, Harjinder. Parallel contour extraction algorithms for image analysis. [s.l.]: typescript, 1989.

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Androutsos, Dimitrios. Photochromic tracer flow extraction via active contours. Ottawa: National Library of Canada, 1994.

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Book chapters on the topic "Contour extraction"

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Lu, ChengEn, Longin Jan Latecki, and Guangxi Zhu. "Contour Extraction Using Particle Filters." In Advances in Visual Computing, 192–201. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89646-3_19.

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Li, Yuan, Jianzhou Zhang, and Ping Jiang. "Contour Extraction Based on Surround Inhibition and Contour Grouping." In Computer Vision – ACCV 2009, 687–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12304-7_65.

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Bernardis, Elena, and Jianbo Shi. "Shape Extraction through Region-Contour Stitching." In Advances in Visual Computing, 393–405. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89639-5_38.

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Cumani, Aldo. "Efficient contour extraction in color images." In Computer Vision — ACCV'98, 582–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63930-6_170.

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Lei, Debin, Chunhong Pan, Qing Yang, and Minyong Shi. "Interactive Contour Extraction Using NURBS-HMM." In Computer Vision – ACCV 2006, 858–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11612032_86.

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Rognant, L., J. G. Planès, M. Memier, and J. M. Chassery. "Contour Lines and DEM: Generation and Extraction." In Digital Earth Moving, 87–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44818-7_13.

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Wang, Junyan, Guangda Su, and Xinggang Lin. "An Effective Approach to Chin Contour Extraction." In Lecture Notes in Computer Science, 203–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11538059_22.

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Ben Chattah, Thoraya, Sébastien Bougleux, Olivier Lézoray, and Atef Hamouda. "Patch-Based Potentials for Interactive Contour Extraction." In Advances in Visual Computing, 587–97. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03801-4_51.

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Nguyen, H. G., B. Kerautret, P. Desbarats, and J. O. Lachaud. "Discrete Contour Extraction from Reference Curvature Function." In Advances in Visual Computing, 1176–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89646-3_117.

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Nagy, Zoltán, Marcin Novotni, and Reinhard Klein. "Enhancing Fourier Volume Rendering Using Contour Extraction." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004, 470–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30136-3_58.

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Conference papers on the topic "Contour extraction"

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Jaiswal, Rachana, and Srikant Satarkar. "Biometric Foetal Contour Extraction using Hybrid Level Set." In 6th International Conference on Signal and Image Processing (SIGI 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.102002.

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In medical imaging, accurate anatomical structure extraction is important for diagnosis and therapeutic interventional planning. So, for easier, quicker and accurate diagnosis of medical images, image processing technologies may be employed in analysis and feature extraction of medical images. In this paper, some modifications to level set algorithm are made and modified algorithm is used for extracting contour of foetal objects in an image. The proposed approach is applied on foetal ultrasound images. In traditional approach, foetal parameters are extracted manually from ultrasound images. Due to lack of consistency and accuracy of manual measurements, an automatic technique is highly desirable to obtain foetal biometric measurements. This proposed approach is based on global & local region information for foetal contour extraction from ultrasonic images. The primary goal of this research is to provide a new methodology to aid the analysis and feature extraction from foetal images.

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Hoang-Nam Nguyen and An-Chen Lee. "Object contour extraction using adaptive NURBS VFC active contour." In 2011 9th World Congress on Intelligent Control and Automation (WCICA 2011). IEEE, 2011. http://dx.doi.org/10.1109/wcica.2011.5970569.

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Hongzhi Diao, Jun Cao, and Guiying Li. "Active Contour Model (Snake) for Wood Cell Contour Extraction." In 2006 6th World Congress on Intelligent Control and Automation. IEEE, 2006. http://dx.doi.org/10.1109/wcica.2006.1714042.

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Ye, Famao, Lin Su, Shukai Li, and Xiangjun Li. "Extraction of complex object contour." In International Conference on Space information Technology, edited by Cheng Wang, Shan Zhong, and Xiulin Hu. SPIE, 2005. http://dx.doi.org/10.1117/12.657837.

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Li, Qi, and Chandra Kambhamettu. "Contour Extraction of Drosophila Embryos." In 2009 21st IEEE International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2009. http://dx.doi.org/10.1109/ictai.2009.44.

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Estrada, F. J., and A. D. Jepson. "Perceptual grouping for contour extraction." In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. IEEE, 2004. http://dx.doi.org/10.1109/icpr.2004.1334008.

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Leong Chee Ken, Gan Yu Han, and Chew Kuew Wai. "Contour positioning system-Methods of data extraction for contour positioning." In 2012 IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (STUDENT2012). IEEE, 2012. http://dx.doi.org/10.1109/student.2012.6408406.

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Xiao-Hua, Liu, Yuan Da, and Li Jin-Jiang. "Image Contour Extraction Based on CNN and Active Contour Model." In 2009 Fifth International Conference on Natural Computation. IEEE, 2009. http://dx.doi.org/10.1109/icnc.2009.86.

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Wang, Zhaobin, Miao Guo, Ying Zhu, Lizhen Yang, and Yi-de Ma. "Macromolecular extraction based on contour evolution." In 2012 International Conference on Graphic and Image Processing, edited by Zeng Zhu. SPIE, 2013. http://dx.doi.org/10.1117/12.2006088.

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Yaling, Liang, and Du Minghui. "Lip Contour Extraction Based on Manifold." In 2008 International Conference on MultiMedia and Information Technology (MMIT). IEEE, 2008. http://dx.doi.org/10.1109/mmit.2008.25.

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Reports on the topic "Contour extraction"

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Andresen, Jens-Bjørn R., and Søren M. Kristiansen. Historic maps as source for hydrological reconstruction of pre-industrial landscape wetness in Denmark: a methodological study. Det Kgl. Bibliotek, 2023. http://dx.doi.org/10.7146/aul.491.

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Historic maps are an important primary source which can be utilized in the reconstruction of environmental variables of the pre-industrial landscape. However, methodological constraints have hitherto prevented large scale and systematic approaches. In this paper a novel methodology is presented, which documents the usefulness of the maps in the study of paleo-hydrology and thus serves a better understanding of the conditions for agricultural production under pre-drainage conditions. The methodology is developed based on eighteenth and nineteenth century maps from a 100 km2 study area in one stream catchment in East Jutland, Denmark. It combines information from two types of historic maps in order to correlate computed soil hydrology (wetness index) and recorded historic land-use. The calculated wetness indexes are derived from contour lines on topographic (military) maps (in Danish: Høje Maalebordsblade), whereas the spatial overlays are land-use classes from economic maps (in Danish: Matrikelkort - Original 1). This study demonstrates – for the first time - that the wetness index is explanatory for the agricultural suitable/non-suitable dichotomy (tilled land versus “wetland”: meadows, fens, and peat bogs) on the historic economic maps. Furthermore, the study shows that pre-industrial arable areas were stretched to their limits in respect to cropping wet soils in this agricultural dominated landscape. The study confirms the existing belief that the historic economic maps constitute the best available source of these mosaic-landscapes for periods before the intense subsurface tile drainage began. This finding opens for further methodological development and up-scaling using automatic feature detection, contour line extraction and text recognition of historical maps.

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Kelly, Luke. What Accountability Means in Somalia. Institute of Development Studies, June 2022. http://dx.doi.org/10.19088/k4d.2022.113.

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This rapid literature review finds that accountability programming in Somalia is focused on working effectively with the country’s hybrid governance. A number of programmes have generated findings on the potential of non-state actors to improve accountability, with a focus on contextual analysis and adaptive programming. Accountability is defined as mechanisms to hold people in power to account according to an agreed standard. Improving accountability may be difficult in fragile and conflict-affected states such as Somalia where power is dispersed and informal. Somalia is commonly described as a hybrid political order. Regions in Somalia have more and less robust governments and non-state actors have a number of important but informal roles in governance. Moreover, the prevalence of clan-based politics and patriarchal norms limits the inclusivity of accountability mechanisms, with women and members of minority clans among those commonly excluded. This report is focused on accountability in governance. It surveys both evidence on the status and contours of accountability in Somalia, and on programmes to improve accountability. It is based on evidence from the Implementation and Analysis in Action of Accountability Programme (IAAAP) Somalia programme, as well as other relevant programmes. It describes the findings on the barriers and enablers to greater accountability in Somalia, as well as lessons on implementing programmes. It does not survey every accountability programme, or programme with accountability components, but instead focused on published evaluations and evidence syntheses. Several programmes, such as IAAAP, have sought to research, improve and learn lessons on accountability in Somalia. IAAAP ran from 2013 to 2019 and had a budget of GBP 23 million. It worked as an innovation laboratory to test models for greater accountability through adaptive programming. IAAAP worked on different themes, including civil society-state engagement, financial flows and extractive industries.

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