Artículos de revistas: "Image Merging"

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Ushakov, K. D. y O. N. Kaneva. "COMPARATIVE ANALYSIS OF IMAGE MERGING ALGORITHMS". Applied Mathematics and Fundamental Informatics 9, n.º 3 (2022): 53–59. http://dx.doi.org/10.25206/2311-4908-2022-9-3-53-59.

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The paper discusses algorithms for merging images by machine learning. Both statistical algorithms and machine learning algorithms are used in image merging. Fusion algorithms based on machine learning give a clearer and sharper image while preserving details. Pairs of images with different blurred areas were used to train the algorithm. The comparison of the developed algorithms by quality metrics is carried out.

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S, Sadagopan. "Image Segmentation and Hiding Using Statistical Region Merging". Journal of Advanced Research in Dynamical and Control Systems 11, n.º 0009-SPECIAL ISSUE (25 de septiembre de 2019): 1010–15. http://dx.doi.org/10.5373/jardcs/v11/20192665.

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3

GU, JIANPING, LI ZHANG y CUN CHENG. "DYNAMIC GRAPH MERGING FOR IMAGE SEGMENTATION". International Journal of Wavelets, Multiresolution and Information Processing 11, n.º 06 (noviembre de 2013): 1350051. http://dx.doi.org/10.1142/s0219691313500513.

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A new algorithm named dynamic graph merging (DGM) for automatic image segmentation is explored. Firstly a novel variational model for multi-section cut is introduced by decomposing the traditional cut into two parts, the harmonic cut and the elastic energy of the boundary. The new energy is called the continuous combined cut. Secondly a new algorithm that removes those edges with higher energy and synchronously merges their starting and ending vertices in an ordered manner is proposed. The continual merging process would iteratively contract the graph, merge those homogeneous vertices into bigger and bigger super-pixels, and fuse the remainder edges into longer and longer boundaries. So we call this algorithm dynamic graph merging. Merging criterions based on the continuous combined cut model are also discussed, which will be used to determine whether a given edge should collapse. Since the merging condition should be highly related to the image content, we present different predicates for structure images and texture images respectively. This algorithm whose efficiency is showed by experiments has a linear time/space complexity, and can efficiently segment gray/color and 2D/3D images.

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Wang, Haoyu, Zhanfeng Shen, Zihan Zhang, Zeyu Xu, Shuo Li, Shuhui Jiao y Yating Lei. "Improvement of Region-Merging Image Segmentation Accuracy Using Multiple Merging Criteria". Remote Sensing 13, n.º 14 (15 de julio de 2021): 2782. http://dx.doi.org/10.3390/rs13142782.

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Image segmentation plays a significant role in remote sensing image processing. Among numerous segmentation algorithms, the region-merging segmentation algorithm is widely used due to its well-organized structure and outstanding results. Many merging criteria (MC) were designed to improve the accuracy of region-merging segmentation, but each MC has its own shortcomings, which can cause segmentation errors. Segmentation accuracy can be improved by referring to the segmentation results. To achieve this, an approach for detecting and correcting region-merging image segmentation errors is proposed, and then an iterative optimization model is established. The main contributions of this paper are as follows: (1) The conflict types of matching segment pairs are divided into scale-expression conflict (SEC) and region-ownership conflict (ROC), and ROC is more suitable for optimization. (2) An equal-scale local evaluation method was designed to quantify the optimization potential of ROC. (3) A regional anchoring strategy is proposed to preserve the results of the previous iteration optimization. Three QuickBird satellite images of different land-cover types were used for validating the proposed approach. Both unsupervised and supervised evaluation results prove that the proposed approach can effectively improve segmentation accuracy. All explicit and implicit optimization modes are concluded, which further illustrate the stability of the proposed approach.

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Andersone, Ilze. "Map Merging in the Context of Image Processing". Scientific Journal of Riga Technical University. Computer Sciences 44, n.º 1 (1 de enero de 2011): 124–30. http://dx.doi.org/10.2478/v10143-011-0030-5.

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Map Merging in the Context of Image ProcessingThe area of map merging is tightly connected to the area of image processing. Usually metric maps created by robots are represented as occupancy grids. It is easy to apply algorithms used in image processing to this kind of map representation. The image processing subfield that is closest to the map merging is the image registration. It can be assumed that metric map merging methods similarly to the image registration methods consist of three components: feature space, search strategy and similarity metric. Algorithms from image processing can also be used in map merging for map preprocessing. The goal of this paper is to explore similarities between the fields of map merging and image processing and to determine how the results of this research can be used for the development of a map merging framework and consequently new map merging approaches.

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Fagel, Sascha. "Merging methods of speech visualization". ZAS Papers in Linguistics 40 (1 de enero de 2005): 19–32. http://dx.doi.org/10.21248/zaspil.40.2005.255.

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The author presents MASSY, the MODULAR AUDIOVISUAL SPEECH SYNTHESIZER. The system combines two approaches of visual speech synthesis. Two control models are implemented: a (data based) di-viseme model and a (rule based) dominance model where both produce control commands in a parameterized articulation space. Analogously two visualization methods are implemented: an image based (video-realistic) face model and a 3D synthetic head. Both face models can be driven by both the data based and the rule based articulation model. The high-level visual speech synthesis generates a sequence of control commands for the visible articulation. For every virtual articulator (articulation parameter) the 3D synthetic face model defines a set of displacement vectors for the vertices of the 3D objects of the head. The vertices of the 3D synthetic head then are moved by linear combinations of these displacement vectors to visualize articulation movements. For the image based video synthesis a single reference image is deformed to fit the facial properties derived from the control commands. Facial feature points and facial displacements have to be defined for the reference image. The algorithm can also use an image database with appropriately annotated facial properties. An example database was built automatically from video recordings. Both the 3D synthetic face and the image based face generate visual speech that is capable to increase the intelligibility of audible speech. Other well known image based audiovisual speech synthesis systems like MIKETALK and VIDEO REWRITE concatenate pre-recorded single images or video sequences, respectively. Parametric talking heads like BALDI control a parametric face with a parametric articulation model. The presented system demonstrates the compatibility of parametric and data based visual speech synthesis approaches.

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Lor, Kuo-Lung y Chung-Ming Chen. "FAST INTERACTIVE REGIONAL PATTERN MERGING FOR GENERIC TISSUE SEGMENTATION IN HISTOPATHOLOGY IMAGES". Biomedical Engineering: Applications, Basis and Communications 33, n.º 02 (9 de marzo de 2021): 2150012. http://dx.doi.org/10.4015/s1016237221500125.

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The image segmentation of histopathological tissue images has always been a challenge due to the overlapping of tissue color distributions, the complexity of extracellular texture and the large image size. In this paper, we introduce a new region-merging algorithm, namely, the Regional Pattern Merging (RPM) for interactive color image segmentation and annotation, by efficiently retrieving and applying the user’s prior knowledge of stroke-based interaction. Low-level color/texture features of each region are used to compose a regional pattern adapted to differentiating a foreground object from the background scene. This iterative region-merging is based on a modified Region Adjacency Graph (RAG) model built from initial segmented results of the mean shift to speed up the merging process. The foreground region of interest (ROI) is segmented by the reduction of the background region and discrimination of uncertain regions. We then compare our method against state-of-the-art interactive image segmentation algorithms in both natural images and histological images. Taking into account the homogeneity of both color and texture, the resulting semi-supervised classification and interactive segmentation capture histological structures more completely than other intensity or color-based methods. Experimental results show that the merging of the RAG model runs in a linear time according to the number of graph edges, which is essentially faster than both traditional graph-based and region-based methods.

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Yocky, David A. "Artifacts in wavelet image merging". Optical Engineering 35, n.º 7 (1 de julio de 1996): 2094. http://dx.doi.org/10.1117/1.600765.

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Panić, Branislav, Marko Nagode, Jernej Klemenc y Simon Oman. "On Methods for Merging Mixture Model Components Suitable for Unsupervised Image Segmentation Tasks". Mathematics 10, n.º 22 (16 de noviembre de 2022): 4301. http://dx.doi.org/10.3390/math10224301.

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Unsupervised image segmentation is one of the most important and fundamental tasks in many computer vision systems. Mixture model is a compelling framework for unsupervised image segmentation. A segmented image is obtained by clustering the pixel color values of the image with an estimated mixture model. Problems arise when the selected optimal mixture model contains a large number of mixture components. Then, multiple components of the estimated mixture model are better suited to describe individual segments of the image. We investigate methods for merging the components of the mixture model and their usefulness for unsupervised image segmentation. We define a simple heuristic for optimal segmentation with merging of the components of the mixture model. The experiments were performed with gray-scale and color images. The reported results and the performed comparisons with popular clustering approaches show clear benefits of merging components of the mixture model for unsupervised image segmentation.

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Gonet, Michal, Boris Epel, Howard J. Halpern y Martyna Elas. "Merging Preclinical EPR Tomography with other Imaging Techniques". Cell Biochemistry and Biophysics 77, n.º 3 (22 de agosto de 2019): 187–96. http://dx.doi.org/10.1007/s12013-019-00880-7.

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Abstract This paper presents a survey of electron paramagnetic resonance (EPR) image registration. Image registration is the process of overlaying images (two or more) of the same scene taken at different times, from different viewpoints and/or different techniques. EPR-imaging (EPRI) techniques belong to the functional-imaging modalities and therefore suffer from a lack of anatomical reference which is mandatory in preclinical imaging. For this reason, it is necessary to merging EPR images with other modalities which allow for obtaining anatomy images. Methodological analysis and review of the literature were done, providing a summary for developing a good foundation for research study in this field which is crucial in understanding the existing levels of knowledge. Out of these considerations, the aim of this paper is to enhance the scientific community’s understanding of the current status of research in EPR preclinical image registration and also communicate to them the contribution of this research in the field of image processing.

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Stawiaski, Jean y Etienne Decenciére. "REGION MERGING VIA GRAPH-CUTS". Image Analysis & Stereology 27, n.º 1 (3 de mayo de 2011): 39. http://dx.doi.org/10.5566/ias.v27.p39-45.

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In this paper, we discuss the use of graph-cuts to merge the regions of the watershed transform optimally. Watershed is a simple, intuitive and efficient way of segmenting an image. Unfortunately it presents a few limitations such as over-segmentation and poor detection of low boundaries. Our segmentation process merges regions of the watershed over-segmentation by minimizing a specific criterion using graph-cuts optimization. Two methods will be introduced in this paper. The first is based on regions histogram and dissimilarity measures between adjacent regions. The second method deals with efficient approximation of minimal surfaces and geodesics. Experimental results show that these techniques can efficiently be used for large images segmentation when a pre-computed low level segmentation is available. We will present these methods in the context of interactive medical image segmentation.

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Cui, Jian, Dong Ling Ma, Ming Yang Yu y Ying Zhou. "Research of Remote Sensing Image Segmentation Based on Mean Shift and Region Merging". Applied Mechanics and Materials 90-93 (septiembre de 2011): 2836–39. http://dx.doi.org/10.4028/www.scientific.net/amm.90-93.2836.

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In order to extract ground information more accurately, it is important to find an image segmentation method to make the segmented features match the ground objects. We proposed an image segmentation method based on mean shift and region merging. With this method, we first segmented the image by using mean shift method and small-scale parameters. According to the region merging homogeneity rule, image features were merged and large-scale image layers were generated. What’s more, Multi-level image object layers were created through scaling method. The test of segmenting remote sensing images showed that the method was effective and feasible, which laid a foundation for object-oriented information extraction.

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Yi, Huiguo, Jie Yang, Pingxiang Li, Lei Shi y Fengkai Lang. "A PolSAR Image Segmentation Algorithm Based on Scattering Characteristics and the Revised Wishart Distance". Sensors 18, n.º 7 (13 de julio de 2018): 2262. http://dx.doi.org/10.3390/s18072262.

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A novel segmentation algorithm for polarimetric synthetic aperture radar (PolSAR) images is proposed in this paper. The method is composed of two essential components: a merging order and a merging predicate. The similarity measured by the complex-kind Hotelling–Lawley trace (HLT) statistic is used to decide the merging order. The merging predicate is determined by the scattering characteristics and the revised Wishart distance between adjacent pixels, which can greatly improve the performance in speckle suppression and detail preservation. A postprocessing step is applied to obtain a satisfactory result after the merging operation. The decomposition and merging processes are iteratively executed until the termination criterion is met. The superiority of the proposed method was verified with experiments on two RADARSAT-2 PolSAR images and a Gaofen-3 PolSAR image, which demonstrated that the proposed method can obtain more accurate segmentation results and shows a better performance in speckle suppression and detail preservation than the other algorithms.

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Gunawan, Wawan, Agus Zainal Arifin, Rarasmaya Indraswari y Dini Adni Navastara. "Fuzzy Region Merging using Fuzzy Similarity Measurement on Image Segmentation". International Journal of Electrical and Computer Engineering (IJECE) 7, n.º 6 (1 de diciembre de 2017): 3402. http://dx.doi.org/10.11591/ijece.v7i6.pp3402-3410.

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Some image’s regions have unbalance information, such as blurred contour, shade, and uneven brightness. Those regions are called as ambiguous regions. Ambiguous region cause problem during region merging process in interactive image segmentation because that region has double information, both as object and background. We proposed a new region merging strategy using fuzzy similarity measurement for image segmentation. The proposed method has four steps; the first step is initial segmentation using mean-shift algorithm. The second step is giving markers manually to indicate the object and background region. The third step is determining the fuzzy region or ambiguous region in the images. The last step is fuzzy region merging using fuzzy similarity measurement. The experimental results demonstrated that the proposed method is able to segment natural images and dental panoramic images successfully with the average value of misclassification error (ME) 1.96% and 5.47%, respectively.

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Gong, Xiu Li y Zhi Ming Wang. "Remote Sensing Image Segmentation Based on Improved Statistical Region Merging". Applied Mechanics and Materials 667 (octubre de 2014): 226–29. http://dx.doi.org/10.4028/www.scientific.net/amm.667.226.

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Statistical Region Merging (SRM) is an efficient image segmentation algorithm for images with noise and partial occlusion. However, due to the complexity of remote sensing image, SRM can’t give satisfactory results. This paper proposes an improved image segmentation algorithm for remote sensing image based on SRM. Firstly, 8-connexity gradient estimation models are used to obtain more precisely edges. Secondly, the dissimilarity criterion between regions is replaced by a normalized distance standard. Finally, it dynamically updates and sorts dissimilarity between regions during region merging. Experimental results show the proposed algorithm can achieve better segmentation results from coarse to fine compared with original SRM.

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Thomas, Irene M. y Michael F. Schmid. "A Cross-Correlation Method for Merging Electron Crystallographic Image Data". Microscopy and Microanalysis 1, n.º 4 (agosto de 1995): 167–73. http://dx.doi.org/10.1017/s1431927695111678.

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An efficient method of merging images from electron crystallographic data has been developed that is several orders of magnitude faster than the method of phase shift origin search. The results of this new cross-correlation method are compared with the phase residual in terms of speed, consistency with the phase residual method, and the ease of incorporating symmetry constraints along with the image-to-image merging. The program is further accelerated by a more efficient sorting procedure.

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Song, Yu Ting, Xiu Hua Ji y Shi Lin Zhao. "An Improved Color Image Enhancement Algorithm Based on 3-D Color Histogram Equalization". Applied Mechanics and Materials 321-324 (junio de 2013): 1133–37. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.1133.

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This paper proposes an improved color image enhancement algorithm based on 3-D color histogram equalization algorithm. When the existed 3-D color histogram equalization algorithms in the literatures are applied in processing dim color images, the processed color images often turn pale due to the decrease of color-saturations and have false contours due to gray-scale merging phenomenon in the histogram equalization algorithm. In this paper, the proposed algorithm can make more pixels of the processed color images keep their color-saturations and reduce the gray-scale merging with Logarithmic histogram equalization algorithm. Test results with dim color images present a better effect of image enhancement.

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Wang, Ping, Zheng Wei, Weihong Cui y Zhiyong Lin. "A MINIMUM SPANNING TREE BASED METHOD FOR UAV IMAGE SEGMENTATION". ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-7 (7 de junio de 2016): 111–17. http://dx.doi.org/10.5194/isprsannals-iii-7-111-2016.

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This paper proposes a Minimum Span Tree (MST) based image segmentation method for UAV images in coastal area. An edge weight based optimal criterion (merging predicate) is defined, which based on statistical learning theory (SLT). And we used a scale control parameter to control the segmentation scale. Experiments based on the high resolution UAV images in coastal area show that the proposed merging predicate can keep the integrity of the objects and prevent results from over segmentation. The segmentation results proves its efficiency in segmenting the rich texture images with good boundary of objects.

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Wang, Ping, Zheng Wei, Weihong Cui y Zhiyong Lin. "A MINIMUM SPANNING TREE BASED METHOD FOR UAV IMAGE SEGMENTATION". ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-7 (7 de junio de 2016): 111–17. http://dx.doi.org/10.5194/isprs-annals-iii-7-111-2016.

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This paper proposes a Minimum Span Tree (MST) based image segmentation method for UAV images in coastal area. An edge weight based optimal criterion (merging predicate) is defined, which based on statistical learning theory (SLT). And we used a scale control parameter to control the segmentation scale. Experiments based on the high resolution UAV images in coastal area show that the proposed merging predicate can keep the integrity of the objects and prevent results from over segmentation. The segmentation results proves its efficiency in segmenting the rich texture images with good boundary of objects.

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Le, Thanh Dat, Seong Young Kwon y Changho Lee. "Performance Comparison of Feature Generation Algorithms for Mosaic Photoacoustic Microscopy". Photonics 8, n.º 9 (25 de agosto de 2021): 352. http://dx.doi.org/10.3390/photonics8090352.

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Mosaic imaging is a computer vision process that is used for merging multiple overlapping imaging patches into a wide-field-of-view image. To achieve a wide-field-of-view photoacoustic microscopy (PAM) image, the limitations of the scan range of PAM require a merging process, such as marking the location of patches or merging overlapping areas between adjacent images. By using the mosaic imaging process, PAM shows a larger field view of targets and preserves the quality of the spatial resolution. As an essential process in mosaic imaging, various feature generation methods have been used to estimate pairs of image locations. In this study, various feature generation algorithms were applied and analyzed using a high-resolution mouse ear PAM image dataset to achieve and optimize a mosaic imaging process for wide-field PAM imaging. We compared the performance of traditional and deep learning feature generation algorithms by estimating the processing time, the number of matches, good matching ratio, and matching efficiency. The analytic results indicate the successful implementation of wide-field PAM images, realized by applying suitable methods to the mosaic PAM imaging process.

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Searcy, Craig, Ken Dean y William Stringer. "Merging remotely sensed data with geophysical models". Polar Record 31, n.º 178 (julio de 1995): 297–304. http://dx.doi.org/10.1017/s003224740001384x.

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AbstractGeophysical models are usually derived from the idealistic viewpoint that all required external parameters are, in principle, measurable. The models are then driven with the best available data for those parameters. In some cases, there are few measurements available, because of factors such as the location of the phenomena modeled. Satellite imagery provides a synoptic overview of a particular environment, supplying spatial and temporal variability as well as spectral data, making this an ideal source of data for some models. In other cases, although frequent satellite-image observations are available, they are of little use to the modeler, because they do not provide values for the parameters demanded by the model. This paper contains two examples of geophysical models that were derived expressly to utilize measurements and qualitative observations taken from satellite images as the major driving elements of the model. The methodology consists of designing a model such that it can be ‘run’ by numerical data extracted from image data sets, and using the image data for verification of the model or adjustment of parameters. The first example is a thermody namic model of springtime removal of nearshore ice from an Arctic river delta area, using the Mackenzie River as a study site. In this example, a multi-date sequence of AVHRR images is used to provide the spatial and temporal patterns of melt, allowing the required physical observations in the model to be parameterized and tested. The second example is a dynamic model simulating thq evolution of a volcanic ash cloud under the influence of atmospheric winds. In this case, AVHRR images are used to determine the position and size of the ash cloud as a function of time, allowing tuning of parameters and verification of the model.

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Li, Qiang Qiang y Wei Li. "An Improved Watershed Segmentation Algorithm for Bridge Image". Applied Mechanics and Materials 513-517 (febrero de 2014): 3691–94. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.3691.

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In order to solve the problem of over-segmentation of traditional watershed algorithm, an improved watershed segmentation algorithm of the bridge image was proposed in this paper. First, the input image was filtered by top-hat transformation and bottom-hat transformation, and then, a multiscale algorithm for computing morphological gradient images is proposed, and the threshold for marker-extraction is automatically calculated according to the statistics of local extreme points in the gradient map. The watershed algorithm is applied on the modified gradient map to segment the image. Then, the over-segmentation regions of the initial watershed segmentation is settled by region merging based on fisher distance.Region merging is ended according to divergence principle. Many contrast experimental results verified the feasibility and validity of the method.

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Sima, Haifeng, Aizhong Mi, Zhiheng Wang y Youfeng Zou. "Objectness Supervised Merging Algorithm for Color Image Segmentation". Mathematical Problems in Engineering 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/3180357.

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Ideal color image segmentation needs both low-level cues and high-level semantic features. This paper proposes a two-hierarchy segmentation model based on merging homogeneous superpixels. First, a region growing strategy is designed for producing homogenous and compact superpixels in different partitions. Total variation smoothing features are adopted in the growing procedure for locating real boundaries. Before merging, we define a combined color-texture histogram feature for superpixels description and, meanwhile, a novel objectness feature is proposed to supervise the region merging procedure for reliable segmentation. Both color-texture histograms and objectness are computed to measure regional similarities between region pairs, and the mixed standard deviation of the union features is exploited to make stop criteria for merging process. Experimental results on the popular benchmark dataset demonstrate the better segmentation performance of the proposed model compared to other well-known segmentation algorithms.

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Naidu, A. Rajesh y D. Bhavana. "Multimodal Medical Image Fusion using Hybrid Domains". Scalable Computing: Practice and Experience 23, n.º 4 (22 de diciembre de 2022): 225–32. http://dx.doi.org/10.12694/scpe.v23i4.2022.

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In a variety of clinical applications, image fusion is critical for merging data from multiple sources into a single, more understandable outcome. The use of medical image fusion technologies to assist the physician in executing combination procedures can be advantageous. The diagnostic process includes preoperative planning, intra operative supervision, an interventional treatment. In this thesis, a technique for image fusion was suggested that used a combination model of PCA and CNN. A method of real-time image fusion that employs pre-trained neural networks to synthesize a single image from several sources in real-time. A innovative technique for merging the images is created based on deep neural network feature maps and a convolution network. Picture fusion has become increasingly popular as a result of the large variety of capturing techniques available. The proposed design is implemented using deep learning technique. The accuracy of the proposed design is around 15% higher than the existing design. The proposed fusion algorithm is verified through a simulation experiment on different multimodality images. Experimental results are evaluated by the number of well-known performance evaluation metrics

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Kumar, Umesh, Neha Gopaliya, Uma Sharma y Sandeep Gupta. "Discrete Transform Based Image Fusion". International Journal of Multimedia Data Engineering and Management 8, n.º 2 (abril de 2017): 43–49. http://dx.doi.org/10.4018/ijmdem.2017040105.

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With the advancement of image processing, the distinct area of image fusion has been explored. The word fusion represents a way of obtaining data acquired in several domains. A technique of merging useful data from input images is defined as image fusion. It improves features and performance. Fused image includes all the important features of input images without introducing any artifacts. This paper depicts the basic of image fusion and fusion techniques. Paper mainly focuses on frequency domain techniques. Image fusion widely used in surveillance, medical diagnosis, biometric, enhanced vision system and remote sensing.

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Xu, Zhi Yuan, Yong Kai Wang, Xiao Hong Su y Yi Liu. "A Defogging Algorithm Based on Statistical Region Merging". Applied Mechanics and Materials 644-650 (septiembre de 2014): 2189–93. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2189.

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Different regions in Foggy images have different contrast attenuations. This paper presented a defogging algorithm based on Statistical Region Merging. Firstly, we segmented the image by Mean Shift; Secondly, we merged these divided regions through Statistical Region Merging; Finally, we enhanced the contrast of different regions by Dynamic Segmentation Histogram Equalization contrast enhancement. The experimental results show that our defogging method is more effective than other methods.

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Umam, Khoirul, Fidi Wincoko Putro y Gulpi Qorik Oktagalu Pratamasunu. "Segmentasi pada Citra Panoramik Gigi dengan Metode Two-Stage SOM dan T-CLUSTER". Jurnal ULTIMA Computing 6, n.º 1 (1 de junio de 2014): 7–13. http://dx.doi.org/10.31937/sk.v6i1.289.

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Segmentation on medical image requires good quality due to affect the interpretation and diagnosis of medical experts. On medical image segmentation, there is merging phase to increase the quality of the segmentation result. However, stopping criteria on merging phase was determined manually by medical experts. It implied the subjectivity of segmentation result. To increase the objectivity of segmentation result, a method to automate merging phase on medical image segmentation is required. Therefore, we propose a novel method on medical image segmentation which combine two-stage SOM and T-cluster method. Experiments were performed on dental panoramic as medical image sample and evaluated by using segmentation quality formula. Experiments show that the proposed method can perform segmentation on dental panoramic image automatically and objectively with the best average of segmentation quality value is 4,40. Index Terms—dental panoramic image, image segmentation, medical image, Self-Organizing Map, T-cluster

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ChiMan Pun y NingYu An. "Image Segmentation using Effective Region Merging Strategy". International Journal of Digital Content Technology and its Applications 5, n.º 8 (31 de agosto de 2011): 59–69. http://dx.doi.org/10.4156/jdcta.vol5.issue8.8.

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Chongbo Zhou y Chuancai Liu. "Interactive Image Semantic Segmentation using Region Merging". International Journal of Digital Content Technology and its Applications 6, n.º 15 (31 de agosto de 2012): 35–44. http://dx.doi.org/10.4156/jdcta.vol6.issue15.5.

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Bo Peng, Lei Zhang y D. Zhang. "Automatic Image Segmentation by Dynamic Region Merging". IEEE Transactions on Image Processing 20, n.º 12 (diciembre de 2011): 3592–605. http://dx.doi.org/10.1109/tip.2011.2157512.

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Archer, Jesse, Geoff Leach y Ron van Schyndel. "GPU based techniques for deep image merging". Computational Visual Media 4, n.º 3 (4 de agosto de 2018): 277–85. http://dx.doi.org/10.1007/s41095-018-0118-8.

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Jian~, WANG. "High Resolution Image Merging Based on EMD". National Remote Sensing Bulletin, n.º 1 (2007): 55–61. http://dx.doi.org/10.11834/jrs.20070108.

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Javid, Tariq, Muhammad Faris y Pervez Akhtar. "Integrated representation for discrete Fourier and wavelet transforms using vector notation". Mehran University Research Journal of Engineering and Technology 41, n.º 3 (1 de julio de 2022): 175–84. http://dx.doi.org/10.22581/muet1982.2203.18.

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Many mathematical operations are implemented easily through transform domain operations. Multiple transform domain operations are used independently in large and complex applications. There is a need to develop integrated representations for multiple transform domain operations. This paper presents an integrated mathematical representation for the discrete Fourier transformation and the discrete wavelet transformation. The proposed combined representation utilizes the powerful vector notation. A mathematical operator, called the star operator, is formulated that merges coefficients from different transform domains. The star operator implements both convolution and correlation processes in a weighted fashion to compute the aggregated representation. The application of the proposed mathematical formulation is demonstrated successfully through merging transform domain representations of time-domain and image-domain representations. Heart sound signals and magnetic resonance images are used to describe transform-domain data merging applications. The significance of the proposed technique is demonstrated through merging time-domain and image-domain representations in a single- stage that may be implemented as the primary processing engine inside a typical digital image processing and analysis system.

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Panguluri, Sumanth Kumar y Laavanya Mohan. "Discrete Wavelet Transform Based Image Fusion Using Unsharp Masking". Periodica Polytechnica Electrical Engineering and Computer Science 64, n.º 2 (2 de diciembre de 2019): 211–20. http://dx.doi.org/10.3311/ppee.14702.

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Nowadays the result of infrared and visible image fusion has been utilized in significant applications like military, surveillance, remote sensing and medical imaging applications. Discrete wavelet transform based image fusion using unsharp masking is presented. DWT is used for decomposing input images (infrared, visible). Approximation and detailed coefficients are generated. For improving contrast unsharp masking has been applied on approximation coefficients. Then for merging approximation coefficients produced after unsharp masking average fusion rule is used. The rule that is used for merging detailed coefficients is max fusion rule. Finally, IDWT is used for generating a fused image. The result produced using the proposed fusion method is providing good contrast and also giving better performance results in reference to mean, entropy and standard deviation when compared with existing techniques.

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Haiyan Yu, Haiyan Yu y Jihong Wang Haiyan Yu. "Infrared Image Segmentation for Power Equipment Using Linear Spectral Clustering and Maximal Similarity-based Region Merging". 電腦學刊 33, n.º 1 (febrero de 2022): 043–53. http://dx.doi.org/10.53106/199115992022023301005.

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<p>The diagnostic method of power equipment based on infrared images is widely used because it has the advantages of non-contact and does not affect the online operation of power equipment. However, in actual using, the power equipment diagnosis method based on infrared image still relies on manual judgment, that is, the detection personnel can judge the fault according to the obtained infrared image of power equipment by experience. This process consumes a lot of time, and the subjectivity is strong, misjudgment rate is higher, which cannot meet the requirements of modern smart grid development. Infrared image of power equipment contains a lot of noise, and the edge is fuzzy. In this paper, we propose a new infrared image segmentation method for power equipment by using linear spectral clustering and maximal similarity-based region merging under complex backgrounds. In this method, the linear spectral clustering algorithm (LSC) is used to segment the image into super-pixels, and the pixels with similar color and distance are clustered to the same center. The calculated OTSU threshold based on the global image is used to pre-label the background of each super-pixel block. The maximum similarity-based region merging algorithm (MSRM) is utilized to merge the super-pixel blocks. Meanwhile, it obtains the target equipment, the over-segmentation and under-segmentation rates are reduced effectively. Finally, the mathematical morphology algorithm is used to post-process the image. Experimental results show that, compared with other algorithms, this new method can obtain more accurate and complete target equipment under complex background. </p> <p> </p>

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Cheng, Cun y Li Zhang. "An efficient segmentation method based on dynamic graph merging". International Journal of Wavelets, Multiresolution and Information Processing 14, n.º 06 (noviembre de 2016): 1650052. http://dx.doi.org/10.1142/s0219691316500521.

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A novel energy functional based on the Mumford–Shah model is established for performing automatic image segmentation. And in order to optimize the global model using graph-based methods, we develop a localized formula. Then, we propose a merging predicate for determining whether an edge connecting two neighboring pixels or regions merge. The dynamic graph merging (DGM) method is applied based on this merging predicate. That is, those edges with large energy merge and the edges with low energy are remained, such that the energy functional is minimized. Compared with other graph-based segmentation methods, our algorithm based on DGM has an important characteristic which is its ability to produce good segmentation on some complex texture images. Another characteristic is that this segmentation algorithm can avoid the “shrinking bias” problem. We also apply DGM to interactive image segmentation and find the results to be encouraging too.

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Juang, Ying-Shen, Hsi-Chin Hsin, Tze-Yun Sung, Yaw-Shih Shieh y Carlo Cattani. "A Rate-Distortion-Based Merging Algorithm for Compressed Image Segmentation". Computational and Mathematical Methods in Medicine 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/648320.

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Original images are often compressed for the communication applications. In order to avoid the burden of decompressing computations, it is thus desirable to segment images in the compressed domain directly. This paper presents a simple rate-distortion-based scheme to segment images in the JPEG2000 domain. It is based on a binary arithmetic code table used in the JPEG2000 standard, which is available at both encoder and decoder; thus, there is no need to transmit the segmentation result. Experimental results on the Berkeley image database show that the proposed algorithm is preferable in terms of the running time and the quantitative measures: probabilistic Rand index (PRI) and boundary displacement error (BDE).

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TANTI, MARC, ALBERT GATT y KENNETH P. CAMILLERI. "Where to put the image in an image caption generator". Natural Language Engineering 24, n.º 3 (23 de abril de 2018): 467–89. http://dx.doi.org/10.1017/s1351324918000098.

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AbstractWhen a recurrent neural network (RNN) language model is used for caption generation, the image information can be fed to the neural network either by directly incorporating it in the RNN – conditioning the language model by ‘injecting’ image features – or in a layer following the RNN – conditioning the language model by ‘merging’ image features. While both options are attested in the literature, there is as yet no systematic comparison between the two. In this paper, we empirically show that it is not especially detrimental to performance whether one architecture is used or another. The merge architecture does have practical advantages, as conditioning by merging allows the RNN’s hidden state vector to shrink in size by up to four times. Our results suggest that the visual and linguistic modalities for caption generation need not be jointly encoded by the RNN as that yields large, memory-intensive models with few tangible advantages in performance; rather, the multimodal integration should be delayed to a subsequent stage.

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Xiong, Ding y Lu Yan. "Early smoke detection of forest fires based on SVM image segmentation". Journal of Forest Science 65, No. 4 (26 de abril de 2019): 150–59. http://dx.doi.org/10.17221/82/2018-jfs.

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A smoke detection method is proposed in single-frame video sequence images for forest fire detection in large space and complex scenes. A new superpixel merging algorithm is further studied to improve the existing horizon detection algorithm. This method performs Simple Linear Iterative Clustering (SLIC) superpixel segmentation on the image, and the over-segmentation problem is solved with a new superpixel merging algorithm. The improved sky horizon line segmentation algorithm is used to eliminate the interference of clouds in the sky for smoke detection. According to the spectral features, the superpixel blocks are classified by support vector machine (SVM). The experimental results show that the superpixel merging algorithm is efficient and simple, and easy to program. The smoke detection technology based on image segmentation can eliminate the interference of noise such as clouds and fog on smoke detection. The accuracy of smoke detection is 77% in a forest scene, it can be used as an auxiliary means of monitoring forest fires. A new attempt is given for forest fire warning and automatic detection.

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Xiao, Yifan, Peter Veelaert y Wilfried Philips. "Deep HDR Deghosting by Motion-Attention Fusion Network". Sensors 22, n.º 20 (16 de octubre de 2022): 7853. http://dx.doi.org/10.3390/s22207853.

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Multi-exposure image fusion (MEF) methods for high dynamic range (HDR) imaging suffer from ghosting artifacts when dealing with moving objects in dynamic scenes. The state-of-the-art methods use optical flow to align low dynamic range (LDR) images before merging, introducing distortion into the aligned LDR images from inaccurate motion estimation due to large motion and occlusion. In place of pre-alignment, attention-based methods calculate the correlation between the reference LDR image and non-reference LDR images, thus excluding misaligned regions in LDR images. Nevertheless, they also exclude the saturated details at the same time. Taking advantage of both the alignment and attention-based methods, we propose an efficient Deep HDR Deghosting Fusion Network (DDFNet) guided by optical flow and image correlation attentions. Specifically, the DDFNet estimates the optical flow of the LDR images by a motion estimation module and encodes that optical flow as a flow feature. Additionally, it extracts correlation features between the reference LDR and other non-reference LDR images. The optical flow and correlation features are employed to adaptably combine information from LDR inputs in an attention-based fusion module. Following the merging of features, a decoder composed of Dense Networks reconstructs the HDR image without ghosting. Experimental results indicate that the proposed DDFNet achieves state-of-the-art image fusion performance on different public datasets.

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Sharifzadeh, Mehdi, Mohammed Aloraini y Dan Schonfeld. "Adaptive Batch Size Image Merging Steganography and Quantized Gaussian Image Steganography". IEEE Transactions on Information Forensics and Security 15 (2020): 867–79. http://dx.doi.org/10.1109/tifs.2019.2929441.

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Baqer Qazzaz, Ali Abdulazeez Mohammed y ABDULHUSSEIN ABDULMOHSON. "A NOVEL PROPOSED FUSION METHOD". International Research Journal of Computer Science 8, n.º 4 (30 de abril de 2021): 74–83. http://dx.doi.org/10.26562/irjcs.2021.v0804.002.

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In this paper, two proposed fusion methods will be proposed for merging important information from more than one source for the purpose of either making final image with high quality compared to the input images or making final image holding more information that captured in the input images depending on some important extracted features from the input images. The first method merge images depending on edges information in each pixel by applying one order prewitt method for edge detection then calculating the ratio related to each image for this position then calculating new pixel value for this place in the output image, while the second proposed method working by estimating texture unit number value in each pixel in the input images and merging pixels by applying proposed techniques for the purpose of calculating output pixel which reflect good degree of the proposed feature with accepted compatible degree with neighbouring eight pixels. The results of the proposed methods checked with some suitable metrics for measuring the quality of the output images compared with the standard methods like maximum, minimum, ratio, wavelet,… and the result of the proposed method reflect good and perfect quality for the output images and this result can be expected because fusion operation performed according to effected visible features that making fused images with good quality.

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Ciupala, Laura, Adrian DEACONU y Delia SPRIDON. "ALGORITHM FOR MERGING AND INTERPOLATING CLUSTERS IN OVERLAPPING IMAGES". SERIES III - MATEMATICS, INFORMATICS, PHYSICS 13(62), n.º 2 (20 de enero de 2021): 697–704. http://dx.doi.org/10.31926/but.mif.2020.13.62.2.25.

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An image overlapping algorithm, taking into account certain properties of objects identified in the images (average intensity, movement speed, etc) is proposed. The algorithm minimizes both memory and time complexity and it can be used in various applications, especially in medical imaging analysis. The idea behind the proposed algorithm is surface merging and interpolation.

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Huang, Zuo Wei, Shu Guang Wu y Tao Xin Zhang. "A Novel Algorithm for Image Data Segmentation". Advanced Materials Research 971-973 (junio de 2014): 1499–503. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.1499.

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With the rapid development of spatial information technology and the increasingly artificial intelligence knowledge, MAS plays a more and more important role in conducting image segmentation.Considering the shortcomings of current segmentation method,a new algorithm based on MAS theory is proposed, It combines spectral and shape information in region merging.employer a number of agents to control the merging procedure in different regions and make the global merging control more optimal by utilizing the advantages of MAS,The results show that the algorithm is very effective for image segmentation both in urban and mountainous areas.

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Shailesh Shivaji Deore, Renu Aggarwal, Vaishali Turai, Rajendra V. Patil, Ritesh Sonawane, Gvoind M. Poddar,. "Automatic Marker Generation to Similarity Based Region Merging Algorithm using Edge Information". Journal of Electrical Systems 20, n.º 3s (4 de abril de 2024): 2227–40. http://dx.doi.org/10.52783/jes.1845.

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One of the most important tasks in computer vision is image segmentation. Several interactive strategies are utilized for picture segmentation because automated techniques are difficult for this kind of work. The outcome of interactive methods is mostly determined by user input. Obtaining excellent interactions for huge datasets is challenging. However, automated picture segmentation is starting to play a significant role in image analysis and computer vision. Effective and efficient segmentation outcomes are obtained using the interacting region merging method suggested by Maximal Similarity based region merging algorithm. Limitation of MSBRM is it requires some efforts on the part of users and is yet not a fully-automatic approach. Here, we suggest a completely fresh unsupervised image segmentation method that combines edge information with MSBRM. We propose an integrated framework to generate object markers for similarity based region algorithm using edge information. Long edges give rough distribution of objects in image. After retrieving edges using phase congruency, edge processing operations are employed to remove small edges and to group color similar long boundaries. Centroids of long boundaries are used as object markers to the MSBRM algorithm. The generation of object markers is done using edge segment grouping. These object markers guide the region merging process. The proposed method shows its effectiveness in segmenting natural real world color images.

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Wu, Wen Huan, Ying Jun Zhao, Dong Hua Lu y Dong Hui Zhang. "The Extraction Research of Urban Road Information Based on the High Resolution QuickBird Image". Advanced Materials Research 718-720 (julio de 2013): 2136–41. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.2136.

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This paper studied the key technology of the extraction of urban road information using high-resolution QuickBird image. And this paper discussed the road information extraction algorithms, K-mean image clustering and segmentation algorithm, lambda-schedule image merging algorithm and the construction of knowledge database using spectral and shape features, from three angles, image segmentation algorithm, image merging algorithm and specific road information extraction algorithm. Studies showed that, after the introduction of the spectral and texture information, road extraction method reached a higher area and shape consistency, and provide a valuable reference for related research in the field.

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MAKROGIANNIS, S., G. ECONOMOU y S. FOTOPOULOS. "A FUZZY DISSIMILARITY FUNCTION FOR REGION BASED SEGMENTATION OF COLOR IMAGES". International Journal of Pattern Recognition and Artificial Intelligence 15, n.º 02 (marzo de 2001): 255–67. http://dx.doi.org/10.1142/s0218001401000861.

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In this paper a novel Fuzzy Rule Based Dissimilarity Function is presented, to determine the hierarchical merging sequence in a region based segmentation scheme. The proposed technique, based on distinct region features and fuzzy logic principles, is designed to cope with the problems inherent in the segmentation task that the traditional merging cost functions cannot overcome. It combines the global (color) and local (spatial) information of the image to compare two adjacent regions in the rgb space. The validity of the approach has been subjectively and objectively verified for several types of color images such as head and shoulders, natural and texture images.

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Li, Guoqing, Guoping Zhang, Chanchan Qin y Anqin Lu. "Automatic RGBD Object Segmentation Based on MSRM Framework Integrating Depth Value". International Journal on Artificial Intelligence Tools 29, n.º 07n08 (30 de noviembre de 2020): 2040009. http://dx.doi.org/10.1142/s0218213020400096.

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In this paper, an automatic RGBD object segmentation method is described. The method integrates depth feature with the cues from RGB images and then uses maximal similarity based region merging (MSRM) method to obtain the segmentation results. Firstly, the depth information is fused to the simple linear iterative clustering (SLIC) method so as to produce superpixels whose boundaries are well adhered to the edges of the natural image. Meanwhile, the depth prior is also incorporated into the saliency estimation, which helps a more accurate localization of representative object and background seeds. By introducing the depth cue into the region merging rule, the maximal geometry weighted similarity (MGWS) is considered, and the resulting segmentation framework has the ability to handle the complex image with similar colour appearance between object and background. Extensive experiments on public RGBD image datasets show that our proposed approach can reliably and automatically provide very promising segmentation results.

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Greer, Sarah y Sergey Fomel. "Matching and merging high-resolution and legacy seismic images". GEOPHYSICS 83, n.º 2 (1 de marzo de 2018): V115—V122. http://dx.doi.org/10.1190/geo2017-0238.1.

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When multiple seismic surveys are acquired over the same area using different technologies that produce data with different frequency content, it may be beneficial to combine these data to produce a broader bandwidth volume. We have developed a workflow for matching and blending seismic images obtained from shallow high-resolution seismic surveys and conventional surveys conducted over the same area. The workflow consists of three distinct steps: (1) balancing the amplitudes and frequency content of the two images by nonstationary smoothing of the high-resolution image, (2) estimating and removing variable time shifts between the two images, and (3) blending the two images together by least-squares inversion. Our workflow is applied successfully to images from the Gulf of Mexico.

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Dong, Xuan, Xiaoyan Hu, Weixin Li, Xiaojie Wang y Yunhong Wang. "MIEHDR CNN: Main Image Enhancement based Ghost-Free High Dynamic Range Imaging using Dual-Lens Systems". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 2 (18 de mayo de 2021): 1264–72. http://dx.doi.org/10.1609/aaai.v35i2.16214.

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We study the High Dynamic Range (HDR) imaging problem using two Low Dynamic Range (LDR) images that are shot from dual-lens systems in a single shot time with different exposures. In most of the related HDR imaging methods, the problem is usually solved by Multiple Images Merging, i.e. the final HDR image is fused from pixels of all the input LDR images. However, ghost artifacts can be hardly avoided using this strategy. Instead of directly merging the multiple LDR inputs, we use an indirect way which enhances the main image, i.e. the short exposure image IS, using the long exposure image IL serving as guidance. In detail, we propose a new model, named MIEHDR CNN model, which consists of three subnets, i.e. Soft Warp CNN, 3D Guided Denoising CNN and Fusion CNN. The Soft Warp CNN aligns IL to get the aligned result ILA using the soft exposed result of IS as reference. The 3D Guided Denoising CNN denoises the soft exposed result of IS using ILA as guidance, whose result are fed into the Fusion CNN with IS to get the HDR result. The MIEHDR CNN model is implemented by MindSpore and experimental results show that we can outperform related methods largely and avoid ghost artifacts.

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