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检索条件"主题词=Image processing algorithms and systems"

共 335 条记录，以下是151-160 订阅

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Semantic photo synthesis

Semantic photo synthesis

引用

Conference on Human Vision and Electronic Imaging XII

作者： Johnson, Matthew Brostow, G. J. Shotton, J. Kwatra, V. Cipolla, R. Univ Cambridge Cambridge CB2 1TN England UNC Chapel Hill NC USA

ISBN: (纸本)9780819466051

Composite images are synthesized from existing photographs by artists who make concept art, e.g. storyboards for movies or architectural planning. Current techniques allow an artist to fabricate such an image by digitally splicing parts of stock photographs. While these images serve mainly to "quickly" convey how a scene should look, their production is laborious. We propose a technique that allows a person to design a new photograph with substantially less effort. This paper presents a method that generates a composite image when a user types in nouns, such as "boat" and "sand." The artist can optionally design an intended image by specifying other constraints. Our algorithm formulates the constraints as queries to search an automatically annotated image database. The desired photograph, not a collage, is then synthesized using graph-cut optimization, optionally allowing for further user interaction to edit or choose among alternative generated photos. Our results demonstrate our contributions of (1) a method of creating specific images with minimal human effort, and (2) a combined algorithm for automatically building an image library with semantic annotations from any photo collection.

关键词： Photography Databases image processing algorithms and systems image segmentation Composites Human-machine interfaces Interfaces Optimization (mathematics) Photosynthesis Fabrication

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image SEGMENTATION BY WAVELET-BASED AUTOMATIC THRESHOLD SELECTION

IMAGE SEGMENTATION BY WAVELET-BASED AUTOMATIC THRESHOLD SELE...

引用

Meeting on Visual Communications and image Communication 93

作者： OLIVO, JC EUROPEAN MOLEC BIOL LAB PHYS INSTRUMENTAT PROGRAMMED-69012 HEIDELBERGGERMANY

ISBN: (纸本)0819413690

A segmentation method using a peak analysis algorithm for threshold selection is presented. It is based on the detection of the zero-crossings and the local extrema of a wavelet transform which give a complete characterization of the peaks in the histogram. These values are used for the unsupervised selection of a sequence of thresholds describing a coarse-to-fine analysis of histogram variation. The results of using the proposed technique are presented in the case of different images.

关键词： image segmentation Wavelet transforms Wavelets Smoothing image processing algorithms and systems image resolution Digital filtering Discrete wavelet transforms Signal processing Statistical analysis

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ALGORITHM EVALUATION FOR AN INFRARED TARGET DETECTION TEST-BED

ALGORITHM EVALUATION FOR AN INFRARED TARGET DETECTION TEST-B...

引用

CONF ON SIGNAL AND image processing systems PERFORMANCE EVALUATION

作者： LITHOPOULOS, E BENDER, E BRONSKILL, J HEPBURN, J Honeywell Ltd. (Canada)

ISBN: (纸本)081940361X

As part of the development of a real-time JR target processor test bed, a number of image processing algorithms were developed, simulated in software, and evaluated for implementation. algorithms performing image pre-processing, target localization, segmentation and target/clutter discrimination were evaluated using an IR image data base. The algorithms selected are being implemented on the test bed using commercially available board-level components, and are capable of pmcessing imagery at real-time rates (30 frames/see).

关键词： Detection and tracking algorithms image segmentation image processing Algorithm development image processing algorithms and systems Target detection Infrared imaging Signal processing Software development Cerium

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Detection of oranges from a color image of an orange tree

Detection of oranges from a color image of an orange tree

引用

Conference on Applications of Digital image processing XXII

作者： Weeks, AR Gallagher, A Eriksson, J Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA

ISBN: (纸本)0819432946

The progress of robotic and machine vision technology has increased the demand for sophisticated methods for performing automatic harvesting of fruit. The harvesting of fruit, until recently, has been performed manually and is quite labor intensive. An automatic robot harvesting system that uses machine vision to locate and extract the fruit would free the agricultural industry from the ups and downs of the labor market. The environment in which robotic fruit harvesters must work presents many challenges due to the inherent variability from one location to the next. This paper takes a step towards this goal by outlining a machine vision algorithm that detects and accurately locates oranges from a color image of an orange tree. Previous work in this area has focused on differentiating the orange regions from the rest of the picture and not locating the actual oranges themselves. Failure to locate the oranges, however, leads to a reduced number of successful pick attempts. This paper presents a new approach for orange region segmentation in which the circumference of the individual oranges as well as partially occluded oranges are located. Accurately defining the circumference of each orange allows a robotic harvester to cut the stem of the orange by either scanning the top of the orange with a laser or by directing a robotic arm towards the stem to automatically cut it. A modified version of the K-means algorithm is used to initially segment the oranges from the canopy of the orange tree. Morphological processing is then used to locate occluded oranges and an iterative circle finding algorithm is used to define the circumference of the segmented oranges.

关键词： image segmentation image processing algorithms and systems image filtering Robotics Detection and tracking algorithms Binary data Machine vision RGB color model Algorithm development Laser cutting

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SEGMENTATION USING RANGE DATA AND STRUCTURED LIGHT

SEGMENTATION USING RANGE DATA AND STRUCTURED LIGHT

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CONF ON INTELLIGENT ROBOTS AND COMPUTER VISION 9 : algorithms AND TECHNIQUES

作者： HU, GZ Central Michigan Univ. (United States)

ISBN: (纸本)0819404489

A segmentation approach is presented that uses combined information of range data and structured light. Structured light are good for extracting surface patches that cannot be easily obtained from gray scale images. Furthermore surface normal discontinuity can be detected at light stripe flection points. Accidental alignments of light stripes may confuse the boundaries of surfaces that are at different depths. This problem can be solved using range data that are specially good for detecting depth discontinuity.

关键词： image segmentation Structured light Robot vision image processing algorithms and systems Computer vision technology Machine vision Robots Algorithm development Detection and tracking algorithms 3D image processing

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SHAPE FROM DEFOCUS AND SHADING

SHAPE FROM DEFOCUS AND SHADING

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CONF ON APPLICATIONS OF DIGITAL image processing 13

作者： CHA, KC CHOI, JS Chung-Ang Univ. (South Korea)

ISBN: (纸本)0819404101

We suggest an active method for overcoming a discontinuous problem of shape from shading. In general the characreteristics of discontinuity doesn''t well come out in scene. Hence we use a camera with the small depth of field so that the attribute of discontinuity is exposed appropriately. In this case 3D information is represented by the degree of defocusing in 2D image plane. And objects in scene can be segmented according to the degree of defocusing. We simulate four algorithms - Brooks Horn''s algorithm the revised its algorithm the resistive net without line processor and the up and low threshold resistive net with respect to the synthetic image. We can see that the success of algorithm depends on the precision of detecting the degree of defocus.

关键词： 3D image processing image processing image processing algorithms and systems Cameras Computer vision technology Digital image processing Machine vision Neurons image segmentation Visualization

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Mixed-Block Neural Architecture Search for Medical image Segmentation

Mixed-Block Neural Architecture Search for Medical Image Seg...

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Conference on Medical Imaging - image processing

作者： Bosma, Martijn M. A. Dushatskiy, Arkadiy Grewal, Monika Alderliesten, Tanja Bosman, Peter A. N. Ctr Wiskunde & Informat Sci Pk 123 NL-1098 XG Amsterdam Netherlands Delft Univ Technol Mekelweg 5 NL-2628 CD Delft Netherlands Leiden Univ Med Ctr Albinusdreef 2 NL-2333 ZA Leiden Netherlands

ISBN: (数字)9781510649408

ISBN: (纸本)9781510649408;9781510649392

Deep Neural Networks (DNNs) have the potential for making various clinical procedures more time-efficient by automating medical image segmentation. Due to their strong, in some cases human-level, performance, they have become the standard approach in this field. The design of the best possible medical image segmentation DNNs, however, is task-specific. Neural Architecture Search (NAS), i.e., the automation of neural network design, has been shown to have the capability to outperform manually designed networks for various tasks. However, the existing NAS methods for medical image segmentation have explored a quite limited range of types of DNN architectures that can be discovered. In this work, we propose a novel NAS search space for medical image segmentation networks. This search space combines the strength of a generalised encoder-decoder structure, well known from U-Net, with network blocks that have proven to have a strong performance in image classification tasks. The search is performed by looking for the best topology of multiple cells simultaneously with the configuration of each cell within, allowing for interactions between topology and cell-level attributes. From experiments on two publicly available datasets, we find that the networks discovered by our proposed NAS method have better performance than well-known handcrafted segmentation networks, and outperform networks found with other NAS approaches that perform only topology search, and topology-level search followed by cell-level search.

关键词： image segmentation Medical imaging Network architectures Prostate Spleen Convolution Neural networks image classification image processing algorithms and systems Medical research

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WAVELET-BASED SENSOR FUSION 6

WAVELET-BASED SENSOR FUSION

引用

CONF ON SENSOR FUSION 6

作者： HUNTSBERGER, T JAWERTH, B Univ. of South Carolina (United States)

ISBN: (纸本)0819413240

Sensor fusion can be performed either on the raw sensor output or after a segmentation step has been done. Our previous work has concentrated on neural network models for sensor fusion after segmentation. Although this method has been shown to be fast and reliable, there is still the overhead entailed from using entire images. The wavelet transform is a multiresolution method that is used to decompose images into detail and average channels. These channels maintain all of the image information and sensor fusion logic operations can be done within the wavelet coefficient space. In addition, image compression can be done within this same space for possible remote transmission. This paper examines sensor fusion within the wavelet coefficient space. The results of some experimental studies performed on the 1024 node NCUBE/10 at the University of South Carolina are also included.

关键词： Wavelets image fusion image segmentation Sensor fusion image compression Wavelet transforms Algorithm development image processing algorithms and systems Logic Sensors

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image ANALYSIS TECHNIQUES FOR VISUALIZING THE SPATIAL ORGANIZATION OF DNA REPLICATION SITES IN THE MAMMALIAN CELL NUCLEUS USING MULTI-CHANNEL CONFOCAL MICROSCOPY

IMAGE ANALYSIS TECHNIQUES FOR VISUALIZING THE SPATIAL ORGANI...

引用

Conference on image processing - Medical Imaging 1995

作者： SAMARABANDU, J MA, H ACHARYA, R CHENG, PC BEREZNEY, R SUNY BUFFALO DEPT BIOL SCIBUFFALONY 14260

ISBN: (纸本)0819417823

Confocal images of double labeled DNA replication sites in mouse 3T3 cells were analyzed using a set of image processing algorithms to investigate the spatial organization of replication sites at different times. Since confocal microscopy images have about a 1:10 ratio of horizontal (X-Y) versus vertical (Z) scale, extending traditional 2D image processing techniques to 3D rarely gives satisfactory results. By using 2D image segmentation on the individual slices to obtain replication site contours and combining this high-level 2D data using a modified 3D connected component labeling algorithm with weak connectivity in the Z direction, we were able to reconstruct the network of 3D replication sites. Instead of using the equivalent of traditional 2D 8-connectivity in 3D, we used a metric which evaluated the amount of overlap between successive contours of a replication site to decide whether the two contours belong to the same replication site. This method is applicable in analyzing the images of replication sites as they are bead like structures. Once the data structures describing the individual sites are obtained, several pattern recognition techniques are applied to elucidate 3D higher order assembly of replication sites. In one method, the amount of site overlap between the two channels was calculated in an attempt to quantify the closeness of sites at different times. Another approach consisted of grouping the replication sites using a pre-set distance between sites required for a set of sites to be included in the same group.

关键词： 3D image processing image processing image analysis Confocal microscopy image segmentation image processing algorithms and systems Particles Visualization Visual analytics Pattern recognition

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Fuzzy model based object delineation via energy minimization

Fuzzy model based object delineation via energy minimization

引用

Conference on Medical Imaging - image processing

作者： Ciesielski, Krzysztof Chris Udupa, Jayaram K. Odhner, Dewey Zhao, Limimg W Virginia Univ Dept Math Morgantown WV 26506 USA

ISBN: (纸本)9780819494436

We study the problem of automatic delineation of an anatomic object in an image, where the object is solely identified by its anatomic prior. We form such priors in the form of fuzzy models to facilitate the segmentation of images acquired via different imaging modalities (like CT, MRI, or PET), in which the recorded image properties are usually different. Our main interest is in delineating different body organs in medical images for automatic anatomy recognition (AAR). The AAR system we are developing consists of three main components: (Cl) building body-wide groupwise fuzzy anatomic models;(C2) recognizing the body organs geographically and then delineating them by employing the models;(C3) generating quantitative descriptions. This paper focuses on (C2) and presents a unified approach for model-based segmentation within which several different strategies can be formulated, ranging from model-based hard/fuzzy thresholding to model-based graph cut, fuzzy connectedness, and random walker methods and algorithms. This is an important theoretical advance. The presented experiments clearly prove, that a fully automatic segmentation system based on the fuzzy models can indeed provide the reliable segmentations. However, the presented experiments utilize only the simplest versions of the methodology presented in the theoretical part of the paper. The full experimental evaluation of the methodology is still a work in progress.

关键词： image segmentation Fuzzy logic Detection and tracking algorithms image processing algorithms and systems Neodymium Digital imaging Remote sensing Medical imaging Computed tomography Magnetic resonance imaging

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