Forward Rasterization - Purdue Computer Science
... are used to create a continuous texture which is used at run time to color the visible surface elements. The method enables high-quality anisotropic filtering within the surface and antialiases edges using coverage masks, bringing to point-based rendering what was previously possible only for polygo ...
... are used to create a continuous texture which is used at run time to color the visible surface elements. The method enables high-quality anisotropic filtering within the surface and antialiases edges using coverage masks, bringing to point-based rendering what was previously possible only for polygo ...
Report of research activities in fuzzy AI and medicine at
... have been developed to detect brain and breast tumors. We brie¯y present only one of these methods. Further information can be obtained from the quoted resources and references. A knowledge-based system integrated with unsupervised fuzzy clustering to automatically segment and label tumors in magnet ...
... have been developed to detect brain and breast tumors. We brie¯y present only one of these methods. Further information can be obtained from the quoted resources and references. A knowledge-based system integrated with unsupervised fuzzy clustering to automatically segment and label tumors in magnet ...
Efficient Histogram Generation Using Scattering on GPUs
... As outlined in section 1, Green’s histogram generation algorithm performs one render pass per bucket, so its asymptotic complexity is O(NB). That of Fluck et al.’s algorithm is also O(NB), because it requires B/4 texture fetches for each input pixel. Assuming we have enough precision in the histogra ...
... As outlined in section 1, Green’s histogram generation algorithm performs one render pass per bucket, so its asymptotic complexity is O(NB). That of Fluck et al.’s algorithm is also O(NB), because it requires B/4 texture fetches for each input pixel. Assuming we have enough precision in the histogra ...
Following non-stationary distributions by controlling the
... The minimization of E is performed by successive stages, until some stopping condition is met. Some methods proceed minimization from a given finite set of examples, chosen from PX , and minimizes the distortion measured on this set [15,11]. Some other methods works on-line, since examples are prov ...
... The minimization of E is performed by successive stages, until some stopping condition is met. Some methods proceed minimization from a given finite set of examples, chosen from PX , and minimizes the distortion measured on this set [15,11]. Some other methods works on-line, since examples are prov ...
Detection and Tracking of Liquids with Fully Convolutional Networks
... The labels are generated for each object (liquid, cup, bowl) as follows. First, all other objects in the scene are set to render as invisible. Next, the material for the object is set to render as a specific, solid color, ignoring lighting. The sequence is then rendered, yielding a class label for t ...
... The labels are generated for each object (liquid, cup, bowl) as follows. First, all other objects in the scene are set to render as invisible. Next, the material for the object is set to render as a specific, solid color, ignoring lighting. The sequence is then rendered, yielding a class label for t ...
Offline Arabic Character Recognition using Genetic Approach
... character recognition, and pre-processing included median and mathematical morphological filtering, linearization, scaling, and centering. Regional projection contour transformation (RPCT) was used , so the image was projected in multiple directions (here, horizontal and vertical), and the chaincode ...
... character recognition, and pre-processing included median and mathematical morphological filtering, linearization, scaling, and centering. Regional projection contour transformation (RPCT) was used , so the image was projected in multiple directions (here, horizontal and vertical), and the chaincode ...
Ms.-Vishakha-R.-Bhadane-el-al
... characteristics. Fingerprint identification and recognition is one of the biometric methods available that has been widely used in variousapplications due to its reliability and accuracy in the process of identification and authentication of a person. The main objective of this work is to develop fi ...
... characteristics. Fingerprint identification and recognition is one of the biometric methods available that has been widely used in variousapplications due to its reliability and accuracy in the process of identification and authentication of a person. The main objective of this work is to develop fi ...
Algorithms for modeling anatomic and target volumes in
... improves the accuracy of the generated volumetric models. Various techniques to improve region growing are also presented. The simplex search method and combinatory similarity terms were used to improve the similarity function with a low additional computational cost and high yield in region correct ...
... improves the accuracy of the generated volumetric models. Various techniques to improve region growing are also presented. The simplex search method and combinatory similarity terms were used to improve the similarity function with a low additional computational cost and high yield in region correct ...
An Analytical Study of the Remote Sensing Image Classification
... Firefly Algorithm etc. Swarm Intelligence is a global research area to improve the optimization of various soft computing and nature inspired techniques. In this paper, we are using these swarm intelligence based techniques for the accurately classification of land cover features. For this classific ...
... Firefly Algorithm etc. Swarm Intelligence is a global research area to improve the optimization of various soft computing and nature inspired techniques. In this paper, we are using these swarm intelligence based techniques for the accurately classification of land cover features. For this classific ...
Dynamic Programming and Graph Algorithms in Computer Vision
... these methods have severe problems near object boundaries, since the majority of pixels near a pixel p may not support the correct label for p. This shows up in the very poor performance of area-based methods on the standard stereo benchmarks [92]. Optimization provides a natural way to address pixe ...
... these methods have severe problems near object boundaries, since the majority of pixels near a pixel p may not support the correct label for p. This shows up in the very poor performance of area-based methods on the standard stereo benchmarks [92]. Optimization provides a natural way to address pixe ...
Volumetric MRI Classification for Alzheimer`s Diseases Based on
... diseases1,2. Morphological analysis of medical images is therefore used in a variety of research and clinical studies that detect and quantify spatially complex and often subtle abnormal imaging patterns of pathology. In neurodegenerative diseases (i.e. the Alzheimer’s disease, AD), the pattern of b ...
... diseases1,2. Morphological analysis of medical images is therefore used in a variety of research and clinical studies that detect and quantify spatially complex and often subtle abnormal imaging patterns of pathology. In neurodegenerative diseases (i.e. the Alzheimer’s disease, AD), the pattern of b ...
IOSR Journal of Electronics and Communication Engineering (IOSR-JECE)
... 3.2 Segmentation Image segmentation is the process of partitioning a digital image into multiple segments. It includes image enhancement, extraction of leaves from background and aligning the leaf image horizontally. The aim of image enhancement [11] is to improve the interpretability or perception ...
... 3.2 Segmentation Image segmentation is the process of partitioning a digital image into multiple segments. It includes image enhancement, extraction of leaves from background and aligning the leaf image horizontally. The aim of image enhancement [11] is to improve the interpretability or perception ...
Comparison of Handwriting characters Accuracy using
... are then classified by the k-Nearest Neighbors algorithm using the Euclidean distance as function for computing distances between data points. In that study, the classification rates obtained from the hotspot, mark direction and direction of chain code techniques were compared. The results showed th ...
... are then classified by the k-Nearest Neighbors algorithm using the Euclidean distance as function for computing distances between data points. In that study, the classification rates obtained from the hotspot, mark direction and direction of chain code techniques were compared. The results showed th ...
Scalable spatial event representation
... 2. VISUAL THESAURUS An image analysis framework requires a representation that allows fast data processing, meaningful data summarization, scalability with respect to dataset size and dimension, multi-feature representation, and efficient data understanding. Limited success towards this end has been ...
... 2. VISUAL THESAURUS An image analysis framework requires a representation that allows fast data processing, meaningful data summarization, scalability with respect to dataset size and dimension, multi-feature representation, and efficient data understanding. Limited success towards this end has been ...
A Watershed-based Social Events Detection Method with Support of
... In this paper, a watershed-based method with support from external data sources is proposed to detect Social Events defined by MediaEval 2012. This method is based on two main observations: (1) people cannot be involved in more than one event at the same time, and (2) people tend to introduce simila ...
... In this paper, a watershed-based method with support from external data sources is proposed to detect Social Events defined by MediaEval 2012. This method is based on two main observations: (1) people cannot be involved in more than one event at the same time, and (2) people tend to introduce simila ...
Image segmentation
In computer vision, image segmentation is the process of partitioning a digital image into multiple segments (sets of pixels, also known as superpixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics.The result of image segmentation is a set of segments that collectively cover the entire image, or a set of contours extracted from the image (see edge detection). Each of the pixels in a region are similar with respect to some characteristic or computed property, such as color, intensity, or texture. Adjacent regions are significantly different with respect to the same characteristic(s).When applied to a stack of images, typical in medical imaging, the resulting contours after image segmentation can be used to create 3D reconstructions with the help of interpolation algorithms like Marching cubes.