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

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EXTRACTION OF EDGE-BASED AND REGION-BASED FEATURES FOR OBJECT RECOGNITION 12

EXTRACTION OF EDGE-BASED AND REGION-BASED FEATURES FOR OBJEC...

引用

12th Conference on Intelligent Robots and Computer Vision: algorithms and Techniques

作者： COUTTS, B RAVI, S HU, GZ SHRIKHANDE, N CENT MICHIGAN UNIV DEPT COMP SCIMT PLEASANTMI 48858

ISBN: (纸本)0819413208

One of the central problems of computer vision is object recognition. A catalogue of model objects is described as a set of features such as edges and surfaces. The same features are extracted from the scene and matched against the models for object recognition. Edges and surfaces extracted from the scenes are often noisy and imperfect. In this paper algorithms are described for improving low level edge and surface features. Existing edge extraction algorithms are applied to the intensity image to obtain edge features. Initial edges are traced by following directions of the current contour. These are improved by using corresponding depth and intensity information for decision making at branch points. Surface fitting routines are applied to the range image to obtain planar surface patches. An algorithm of region growing is developed that starts with a coarse segmentation and uses quadric surface fitting to iteratively merge adjacent regions into quadric surfaces based on approximate orthogonal distance regression. Surface information obtained is returned to the edge extraction routine to detect and remove fake edges. This process repeats until no more merging or edge improvement can take place. Both synthetic (with Gaussian noise) and real images containing multiple object scenes have been tested using the merging criteria. Results appeared quite encouraging.

关键词： Edge detection Object recognition image segmentation Algorithm development Feature extraction Detection and tracking algorithms image processing algorithms and systems Computer vision technology Machine vision Visual process modeling

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FONT IDENTIFICATION USING VISUAL GLOBAL CONTEXT

FONT IDENTIFICATION USING VISUAL GLOBAL CONTEXT

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Conference on Document Recognition

作者： KHOUBYARI, S HULL, JJ SUNY BUFFALO CTR EXCELLENCE DOCUMENT ANAL & RECOGNITDEPT COMP SCIBUFFALONY 14260

ISBN: (纸本)081941476X

An important part of many algorithms that convert digital images of machine-printed text into their ASCII equivalent is information about fonts. This paper presents an algorithm for identifying the font in which a document is printed. The algorithm matches word-level information gathered from the document image to fonts in an image database. This method is more robust in the presence of noise than font recognition algorithms that use character-level information. Clusters of frequent function words (such as the, of, and, a, and to) are constructed from an input document image. The clusters are then matched to a database of function words derived from document images, and the document that matches best provides the identification of the input font. This technique utilizes the context from many words in an input document to overcome noise. Experimental results are presented that show near-perfect recognition of fonts, even in noisy documents.

关键词： Detection and tracking algorithms Prototyping Databases Visualization image segmentation Information visualization Curium image processing algorithms and systems Reliability Visual analytics

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Object based very low bit rate coding with robust global motion estimation and background/foregound separation

Object based very low bit rate coding with robust global mot...

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Conference on Digital Compression Technologies and systems for Video Communications

作者： Saflekos, A Strintzis, MG BenoisPineau, J Barba, D UNIV THESSALONIKI DEPT ELECT & ELECT ENGNINFORMAT PROC LABTHESSALONIKI 54006GREECE

ISBN: (纸本)0819423564

We present a two-step segmentation scheme for the very low bit rate coding of general purpose video scenes. Our objective is to determine the regions of interest before the actual segmentation procedure, so as to reduce the computational overhead introduced by this relatively complex process, and to avoid the phenomenon of over-segmentation of the scene. Simulation results show that the proposed scheme results in a radical reduction of the number of discrete spatio-temporal regions, while the background is identified as one uniform region, even when it is characterized by complex global motion.

关键词： image segmentation Motion estimation Video Video coding Motion models Cameras image processing algorithms and systems Computer simulations image quality Visualization

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CLUSTERING algorithms FOR A PC BASED HARDWARE IMPLEMENTATION OF THE UNSUPERVISED CLASSIFIER FOR THE SHUTTLE ICE DETECTION SYSTEM

CLUSTERING ALGORITHMS FOR A PC BASED HARDWARE IMPLEMENTATION...

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CONF ON NONLINEAR image processing 2

作者： JAGGI, S Lockheed Engineering & Sciences Co. (United States)

ISBN: (纸本)0819405507

This paper introduces a near-real time method of image processing in a PC-based environment. A segmentation technique based on unsupervised classification is implemented. A prototype for the detection of ice formation on the external tank (ET) of the Space Shuttle is being developed for NASA Science and Technology Laboratory by Lockheed Engineering and Sciences Company at Stennis Space Center, MS. The objective is to be able to do an online classification of the ET images into distinct regions denoting ice, frost, wet, or dry areas. The images are acquired with an infrared camera and digitized before being processed by a computer to yield a false color-coded pattern, with each color representing a region. A two-monitor PC-based setup is used for image processing. Various techniques for classification, both supervised and unsupervised, are being investigated for developing a methodology. This paper discusses the implementation of two adaptive algorithms for image segmentation. The K-means algorithm is compared to another algorithm based on adaptive estimation of region boundaries.

关键词： image processing image processing algorithms and systems Cameras image segmentation Detection and tracking algorithms Nonlinear image processing Thermography image classification Optical filtering Video

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A unifying graph-cut image segmentation framework: algorithms it encompasses and equivalences among them

A unifying graph-cut image segmentation framework: algorithm...

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Conference on Medical Imaging - image processing

作者： Ciesielski, Krzysztof Chris Udupa, Jayaram K. Falcao, A. X. Miranda, P. A. V. W Virginia Univ Dept Math Morgantown WV 26506 USA Univ Penn MIPG Dept Radiol Philadelphia PA 19104 USA Univ Estadual Campinas Inst Comp Campinas SP Brazil Univ Sao Paulo IME Dept Comp Sci Sao Paulo Brazil

ISBN: (纸本)9780819489630

We present a general graph-cut segmentation framework GGC, in which the delineated objects returned by the algorithms optimize the energy functions associated with the l(p) norm, 1 <= p <= infinity. Two classes of well known algorithms belong to GGC: the standard graph cut GC (such as the min-cut/max-flow algorithm) and the relative fuzzy connectedness algorithms RFC (including iterative RFC, IRFC). The norm-based description of GGC provides more elegant and mathematically better recognized framework of our earlier results from [18, 19]. Moreover, it allows precise theoretical comparison of GGC representable algorithms with the algorithms discussed in a recent paper [22] (min-cut/max-flow graph cut, random walker, shortest path/geodesic, Voronoi diagram, power watershed/shortest path forest), which optimize, via l(p) norms, the intermediate segmentation step, the labeling of scene voxels, but for which the final object need not optimize the used l(p) energy function. Actually, the comparison of the GGC representable algorithms with that encompassed in the framework described in [22] constitutes the main contribution of this work.

关键词： Fuzzy logic Detection and tracking algorithms Evolutionary algorithms image segmentation algorithms image processing algorithms and systems Artificial intelligence Optimization (mathematics) Digital imaging Surface plasmons

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INTEGRATING REGION GROWING AND EDGE-DETECTION USING REGULARIZATION

INTEGRATING REGION GROWING AND EDGE-DETECTION USING REGULARI...

引用

Conference on image processing - Medical Imaging 1995

作者： CHALANA, V COSTA, W KIM, YM UNIV WASHINGTON CTR BIOENGN SEATTLE WA 98195 USA

ISBN: (纸本)0819417823

We propose a segmentation approach which integrates region growing and edge detection in a regularization framework. Our method is a modified active contour model and uses region statistics in addition to gradient information. We formulate the active contour model using a Bayesian approach. We have implemented this integrated approach and characterized its performance on synthetic images and on 36 short-axis cardiac ultrasound images. The resulting boundaries are compared to true boundaries in the case of the synthetic images and to manually outlined boundaries in the case of ultrasound images. The results are also compared with those obtained using the balloon force to expand the active contour model. We found that our integrated algorithm detects boundaries more accurately than the active contour method using a balloon force. Furthermore, the integrated algorithm is less sensitive to the placement of the initial contour inside the LV cavity than the active contour algorithm using a balloon force.

关键词： image segmentation Data modeling Sensors Edge detection Statistical modeling Echocardiography image processing image processing algorithms and systems Ultrasonography Convolution

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Anatomy-Aware Measurement of Segmentation Accuracy

Anatomy-Aware Measurement of Segmentation Accuracy

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Conference on Medical Imaging - image processing

作者： Tizhoosh, H. R. Othman, A. A. Univ Waterloo KIMIA Lab 200 Univ Ave West Waterloo ON Canada Suez Canal Univ Dept Informat Syst Comp & Informat Ismailia Egypt

ISBN: (纸本)9781510600195

Quantifying the accuracy of segmentation and manual delineation of organs, tissue types and tumors in medical images is a necessary measurement that suffers from multiple problems. One major shortcoming of all accuracy measures is that they neglect the anatomical significance or relevance of different zones within a given segment. Hence, existing accuracy metrics measure the overlap of a given segment with a ground-truth without any anatomical discrimination inside the segment. For instance, if we understand the rectal wall or urethral sphincter as anatomical zones, then current accuracy measures ignore their significance when they are applied to assess the quality of the prostate gland segments. In this paper, we propose an anatomy-aware measurement scheme for segmentation accuracy of medical images. The idea is to create a "master gold" based on a consensus shape containing not just the outline of the segment but also the outlines of the internal zones if existent or relevant. To apply this new approach to accuracy measurement, we introduce the anatomy-aware extensions of both Dice coefficient and Jaccard index and investigate their effect using 500 synthetic prostate ultrasound images with 20 different segments for each image. We show that through anatomy-sensitive calculation of segmentation accuracy, namely by considering relevant anatomical zones, not only the measurement of individual users can change but also the ranking of users' segmentation skills may require reordering.

关键词： image segmentation Gold Prostate Magnesium image processing algorithms and systems Tumors Ultrasonography Buildings Accuracy assessment Shape analysis

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MORPHOLOGICAL SEGMENTATION OF BINARY SHAPES INTO CONVEX POLYGONS

MORPHOLOGICAL SEGMENTATION OF BINARY SHAPES INTO CONVEX POLY...

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CONF ON MACHINE VISION ARCHITECTURES, INTEGRATION, AND APPLICATIONS

作者： XU, JN Glassboro State College (United States)

ISBN: (纸本)0819407526

Mathematical morphology is an approach to image analysis based on geometric concept of shape and size. Naturally, it provides a very effective tool for shape analysis. In this paper, we present a new morphological shape segmentation algorithm which decomposes a 2-D binary shape into a class of convex polygonal components. In this algorithm, shape information is extracted by using a number of different small shape patterns as structuring elements to probe the given shape through basic morphological operations. Basic morphological operations are also used to transform the given image so that in the transformed image global shape information is still retained and can be extracted efficiently by using only 'neighborhood' operations. The resulting algorithm is very simple and yet very effective. Decomposition examples presented show good agreements between the decomposition results and the natural structures of the given shapes. The shape segments produced can be used to construct structural shape description and for other shape analysis purposes.

关键词： Shape analysis Binary data image processing algorithms and systems image segmentation image analysis Mathematical morphology

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Edge-Preserving Metal Artifact Reduction

Edge-Preserving Metal Artifact Reduction

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Conference on Medical Imaging - Physics of Medical Imaging

作者： Meyer, Esther Raupach, Rainer Lell, Michael Schmidt, Bernhard Kachelriess, Marc Univ Erlangen Nurnberg Inst Med Phys D-91054 Erlangen Germany Siemens Hlthcare Forchheim Forchheim Germany Univ Erlangen Nurnberg Inst Diagnost Radiol Erlangen Germany DKFZ Heidelberg Germany

ISBN: (纸本)9780819489623

Metal implants in the field of measurement lead to strong artifacts in CT images and reduce the image quality and the diagnostic value severely. We introduce frequency split metal artifact reduction (FSMAR), a conceptually new MAR method which is designed to reduce metal artifacts and preserve details and edges of structures even close to metal implants. There are many MAR methods which simply replace unreliable parts of the projection data by inpainting. FSMAR is a combination of an inpainting-based MAR method with a frequency split approach. Normalized metal artifact reduction (NMAR) is chosen as the inpainting-based MAR method in this work. The high frequencies of the original image, where all rawdata were used for the reconstruction, are combined with an NMAR-corrected image. NMAR uses a normalization step to reduce metal artifacts without introducing severe new artifacts. algorithms using a frequency split were already used in CT for example to reduce cone-beam artifacts. FSMAR is tested for patient datasets with different metal implants. The study includes patients with hip prostheses, a neuro coil, and a spine fixation. All datasets were scanned with modern clinical dual source CT scanners. In contrast to other MAR methods, FSMAR yields images without the usual blurring close to metal implants.

关键词： Metals Computed tomography image segmentation Spine Bone image filtering image quality Diagnostics Linear filtering image processing algorithms and systems

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MAP SEGMENTATION OF MAGNETIC-RESONANCE imageS USING MEAN FIELD ANNEALING 2

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CONF ON image processing algorithms AND TECHNIQUES

作者： LOGENTHIRAN, A SNYDER, WE SANTAGO, P LINK, KM AT&T Bell Labs. (United States) Bowman Gray School of Medicine/Wake Forest Univ. (United States)

ISBN: (纸本)0819405515

An algorithm is described which segments magnetic resonance images while removing the noise from the images without blurring or other distortion of edges. The problem of segmentation and noise removal is posed as a restoration of an uncorrupted image, given additive white Gaussian noise and a segmentation cost. The problem is solved using a strategy called Mean Field Annealing. An a priori statistical model of the image, which includes the region classification, is chosen which drives the minimization toward solutions which are locally homogeneous and globally classified. Application of the algorithm to brain and knee images is presented.

关键词： image segmentation image processing algorithms and systems image processing Annealing Brain mapping Magnetic resonance imaging Magnetism Signal to noise ratio Brain Neuroimaging

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