Image segmentation is still a challenging issue in pattern recognition. Among the various segmentation approaches are those based on graph partitioning, which present some drawbacks, one being high processing times. W...
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Searching for people through their personal traits has been largely required for several areas and, consequently, has become the center of attention in the scientific community. Locating a suspect or finding missing p...
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this book contains five survey papers written on the topics of the tutorials presented atthe 25th sibgrapi - conference on graphics, patterns and images, held in Ouro Preto, Minas Gerais, Brazil from August 22-25, 20...
this book contains five survey papers written on the topics of the tutorials presented atthe 25th sibgrapi - conference on graphics, patterns and images, held in Ouro Preto, Minas Gerais, Brazil from August 22-25, 2012. this is the fourth year thattutorial papers from sibgrapi are published by IEEE CPS. the authors of accepted tutorials are invited to write survey papers aboutthe topics and concepts presented during the tutorial sessions. this selection includes survey papers on exciting topics in computer graphics, image processing and computer vision. We hope this material will inspire new exciting research in our fields.
Kinect is a device introduced in November 2010 as an accessory of Xbox 360. the acquired data has different and complementary natures, combining geometry with visual attributes. For this reason, Kinect is a flexible t...
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Kinect is a device introduced in November 2010 as an accessory of Xbox 360. the acquired data has different and complementary natures, combining geometry with visual attributes. For this reason, Kinect is a flexible tool that can be used in applications from several areas such as: Computer graphics, Image Processing, Computer Vision and Human-Machine Interaction. In this way, the Kinect is a widely used device in industry (games, robotics, theater performers, natural interfaces, etc.) and in research. We will initially present some concepts aboutthe device: the architecture and the sensor. We then will discuss aboutthe data acquisition process: capturing, representation and filtering. Capturing process consists of obtaining a colored image (RGB) and performing a depth measurement (D), with structured lighttechnique. this data is represented by a structure called RGBD Image. We will also talk aboutthe main tools available for developing applications on various platforms. Furthermore, we will discuss some recent projects based on RGBD images. In particular, those related to Object Recognition, 3D Reconstruction, Augmented Reality, Image Processing, Robotic, and Interaction. In this survey, we will show some research developed by the academic community and some projects developed for the industry. We intend to show the basic principles to begin developing applications using Kinect, and present some projects developed atthe VISGRAF Lab. And finally, we intend to discuss the new possibilities, challenges and trends raised by Kinect.
In this paper, histogram uniformization of digital images by means of the finite field cosine transform (FFCt) is examined. the approach consists in dividing the image into blocks and applying the FFCt, in a recursive...
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In this paper, histogram uniformization of digital images by means of the finite field cosine transform (FFCt) is examined. the approach consists in dividing the image into blocks and applying the FFCt, in a recursive manner, to each block. Simulations of the procedure show thatthe histogram of the transformed image exhibits a uniform shape and its pixels have low correlation withtheir neighbors. this result is achieved due to the modular arithmetic used in the application of the FFCtto the image blocks. the suitability of the proposed technique in the context of image encryption is discussed.
this paper proposes a new methodology for micro pattern analysis in digital images based on fuzzy numbers. A micro-pattern is the structure of the gray-level pixels within a neighborhood and can describe the spatial c...
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this paper proposes a new methodology for micro pattern analysis in digital images based on fuzzy numbers. A micro-pattern is the structure of the gray-level pixels within a neighborhood and can describe the spatial context of the image, such as edge, line, spot, blob, corner, texture, and more complex patterns. By treating a pixel neighborhood as a fuzzy set and each pixel gray-level as a fuzzy number, we can evaluate the membership degree of the central pixel to the others. We have called this method the Local Fuzzy Pattern (LFP). Using a sigmoid membership function, we proved thatthe proposed methodology surpasses the Hit-rate of the Local Binary Pattern (LBP), for texture classification. the LFP proved to be robustto image rotation. Moreover, our proposed formulation for the LFP is a generalization of previously published techniques, such as texture Unit, LBP, FUNED, and Census transform.
the use of computational techniques in the processing of histopathological images allows the study of the structural organization of tissues and their pathological changes. the overall objective of this work includes ...
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the use of computational techniques in the processing of histopathological images allows the study of the structural organization of tissues and their pathological changes. the overall objective of this work includes the proposal, the implementation and the evaluation of a methodology for the analysis of cervical intraepithelial neoplasia (CIN) from histopathological images. For this purpose, a pipeline of morphological operators were implemented for the segmentation of cell nuclei and the Delaunay triangulation were used in order to representthe tissue architecture. Also, clustering algorithms and graph morphology were used to automatically obtain the boundary between the histological layers of the epithelial tissue. Similarity criteria and adjacency relations between the triangles of the network were explored. the proposed method was evaluated concerning the detection of the presence of lesions in the tissue as well as the their malignancy grading.
Recently, investigations on medical imaging have been indicating a strong correlation between cases of cancers and the increasing number of Computed tomography (Ct) exams, mainly due to high radiation doses to which p...
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Recently, investigations on medical imaging have been indicating a strong correlation between cases of cancers and the increasing number of Computed tomography (Ct) exams, mainly due to high radiation doses to which patients are exposed during the data acquisition process. thus, there is a need to reduce the radiation doses whereas still keeping satisfactory quality images for diagnosis. In this paper, we propose to filter noise in Ctimages using contextual versions of Wiener Filter such as Generalized Wiener Filter (GWF) and Non Local Means approach for parameter estimation. Experiments show thatthe proposed methods are promising, since they provide good results with no significant increase in the computational cost.
Bias field (in homogeneity) correction and skull stripping are initial standard procedures in medical image analysis of the human brain. Several works have investigated the effects of prior in homogeneity correction o...
Bias field (in homogeneity) correction and skull stripping are initial standard procedures in medical image analysis of the human brain. Several works have investigated the effects of prior in homogeneity correction on skull stripping using 1.5 tesla magnetic resonance (MR) images, butthis question remains unanswered for higher magnetic fields. this paper fills this gap using 3 tesla MR-images, by proposing a novel alternate sequence of skull stripping, morphological operations, in homogeneity correction and intensity standardization, withthe first atthe beginning and end of the sequence, denominated Iterative Skull Stripping methodology. Conversely to what happens in 1.5timages, experimental evaluation shows that, in 3 tesla datasets, in homogeneity effect plays an important role in stripping the brain. this observation produces a deep impact on previous and future studies that rely on skull stripping operation.
Image segmentation is still a challenging issue in pattern recognition. Among the various segmentation approaches are those based on graph partitioning, which present some drawbacks, one being high processing times. W...
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Image segmentation is still a challenging issue in pattern recognition. Among the various segmentation approaches are those based on graph partitioning, which present some drawbacks, one being high processing times. Withthe recent developments on complex networks theory, pattern recognition techniques based on graphs have improved considerably. the identification of cluster of vertices can be considered a process of community identification according to complex networks theory. Since data clustering is related with image segmentation, image segmentation can also be approached via complex networks. However, image segmentation based on complex networks poses a fundamental limitation which is the excessive numbers of nodes in the network. this paper presents a complex network approach for large image segmentation that is both accurate and fast. to that, we incorporate the concept of super pixels, to reduce the number of nodes in the network. We evaluate our method for both synthetic and real images. Results show that our method can outperform other graph-based methods both in accuracy and processing times.
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