Journal articles: 'Transformation of image'

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Kim, J., T. Kim, D. Shin, and S. H. Kim. "ROBUST MOSAICKING OF UAV IMAGES WITH NARROW OVERLAPS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 6, 2016): 879–83. http://dx.doi.org/10.5194/isprsarchives-xli-b1-879-2016.

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This paper considers fast and robust mosaicking of UAV images under a circumstance that each UAV images have very narrow overlaps in-between. Image transformation for image mosaicking consists of two estimations: relative transformations and global transformations. For estimating relative transformations between adjacent images, projective transformation is widely considered. For estimating global transformations, panoramic constraint is widely used. While perspective transformation is a general transformation model in 2D-2D transformation, this may not be optimal with weak stereo geometry such as images with narrow overlaps. While panoramic constraint works for reliable conversion of global transformation for panoramic image generation, this constraint is not applicable to UAV images in linear motions. For these reasons, a robust approach is investigated to generate a high quality mosaicked image from narrowly overlapped UAV images. For relative transformations, several transformation models were considered to ensure robust estimation of relative transformation relationship. Among them were perspective transformation, affine transformation, coplanar relative orientation, and relative orientation with reduced adjustment parameters. Performance evaluation for each transformation model was carried out. The experiment results showed that affine transformation and adjusted coplanar relative orientation were superior to others in terms of stability and accuracy. For global transformation, we set initial approximation by converting each relative transformation to a common transformation with respect to a reference image. In future work, we will investigate constrained relative orientation for enhancing geometric accuracy of image mosaicking and bundle adjustments of each relative transformation model for optimal global transformation.

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Kim, J., T. Kim, D. Shin, and S. H. Kim. "ROBUST MOSAICKING OF UAV IMAGES WITH NARROW OVERLAPS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 6, 2016): 879–83. http://dx.doi.org/10.5194/isprs-archives-xli-b1-879-2016.

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This paper considers fast and robust mosaicking of UAV images under a circumstance that each UAV images have very narrow overlaps in-between. Image transformation for image mosaicking consists of two estimations: relative transformations and global transformations. For estimating relative transformations between adjacent images, projective transformation is widely considered. For estimating global transformations, panoramic constraint is widely used. While perspective transformation is a general transformation model in 2D-2D transformation, this may not be optimal with weak stereo geometry such as images with narrow overlaps. While panoramic constraint works for reliable conversion of global transformation for panoramic image generation, this constraint is not applicable to UAV images in linear motions. For these reasons, a robust approach is investigated to generate a high quality mosaicked image from narrowly overlapped UAV images. For relative transformations, several transformation models were considered to ensure robust estimation of relative transformation relationship. Among them were perspective transformation, affine transformation, coplanar relative orientation, and relative orientation with reduced adjustment parameters. Performance evaluation for each transformation model was carried out. The experiment results showed that affine transformation and adjusted coplanar relative orientation were superior to others in terms of stability and accuracy. For global transformation, we set initial approximation by converting each relative transformation to a common transformation with respect to a reference image. In future work, we will investigate constrained relative orientation for enhancing geometric accuracy of image mosaicking and bundle adjustments of each relative transformation model for optimal global transformation.

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Sempio, J. N. H., R. K. D. Aranas, B. P. Lim, B. J. Magallon, M. E. A. Tupas, and I. A. Ventura. "ASSESSMENT OF DIFFERENT IMAGE TRANSFORMATION METHODS ON DIWATA-1 SMI IMAGES USING STRUCTURAL SIMILARITY MEASURE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W19 (December 23, 2019): 393–400. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w19-393-2019.

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Abstract. This paper aims to provide a qualitative assessment of different image transformation parameters as applied on images taken by the spaceborne multispectral imager (SMI) sensor installed in Diwata-1, the Philippines’ first Earth observation microsatellite, with the aim of determining the order of transformation that is sufficient for operationalization purposes. Images of the Palawan area were subjected to different image transformations by manual georeferencing using QGIS 3, and cloud masks generated and applied to remove the effects of clouds. The resulting images were then subjected to structural similarity (SSIM) tests using resampled and cloud masked Landsat 8 images of the same area to generate SSIM indices, which are then used as a quantitative means to assess the best performing transformation. The results of this study point to all transformed images having good SSIM ratings with their Landsat 8 counterparts, indicating that features shown in a Diwata-1 SMI image are structurally similar to the same features in a resampled Landsat 8 data. This implies that for Diwata-1 data processing operationalization purposes, higher order transformations, with the necessary effort to implement them, offer little advantage to lower order counterparts.

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Kim, Jae-In, Hyun-cheol Kim, and Taejung Kim. "Robust Mosaicking of Lightweight UAV Images Using Hybrid Image Transformation Modeling." Remote Sensing 12, no. 6 (March 20, 2020): 1002. http://dx.doi.org/10.3390/rs12061002.

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This paper proposes a robust feature-based mosaicking method that can handle images obtained by lightweight unmanned aerial vehicles (UAVs). The imaging geometry of small UAVs can be characterized by unstable flight attitudes and low flight altitudes. These can reduce mosaicking performance by causing insufficient overlaps, tilted images, and biased tiepoint distributions. To solve these problems in the mosaicking process, we introduce the tiepoint area ratio (TAR) as a geometric stability indicator and orthogonality as an image deformation indicator. The proposed method estimates pairwise transformations with optimal transformation models derived by geometric stability analysis between adjacent images. It then estimates global transformations from optimal pairwise transformations that maximize geometric stability between adjacent images and minimize mosaic deformation. The valid criterion for the TAR in selecting an optimal transformation model was found to be about 0.3 from experiments with two independent image datasets. The results of a performance evaluation showed that the problems caused by the imaging geometry characteristics of small UAVs could actually occur in image datasets and showed that the proposed method could reliably produce image mosaics for image datasets obtained in both general and extreme imaging environments.

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Sarid, Orly, and Ephrat Huss. "Image formation and image transformation." Arts in Psychotherapy 38, no. 4 (September 2011): 252–55. http://dx.doi.org/10.1016/j.aip.2011.07.001.

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Liu, Hongbing, Gengyi Liu, Xuewen Ma, and Daohua Liu. "Training dictionary by granular computing with L∞-norm for patch granule–based image denoising." Journal of Algorithms & Computational Technology 12, no. 2 (March 2, 2018): 136–46. http://dx.doi.org/10.1177/1748301818761131.

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Considering the objects by different granularity reflects the recognition common law of people, granular computing embodies the transformation between different granularity spaces. We present the image denoising algorithm by using the dictionary trained by granular computing with L∞-norm, which realizes three transformations, (1) the transformation from image space to patch granule space, (2) the transformation between granule spaces with different granularities, and (3) the transformation from patch granule space to image space. We demonstrate that the granular computing with L∞-norm achieved the comparable peak signal to noise ratio (PSNR) measure compared with BM3D and patch group prior based denoising for eight natural images.

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Wang, Nannan, Jie Li, Dacheng Tao, Xuelong Li, and Xinbo Gao. "Heterogeneous image transformation." Pattern Recognition Letters 34, no. 1 (January 2013): 77–84. http://dx.doi.org/10.1016/j.patrec.2012.04.005.

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Hou, Dongdong, Weiming Zhang, and Nenghai Yu. "Image camouflage by reversible image transformation." Journal of Visual Communication and Image Representation 40 (October 2016): 225–36. http://dx.doi.org/10.1016/j.jvcir.2016.06.018.

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Mat Jizat, Jessnor Arif, Ahmad Fakhri Ab. Nasir, Anwar P.P Abdul Majeed, and Edmund Yuen. "Effect of Image Compression using Fast Fourier Transformation and Discrete Wavelet Transformation on Transfer Learning Wafer Defect Image Classification." MEKATRONIKA 2, no. 1 (June 5, 2020): 16–22. http://dx.doi.org/10.15282/mekatronika.v2i1.6704.

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Automated inspection machines for wafer defects usually captured thousands of images on a large scale to preserve the detail of defect features. However, most transfer learning architecture requires smaller images as input images. Thus, proper compression is required to preserve the defect features whilst maintaining an acceptable classification accuracy. This paper reports on the effect of image compression using Fast Fourier Transformation and Discrete Wavelet Transformation on transfer learning wafer defect image classification. A total of 500 images with 5 classes with 4 defect classes and 1 non-defect class were split to 60:20:20 ratio for training, validating and testing using InceptionV3 and Logistic Regression classifier. However, the input images were compressed using Fast Fourier Transformation and Discrete Wavelet Transformation using 4 level decomposition and Debauchies 4 wavelet family. The images were compressed by 50%, 75%, 90%, 95%, and 99%. As a result, the Fast Fourier Transformation compression show an increase from 89% to 94% in classification accuracy up to 95% compression, while Discrete Wavelet Transformation shows consistent classification accuracy throughout albeit diminishing image quality. From the experiment, it can be concluded that FFT and DWT image compression can be a reliable method for image compression for grayscale image classification as the image memory space drop 56.1% while classification accuracy increased by 5.6% with 95% FFT compression and memory space drop 55.6% while classification accuracy increased 2.2% with 50% DWT compression.

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G., Sindhu Madhuri, and Indra Gandhi M. P. "New Image Registration Techniques: Development and Comparative Analysis." International Journal of Emerging Research in Management and Technology 6, no. 7 (June 29, 2018): 146. http://dx.doi.org/10.23956/ijermt.v6i7.204.

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Design and Development of new Image Registration Techniques by using complex mathematical transformation functions are attempted in this research work as there is a requirement for the performance measurement of image registration complexity. The design and development of new image registration techniques are carried out with complex mathematical transformations of Radon and Slant functions due to their importance. And the rotation and translation geometric function are considered for better insight into the complex image registration process. The newly developed image registration techniques areevaluated and analyzed with openly available images of Lena, Cameraman and VegCrop. The accuracy as a performance measure of the newly developed image registration techniques are attempted to measure with popularly known metrics of RMSE, PSNR and Entropy. And the results obtained after successful image registration process are compared are presented. It is observed from the results that the developed new image registration techniques using Radon and Slant transformation functions with rotation and translation are superior and useful for the requirement and purpose in the digital image processing domain. Finally a research effort is made to development of new image registration techniques that are useful to extract intelligence embedded in the images with complex transformation function and an attempt is made to measure its performance also.

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Dixit, Karnika, and Mr Kamlesh Lakhwani. "A NOVEL METHOD OF COLOR IMAGE ENHANCEMENT BY COLOR SPACE TRANSFORMATION FOLLOWED BY GAMMA/LOGARITHMIC TRANSFORMATION." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 8, no. 1 (June 20, 2013): 707–11. http://dx.doi.org/10.24297/ijct.v8i1.3430.

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Visual enhancement of image plays a very important role in the field of medical imaging. Enhanced medical images are more suitable for analysis and proper diagnosis. We present a novel method of enhancement of color medical images in this paper. We are transforming color space of the image from RGB to HIS followed by application of logarithmic and gamma transformation on saturation and intensity component respectively. Hence we obtain a visually enhanced version of the original image. We have obtained excellent color medical image enhancement results presented in this paper.

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Saliby, Joe G. "Design and Implementation of Digital Image Transformation Algorithms." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (April 30, 2019): 623–31. http://dx.doi.org/10.31142/ijtsrd22918.

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WANG, XIUYING, and DAVID DAGAN FENG. "AUTOMATIC ELASTIC MEDICAL IMAGE REGISTRATION BASED ON IMAGE INTENSITY." International Journal of Image and Graphics 05, no. 02 (April 2005): 351–69. http://dx.doi.org/10.1142/s0219467805001793.

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An automatic elastic medical image registration approach is proposed, based on image intensity. The algorithm is divided into two steps. In Step 1, global affine registration is first used to establish an initial guess and the resulting images can be assumed to have only small local elastic deformations. The mapped images are then used as inputs in Step 2, during which, the study image is modeled as elastic sheet by being divided into sub-images. Moving the individual sub-image in the reference image, the local displacement vectors are found and the global elastic transformation is achieved by assimilating all of the local transformation into a continuous transformation. The algorithm has been validated by simulated data, noisy data and clinical tomographic data. Both experiments and theoretical analysis have demonstrated that the proposed algorithm has a superior computational performance and can register images automatically with an improved accuracy.

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Li, Dongyang, Lin Yang, Hongguang Zhang, Xiaolei Wang, Linru Ma, and Junchao Xiao. "Image-Based Insider Threat Detection via Geometric Transformation." Security and Communication Networks 2021 (September 13, 2021): 1–18. http://dx.doi.org/10.1155/2021/1777536.

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Insider threat detection has been a challenging task over decades; existing approaches generally employ the traditional generative unsupervised learning methods to produce normal user behavior model and detect significant deviations as anomalies. However, such approaches are insufficient in precision and computational complexity. In this paper, we propose a novel insider threat detection method, Image-based Insider Threat Detector via Geometric Transformation (IGT), which converts the unsupervised anomaly detection into supervised image classification task, and therefore the performance can be boosted via computer vision techniques. To illustrate, our IGT uses a novel image-based feature representation of user behavior by transforming audit logs into grayscale images. By applying multiple geometric transformations on these behavior grayscale images, IGT constructs a self-labelled dataset and then trains a behavior classifier to detect anomaly in a self-supervised manner. The motivation behind our proposed method is that images converted from normal behavior data may contain unique latent features which remain unchanged after geometric transformation, while malicious ones cannot. Experimental results on CERT dataset show that IGT outperforms the classical autoencoder-based unsupervised insider threat detection approaches, and improves the instance and user based Area under the Receiver Operating Characteristic Curve (AUROC) by 4% and 2%, respectively.

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Golub, Yu I. "COMPRESSION OF HIGH DYNAMIC RANGE OF SAR IMAGES." «System analysis and applied information science», no. 1 (June 12, 2018): 51–57. http://dx.doi.org/10.21122/2309-4923-2018-1-51-57.

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The paper presents results of our experiments on compression of the high dynamic range SAR images. The range is equal to 16-bit. Objectives of study were comparison of known approaches to compression of the high dynamic range images; selection of optimal parameters for compression algorithms, and selection of a no-reference measure for image quality assessment after compression. Tone-mapping transformations like gamma correction, Ashikhmin-operator, mu-transformation, as well as no-reference image quality assessment measures were tested. The results of the experiments are presented in the article. It was concluded that further research and analysis of various functions and approaches to compression of dynamic range of SAR images is necessary, since including in the article approaches do not give stable and positive results on all SAR images. It was also concluded that after transformation 16-bit images, it is very difficult to estimate which image is better, and it is necessary to use no-reference image quality assessment measure.

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Бродская, Юлия Алексеевна, and Светлана Ивановна Яковлева. "KONIGSBERG-KALININGRAD IMAGE TRANSFORMATION." Вестник Тверского государственного университета. Серия: География и геоэкология, no. 1(33) (March 23, 2021): 82–92. http://dx.doi.org/10.26456/2226-7719-2021-1-82-92.

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Цель исследования - анализ трансформации городского образа на примере Кёнигсберга-Калининграда. Новизна исследования - в применение градостроительной схемы К.Линча (1960) к анализу разновременной пространственной структуры крупного старого немецкого города Кёнигсберга и послевоенного (современного) Калининграда. The aim of the research is to analyze the transformation of the urban image on the example of Königsberg-Kaliningrad. The novelty of the research lies in the application of the urban planning scheme of K. Linch (1960) to the analysis of the multi-temporal spatial structure of the large old German city of Königsberg and post-war (modern) Kaliningrad.

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Barthel, Kai Uwe. "Entropy Constrained Fractal Image Coding." Fractals 05, supp01 (April 1997): 17–26. http://dx.doi.org/10.1142/s0218348x97000607.

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In this paper we present an entropy constrained fractal coding scheme. In order to get high compression rates, previous fractal coders used hierarchical coding schemes with variable range block sizes. Our scheme uses constant range block sizes, but the complexity of the fractal transformations is adapted to the image contents. The entropy of the fractal code can be significantly reduced by introducing geometrical codebooks of variable size and a variable order luminance transformation. We propose a luminance transformation consisting of a unification of fractal and transform coding. With this transformation both inter- and intra- block redundancy of an image can be exploited to get higher coding gain. The coding results obtained with our new scheme are superior compared to conventional fractal and transform coding schemes.

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Lee, Yun-Shan, Wen-Liang Hwang, and Xiaolin Tian. "Continuous piecewise affine transformation for image registration." International Journal of Wavelets, Multiresolution and Information Processing 13, no. 01 (January 2015): 1550006. http://dx.doi.org/10.1142/s021969131550006x.

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The image registration problem involves determining a geometric transformation to properly align images of interest. This paper proposes a transformation approach called Continuous Piecewise Affine Transformation (CPAT) to model the geometric distortion in images. The associated design methodology for the 2D registration problem is also discussed. Registration on CPAT has two advantages: (1) The optimal transformation has a closed form analytical solution; and (2) the transformation is invertible and transitive. Because of these advantages, CPAT could be used for real-time registration and progressive registration, where the registration speed and transitivity property are important for the successful completion of the tasks. The results of experiments demonstrate the efficacy of CPAT in resolving the image registration problem.

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Wu, Dan. "Reversible Data Hiding for Encrypted Image Based on Arnold Transformation." MATEC Web of Conferences 173 (2018): 03088. http://dx.doi.org/10.1051/matecconf/201817303088.

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A reversible data hiding scheme for encrypted image was proposed based on Arnold transformation. In this scheme, the original image was divided into four sub-images by sampling, the sub-images were scrambled by Arnold transformation using two secret keys, then the scrambled sub-images were reconstituted an encrypted image. Subsequently, additional data was embedded into the encrypted image by modifying the difference between two adjacent pixels. With an encrypted image containing additional data, the receiver can obtain a decrypt image using the decryption key. Meanwhile, with the aid of the decryption key and information hiding key, the receiver can pick the hiding information and recover the original image without any error. Experiment result shows that the proposed scheme can obtain a higher payload with good image quality.

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CHANG, CHIN-CHEN, JUN-CHOU CHUANG, and YIH-SHIN HU. "SIMILAR IMAGE RETRIEVAL BASED ON WAVELET TRANSFORMATION." International Journal of Wavelets, Multiresolution and Information Processing 02, no. 02 (June 2004): 111–20. http://dx.doi.org/10.1142/s021969130400038x.

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In this paper, a novel similar image retrieval scheme based on wavelet transformation will be presented. Our scheme is built upon a block-based query system. Our new scheme employs the wavelet transformation technique to transform each block in the spatial domain to the wavelet domain. Then, from each transformed block, the mean value and the edge types are extracted. These extracted features are then used to compute the similarity between a query image and the images in the database. In order to increase the similarity in the query result, the current block can be further divided into many sub-blocks, and then features can be extracted from these sub-blocks. Finally, the query result will be a set of ranked images in the database with respect to the query. According to our experiment, the proposed scheme can obtain satisfactory results.

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Pandurangan, Durai, R. Saravana Kumar, Lukas Gebremariam, L. Arulmurugan, and S. Tamilselvan. "Combined Gray Level Transformation Technique for Low Light Color Image Enhancement." Journal of Computational and Theoretical Nanoscience 18, no. 4 (April 1, 2021): 1221–26. http://dx.doi.org/10.1166/jctn.2021.9392.

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Insufficient and poor lightning conditions affect the quality of videos and images captured by the camcorders. The low quality images decrease the performances of computer vision systems in smart traffic, video surveillance, and other imaging systems applications. In this paper, combined gray level transformation technique is proposed to enhance the less quality of illuminated images. This technique is composed of log transformation, power law transformation and adaptive histogram equalization process to improve the low light illumination image estimated using HIS color model. Finally, the enhanced illumination image is blended with original reflectance image to get enhanced color image. This paper shows that the proposed algorithm on various weakly illuminated images is enhanced better and has taken reduced computation time than previous image processing techniques.

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Hocevar, Erwin, and Walter G. Kropatsch. "Inventing the Formula of the Trees: A Solution of the Representation of Self Similar Objects." Fractals 05, supp01 (April 1997): 51–64. http://dx.doi.org/10.1142/s0218348x97000632.

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Iterated Function Systems (IFS) seem to be used best to represent objects in the nature, because many of them are self similar. An IFS is a set of affine and contractive transformations. The union (so-called collage) of the subimages generated by transforming the whole image produces the image again - the self similar attractor of these transformations, which can be described by a binary image. For a fast and compact representation of those images, it would be desirable to calculate the transformations (the IFS-Codes) directly from the image that means to solve the inverse IFS-Problem. The solution presented in this paper will directly use the features of the self similar image. Subsets of the entire image and the subimage to be calculated are identified by the computation of the set difference between the pixels of the original and a rotated copy. The rotation and the scale factor of the transformation can be computed by the mapping of this two subsets onto each other, if the translation part - the fixed point - is predefined. The calculation of the transformation has to be repeated for each subimage. It will be proved, that with this method the IFS-Codes can be calculated for not convex, undistorted, and self similar images as long as the fixed point is known. An efficient algorithm for the identification of these fixed points within the image is introduced. Different ways to achieve this solutions are presented. In the conclusion the class of images, which can be coded by this method is defined, the results are pointed out, the advantages resp. the disadvantages of the method are evaluated, and possible ways to extend the method are discussed.

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Wang, Chaoyue, Chang Xu, Chaohui Wang, and Dacheng Tao. "Perceptual Adversarial Networks for Image-to-Image Transformation." IEEE Transactions on Image Processing 27, no. 8 (August 2018): 4066–79. http://dx.doi.org/10.1109/tip.2018.2836316.

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Grimes, David B., and Rajesh P. N. Rao. "Bilinear Sparse Coding for Invariant Vision." Neural Computation 17, no. 1 (January 1, 2005): 47–73. http://dx.doi.org/10.1162/0899766052530893.

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Recent algorithms for sparse coding and independent component analysis (ICA) have demonstrated how localized features can be learned from natural images. However, these approaches do not take image transformations into account. We describe an unsupervised algorithm for learning both localized features and their transformations directly from images using a sparse bilinear generative model. We show that from an arbitrary set of natural images, the algorithm produces oriented basis filters that can simultaneously represent features in an image and their transformations. The learned generative model can be used to translate features to different locations, thereby reducing the need to learn the same feature at multiple locations, a limitation of previous approaches to sparse coding and ICA. Our results suggest that by explicitly modeling the interaction between local image features and their transformations, the sparse bilinear approach can provide a basis for achieving transformation-invariant vision.

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Sujatha, M., G. Srilekha, Kommula Tina, Tella Sri Tulasi, and K. Harish. "Image Enhancement Using Wavelet Based Image Fusion and Power Law Transform." Journal of Computational and Theoretical Nanoscience 17, no. 5 (May 1, 2020): 2405–8. http://dx.doi.org/10.1166/jctn.2020.8904.

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One of the best branches of data fusion is Image Fusion and Power law transform is having its own importance in image enhancement. The future applications will be mainly based on image brightness and image sharpness. The enhanced images provide better information than the low-quality blur images. In this paper, we acquired a solitary image which introduces better execution under a few assessment criteria’s by combining wavelet transform with power law transformation for the images at around various locales. Wavelet transform is basically utilized as a result of its natural property that is, they are repetitive and move variation. It changes image into various scales. In power law transformation one must pick the example showing up in the transform capacity with the end goal that it doesn’t corrupt the nature of the image. The empirical results are better for proposed algorithm compare with state of art algorithms.

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Acho, Leonardo, and Pablo Buenestado. "Image Fusion Based on Principal Component Analysis and Slicing Image Transformation." MATEC Web of Conferences 210 (2018): 04020. http://dx.doi.org/10.1051/matecconf/201821004020.

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Image fusion deals with the ability to integrate data from image sensors at different instants when the source information is uncertain. Although there exist many techniques on the subject, in this paper, we develop two originative techniques based on principal component analysis and slicing image transformation to efficiently fuse a small set of noisy images. For instance, in neural data fusion, this approach requires a considerable number of corrupted images to efficiently produce the desired outcome and also requiring a considerable computing time because of the dynamics involved in the fusion data process. In our approaches, the computation time is considerably smaller. This results appealing to increasing feasibility, for instance, in remote sensing or wireless sensor network. Moreover, and according to our numerical experiments, when our methods are compared against the neural data fusion algorithm, they present better performance.

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Watcharawipha, Anirut, Nipon Theera-Umpon, and Sansanee Auephanwiriyakul. "Space Independent Image Registration Using Curve-Based Method with Combination of Multiple Deformable Vector Fields." Symmetry 11, no. 10 (September 28, 2019): 1210. http://dx.doi.org/10.3390/sym11101210.

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This paper proposes a novel curve-based or edge-based image registration technique that utilizes the curve transformation function and Gaussian function. It enables deformable image registration between images in different spaces, e.g., different color spaces or different medical image modalities. In particular, piecewise polynomial fitting is used to fit a curve and convert it to the global cubic B-spline control points. The transformation between the curves in the reference and source images are performed by using these control points. The image area is segmented with respect to the reference curve for the moving pixels. The Gaussian function, which is symmetric about the coordinates of the points of the reference curve, was used to improve the continuity in the intra- and inter-segmented areas. The overall result on curve transformation by means of the Hausdroff distance was 5.820 ± 1.127 pixels on average on several 512 × 512 synthetic images. The proposed method was compared with an ImageJ plugin, namely bUnwarpJ, and a software suite for deformable image registration and adaptive radiotherapy research, namely DIRART, to evaluate the image registration performance. The experimental result shows that the proposed method yielded better image registration performance than its counterparts. On average, the proposed method could reduce the root mean square error from 2970.66 before registration to 1677.94 after registration and can increase the normalized cross-correlation coefficient from 91.87% before registration to 97.40% after registration.

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Masunaga, Shinya, and Tomoharu Nagao. "Automatic Construction of Image Transformation Processes from Sample Images." Journal of the Institute of Image Information and Television Engineers 51, no. 8 (1997): 1278–83. http://dx.doi.org/10.3169/itej.51.1278.

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Arya, Sandeep. "Image enhancement technique on Ultrasound Images using Aura Transformation." International Journal in Foundations of Computer Science & Technology 2, no. 3 (May 31, 2012): 1–10. http://dx.doi.org/10.5121/ijfcst.2012.2301.

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Fernández, Claudio Ignacio, Ata Haddadi, Brigitte Leblon, Jinfei Wang, and Keri Wang. "Comparison between Three Registration Methods in the Case of Non-Georeferenced Close Range of Multispectral Images." Remote Sensing 13, no. 3 (January 24, 2021): 396. http://dx.doi.org/10.3390/rs13030396.

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Cucumber powdery mildew, which is caused by Podosphaera xanthii, is a major disease that has a significant economic impact in cucumber greenhouse production. It is necessary to develop a non-invasive fast detection system for that disease. Such a system will use multispectral imagery acquired at a close range with a camera attached to a mobile cart’s mechanic extension. This study evaluated three image registration methods applied to non-georeferenced multispectral images acquired at close range over greenhouse cucumber plants with a MicaSense® RedEdge camera. The detection of matching points was performed using Speeded-Up Robust Features (SURF), and outliers matching points were removed using the M-estimator Sample Consensus (MSAC) algorithm. Three geometric transformations (affine, similarity, and projective) were considered in the registration process. For each transformation, we mapped the matching points of the blue, green, red, and NIR band images into the red-edge band space and computed the root mean square error (RMSE in pixel) to estimate the accuracy of each image registration. Our results achieved an RMSE of less than 1 pixel with the similarity and affine transformations and of less than 2 pixels with the projective transformation, whatever the band image. We determined that the best image registration method corresponded to the affine transformation because the RMSE is less than 1 pixel and the RMSEs have a Gaussian distribution for all of the bands, but the blue band.

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Zhang, Yu Jun, Mei Xiang, and Ying Tian. "An Efficient Ear Recognition Method from Two-Dimensional Images." Advanced Materials Research 1049-1050 (October 2014): 1531–35. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.1531.

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An efficient ear recognition method by weighted wavelet transformation and Bi-Directional principal component analysis was proposed. First, each ear image was decomposed into four sub-images by wavelet transformation ,the four sub-images were low frequency image , vertical detail image ,horizontal detail image and high frequency image .Then the low frequency image was decomposed into four sub-images, the four-images were weighted by different coefficients, then ,the four sub-images were reconstructed into a image .On this basis ,the feature was extraction by the BDPCA method ,and then we use the k-Nearest Neighbor Classification to recognition .Experimental results show that the method have high recognition rate and shorted training time.

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Qiu, Peng Rui, Ying Liang, and Hui Rong. "Image Mosaics Algorithm Combined Feature Point Matching with Transformation Parameters." Applied Mechanics and Materials 380-384 (August 2013): 4136–39. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.4136.

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To solve the problem of the large amount of calculation, poor robustness and do not well in image mosaic of images who are in different scales in the traditional image mosaic method ,the article arise a mosaic algorithm of different scales images registration and adaptive. Through feature point matching and automatically recognizing of transform geometric parameters between images,It achieves the match and mosaic of different scale and rotated images. First, using SIFT to extract the feature points of the images and matching feature points according to the principal of mutual information maximum. Then based on the geometric information of the matching pairs, automatically recognize the relationship of transformation parameters. In the end, obtain the projective transformation and achieve the image stable mosaic.

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Wang, Hai Feng, Kun Zhang, and Hong E. Ren. "A Gabor Wavelet Transformation-Based Texture Images Classification Algorithm." Advanced Materials Research 811 (September 2013): 430–34. http://dx.doi.org/10.4028/www.scientific.net/amr.811.430.

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In this paper, we introduce a texture image classification algorithm based on Gabor wavelet transform. Using Gabor wavelet transform, image is decomposed into sub-bands images in multiresolution and multi-direction, and we extract texture feature from all sub-bands images. Then the algorithm groups feature image into clusters by the k near neighbor algorithm. The experimental results on dataset Brodatz showed that the proposed algorithm can achieve an ideal accuracy rate and excellent classification effect.

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Olesia Prysiazhna, Liudmyla Tulchak, Nataliia Hadaichuk, and Svitlana Medvedieva. "ENGLISH IDIOMS: TRANSFORMATION OF IMAGE." World Science, no. 9(37) (September 30, 2018): 66–68. http://dx.doi.org/10.31435/rsglobal_ws/30092018/6138.

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The study was aimed to analyze theoretical material on the issues of idiomatic expressions and their translation, as well as to define and assess the importance of the translation of idiomatic expressions, which students can encounter in technical literature, into Ukrainian. The transformation of image of English idioms is a very difficult task because of their semantic richness, imagery, brevity and the brightness. On the one hand, English idioms are widely used in oral speech, artistic and political literature. On the other hand, English idioms are used in technical literature, though not so often. The choice of one or another type of translation depends on the features of idiomatic expressions that students must recognize and be able to transform their meaning, brightness and clarity.

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Gokcen, Ibrahim. "Chaotic gray-level image transformation." Journal of Electronic Imaging 14, no. 4 (October 1, 2005): 043001. http://dx.doi.org/10.1117/1.2135792.

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Kellnhofer, Petr, Tobias Ritschel, Karol Myszkowski, and Hans-Peter Seidel. "Transformation-aware perceptual image metric." Journal of Electronic Imaging 25, no. 5 (September 21, 2016): 053014. http://dx.doi.org/10.1117/1.jei.25.5.053014.

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Cheng, H. D., Y. Y. Tang, and C. Y. Suen. "VLSI architectures for image transformation." IEEE Transactions on Systems, Man, and Cybernetics 21, no. 2 (1991): 409–13. http://dx.doi.org/10.1109/21.87076.

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Pflugfelder, Daniel, and Hanno Scharr. "Practically Lossless Affine Image Transformation." IEEE Transactions on Image Processing 29 (2020): 5367–73. http://dx.doi.org/10.1109/tip.2020.2982260.

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Leys, Jos. "The Droste effect image transformation." Computers & Graphics 31, no. 3 (June 2007): 516–23. http://dx.doi.org/10.1016/j.cag.2006.12.001.

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Miao, Xu, and Rajesh P. N. Rao. "Learning the Lie Groups of Visual Invariance." Neural Computation 19, no. 10 (October 2007): 2665–93. http://dx.doi.org/10.1162/neco.2007.19.10.2665.

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A fundamental problem in biological and machine vision is visual invariance: How are objects perceived to be the same despite transformations such as translations, rotations, and scaling? In this letter, we describe a new, unsupervised approach to learning invariances based on Lie group theory. Unlike traditional approaches that sacrifice information about transformations to achieve invariance, the Lie group approach explicitly models the effects of transformations in images. As a result, estimates of transformations are available for other purposes, such as pose estimation and visuomotor control. Previous approaches based on first-order Taylor series expansions of images can be regarded as special cases of the Lie group approach, which utilizes a matrix-exponential-based generative model of images and can handle arbitrarily large transformations. We present an unsupervised expectation-maximization algorithm for learning Lie transformation operators directly from image data containing examples of transformations. Our experimental results show that the Lie operators learned by the algorithm from an artificial data set containing six types of affine transformations closely match the analytically predicted affine operators. We then demonstrate that the algorithm can also recover novel transformation operators from natural image sequences. We conclude by showing that the learned operators can be used to both generate and estimate transformations in images, thereby providing a basis for achieving visual invariance.

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Hjouj, Fawaz. "Linear Transformation Recognition Using Radon Transform." Journal of Mathematical Sciences & Computer Applications 1, no. 2 (June 10, 2017): 40–47. http://dx.doi.org/10.5147/jmsca.v1i2.90.

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Given two regular functions (images) f and g on R2 where g is formed from f by a general linear transformation, g(x) = f (Ax + b). We present a procedure to determine the transformation ‘parameters’ A and b using Radon projections of f and only two projections of g. We use these projections together with simple facts on matrix vector multiplication to recover the matrix A. The assumptions we have here are: f is nonnegative and A is nonsingular. Commonly used transformations in image processing such as rotation, scaling and others are special cases of our approach.

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Foroughi Sabzevar, Mohsen, Masoud Gheisari, and James Lo. "Development and Assessment of a Sensor-Based Orientation and Positioning Approach for Decreasing Variation in Camera Viewpoints and Image Transformations at Construction Sites." Applied Sciences 10, no. 7 (March 27, 2020): 2305. http://dx.doi.org/10.3390/app10072305.

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Image matching techniques offer valuable opportunities for the construction industry. Image matching, a fundamental process in computer vision, is required for different purposes such as object and scene recognition, video data mining, reconstruction of three-dimensional (3D) objects, etc. During the image matching process, two images that are randomly (i.e., from different position and orientation) captured from a scene are compared using image matching algorithms in order to identify their similarity. However, this process is very complex and error prone, because pictures that are randomly captured from a scene vary in viewpoints. Therefore, some main features in images such as position, orientation, and scale of objects are transformed. Sometimes, these image matching algorithms cannot correctly identify the similarity between these images. Logically, if these features remain unchanged during the picture capturing process, then image transformations are reduced, similarity increases, and consequently, the chances of algorithms successfully conducting the image matching process increase. One way to improve these chances is to hold the camera at a fixed viewpoint. However, in messy, dusty, and temporary locations such as construction sites, holding the camera at a fixed viewpoint is not always feasible. Is there any way to repeat and retrieve the camera’s viewpoints during different captures at locations such as construction sites? This study developed and evaluated an orientation and positioning approach that decreased the variation in camera viewpoints and image transformation on construction sites. The results showed that images captured while using this approach had less image transformation in contrast to images not captured using this approach.

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Bao, Jun Wei, Qiang Chen, and Fu Qiang Peng. "Nonlinear Image Mosaic of Pipe Inner Surface." Applied Mechanics and Materials 427-429 (September 2013): 1620–24. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.1620.

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The present study is concerned about image mosaic in single reflector panoramic imaging system (SRPIS). A nonlinear image mosaic algorithm is proposed to get the panoramic image of pipe inner surface. Because of nonlinear distortion in the images which are unwrapped from the original images, its practically impossible for traditional image mosaic method based on 2D planar projective transformation to eliminate phenomenon of ghost and blur in the seam. Nonlinear image mosaic algorithm is performed by projecting many pieces of image divided from right image onto the left image. The position-variant parameters of transformation model are got by quadratic interpolation. The results show that nonlinear image mosaic algorithm overcomes the limitations of traditional image mosaic method in images with distortion and the mosaic image is clearer than that by traditional image mosaic method.

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Chen, Feiran, Jianlin Zhang, Jingju Cai, Tao Xu, Gang Lu, and Xianrong Peng. "Infrared Image Adaptive Enhancement Guided by Energy of Gradient Transformation and Multiscale Image Fusion." Applied Sciences 10, no. 18 (September 9, 2020): 6262. http://dx.doi.org/10.3390/app10186262.

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The detail enhancement and dynamic range compression of infrared (IR) images is an important issue and a necessary practical application in the domain of IR image processing. This paper provides a novel approach to displaying high dynamic range infrared images on common display equipment with appropriate contrast and clear detail information. The steps are chiefly as follows. First, in order to protect the weak global details in different regions of the image, we adjust the original normalized image into multiple brightness levels by adaptive Gamma transformation. Second, each brightness image is decomposed into a base layer and several detail layers by the multiscale guided filter. Details in each image are enhanced separately. Third, to obtain the image with global details of the input image, enhanced images in each brightness are fused together. Last, we filter out the outliers and adjust the dynamic range before outputting the image. Compared with other conventional or cutting-edge methods, the experimental results demonstrate that the proposed approach is effective and robust in dynamic range compression and detail information enhancement of IR image.

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Han, Yuan Li, and Yan Ping Chen. "Image Overlapping Technique without Model for Large LoD Terrain Based on GPU." Applied Mechanics and Materials 556-562 (May 2014): 4792–96. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4792.

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There are many difficulties in image overlapping in 3D terrain of dynamic LoD quad tree model, it will cause gaps between terrain tiles and images if both of them are expressed independently. This paper breaks through the LoD mesh difficulties, and realizes image overlapping based on GPU programming without modeling. It establishes different texture coordinate transformational relationships in GPU to realize seamless expression between terrain texture and overlapping image based on setting terrain vertexes with global unified texture coordinate. The affine transformation is adopted to solve the difficulty of multiple spatial nonlinear transformations aiming to the interaction of image overlapping, and also to solve the non-synchronous problem of texture coordinate in interaction. At last, the conclusion may be achieved. Image overlapping technique in LoD terrain without modeling based on GPU is a typical case of render technology to solve application difficulties, the realization is simple, efficient and steady which only faces to single terrain tile. This research will greatly enhance the ability of Digital Earth, which completely clear an important technical barriers and promote the applications in GeoDesign.

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Han, Kim, and Yeom. "Improved Piecewise Linear Transformation for Precise Warping of Very-High-Resolution Remote Sensing Images." Remote Sensing 11, no. 19 (September 25, 2019): 2235. http://dx.doi.org/10.3390/rs11192235.

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A large number of evenly distributed conjugate points (CPs) in entirely overlapping regions of the images are required to achieve successful co-registration between very-high-resolution (VHR) remote sensing images. The CPs are then used to construct a non-linear transformation model that locally warps a sensed image to a reference image’s coordinates. Piecewise linear (PL) transformation is largely exploited for warping VHR images because of its superior performance as compared to the other methods. The PL transformation constructs triangular regions on a sensed image from the CPs by applying the Delaunay algorithm, after which the corresponding triangular regions in a reference image are constructed using the same CPs on the image. Each corresponding region in the sensed image is then locally warped to the regions of the reference image through an affine transformation estimated from the CPs on the triangle vertices. The warping performance of the PL transformation shows reliable results, particularly in regions inside the triangles, i.e., within the convex hulls. However, the regions outside the triangles, which are warped when the extrapolated boundary planes are extended using CPs located close to the regions, incur severe geometric distortion. In this study, we propose an effective approach that focuses on the improvement of the warping performance of the PL transformation over the external area of the triangles. Accordingly, the proposed improved piecewise linear (IPL) transformation uses additional pseudo-CPs intentionally extracted from positions on the boundary of the sensed image. The corresponding pseudo-CPs on the reference image are determined by estimating the affine transformation from CPs located close to the pseudo-CPs. The latter are simultaneously used with the former to construct the triangular regions, which are enlarged accordingly. Experiments on both simulated and real datasets, constructed from Worldview-3 and Kompsat-3A satellite images, were conducted to validate the effectiveness of the proposed IPL transformation. That transformation was shown to outperform the existing linear/non-linear transformation models such as an affine, third and fourth polynomials, local weighted mean, and PL. Moreover, we demonstrated that the IPL transformation improved the warping performance over the PL transformation outside the triangular regions by increasing the correlation coefficient values from 0.259 to 0.304, 0.603 to 0.657, and 0.180 to 0.338 in the first, second, and third real datasets, respectively.

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Kumar, C., and R. Prakash. "Ultrasound Medical Image Denoising Using Threshold Based Wavelet Transformation Method." Journal of Medical Imaging and Health Informatics 10, no. 8 (August 1, 2020): 1825–30. http://dx.doi.org/10.1166/jmihi.2020.3195.

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Ultrasound imaging is most commonly used and secure medical diagnostic approach its low cost, noninvasive nature of real time image construction. However because of signal dependant noise existence, ultrasound imaging gets degraded: A baseline phase in image processing is removing diverse noise types from image. Noise sources of image generally occur during storage, transmission and image acquisition. Image denoising is an issue determined in diverse computer vision and image processing crisis. There are diverse prevailing approaches to denoise image. The significant property of finest denoising of an image model has to remove noise while preserving edges. For wavelet thresholding paradigm, Fisz transformation method is brought-in in the current work. Furthermore, it has problem with poor denoising performance and hence the quality of image is minimized considerably. In this paper, Improved Threshold based Wavelet Transformation Method (ITWTM) is suggested to rectify above mentioned crisis, to enhance the better denoising images, the soft threshold method is introduced. To enhance visual quality of noisy image, it simply changes coefficients with help of softthresholding method. Speckle noise, additive noise, Gaussian noise and multiplicative noise factors affect the ultrasound images, which minimize the image quality and effects the human interpretation. So, ITWTM helps to minimize the noise rate considerably for the provided US image. The experimental result confirms that the proposed ITWTM provides better performance with respect to higher PSNR, SSIM and lower MSE, execution time rather than the previous Fisz transformation and DWT methods.

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Martey, Ezekiel Mensah, Hang Lei, Xiaoyu Li, and Obed Appiah. "Image Representation Using Stacked Colour Histogram." Algorithms 14, no. 8 (July 30, 2021): 228. http://dx.doi.org/10.3390/a14080228.

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Image representation plays a vital role in the realisation of Content-Based Image Retrieval (CBIR) system. The representation is performed because pixel-by-pixel matching for image retrieval is impracticable as a result of the rigid nature of such an approach. In CBIR therefore, colour, shape and texture and other visual features are used to represent images for effective retrieval task. Among these visual features, the colour and texture are pretty remarkable in defining the content of the image. However, combining these features does not necessarily guarantee better retrieval accuracy due to image transformations such rotation, scaling, and translation that an image would have gone through. More so, concerns about feature vector representation taking ample memory space affect the running time of the retrieval task. To address these problems, we propose a new colour scheme called Stack Colour Histogram (SCH) which inherently extracts colour and neighbourhood information into a descriptor for indexing images. SCH performs recurrent mean filtering of the image to be indexed. The recurrent blurring in this proposed method works by repeatedly filtering (transforming) the image. The output of a transformation serves as the input for the next transformation, and in each case a histogram is generated. The histograms are summed up bin-by-bin and the resulted vector used to index the image. The image blurring process uses pixel’s neighbourhood information, making the proposed SCH exhibit the inherent textural information of the image that has been indexed. The SCH was extensively tested on the Coil100, Outext, Batik and Corel10K datasets. The Coil100, Outext, and Batik datasets are generally used to assess image texture descriptors, while Corel10K is used for heterogeneous descriptors. The experimental results show that our proposed descriptor significantly improves retrieval and classification rate when compared with (CMTH, MTH, TCM, CTM and NRFUCTM) which are the start-of-the-art descriptors for images with textural features.

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Kim, J., and T. Kim. "DEVELOPMENT OF A ROBUST IMAGE MOSAICKING METHOD FOR SMALL UNMANNED AERIAL VEHICLE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W6 (August 23, 2017): 183–88. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w6-183-2017.

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In this paper, a tie-point based image mosaicking method considering imaging characteristics of small UAVs is proposed. Small UAVs can be characterized to have unstable flight trajectory and lower flight height. The proposed method considers the imaging characteristics in image transformation estimation and image blending process. For image transformation estimation, an optimal transformation model is variably applied by using tie-point area ratio. The optimal tie-point area ratio was about 0.3. Mosaicking error was largely decreased by using this tie-point area ratio. For image blending, a composite area minimization is introduced as a preceding step of image resampling. Composite areas of individual images were minimized by analyzing image overlaps between adjacent images. The proposed method was evaluated over flat area and urban area with highly overlapping multi-strip and inconsistently overlapping strip. Experiment results showed that the proposed method can reliably generate mosaics not only from UAV images acquired in good environment but also from extreme environment.

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Suryaprabha, D., J. Satheeshkumar, and N. Seenivasan. "Classical and Fuzzy Based Image Enhancement Techniques for Banana Root Disease Diagnosis: A Review and Validation." Oriental journal of computer science and technology 13, no. 1 (May 19, 2020): 50–62. http://dx.doi.org/10.13005/ojcst13.01.05.

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A vital step in automation of plant root disease diagnosis is to extract root region from the input images in an automatic and consistent manner. However, performance of segmentation algorithm over root images directly depends on the quality of input images. During acquisition, the captured root images are distorted by numerous external factors like lighting conditions, dust and so on. Hence it is essential to incorporate an image enhancement algorithm as a pre-processing step in the plant root disease diagnosis module. Image quality can be improved either by manipulating the pixels through spatial or frequency domain. In spatial domain, images are directly manipulated using their pixel values and alternatively in frequency domain, images are indirectly manipulated using transformations. Spatial based enhancement methods are considered as favourable approach for real time root images as it is simple and easy to understand with low computational complexity. In this study, real time banana root images were enhanced by attempting with different spatial based image enhancement techniques. Different classical point processing methods (contrast stretching, logarithmic transformation, power law transformation, histogram equalization, adaptive histogram equalization and histogram matching) and fuzzy based enhancement methods using fuzzy intensification operator and fuzzy if-then rule based methods were tried to enhance the banana root images. Quality of the enhanced root images obtained through different classical point processing and fuzzy based methods were measured using no-reference image quality metrics, entropy and blind image quality index. Hence, this study concludes that fuzzy based method could be deployed as a suitable image enhancement algorithm while devising the image processing modules for banana root disease diagnosis.

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Journal articles on the topic 'Transformation of image'

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