Relevant bibliographies by topics / Image representation methods

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Image representation methods'

Author: Grafiati
Published: 19 February 2023

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Journal articles on the topic "Image representation methods"

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HU, CHAO, LI LIU, BO SUN, and MAX Q. H. MENG. "COMPACT REPRESENTATION AND PANORAMIC REPRESENTATION FOR CAPSULE ENDOSCOPE IMAGES." International Journal of Information Acquisition 06, no. 04 (December 2009): 257–68. http://dx.doi.org/10.1142/s0219878909001989.

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A capsule endoscope robot is a miniature medical instrument for inspection of gastrointestinal tract. In this paper, we present image compact representation and preliminary panoramic representation methods for the capsule endoscope. First, the characteristics of the capsule endoscopic images are investigated and different coordinate representations of the circular image are discussed. Secondly, effective compact representation methods including special DPCM and wavelet compression techniques are applied to the endoscopic images to get high compression ratio and signal to noise ratio. Then, a preliminary approach to panoramic representation of endoscopic images is presented.
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Cortez, Diogo, Paulo Nunes, Manuel Menezes de Sequeira, and Fernando Pereira. "Image segmentation towards new image representation methods." Signal Processing: Image Communication 6, no. 6 (February 1995): 485–98. http://dx.doi.org/10.1016/0923-5965(94)00031-d.

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Al-Obaide, Zahraa H., and Ayad A. Al-Ani. "COMPARISON STUDY BETWEEN IMAGE RETRIEVAL METHODS." Iraqi Journal of Information and Communication Technology 5, no. 1 (April 29, 2022): 16–30. http://dx.doi.org/10.31987/ijict.5.1.182.

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Searching for a relevant image in an archive is a problematic research issue for the computer vision research community. The majority of search engines retrieve images using traditional text-based approaches that rely on captions and metadata. Extensive research has been reported in the last two decades for content-based image retrieval (CBIR), analysis, and image classification. Content-Based Image Retrieval is a process that provides a framework for image search, and low-level visual features are commonly used to retrieve the images from the image database. The essential requirement in any image retrieval process is to sort the images with a close similarity in terms of visual appearance. The shape, color, and texture are examples of low-level image features. In image classification-based models and CBIR, high-level image visuals are expressed in the form of feature vectors made up of numerical values. The researcher's findings a significant gap between human visual comprehension and image feature representation. In this paper, we plan to present a comparison study and a comprehensive overview of the recent developments in the field of CBIR and image representation.
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Choi, Jaewoong, Daeha Kim, and Byung Cheol Song. "Style-Guided and Disentangled Representation for Robust Image-to-Image Translation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (June 28, 2022): 463–71. http://dx.doi.org/10.1609/aaai.v36i1.19924.

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Recently, various image-to-image translation (I2I) methods have improved mode diversity and visual quality in terms of neural networks or regularization terms. However, conventional I2I methods relies on a static decision boundary and the encoded representations in those methods are entangled with each other, so they often face with ‘mode collapse’ phenomenon. To mitigate mode collapse, 1) we design a so-called style-guided discriminator that guides an input image to the target image style based on the strategy of flexible decision boundary. 2) Also, we make the encoded representations include independent domain attributes. Based on two ideas, this paper proposes Style-Guided and Disentangled Representation for Robust Image-to-Image Translation (SRIT). SRIT showed outstanding FID by 8%, 22.8%, and 10.1% for CelebA-HQ, AFHQ, and Yosemite datasets, respectively. The translated images of SRIT reflect the styles of target domain successfully. This indicates that SRIT shows better mode diversity than previous works.
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Lu, Jiahao, Johan Öfverstedt, Joakim Lindblad, and Nataša Sladoje. "Is image-to-image translation the panacea for multimodal image registration? A comparative study." PLOS ONE 17, no. 11 (November 28, 2022): e0276196. http://dx.doi.org/10.1371/journal.pone.0276196.

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Despite current advancement in the field of biomedical image processing, propelled by the deep learning revolution, multimodal image registration, due to its several challenges, is still often performed manually by specialists. The recent success of image-to-image (I2I) translation in computer vision applications and its growing use in biomedical areas provide a tempting possibility of transforming the multimodal registration problem into a, potentially easier, monomodal one. We conduct an empirical study of the applicability of modern I2I translation methods for the task of rigid registration of multimodal biomedical and medical 2D and 3D images. We compare the performance of four Generative Adversarial Network (GAN)-based I2I translation methods and one contrastive representation learning method, subsequently combined with two representative monomodal registration methods, to judge the effectiveness of modality translation for multimodal image registration. We evaluate these method combinations on four publicly available multimodal (2D and 3D) datasets and compare with the performance of registration achieved by several well-known approaches acting directly on multimodal image data. Our results suggest that, although I2I translation may be helpful when the modalities to register are clearly correlated, registration of modalities which express distinctly different properties of the sample are not well handled by the I2I translation approach. The evaluated representation learning method, which aims to find abstract image-like representations of the information shared between the modalities, manages better, and so does the Mutual Information maximisation approach, acting directly on the original multimodal images. We share our complete experimental setup as open-source (https://github.com/MIDA-group/MultiRegEval), including method implementations, evaluation code, and all datasets, for further reproducing and benchmarking.
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RIZO-RODRÍGUEZ, DAYRON, HEYDI MÉNDEZ-VAZQUEZ, and EDEL GARCÍA-REYES. "ILLUMINATION INVARIANT FACE RECOGNITION IN QUATERNION DOMAIN." International Journal of Pattern Recognition and Artificial Intelligence 27, no. 03 (May 2013): 1360004. http://dx.doi.org/10.1142/s0218001413600045.

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The performance of face recognition systems tends to decrease when images are affected by illumination. Feature extraction is one of the main steps of a face recognition process, where it is possible to alleviate the illumination effects on face images. In order to increase the accuracy of recognition tasks, different methods for obtaining illumination invariant features have been developed. The aim of this work is to compare two different ways to represent face image descriptions in terms of their illumination invariant properties for face recognition. The first representation is constructed following the structure of complex numbers and the second one is based on quaternion numbers. Using four different face description approaches both representations are constructed, transformed into frequency domain and expressed in polar coordinates. The most illumination invariant component of each frequency domain representation is determined and used as the representative information of the face image. Verification and identification experiments are then performed in order to compare the discriminative power of the selected components. Representative component of the quaternion representation overcame the complex one.
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Lu, Xuchao, Li Song, Rong Xie, Xiaokang Yang, and Wenjun Zhang. "Deep Binary Representation for Efficient Image Retrieval." Advances in Multimedia 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/8961091.

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With the fast growing number of images uploaded every day, efficient content-based image retrieval becomes important. Hashing method, which means representing images in binary codes and using Hamming distance to judge similarity, is widely accepted for its advantage in storage and searching speed. A good binary representation method for images is the determining factor of image retrieval. In this paper, we propose a new deep hashing method for efficient image retrieval. We propose an algorithm to calculate the target hash code which indicates the relationship between images of different contents. Then the target hash code is fed to the deep network for training. Two variants of deep network, DBR and DBR-v3, are proposed for different size and scale of image database. After training, our deep network can produce hash codes with large Hamming distance for images of different contents. Experiments on standard image retrieval benchmarks show that our method outperforms other state-of-the-art methods including unsupervised, supervised, and deep hashing methods.
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Bouarara, Hadj Ahmed, and Yasmin Bouarara. "Swarm Intelligence Methods for Unsupervised Images Classification." International Journal of Organizational and Collective Intelligence 6, no. 2 (April 2016): 50–74. http://dx.doi.org/10.4018/ijoci.2016040104.

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Nowadays, Google estimates that more than 1000 billion the number of images on the internet where the classification of this type of data represents a big problem in the scientific community. Several techniques have been proposed belonging to the world of image-mining. The substance of our work is the application of swarm intelligence methods for the unsupervised image classification (UIC) problem following four steps: image digitalization by developing a new representation approach in order to transform each image into a set of term (set of pixels); image clustering using three methods: firstly a distances combination by social worker bees (DC-SWBs) based on the principle of filtering where each image must successfully pass three filters, secondly Artificial social spiders (ASS) method based on the silky structure and the principle of weaving and the third method called artificial immune system (AIS); For the authors' experiment they use the benchmark MuHavi with changing for each test the configuration (image representation, distance measures and threshold).
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Cohen, Ido, Eli David, and Nathan Netanyahu. "Supervised and Unsupervised End-to-End Deep Learning for Gene Ontology Classification of Neural In Situ Hybridization Images." Entropy 21, no. 3 (February 26, 2019): 221. http://dx.doi.org/10.3390/e21030221.

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In recent years, large datasets of high-resolution mammalian neural images have become available, which has prompted active research on the analysis of gene expression data. Traditional image processing methods are typically applied for learning functional representations of genes, based on their expressions in these brain images. In this paper, we describe a novel end-to-end deep learning-based method for generating compact representations of in situ hybridization (ISH) images, which are invariant-to-translation. In contrast to traditional image processing methods, our method relies, instead, on deep convolutional denoising autoencoders (CDAE) for processing raw pixel inputs, and generating the desired compact image representations. We provide an in-depth description of our deep learning-based approach, and present extensive experimental results, demonstrating that representations extracted by CDAE can help learn features of functional gene ontology categories for their classification in a highly accurate manner. Our methods improve the previous state-of-the-art classification rate (Liscovitch, et al.) from an average AUC of 0.92 to 0.997, i.e., it achieves 96% reduction in error rate. Furthermore, the representation vectors generated due to our method are more compact in comparison to previous state-of-the-art methods, allowing for a more efficient high-level representation of images. These results are obtained with significantly downsampled images in comparison to the original high-resolution ones, further underscoring the robustness of our proposed method.
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Li, Fengpeng, Jiabao Li, Wei Han, Ruyi Feng, and Lizhe Wang. "Unsupervised Representation High-Resolution Remote Sensing Image Scene Classification via Contrastive Learning Convolutional Neural Network." Photogrammetric Engineering & Remote Sensing 87, no. 8 (August 1, 2021): 577–91. http://dx.doi.org/10.14358/pers.87.8.577.

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Inspired by the outstanding achievement of deep learning, supervised deep learning representation methods for high-spatial-resolution remote sensing image scene classification obtained state-of-the-art performance. However, supervised deep learning representation methods need a considerable amount of labeled data to capture class-specific features, limiting the application of deep learning-based methods while there are a few labeled training samples. An unsupervised deep learning representation, high-resolution remote sensing image scene classification method is proposed in this work to address this issue. The proposed method, called contrastive learning, narrows the distance between positive views: color channels belonging to the same images widens the gaps between negative view pairs consisting of color channels from different images to obtain class-specific data representations of the input data without any supervised information. The classifier uses extracted features by the convolutional neural network (CNN)-based feature extractor with labeled information of training data to set space of each category and then, using linear regression, makes predictions in the testing procedure. Comparing with existing unsupervised deep learning representation high-resolution remote sensing image scene classification methods, contrastive learning CNN achieves state-of-the-art performance on three different scale benchmark data sets: small scale RSSCN7 data set, midscale aerial image data set, and large-scale NWPU-RESISC45 data set.
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Dissertations / Theses on the topic "Image representation methods"

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Chang, William. "Representation Theoretical Methods in Image Processing." Scholarship @ Claremont, 2004. https://scholarship.claremont.edu/hmc_theses/160.

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Image processing refers to the various operations performed on pictures that are digitally stored as an aggregate of pixels. One can enhance or degrade the quality of an image, artistically transform the image, or even find or recognize objects within the image. This paper is concerned with image processing, but in a very mathematical perspective, involving representation theory. The approach traces back to Cooley and Tukey’s seminal paper on the Fast Fourier Transform (FFT) algorithm (1965). Recently, there has been a resurgence in investigating algebraic generalizations of this original algorithm with respect to different symmetry groups. My approach in the following chapters is as follows. First, I will give necessary tools from representation theory to explain how to generalize the Discrete Fourier Transform (DFT). Second, I will introduce wreath products and their application to images. Third, I will show some results from applying some elementary filters and compression methods to spectrums of images. Fourth, I will attempt to generalize my method to noncyclic wreath product transforms and apply it to images and three-dimensional geometries.
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Karmakar, Priyabrata. "Effective and efficient kernel-based image representations for classification and retrieval." Thesis, Federation University Australia, 2018. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/165515.

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Image representation is a challenging task. In particular, in order to obtain better performances in different image processing applications such as video surveillance, autonomous driving, crime scene detection and automatic inspection, effective and efficient image representation is a fundamental need. The performance of these applications usually depends on how accurately images are classified into their corresponding groups or how precisely relevant images are retrieved from a database based on a query. Accuracy in image classification and precision in image retrieval depend on the effectiveness of image representation. Existing image representation methods have some limitations. For example, spatial pyramid matching, which is a popular method incorporating spatial information in image-level representation, has not been fully studied to date. In addition, the strengths of pyramid match kernel and spatial pyramid matching are not combined for better image matching. Kernel descriptors based on gradient, colour and shape overcome the limitations of histogram-based descriptors, but suffer from information loss, noise effects and high computational complexity. Furthermore, the combined performance of kernel descriptors has limitations related to computational complexity, higher dimensionality and lower effectiveness. Moreover, the potential of a global texture descriptor which is based on human visual perception has not been fully explored to date. Therefore, in this research project, kernel-based effective and efficient image representation methods are proposed to address the above limitations. An enhancement is made to spatial pyramid matching in terms of improved rotation invariance. This is done by investigating different partitioning schemes suitable to achieve rotation-invariant image representation and the proposal of a weight function for appropriate level contribution in image matching. In addition, the strengths of pyramid match kernel and spatial pyramid are combined to enhance matching accuracy between images. The existing kernel descriptors are modified and improved to achieve greater effectiveness, minimum noise effects, less dimensionality and lower computational complexity. A novel fusion approach is also proposed to combine the information related to all pixel attributes, before the descriptor extraction stage. Existing kernel descriptors are based only on gradient, colour and shape information. In this research project, a texture-based kernel descriptor is proposed by modifying an existing popular global texture descriptor. Finally, all the contributions are evaluated in an integrated system. The performances of the proposed methods are qualitatively and quantitatively evaluated on two to four different publicly available image databases. The experimental results show that the proposed methods are more effective and efficient in image representation than existing benchmark methods.
Doctor of Philosophy
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Nygaard, Ranveig. "Shortest path methods in representation and compression of signals and image contours." Doctoral thesis, Norwegian University of Science and Technology, Department of Electronics and Telecommunications, 2000. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-1182.

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Signal compression is an important problem encountered in many applications. Various techniques have been proposed over the years for adressing the problem. The focus of the dissertation is on signal representation and compression by the use of optimization theory, more shortest path methods.

Several new signal compression algorithms are presented. They are based on the coding of line segments which are used to spproximate, and thereby represent, the signal. These segments are fit in a way that is optimal given some constraints on the solution. By formulating the compession problem as a graph theory problem, shortest path methods can be applied in order to yeild optimal compresson with respect to the given constraints.

The approaches focused on in this dissertaion mainly have their origin in ECG comression and is often referred to as time domain compression methods. Coding by time domain methods is based on the idea of extracting a subset of significant signals samples to represent the signal. The key to a successful algoritm is a good rule for determining the most significant samples. Between any two succeeding samples in the extracted smaple set, different functions are applied in reconstruction of the signal. These functions are fitted in a wy that guaratees minimal reconstruction error under the gien constraints. Two main categories of compression schemes are developed:

1. Interpolating methods, in which it is insisted on equality between the original and reconstructed signal at the points of extraction.

2. Non-interpolating methods, where the inerpolatian restriction is released.

Both first and second order polynomials are used in reconstruction of the signal. There is solso developed an approach were multiple error measures are applied within one compression algorithm.

The approach of extracting the most significant smaples are further developed by measuring the samples in terms of the number of bits needed to encode such samples. This way we develop an approach which is optimal in the ratedistortion sense.

Although the approaches developed are applicable to any type of signal, the focus of this dissertaion is on the compression of electrodiogram (ECG) signals and image contours, ECG signal compression has traditionally been

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Sampaio, de Rezende Rafael. "New methods for image classification, image retrieval and semantic correspondence." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEE068/document.

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Le problème de représentation d’image est au cœur du domaine de vision. Le choix de représentation d’une image change en fonction de la tâche que nous voulons étudier. Un problème de recherche d’image dans des grandes bases de données exige une représentation globale compressée, alors qu’un problème de segmentation sémantique nécessite une carte de partitionnement de ses pixels. Les techniques d’apprentissage statistique sont l’outil principal pour la construction de ces représentations. Dans ce manuscrit, nous abordons l’apprentissage des représentations visuels dans trois problèmes différents : la recherche d’image, la correspondance sémantique et classification d’image. Premièrement, nous étudions la représentation vectorielle de Fisher et sa dépendance sur le modèle de mélange Gaussien employé. Nous introduisons l’utilisation de plusieurs modèles de mélange Gaussien pour différents types d’arrière-plans, e.g., différentes catégories de scènes, et analyser la performance de ces représentations pour objet classification et l’impact de la catégorie de scène en tant que variable latente. Notre seconde approche propose une extension de la représentation l’exemple SVM pipeline. Nous montrons d’abord que, en remplaçant la fonction de perte de la SVM par la perte carrée, on obtient des résultats similaires à une fraction de le coût de calcul. Nous appelons ce modèle la « square-loss exemplar machine », ou SLEM en anglais. Nous introduisons une variante de SLEM à noyaux qui bénéficie des même avantages computationnelles mais affiche des performances améliorées. Nous présentons des expériences qui établissent la performance et l’efficacité de nos méthodes en utilisant une grande variété de représentations de base et de jeux de données de recherche d’images. Enfin, nous proposons un réseau neuronal profond pour le problème de l’établissement sémantique correspondance. Nous utilisons des boîtes d’objets en tant qu’éléments de correspondance pour construire une architecture qui apprend simultanément l’apparence et la cohérence géométrique. Nous proposons de nouveaux scores géométriques de cohérence adaptés à l’architecture du réseau de neurones. Notre modèle est entrainé sur des paires d’images obtenues à partir des points-clés d’un jeu de données de référence et évaluées sur plusieurs ensembles de données, surpassant les architectures d’apprentissage en profondeur récentes et méthodes antérieures basées sur des caractéristiques artisanales. Nous terminons la thèse en soulignant nos contributions et en suggérant d’éventuelles directions de recherche futures
The problem of image representation is at the heart of computer vision. The choice of feature extracted of an image changes according to the task we want to study. Large image retrieval databases demand a compressed global vector representing each image, whereas a semantic segmentation problem requires a clustering map of its pixels. The techniques of machine learning are the main tool used for the construction of these representations. In this manuscript, we address the learning of visual features for three distinct problems: Image retrieval, semantic correspondence and image classification. First, we study the dependency of a Fisher vector representation on the Gaussian mixture model used as its codewords. We introduce the use of multiple Gaussian mixture models for different backgrounds, e.g. different scene categories, and analyze the performance of these representations for object classification and the impact of scene category as a latent variable. Our second approach proposes an extension to the exemplar SVM feature encoding pipeline. We first show that, by replacing the hinge loss by the square loss in the ESVM cost function, similar results in image retrieval can be obtained at a fraction of the computational cost. We call this model square-loss exemplar machine, or SLEM. Secondly, we introduce a kernelized SLEM variant which benefits from the same computational advantages but displays improved performance. We present experiments that establish the performance and efficiency of our methods using a large array of base feature representations and standard image retrieval datasets. Finally, we propose a deep neural network for the problem of establishing semantic correspondence. We employ object proposal boxes as elements for matching and construct an architecture that simultaneously learns the appearance representation and geometric consistency. We propose new geometrical consistency scores tailored to the neural network’s architecture. Our model is trained on image pairs obtained from keypoints of a benchmark dataset and evaluated on several standard datasets, outperforming both recent deep learning architectures and previous methods based on hand-crafted features. We conclude the thesis by highlighting our contributions and suggesting possible future research directions
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Budinich, Renato [Verfasser], Gerlind [Akademischer Betreuer] Plonka-Hoch, Gerlind [Gutachter] Plonka-Hoch, and Armin [Gutachter] Iske. "Adaptive Multiscale Methods for Sparse Image Representation and Dictionary Learning / Renato Budinich ; Gutachter: Gerlind Plonka-Hoch, Armin Iske ; Betreuer: Gerlind Plonka-Hoch." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2019. http://d-nb.info/1175625396/34.

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Jia, Yue Verfasser], Timon [Akademischer Betreuer] Rabczuk, Klaus [Gutachter] [Gürlebeck, and Alessandro [Gutachter] Reali. "Methods based on B-splines for model representation, numerical analysis and image registration / Yue Jia ; Gutachter: Klaus Gürlebeck, Alessandro Reali ; Betreuer: Timon Rabczuk." Weimar : Institut für Strukturmechanik, 2015. http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20151210-24849.

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Jia, Yue [Verfasser], Timon [Akademischer Betreuer] Rabczuk, Klaus [Gutachter] Gürlebeck, and Alessandro [Gutachter] Reali. "Methods based on B-splines for model representation, numerical analysis and image registration / Yue Jia ; Gutachter: Klaus Gürlebeck, Alessandro Reali ; Betreuer: Timon Rabczuk." Weimar : Institut für Strukturmechanik, 2015. http://d-nb.info/1116366770/34.

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Sjöberg, Oscar. "Evaluating Image Compression Methods on Two DimensionalHeight Representations." Thesis, Linköpings universitet, Informationskodning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-171227.

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Wei, Qi. "Bayesian fusion of multi-band images : A powerful tool for super-resolution." Phd thesis, Toulouse, INPT, 2015. http://oatao.univ-toulouse.fr/14398/1/wei.pdf.

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Hyperspectral (HS) imaging, which consists of acquiring a same scene in several hundreds of contiguous spectral bands (a three dimensional data cube), has opened a new range of relevant applications, such as target detection [MS02], classification [C.-03] and spectral unmixing [BDPD+12]. However, while HS sensors provide abundant spectral information, their spatial resolution is generally more limited. Thus, fusing the HS image with other highly resolved images of the same scene, such as multispectral (MS) or panchromatic (PAN) images is an interesting problem. The problem of fusing a high spectral and low spatial resolution image with an auxiliary image of higher spatial but lower spectral resolution, also known as multi-resolution image fusion, has been explored for many years [AMV+11]. From an application point of view, this problem is also important as motivated by recent national programs, e.g., the Japanese next-generation space-borne hyperspectral image suite (HISUI), which fuses co-registered MS and HS images acquired over the same scene under the same conditions [YI13]. Bayesian fusion allows for an intuitive interpretation of the fusion process via the posterior distribution. Since the fusion problem is usually ill-posed, the Bayesian methodology offers a convenient way to regularize the problem by defining appropriate prior distribution for the scene of interest. The aim of this thesis is to study new multi-band image fusion algorithms to enhance the resolution of hyperspectral image. In the first chapter, a hierarchical Bayesian framework is proposed for multi-band image fusion by incorporating forward model, statistical assumptions and Gaussian prior for the target image to be restored. To derive Bayesian estimators associated with the resulting posterior distribution, two algorithms based on Monte Carlo sampling and optimization strategy have been developed. In the second chapter, a sparse regularization using dictionaries learned from the observed images is introduced as an alternative of the naive Gaussian prior proposed in Chapter 1. instead of Gaussian prior is introduced to regularize the ill-posed problem. Identifying the supports jointly with the dictionaries circumvented the difficulty inherent to sparse coding. To minimize the target function, an alternate optimization algorithm has been designed, which accelerates the fusion process magnificently comparing with the simulation-based method. In the third chapter, by exploiting intrinsic properties of the blurring and downsampling matrices, a much more efficient fusion method is proposed thanks to a closed-form solution for the Sylvester matrix equation associated with maximizing the likelihood. The proposed solution can be embedded into an alternating direction method of multipliers or a block coordinate descent method to incorporate different priors or hyper-priors for the fusion problem, allowing for Bayesian estimators. In the last chapter, a joint multi-band image fusion and unmixing scheme is proposed by combining the well admitted linear spectral mixture model and the forward model. The joint fusion and unmixing problem is solved in an alternating optimization framework, mainly consisting of solving a Sylvester equation and projecting onto a simplex resulting from the non-negativity and sum-to-one constraints. The simulation results conducted on synthetic and semi-synthetic images illustrate the advantages of the developed Bayesian estimators, both qualitatively and quantitatively.
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Slobodan, Dražić. "Shape Based Methods for Quantification and Comparison of Object Properties from Their Digital Image Representations." Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2019. https://www.cris.uns.ac.rs/record.jsf?recordId=107871&source=NDLTD&language=en.

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The thesis investigates development, improvement and evaluation of methods for quantitative characterization of objects from their digital images and similarity measurements between digital images. Methods for quantitative characterization of objects from their digital images are increasingly used in applications in which error can have crtical consequences, but the traditional methods for shape quantification are of low precision and accuracy. In the thesis is shown that the coverage of a pixel by a shape can be used to highly improve the accuracy and precision of using digital images to estimate the maximal distance between objects furthest points measured in a given direction. It is highly desirable that a distance measure between digital images can be related to a certain shape property and morphological operations are used when defining a distance for this purpose. Still, the distances defined in this manner turns out to be insufficiently sensitive to relevant data representing shape properties in images. We show that the idea of adaptive mathematical morphology can be used successfully to overcome problems related to sensitivity of distances defined via morphological operations when comparing objects from their digital image representations.
У тези су размотрени развој, побољшање и евалуација метода за квантитативну карактеризацију објеката приказаних дигиталним сликама, као и мере растојања између дигиталних слика. Методе за квантитативну карактеризацију објеката представљених дигиталним сликама се  све више користе у применама у којима грешка може имати критичне последице, а традиционалне методе за  квантитативну карактеризацију су мале прецизности и тачности. У тези се показује да се коришћењем информације о покривеност пиксела обликом може значајно побољшати прецизност и тачност оцене растојања између две најудаљеније тачке облика мерено у датом правцу. Веома је пожељно да мера растојања између дигиталних слика може да се веже за одређену особину облика и морфолошке операције се користе приликом дефинисања растојања у ту сврху. Ипак, растојања дефинисана на овај начин показују се недовољно осетљива на релевантне податке дигиталних слика који представљају особине облика. У тези се показује да идеја адаптивне математичке морфологије може успешно да се користи да би се превазишао поменути  проблем осетљивости растојања дефинисаних користећи морфолошке операције.
U tezi su razmotreni razvoj, poboljšanje i evaluacija metoda za kvantitativnu karakterizaciju objekata prikazanih digitalnim slikama, kao i mere rastojanja između digitalnih slika. Metode za kvantitativnu karakterizaciju objekata predstavljenih digitalnim slikama se  sve više koriste u primenama u kojima greška može imati kritične posledice, a tradicionalne metode za  kvantitativnu karakterizaciju su male preciznosti i tačnosti. U tezi se pokazuje da se korišćenjem informacije o pokrivenost piksela oblikom može značajno poboljšati preciznost i tačnost ocene rastojanja između dve najudaljenije tačke oblika mereno u datom pravcu. Veoma je poželjno da mera rastojanja između digitalnih slika može da se veže za određenu osobinu oblika i morfološke operacije se koriste prilikom definisanja rastojanja u tu svrhu. Ipak, rastojanja definisana na ovaj način pokazuju se nedovoljno osetljiva na relevantne podatke digitalnih slika koji predstavljaju osobine oblika. U tezi se pokazuje da ideja adaptivne matematičke morfologije može uspešno da se koristi da bi se prevazišao pomenuti  problem osetljivosti rastojanja definisanih koristeći morfološke operacije.
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Books on the topic "Image representation methods"

1

Florack, Luc, Remco Duits, Geurt Jongbloed, Marie-Colette van Lieshout, and Laurie Davies, eds. Mathematical Methods for Signal and Image Analysis and Representation. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-4471-2353-8.

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Remco, Duits, Jongbloed Geurt, Lieshout Marie-Colette, Davies Laurie, and SpringerLink (Online service), eds. Mathematical Methods for Signal and Image Analysis and Representation. London: Springer London, 2012.

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Martial, Hebert, National Science Foundation (U.S.), and United States. Advanced Research Projects Agency., eds. Object representation in computer vision: International NSF-ARPA Workshop, New York City, NY, USA, December 5-7, 1994 : proceedings. Berlin: Springer, 1995.

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ECCV '96 International Workshop (1996 Cambridge, England). Object representation in computer vision II: ECCV '96 International Workshop, Cambridge, UK, April 13-14, 1996 : proceedings. Berlin: Springer, 1996.

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Pink, Sarah. Doing visual ethnography: Images, media, and representation in research. London: Sage, 2001.

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Doing visual ethnography: Images, media and representation in research. 2nd ed. London: SAGE, 2007.

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Doing visual ethnography: Images, media, and representation in research. London: Sage, 2001.

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Dibazar, Pedram, and Judith Naeff, eds. Visualizing the Street. NL Amsterdam: Amsterdam University Press, 2018. http://dx.doi.org/10.5117/9789462984356.

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From user-generated images of streets to professional architectural renderings, and from digital maps and drone footages to representations of invisible digital ecologies, this collection of essays analyses the emergent practices of visualizing the street. Today, advancements in digital technologies of the image have given rise to the production and dissemination of imagery of streets and urban realities in multiple forms. The ubiquitous presence of digital visualizations has in turn created new forms of urban practice and modes of spatial encounter. Everyone who carries a smartphone not only plays an increasingly significant role in the production, editing and circulation of images of the street, but also relies on those images to experience urban worlds and to navigate in them. Such entangled forms of image-making and image-sharing have constructed new imaginaries of the street and have had a significant impact on the ways in which contemporary and future streets are understood, imagined, documented, navigated, mediated and visualized. Visualizing the Street investigates the social and cultural significance of these new developments at the intersection of visual culture and urban space. The interdisciplinary essays provide new concepts, theories and research methods that combine close analyses of street images and imaginaries with the study of the practices of their production and circulation. The book covers a wide range of visible and invisible geographies — From Hong Kong’s streets to Rio’s favelas, from Sydney’s suburbs to London’s street markets, and from Damascus’ war-torn streets to Istanbul’s sidewalks — and engages with multiple ways in which visualizations of the street function to document street protests and urban change, to build imaginaries of urban communities and alternate worlds, and to help navigate streetscapes.
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Robert, Hopkins. Picture, image and experience: A philosophical inquiry. Cambridge: Cambridge University Press, 1998.

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Picture, image and experience: A philosophical inquiry. Cambridge: Cambridge University Press, 1998.

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Book chapters on the topic "Image representation methods"

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Kanatani, Kenichi. "Representation of 3D Rotations." In Group-Theoretical Methods in Image Understanding, 197–235. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-61275-6_6.

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Rosário Lucas, Luís Filipe, Eduardo Antônio Barros da Silva, Sérgio Manuel Maciel de Faria, Nuno Miguel Morais Rodrigues, and Carla Liberal Pagliari. "Sparse Representation Methods for Image Prediction." In Efficient Predictive Algorithms for Image Compression, 97–115. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51180-1_5.

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DeCost, Brian L., and Elizabeth A. Holm. "Computer Vision for Microstructural Image Representation: Methods and Applications." In Statistical Methods for Materials Science, 241–58. Boca Raton, Florida : CRC Press, [2019]: CRC Press, 2019. http://dx.doi.org/10.1201/9781315121062-18.

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Zhang, Qinghui, and Yi Chen. "A Survey of Literature Analysis Methods Based on Representation Learning." In Image and Graphics Technologies and Applications, 249–63. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5096-4_19.

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Dobrosotskaya, J., M. Ehler, E. King, R. Bonner, and W. Czaja. "Sparse Representation and Variational Methods in Retinal Image Processing." In IFMBE Proceedings, 361–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14998-6_92.

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Park, Chul-Hyun, Joon-Jae Lee, Sang-Keun Oh, Young-Chul Song, Doo-Hyun Choi, and Kil-Houm Park. "Iris Feature Extraction and Matching Based on Multiscale and Directional Image Representation." In Scale Space Methods in Computer Vision, 576–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44935-3_40.

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Zhou, Bolei. "Interpreting Generative Adversarial Networks for Interactive Image Generation." In xxAI - Beyond Explainable AI, 167–75. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04083-2_9.

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AbstractSignificant progress has been made by the advances in Generative Adversarial Networks (GANs) for image generation. However, there lacks enough understanding of how a realistic image is generated by the deep representations of GANs from a random vector. This chapter gives a summary of recent works on interpreting deep generative models. The methods are categorized into the supervised, the unsupervised, and the embedding-guided approaches. We will see how the human-understandable concepts that emerge in the learned representation can be identified and used for interactive image generation and editing.
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Ashour, Mohammed W., Fatimah Khalid, Alfian Abdul Halin, Samy H. Darwish, and M. M. Abdulrazzaq. "A Review on Steel Surface Image Features Extraction and Representation Methods." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 239–50. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60036-5_17.

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Deng, Limiao. "Research on insect pest image detection and recognition based on bio-inspired methods." In Cognitive and Neural Modelling for Visual Information Representation and Memorization, 205–24. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003281641-8.

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Pajarola, Renato, Susanne K. Suter, Rafael Ballester-Ripoll, and Haiyan Yang. "Tensor Approximation for Multidimensional and Multivariate Data." In Mathematics and Visualization, 73–98. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56215-1_4.

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AbstractTensor decomposition methods and multilinear algebra are powerful tools to cope with challenges around multidimensional and multivariate data in computer graphics, image processing and data visualization, in particular with respect to compact representation and processing of increasingly large-scale data sets. Initially proposed as an extension of the concept of matrix rank for 3 and more dimensions, tensor decomposition methods have found applications in a remarkably wide range of disciplines. We briefly review the main concepts of tensor decompositions and their application to multidimensional visual data. Furthermore, we will include a first outlook on porting these techniques to multivariate data such as vector and tensor fields.
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Conference papers on the topic "Image representation methods"

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Xie, Ruobing, Zhiyuan Liu, Huanbo Luan, and Maosong Sun. "Image-embodied Knowledge Representation Learning." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/438.

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Entity images could provide significant visual information for knowledge representation learning. Most conventional methods learn knowledge representations merely from structured triples, ignoring rich visual information extracted from entity images. In this paper, we propose a novel Image-embodied Knowledge Representation Learning model (IKRL), where knowledge representations are learned with both triple facts and images. More specifically, we first construct representations for all images of an entity with a neural image encoder. These image representations are then integrated into an aggregated image-based representation via an attention-based method. We evaluate our IKRL models on knowledge graph completion and triple classification. Experimental results demonstrate that our models outperform all baselines on both tasks, which indicates the significance of visual information for knowledge representations and the capability of our models in learning knowledge representations with images.
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Bouyerbou, H., S. Oukid, N. Benblidia, and K. Bechkoum. "Hybrid image representation methods for automatic image annotation: A survey." In 2012 International Conference on Signals and Electronic Systems (ICSES 2012). IEEE, 2012. http://dx.doi.org/10.1109/icses.2012.6382246.

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Tan, Ruiguang. "Research methods of product perceptual image recognition in Kansei Engineering." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001764.

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The paper studies the image recognition in Kansei engineering. Firstly, the development history of Kansei Engineering is reviewed. The early research object of ergonomics is the explicit physiological and psychological reaction generated after the interaction between human and artificial objects. This type of research object is characterized by objectivity, universality, stability and easiness to measure. Kansei Engineering extends the research object of ergonomics to the implicit psychological reaction generated by the interaction between human beings and artificial objects, which is characterized by subjectivity, difference, fuzziness and unpredictability.Secondly,the perceptual image of the product is discussed. Perceptual images in Kansei engineering are human's feelings and psychological expectations for artificial objects, as well as highly condensed and deep emotional activities. Different groups may have different perceptual images in different stages. In the study of perceptual image, it is necessary to clarify the subject and stage of the perceptual image. Thirdly, the method of product perceptual image recognition is studied. Perceptual image recognition can be divided into two stages: experiment and statistical analysis, which involves the acquisition, representation, modeling and mapping of user perceptual image with product modeling elements. Generally, the test method is to collect product sample pictures and product perceptual evaluation words, combine morphological analysis method and semantic difference method to form a questionnaire, and then select subjects for testing. A series of statistical analyses should be carried out on the questionnaire data, including factor analysis, cluster analysis, multidimensional scale analysis, artificial neural network analysis, etc. The aim of statistical analyses is to establish the mapping relationship between perceptual image and form. Finally, it points out the problems existing in perceptual image recognition, including "the purpose and subject of perceptual image recognition","Questions of subject representation". A more comprehensive research perspective and technical means should be established to study the above problems.
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Xiaojie Huang, Ben A. Lin, Colin B. Compas, Albert J. Sinusas, Lawrence H. Staib, and James S. Duncan. "Segmentation of left ventricles from echocardiographic sequences via sparse appearance representation." In 2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA). IEEE, 2012. http://dx.doi.org/10.1109/mmbia.2012.6164769.

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Özay, Evrim Korkmaz, and Metin Demiralp. "Combined small scale enhanced multivariance product representation (CSSEMPR) for image reconstruction." In INTERNATIONAL CONFERENCE OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2015 (ICCMSE 2015). AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4938938.

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Bui, Manh-Quan, Viet-Hang Duong, Yung-Hui Li, Tzu-Chiang Tai, and Jia-Ching Wang. "Image Representation Using Supervised and Unsupervised Learning Methods on Complex Domain." In ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8462222.

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Eichmann, G., and M. Jankowski. "Surface Representation and Shape Description of Solid Bodies." In Machine Vision. Washington, D.C.: Optica Publishing Group, 1987. http://dx.doi.org/10.1364/mv.1987.fb5.

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Modern image data collection techniques such as computed tomography, scanning electron microscopy, and digital stereoscopy make possible the computation of three-dimensional (3D) structure of scenes or objects. Such images are encountered in medical imaging, modeling of physical phenomena, geometric modeling and computer vision. There is a growing need in all these fields for efficient representation schemes of the 3D digital image. Of special interest in image description and modeling are surface methods, of which there are two general types. One is an interpolative method which expresses the surface by subdivisions known as surface primitives or patches (1,2). It is used in computer aided geometric design and computer graphics. The other is concerned with describing surfaces "in the large", mainly in terms of shape descriptors or globaly defined functions of the boundary(3). In either case parametric techniques are preferred since the representations are of lower dimensionality, are axes independent, and unambiguous for multivalued surfaces. Unfortunately 3D spatial data is most often encountered as objects in cellular space and the problem of parametrizing an arbitrary surface cannot in general be solved(4).
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Kim, Seung-Goo, Moo K. Chung, Stacey M. Schaefer, Carien van Reekum, and Richard J. Davidson. "Sparse shape representation using the Laplace-Beltrami eigenfunctions and its application to modeling subcortical structures." In 2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA). IEEE, 2012. http://dx.doi.org/10.1109/mmbia.2012.6164736.

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Zhao, Sicheng, Guiguang Ding, Qingming Huang, Tat-Seng Chua, Björn W. Schuller, and Kurt Keutzer. "Affective Image Content Analysis: A Comprehensive Survey." 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.2018/780.

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Images can convey rich semantics and induce strong emotions in viewers. Recently, with the explosive growth of visual data, extensive research efforts have been dedicated to affective image content analysis (AICA). In this paper, we review the state-of-the-art methods comprehensively with respect to two main challenges -- affective gap and perception subjectivity. We begin with an introduction to the key emotion representation models that have been widely employed in AICA. Available existing datasets for performing evaluation are briefly described. We then summarize and compare the representative approaches on emotion feature extraction, personalized emotion prediction, and emotion distribution learning. Finally, we discuss some future research directions.
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Chen, Tianshui, Liang Lin, Riquan Chen, Yang Wu, and Xiaonan Luo. "Knowledge-Embedded Representation Learning for Fine-Grained Image Recognition." 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.2018/87.

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Humans can naturally understand an image in depth with the aid of rich knowledge accumulated from daily lives or professions. For example, to achieve fine-grained image recognition (e.g., categorizing hundreds of subordinate categories of birds) usually requires a comprehensive visual concept organization including category labels and part-level attributes. In this work, we investigate how to unify rich professional knowledge with deep neural network architectures and propose a Knowledge-Embedded Representation Learning (KERL) framework for handling the problem of fine-grained image recognition. Specifically, we organize the rich visual concepts in the form of knowledge graph and employ a Gated Graph Neural Network to propagate node message through the graph for generating the knowledge representation. By introducing a novel gated mechanism, our KERL framework incorporates this knowledge representation into the discriminative image feature learning, i.e., implicitly associating the specific attributes with the feature maps. Compared with existing methods of fine-grained image classification, our KERL framework has several appealing properties: i) The embedded high-level knowledge enhances the feature representation, thus facilitating distinguishing the subtle differences among subordinate categories. ii) Our framework can learn feature maps with a meaningful configuration that the highlighted regions finely accord with the nodes (specific attributes) of the knowledge graph. Extensive experiments on the widely used Caltech-UCSD bird dataset demonstrate the superiority of our KERL framework over existing state-of-the-art methods.
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Reports on the topic "Image representation methods"

1

Varastehpour, Soheil, Hamid Sharifzadeh, and Iman Ardekani. A Comprehensive Review of Deep Learning Algorithms. Unitec ePress, 2021. http://dx.doi.org/10.34074/ocds.092.

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Deep learning algorithms are a subset of machine learning algorithms that aim to explore several levels of the distributed representations from the input data. Recently, many deep learning algorithms have been proposed to solve traditional artificial intelligence problems. In this review paper, some of the up-to-date algorithms of this topic in the field of computer vision and image processing are reviewed. Following this, a brief overview of several different deep learning methods and their recent developments are discussed.
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Yan, Yujie, and Jerome F. Hajjar. Automated Damage Assessment and Structural Modeling of Bridges with Visual Sensing Technology. Northeastern University, May 2021. http://dx.doi.org/10.17760/d20410114.

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Recent advances in visual sensing technology have gained much attention in the field of bridge inspection and management. Coupled with advanced robotic systems, state-of-the-art visual sensors can be used to obtain accurate documentation of bridges without the need for any special equipment or traffic closure. The captured visual sensor data can be post-processed to gather meaningful information for the bridge structures and hence to support bridge inspection and management. However, state-of-the-practice data postprocessing approaches require substantial manual operations, which can be time-consuming and expensive. The main objective of this study is to develop methods and algorithms to automate the post-processing of the visual sensor data towards the extraction of three main categories of information: 1) object information such as object identity, shapes, and spatial relationships - a novel heuristic-based method is proposed to automate the detection and recognition of main structural elements of steel girder bridges in both terrestrial and unmanned aerial vehicle (UAV)-based laser scanning data. Domain knowledge on the geometric and topological constraints of the structural elements is modeled and utilized as heuristics to guide the search as well as to reject erroneous detection results. 2) structural damage information, such as damage locations and quantities - to support the assessment of damage associated with small deformations, an advanced crack assessment method is proposed to enable automated detection and quantification of concrete cracks in critical structural elements based on UAV-based visual sensor data. In terms of damage associated with large deformations, based on the surface normal-based method proposed in Guldur et al. (2014), a new algorithm is developed to enhance the robustness of damage assessment for structural elements with curved surfaces. 3) three-dimensional volumetric models - the object information extracted from the laser scanning data is exploited to create a complete geometric representation for each structural element. In addition, mesh generation algorithms are developed to automatically convert the geometric representations into conformal all-hexahedron finite element meshes, which can be finally assembled to create a finite element model of the entire bridge. To validate the effectiveness of the developed methods and algorithms, several field data collections have been conducted to collect both the visual sensor data and the physical measurements from experimental specimens and in-service bridges. The data were collected using both terrestrial laser scanners combined with images, and laser scanners and cameras mounted to unmanned aerial vehicles.
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