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Relevant bibliographies by topics / Image Clustering

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Academic literature on the topic 'Image Clustering'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 March 2023

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Journal articles on the topic "Image Clustering"

1

Miklautz, Lukas, Dominik Mautz, Muzaffer Can Altinigneli, Christian Böhm, and Claudia Plant. "Deep Embedded Non-Redundant Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5174–81. http://dx.doi.org/10.1609/aaai.v34i04.5961.

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Complex data types like images can be clustered in multiple valid ways. Non-redundant clustering aims at extracting those meaningful groupings by discouraging redundancy between clusterings. Unfortunately, clustering images in pixel space directly has been shown to work unsatisfactory. This has increased interest in combining the high representational power of deep learning with clustering, termed deep clustering. Algorithms of this type combine the non-linear embedding of an autoencoder with a clustering objective and optimize both simultaneously. None of these algorithms try to find multiple non-redundant clusterings. In this paper, we propose the novel Embedded Non-Redundant Clustering algorithm (ENRC). It is the first algorithm that combines neural-network-based representation learning with non-redundant clustering. ENRC can find multiple highly non-redundant clusterings of different dimensionalities within a data set. This is achieved by (softly) assigning each dimension of the embedded space to the different clusterings. For instance, in image data sets it can group the objects by color, material and shape, without the need for explicit feature engineering. We show the viability of ENRC in extensive experiments and empirically demonstrate the advantage of combining non-linear representation learning with non-redundant clustering.

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Soleh, Muhamad, Aniati Murni Arymurthy, and Sesa Wiguna. "CHANGE DETECTION IN MULTI-TEMPORAL IMAGES USING MULTISTAGE CLUSTERING FOR DISASTER RECOVERY PLANNING." Jurnal Ilmu Komputer dan Informasi 11, no. 2 (June 29, 2018): 110. http://dx.doi.org/10.21609/jiki.v11i2.623.

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Change detection analysis on multi-temporal images using various methods have been developed by many researchers in the field of spatial data analysis and image processing. Change detection analysis has many benefit for real world applications such as medical image analysis, valuable material detector, satellite image analysis, disaster recovery planning, and many others. Indonesia is one of the most country that encounter natural disaster. The most memorable disaster was happened in December 26, 2004. Change detection is one of the important part management planning for natural disaster recovery. This article present the fast and accurate result of change detection on multi-temporal images using multistage clustering. There are three main step for change detection in this article, the first step is to find the image difference of two multi-temporal images between the time before disaster and after disaster using operation log ratio between those images. The second step is clustering the difference image using Fuzzy C means divided into three classes. Change, unchanged, and intermediate change region. Afterword the last step is cluster the change map from fuzzy C means clustering using k means clustering, divided into two classes. Change and unchanged region. Both clustering’s based on Euclidian distance.

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Tang, Jun. "Image Registration Using Clustering Algorithm." Advanced Materials Research 108-111 (May 2010): 63–68. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.63.

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This paper proposed a new method of image registration based on clustering algorithm. It used clustering algorithm to cluster all the feature vectors of images, and adopted EM algorithm to optimize the parameters and algorithm. Experimental result shows that the proposed image registration method can improve the precise of image registration, and reduce error.

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Zhu, Wencheng, Jiwen Lu, and Jie Zhou. "Nonlinear subspace clustering for image clustering." Pattern Recognition Letters 107 (May 2018): 131–36. http://dx.doi.org/10.1016/j.patrec.2017.08.023.

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Li, Zhihui, Lina Yao, Sen Wang, Salil Kanhere, Xue Li, and Huaxiang Zhang. "Adaptive Two-Dimensional Embedded Image Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4796–803. http://dx.doi.org/10.1609/aaai.v34i04.5914.

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With the rapid development of mobile devices, people are generating huge volumes of images data every day for sharing on social media, which draws much research attention to understanding the contents of images. Image clustering plays an important role in image understanding systems. Often, most of the existing image clustering algorithms flatten digital images that are originally represented by matrices into 1D vectors as the image representation for the subsequent learning. The drawbacks of vector-based algorithms include limited consideration of spatial relationship between pixels and computational complexity, both of which blame to the simple vectorized representation. To overcome the drawbacks, we propose a novel image clustering framework that can work directly on matrices of images instead of flattened vectors. Specifically, the proposed algorithm simultaneously learn the clustering results and preserve the original correlation information within the image matrix. To solve the challenging objective function, we propose a fast iterative solution. Extensive experiments have been conducted on various benchmark datasets. The experimental results confirm the superiority of the proposed algorithm.

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García Villalba, Luis Javier, Ana Lucila Sandoval Orozco, and Jocelin Rosales Corripio. "Smartphone image clustering." Expert Systems with Applications 42, no. 4 (March 2015): 1927–40. http://dx.doi.org/10.1016/j.eswa.2014.10.018.

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Li, Xutong, Taoying Li, and Yan Wang. "GW-DC: A Deep Clustering Model Leveraging Two-Dimensional Image Transformation and Enhancement." Algorithms 14, no. 12 (November 29, 2021): 349. http://dx.doi.org/10.3390/a14120349.

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Traditional time-series clustering methods usually perform poorly on high-dimensional data. However, image clustering using deep learning methods can complete image annotation and searches in large image databases well. Therefore, this study aimed to propose a deep clustering model named GW_DC to convert one-dimensional time-series into two-dimensional images and improve cluster performance for algorithm users. The proposed GW_DC consisted of three processing stages: the image conversion stage, image enhancement stage, and image clustering stage. In the image conversion stage, the time series were converted into four kinds of two-dimensional images by different algorithms, including grayscale images, recurrence plot images, Markov transition field images, and Gramian Angular Difference Field images; this last one was considered to be the best by comparison. In the image enhancement stage, the signal components of two-dimensional images were extracted and processed by wavelet transform to denoise and enhance texture features. Meanwhile, a deep clustering network, combining convolutional neural networks with K-Means, was designed for well-learning characteristics and clustering according to the aforementioned enhanced images. Finally, six UCR datasets were adopted to assess the performance of models. The results showed that the proposed GW_DC model provided better results.

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Prades, José, Gonzalo Safont, Addisson Salazar, and Luis Vergara. "Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering." Remote Sensing 12, no. 21 (November 1, 2020): 3585. http://dx.doi.org/10.3390/rs12213585.

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Many tasks in hyperspectral imaging, such as spectral unmixing and sub-pixel matching, require knowing how many substances or materials are present in the scene captured by a hyperspectral image. In this paper, we present an algorithm that estimates the number of materials in the scene using agglomerative clustering. The algorithm is based on the assumption that a valid clustering of the image has one cluster for each different material. After reducing the dimensionality of the hyperspectral image, the proposed method obtains an initial clustering using K-means. In this stage, cluster densities are estimated using Independent Component Analysis. Based on the K-means result, a model-based agglomerative clustering is performed, which provides a hierarchy of clusterings. Finally, a validation algorithm selects a clustering of the hierarchy; the number of clusters it contains is the estimated number of materials. Besides estimating the number of endmembers, the proposed method can approximately obtain the endmember (or spectrum) of each material by computing the centroid of its corresponding cluster. We have tested the proposed method using several hyperspectral images. The results show that the proposed method obtains approximately the number of materials that these images contain.

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Tongbram, Simon. "Clustering-based Image Segmentation Techniques: A Review." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 701–7. http://dx.doi.org/10.5373/jardcs/v12sp7/20202160.

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Mohammed, Shatha J. "Brain Image Segmentation Based on Fuzzy Clustering." Al-Mustansiriyah Journal of Science 28, no. 3 (July 3, 2018): 220. http://dx.doi.org/10.23851/mjs.v28i3.553.

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The segmentation performance is topic to suitable initialization and best configuration of supervisory parameters. In medical image segmentation, the segmentation is very important when the diagnosing becomes very hard in medical images which are not properly illuminated. This paper proposes segmentation of brain tumour image of MRI images based on spatial fuzzy clustering and level set algorithm. After performance evaluation of the proposed algorithm was carried on brain tumour images, the results showed confirm its effectiveness for medical image segmentation, where the brain tumour is detected properly.

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Dissertations / Theses on the topic "Image Clustering"

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U, Leong Hou. "Web image clustering and retrieval." Thesis, University of Macau, 2005. http://umaclib3.umac.mo/record=b1445902.

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Sayar, Ahmet. "Image Annotation With Semi-supervised Clustering." Phd thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/3/12611251/index.pdf.

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Image annotation is defined as generating a set of textual words for a given image, learning from the available training data consisting of visual image content and annotation words. Methods developed for image annotation usually make use of region clustering algorithms to quantize the visual information. Visual codebooks are generated from the region clusters of low level visual features. These codebooks are then, matched with the words of the text document related to the image, in various ways. In this thesis, we propose a new image annotation technique, which improves the representation and quantization of the visual information by employing the available but unused information, called side information, which is hidden in the system. This side information is used to semi-supervise the clustering process which creates the visterms. The selection of side information depends on the visual image content, the annotation words and the relationship between them. Although there may be many different ways of defining and selecting side information, in this thesis, three types of side information are proposed. The first one is the hidden topic probability information obtained automatically from the text document associated with the image. The second one is the orientation and the third one is the color information around interest points that correspond to critical locations in the image. The side information provides a set of constraints in a semi-supervised K-means region clustering algorithm. Consequently, in generation of the visual terms from the regions, not only low level features are clustered, but also side information is used to complement the visual information, called visterms. This complementary information is expected to close the semantic gap between the low level features extracted from each region and the high level textual information. Therefore, a better match between visual codebook and the annotation words is obtained. Moreover, a speedup is obtained in the modified K-means algorithm because of the constraints brought by the side information. The proposed algorithm is implemented in a high performance parallel computation environment.

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Spång, Anton. "Automatic Image Annotation by Sharing Labels Based on Image Clustering." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210164.

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The growth of image collection sizes during the development has currently made manual annotation unfeasible, leading to the need for accurate and time efficient image annotation methods. This project evaluates a system for Automatic Image Annotation to see if it is possible to share annotations between images based on un-supervised clustering. The evaluation of the system included performing experiments with different algorithms and different unlabeled data sets. The system is also compared to an award winning Convolutional Neural Network model, used as a baseline, to see if the system’s precision and/or recall could be better than the baseline model’s. The results of the experiment conducted in this work showed that the precision and recall could be increased on the data used in this thesis, an increase of 0.094 in precision and 0.049 in recall in average for the system compared to the baseline.
Utvecklingen av bildkollektioners storlekar har fram till idag ökat behovet av ett pålitligt och effektivt annoteringsverktyg i och med att manuell annotering har blivit ineffektivt. Denna rapport utvärderar möjligheterna att dela bildtaggar mellan visuellt lika bilder med ett system för automatisk bildannotering baserat på klustring. Utvärderingen sker i form av flera experiment med olika algoritmer och olika omärkta datamängder. I experimenten är systemet jämfört med en prisbelönt konvolutionell neural nätverksmodell, vilken är använd som utgångspunkt, för att undersöka om systemets resultat kan bli bättre än utgångspunktens resultat. Resultaten visar att både precisionen och återkallelsen förbättrades i de experiment som genomfördes på den data använd i detta arbete. En precisionsökning med 0.094 och en återkallelseökning med 0.049 för det implementerade systemet jämfört med utgångspunkten, över det genomförda experimenten.

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Chang, Soong Uk. "Clustering with mixed variables /." [St. Lucia, Qld.], 2005. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe19086.pdf.

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Daniels, Kristine Jean. "Clustering of Database Query Results." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1282.pdf.

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Ekstrom, Nathan Hyrum. "Increasing DOGMA Scaling Through Clustering." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2359.pdf.

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Kong, Tian Fook. "Multilevel spectral clustering : graph partitions and image segmentation." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45275.

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Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2008.
Includes bibliographical references (p. 145-146).
While the spectral graph partitioning method gives high quality segmentation, segmenting large graphs by the spectral method is computationally expensive. Numerous multilevel graph partitioning algorithms are proposed to reduce the segmentation time for the spectral partition of large graphs. However, the greedy local refinement used in these multilevel schemes has the tendency of trapping the partition in poor local minima. In this thesis, I develop a multilevel graph partitioning algorithm that incorporates the inverse powering method with greedy local refinement. The combination of the inverse powering method with greedy local refinement ensures that the partition quality of the multilevel method is as good as, if not better than, segmenting the large graph by the spectral method. In addition, I present a scheme to construct the adjacency matrix, W and degree matrix, D for the coarse graphs. The proposed multilevel graph partitioning algorithm is able to bisect a graph (k = 2) with significantly shorter time than segmenting the original graph without the multilevel implementation, and at the same time achieving the same normalized cut (Ncut) value. The starting eigenvector, obtained by solving a generalized eigenvalue problem on the coarsest graph, is close to the Fiedler vector of the original graph. Hence, the inverse iteration needs only a few iterations to converge the starting vector. In the k-way multilevel graph partition, the larger the graph, the greater the reduction in the time needed for segmenting the graph. For the multilevel image segmentation, the multilevel scheme is able to give better segmentation than segmenting the original image. The multilevel scheme has higher success of preserving the salient part of an object.
(cont.) In this work, I also show that the Ncut value is not the ultimate yardstick for the segmentation quality of an image. Finding a partition that has lower Ncut value does not necessary means better segmentation quality. Segmenting large images by the multilevel method offers both speed and quality.
by Tian Fook Kong.
S.M.

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Fang, Yan. "Data clustering and graph-based image matching methods." Thesis, University of York, 2012. http://etheses.whiterose.ac.uk/4778/.

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This thesis describes our novel methods for data clustering, graph characterizing and image matching. In Chapter 3, our main contribution is the M1NN agglomerative clustering method with a new parallel merging algorithm. A cluster characterizing quantity is derived from the path-based dissimilarity measure. In Chapter 4, our main contribution is the modified log likelihood model for quantitative clustering analysis. The energy of a graph is adopted to define the description length to measure the complexity of a clustering. In Chapter 5, our main contribution is an image matching method based on Delaunay graph characterization and node selection. A normalized Euclidean distance on Delaunay graphs is found useful to estimate pairwise distances.

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Davis, Aaron Samuel. "Bisecting Document Clustering Using Model-Based Methods /." Diss., CLICK HERE for online access, 2010. http://contentdm.lib.byu.edu/ETD/image/etd3332.pdf.

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Piatrik, Tomas. "Image clustering and Video Summarisation using ant-inspired methods." Thesis, University of London, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.509746.

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Books on the topic "Image Clustering"

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Siddiqui, Fasahat Ullah, and Abid Yahya. Clustering Techniques for Image Segmentation. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-81230-0.

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Köster, Klaus. Robust clustering and image segmentation. Birmingham: University of Birmingham, 1999.

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Borra, Surekha, Rohit Thanki, and Nilanjan Dey. Satellite Image Analysis: Clustering and Classification. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6424-2.

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Ramadas, Meera, and Ajith Abraham. Metaheuristics for Data Clustering and Image Segmentation. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04097-0.

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K, Kokula Krishna Hari, and K. Saravanan, eds. Identification of Brain Regions Related to Alzheimers’ Diseases using MRI Images Based on Eigenbrain and K-means Clustering. Tiruppur, Tamil Nadu, India: Association of Scientists, Developers and Faculties, 2016.

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Yahya, Abid, and Fasahat Ullah Siddiqui. Clustering Techniques for Image Segmentation. Springer International Publishing AG, 2021.

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Yahya, Abid, and Fasahat Ullah Siddiqui. Clustering Techniques for Image Segmentation. Springer International Publishing AG, 2022.

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Dey, Nilanjan, Rohit Thanki, and Surekha Borra. Satellite Image Analysis: Clustering and Classification. Springer, 2019.

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Abraham, Ajith, and Meera Ramadas. Metaheuristics for Data Clustering and Image Segmentation. Springer, 2018.

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Bhattacharyya, Siddhartha, Sourav De, Paramartha Dutta, and Indrajit Pan. Intelligent Multidimensional Data Clustering and Analysis. IGI Global, 2017.

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Book chapters on the topic "Image Clustering"

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Borra, Surekha, Rohit Thanki, and Nilanjan Dey. "Satellite Image Clustering." In Satellite Image Analysis: Clustering and Classification, 31–52. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6424-2_3.

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Fernandez, Gregory, Abdelouaheb Meckaouche, Philippe Peter, and Chabane Djeraba. "Intelligent Image Clustering." In XML-Based Data Management and Multimedia Engineering — EDBT 2002 Workshops, 406–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36128-6_24.

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Cleju, Ioan, Pasi Fränti, and Xiaolin Wu. "Clustering Based on Principal Curve." In Image Analysis, 872–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11499145_88.

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Siddiqui, Fasahat Ullah, and Abid Yahya. "Novel Partitioning Clustering." In Clustering Techniques for Image Segmentation, 69–91. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81230-0_3.

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Siddiqui, Fasahat Ullah, and Abid Yahya. "Partitioning Clustering Techniques." In Clustering Techniques for Image Segmentation, 35–67. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81230-0_2.

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Borra, Surekha, Rohit Thanki, and Nilanjan Dey. "Satellite Image Classification." In Satellite Image Analysis: Clustering and Classification, 53–81. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6424-2_4.

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Myhre, Jonas Nordhaug, Karl Øyvind Mikalsen, Sigurd Løkse, and Robert Jenssen. "Consensus Clustering Using kNN Mode Seeking." In Image Analysis, 175–86. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19665-7_15.

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Gong, Zhiguo, Leong Hou U, and Chan Wa Cheang. "Web Image Semantic Clustering." In Lecture Notes in Computer Science, 1416–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11575801_30.

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Toennies, Klaus D. "Classification and Clustering." In Guide to Medical Image Analysis, 473–528. London: Springer London, 2017. http://dx.doi.org/10.1007/978-1-4471-7320-5_12.

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Toennies, Klaus D. "Classification and Clustering." In Guide to Medical Image Analysis, 379–412. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2751-2_12.

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Conference papers on the topic "Image Clustering"

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chen, Wei-Bang, and Chengcui Zhang. "Image spam clustering." In the First ACM workshop. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1631081.1631088.

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El Choubassi, Maha, Ara V. Nefian, Igor Kozintse, Jean-Yves Bouguet, and Yi Wu. "Web Image Clustering." In 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/icassp.2007.367296.

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Tariq, Amara, and Hassan Foroosh. "T-clustering: Image clustering by tensor decomposition." In 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015. http://dx.doi.org/10.1109/icip.2015.7351719.

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Sawant, Rudra, Gianluca Demartini, and Tom Bridge. "Hierarchical Clustering of Corals using Image Clustering." In ADCS '21: Australasian Document Computing Symposium. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3503516.3503531.

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Ahmed, Nasir, and Abdul Jalil. "Image Clustering Using Discriminant Image Features." In 2013 11th International Conference on Frontiers of Information Technology (FIT). IEEE, 2013. http://dx.doi.org/10.1109/fit.2013.13.

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Caldelli, Roberto, Irene Amerini, Francesco Picchioni, and Matteo Innocenti. "Fast image clustering of unknown source images." In 2010 IEEE International Workshop on Information Forensics and Security (WIFS). IEEE, 2010. http://dx.doi.org/10.1109/wifs.2010.5711454.

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Liu, Yang, Quanxue Gao, Zhaohua Yang, and Shujian Wang. "Learning with Adaptive Neighbors for Image Clustering." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/344.

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Due to the importance and efficiency of learning complex structures hidden in data, graph-based methods have been widely studied and get successful in unsupervised learning. Generally, most existing graph-based clustering methods require post-processing on the original data graph to extract the clustering indicators. However, there are two drawbacks with these methods: (1) the cluster structures are not explicit in the clustering results; (2) the final clustering performance is sensitive to the construction of the original data graph. To solve these problems, in this paper, a novel learning model is proposed to learn a graph based on the given data graph such that the new obtained optimal graph is more suitable for the clustering task. We also propose an efficient algorithm to solve the model. Extensive experimental results illustrate that the proposed model outperforms other state-of-the-art clustering algorithms.

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Yin, Qiyue, Shu Wu, and Liang Wang. "Partially tagged image clustering." In 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015. http://dx.doi.org/10.1109/icip.2015.7351559.

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Chang, Jianlong, Lingfeng Wang, Gaofeng Meng, Shiming Xiang, and Chunhong Pan. "Deep Adaptive Image Clustering." In 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, 2017. http://dx.doi.org/10.1109/iccv.2017.626.

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Chang, C. C., and D. J. Buehrer. "DE-CLUSTERING IMAGE DATABASES." In Proceedings of the Second Far-East Workshop on Future Database Systems. WORLD SCIENTIFIC, 1992. http://dx.doi.org/10.1142/9789814503624_0017.

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Reports on the topic "Image Clustering"

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Silverman, Jerry, and Charlene Caefer. Use of Eigenvector-Generated Scatter Plots in Clustering Image Data. Fort Belvoir, VA: Defense Technical Information Center, July 2008. http://dx.doi.org/10.21236/ada483563.

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Wehrens, Ron, Lutgarde M. Buydens, Chris Fraley, and Adrian E. Raftery. Model-Based Clustering for Image Segmentation and Large Datasets Via Sampling. Fort Belvoir, VA: Defense Technical Information Center, February 2003. http://dx.doi.org/10.21236/ada459638.

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Murtagh, Fionn, Adrian E. Raftery, and Jean-Luc Starck. Bayesian Inference for Color Image Quantization via Model-Based Clustering Trees. Fort Belvoir, VA: Defense Technical Information Center, November 2001. http://dx.doi.org/10.21236/ada459791.

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4

Harris, J. Clustering of gamma ray spectrometer data using a computer image analysis system. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/128043.

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Kersten, Paul R., Roger R. Lee, Jim S. Verdi, Ron M. Carvlho, and Stephen P. Yankovich. Segmenting SAR Images Using Fuzzy Clustering. Fort Belvoir, VA: Defense Technical Information Center, July 1999. http://dx.doi.org/10.21236/ada378143.

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Kersten, Paul, and Roger Lee. Segmenting Polarimetric SAR Images Using Robust Competitive Clustering. Fort Belvoir, VA: Defense Technical Information Center, January 2001. http://dx.doi.org/10.21236/ada389803.

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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Abstract:

The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.

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