Gotowe bibliografie tematyczne / Object matching

Gotowa bibliografia na temat „Object matching”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 8 czerwca 2024

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Artykuły w czasopismach na temat "Object matching"

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Li, Stan Z. "Bayesian object matching". Journal of Applied Statistics 25, nr 3 (czerwiec 1998): 425–43. http://dx.doi.org/10.1080/02664769823142.

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Leme, Luiz André P., Daniela F. Brauner, Karin K. Breitman, Marco A. Casanova i Alexandre Gazola. "Matching object catalogues". Innovations in Systems and Software Engineering 4, nr 4 (31.10.2008): 315–28. http://dx.doi.org/10.1007/s11334-008-0070-3.

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Klami, Arto. "Bayesian object matching". Machine Learning 92, nr 2-3 (30.04.2013): 225–50. http://dx.doi.org/10.1007/s10994-013-5357-4.

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Newell, F. N. "Searching for Objects in the Visual Periphery: Effects of Orientation". Perception 25, nr 1_suppl (sierpień 1996): 110. http://dx.doi.org/10.1068/v96l1111.

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Previous studies have found that the recognition of familiar objects is dependent on the orientation of the object in the picture plane. Here the time taken to locate rotated objects in the periphery was examined. Eye movements were also recorded. In all experiments, familiar objects were arranged in a clock face display. In experiment 1, subjects were instructed to locate a match to a central, upright object from amongst a set of randomly rotated objects. The target object was rotated in the frontoparallel plane. Search performance was dependent on rotation, yielding the classic ‘M’ function found in recognition tasks. When matching a single object in periphery, match times were dependent on the angular deviations between the central and target objects and showed no advantage for the upright (experiment 2). In experiment 3 the central object was shown in either the upright rotation or rotated by 120° from the upright. The target object was similarly rotated given four different match conditions. Distractor objects were aligned with the target object. Search times were faster when the centre and target object were aligned and also when the centre object was rotated and the target was upright. Search times were slower when matching a central upright object to a rotated target object. These results suggest that in simple tasks matching is based on image characteristics. However, in complex search tasks a contribution from the object's representation is made which gives an advantage to the canonical, upright view in peripheral vision.
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Koning, Arno, i Johan Wagemans. "Detection of Symmetry and Repetition in One and Two Objects". Experimental Psychology 56, nr 1 (styczeń 2009): 5–17. http://dx.doi.org/10.1027/1618-3169.56.1.5.

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Symmetry is usually easier to detect within a single object than in two objects (one-object advantage), while the reverse is true for repetition (two-objects advantage). This interaction between regularity and number of objects could reflect an intrinsic property of encoding spatial relations within and across objects or it could reflect a matching strategy. To test this, regularities between two contours (belonging to a single object or two objects) had to be detected in two experiments. Projected three-dimensional (3-D) objects rotated in depth were used to disambiguate figure-ground segmentation and to make matching based on simple translations of the two-dimensional (2-D) contours unlikely. Experiment 1 showed the expected interaction between regularity and number of objects. Experiment 2 used two-objects displays only and prevented a matching strategy by also switching the positions of the two objects. Nevertheless, symmetry was never detected more easily than repetition in these two-objects displays. We conclude that structural coding, not matching strategies, underlies the one-object advantage for symmetry and the two-objects advantage for repetition.
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Flusser, Jan. "Object matching by means of matching likelihood coefficients". Pattern Recognition Letters 16, nr 9 (wrzesień 1995): 893–900. http://dx.doi.org/10.1016/0167-8655(95)00032-c.

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Auclair-Ouellet, Noémie, Marion Fossard, Joël Macoir i Robert Laforce. "The Nonverbal Processing of Actions Is an Area of Relative Strength in the Semantic Variant of Primary Progressive Aphasia". Journal of Speech, Language, and Hearing Research 63, nr 2 (26.02.2020): 569–84. http://dx.doi.org/10.1044/2019_jslhr-19-00271.

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Purpose Better performance for actions compared to objects has been reported in the semantic variant of primary progressive aphasia (svPPA). This study investigated the influence of the assessment task (naming, semantic picture matching) over the dissociation between objects and actions. Method Ten individuals with svPPA and 17 matched controls completed object and action naming tests, and object and action semantic picture matching tests. Performance was compared between the svPPA and control groups, within the svPPA group, and for each participant with svPPA versus the control group individually. Results Compared to controls, participants with svPPA were impaired on object and action naming, and object and action semantic picture matching. As a group, participants with svPPA had an advantage for actions over objects and for semantic picture matching tests over naming tests. Eight participants had a better performance for actions compared to objects in naming, with three showing a significant difference. Nine participants had a better performance for actions compared to objects in semantic picture matching, with six showing a significant difference. For objects, semantic picture matching was better than naming in nine participants, with five showing a significant difference. For actions, semantic picture matching was better than naming in all 10 participants, with nine showing a significant difference. Conclusion The nonverbal processing of actions, as assessed with a semantic picture matching test, is an area of relative strength in svPPA. Clinical implications for assessment planning and interpretation and theoretical implications for current models of semantic cognition are discussed.
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Ulrich, Markus, Patrick Follmann i Jan-Hendrik Neudeck. "A comparison of shape-based matching with deep-learning-based object detection". tm - Technisches Messen 86, nr 11 (26.11.2019): 685–98. http://dx.doi.org/10.1515/teme-2019-0076.

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AbstractMatching, i. e. determining the exact 2D pose (e. g., position and orientation) of objects, is still one of the key tasks in machine vision applications like robot navigation, measuring, or grasping an object. There are many classic approaches for matching, based on edges or on the pure gray values of the template. In recent years, deep learning has been utilized mainly for more difficult tasks where the objects of interest are from many different categories with high intra-class variations and classic algorithms are failing. In this work, we compare one of the latest deep-learning-based object detectors with classic shape-based matching. We evaluate the methods both on a matching dataset as well as an object detection dataset that contains rigid objects and is thus also suitable for shape-based matching. We show that for datasets of this type, where rigid objects appear with rigid transformations, shape-based matching still outperforms recent object detectors regarding runtime, robustness, and precision if only a single template image per object is used. On the other hand, we show that for the application of object detection, the deep-learning-based approach outperforms the classic approach if annotated data is used for training. Ultimately, the choice of the best suited approach depends on the conditions and requirements of the application.
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Yang, Yingpeng. "A Stereo Matching System". International Journal of Advanced Pervasive and Ubiquitous Computing 5, nr 2 (kwiecień 2013): 1–8. http://dx.doi.org/10.4018/japuc.2013040101.

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Determination of the depth of the image feature distinctive automation and other industries of machine vision and computer vision technology in everyday life are becoming increasingly popular. Some techniques have been proposed to extract from the current depth of a 2D image of the feature, which defines a particular object or structure of the information. In many cases, these techniques are automatic, such as a suitable carrier moving average depth identify objects placed in the 2D image. For this intensive depth cues to solve two stereo matching algorithm using a machine learning algorithm. Other methods, relative to the camera based on the motion of the object have been proposed and analyzed by estimating the optical flow calculation depth map. The method of dense and sparse three-dimensional surface of the object to provide the three-dimensional information. This paper discusses the evaluation of the depth cues, through intensive two standard fast algorithm for real-time stereo image matching algorithm.
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Zhu, Daoye, Chengqi Cheng, Weixin Zhai, Yihang Li, Shizhong Li i Bo Chen. "Multiscale Spatial Polygonal Object Granularity Factor Matching Method Based on BPNN". ISPRS International Journal of Geo-Information 10, nr 2 (13.02.2021): 75. http://dx.doi.org/10.3390/ijgi10020075.

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Spatial object matching is one of the fundamental technologies used for updating and merging spatial data. This study focused mainly on the matching optimization of multiscale spatial polygonal objects. We proposed a granularity factor evaluation index that was developed to promote the recognition ability of complex matches in multiscale spatial polygonal object matching. Moreover, we designed the granularity factor matching model based on a backpropagation neural network (BPNN) and designed a multistage matching workflow. Our approach was validated experimentally using two topographical datasets at two different scales: 1:2000 and 1:10,000. Our results indicate that the granularity factor is effective both in improving the matching score of complex matching and reducing the occurrence of missing matching, and our matching model is suitable for multiscale spatial polygonal object matching, with a high precision and recall reach of 97.2% and 90.6%.
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Rozprawy doktorskie na temat "Object matching"

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Lennartsson, Mattias. "Object Recognition with Cluster Matching". Thesis, Linköping University, Department of Electrical Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-51494.

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Within this thesis an algorithm for object recognition called Cluster Matching has been developed, implemented and evaluated. The image information is sampled at arbitrary sample points, instead of interest points, and local image features are extracted. These sample points are used as a compact representation of the image data and can quickly be searched for prior known objects. The algorithm is evaluated on a test set of images and the result is surprisingly reliable and time efficient.

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Anderson, R. "Phase-based object matching using complex wavelets". Thesis, University of Cambridge, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.595514.

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This thesis investigates the use of phase information from the Dual-Tree Complex Wavelet Transform (DT CWT) for the purpose of image matching. We review methods for image matching with a particular emphasis on matching with local features. We review the meaning and current uses of local phase, and introduce the DT CWT. We highlight the shortcomings of decimated wavelets for typical local phase analysis, and introduce two new functions that can extract useful information from the phases of decimated complex wavelets. The first, the InterLevel Product (ILP), is a pyramidal representation where the phases are equal to the phase difference between co-located, same-direction DT CWT coefficients at different scales. The second function, the Same-Level Product (SLP), has phases proportional to the phase differences between adjacent coefficients in the same level; these SLP phases are parallel to the gradient of dominant local features. We seek to represent dominant local features sparsely by clustering areas where the ILP phase is large and same-phase. Three novel clustering techniques are introduced and discussed, with the Line-Growing technique shown to be the best. Line-Growing is a technique similar to the Canny edge detector, operating upon decimated coefficients and sensitive to phase symmetry changes. We call the resulting clusters Edge-Profile Clusters (EPC’s). We explore three different matching techniques based upon local phase information. The first technique shows how ILP information can be combined with the normalized cross correlation (template matching) to accelerate traditional matching. We also show a hybrid ILP/SLP format, where the target is abstracted into EPC cluster constellations that may be rotated quickly to test different match hypotheses. Finally, we show a method where EPC parameters are used to represent both images in the comparison, and a geometric hashing algorithm is combined with a cluster overlap metric to evaluated matches in a fully affine-invariant manner. We highlight the compatibility of our EPC and ILP representations and current physiological/psychovisual observations regarding the mammalian visual cortex, including an ILP phase-based explanation of the “pop-out” effect and perceptual grouping regarding “Glass patterns”.
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Ahn, Yushin. "Object space matching and reconstruction using multiple images". Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1213375997.

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Kwon, Ohkyu. "Similarity measures for object matching in computer vision". Thesis, University of Bolton, 2016. http://ubir.bolton.ac.uk/890/.

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The similarity measures for object matching and their applications have been important topics in many fields of computer vision such as those of image recognition, image fusion, image analysis, video sequence matching, and so on. This critical commentary presents the efficiency of new metric methods such as the robust Hausdorff distance (RHD), the accurate M-Hausdorff distance (AMHD), and the fast sum of absolute differences (FSAD). The RHD measure computes the similarity distance of the occluded/noisy image pair and evaluates the performances of the multi-modal registration algorithms. The AMHD measure is utilised for aligning the pair of the occluded/noisy multi-sensor face images, and the FSAD measure in adaptive-template matching method finds the zero location of the slide in an automatic scanning microscope system. A Hausdorff distance (HD) similarity measure has been widely investigated to compare the pair of two-dimensional (2-D) images by low-level features since it is simple and insensitive to the changes in an image characteristic. In this research, novel HD measures based on the robust statistics of regression analysis are addressed for occluded and noisy object matching, resulting in two RHD measures such as M-HD based on the M-estimation and LTS-HD based on the least trimmed squares (LTS). The M-HD is extended to three-dimensional (3-D) version for scoring the registration algorithms of the multi-modal medical images. This 3-D measure yields the comparison results with different outlier-suppression parameters (OSP) quantitatively, even though the Computed Tomography (CT) and emission-Positron Emission Tomography (PET) images have different distinctive features. The RHD matching technique requires a high level of complexity in computing the minimum distance from one point to the nearest point between two edge point sets and searching for the best fit of matching position. To overcome these problems, the improved 3×3 distance transform (DT) is employed. It has a separable scan structure to reduce the calculation time of the minimum distance in multi-core processors. The object matching algorithm with hierarchical structures is also demonstrated to minimize the computational complexity dramatically without failing the matching position. The object comparison between different modality images is still challenging due to the poor edge correspondence coming from heterogeneous characteristics. To improve the robustness of HD measures in comparing the pair of multi-modal sensor images, an accurate M-HD (AMHD) is proposed by utilizing the orientation information of each point in addition to the DT map. This similarity measure can precisely analyse the non-correspondent edges and noises by using the distance orientation information. The AMHD measure yields superior performance at aligning the pairs of multi-modal face images over those achieved by the conventional robust HD schemes. The sum of absolute differences (SAD) is popular similarity measure in template matching technique. This thesis shows the adaptive-template matching method based on the FSAD for accurately locating the slide in automated microscope. The adaptive-template matching method detects the fiduciary ring mark in the slide by predicting the constant used in the template, where the FSAD reduces the processing time with a low rate of error of the template matching by inducing 1-D vertical and horizontal SAD. The proposed scheme results in an accurate performance in terms of detecting the ring mark and estimating the relative offset in slide alignment during the on-line calibration process.
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Tieu, Kinh H. (Kinh Han) 1976. "Statistical dependence estimation for object interaction and matching". Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/38316.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.
Includes bibliographical references (p. 97-103).
This dissertation shows how statistical dependence estimation underlies two key problems in visual surveillance and wide-area tracking. The first problem is to detect and describe interactions between moving objects. The goal is to measure the influence objects exert on one another. The second problem is to match objects between non-overlapping cameras. There, the goal is to pair the departures in one camera with the arrivals in a different camera so that the resulting distribution of relationships best models the data. Both problems have become important for scaling up surveillance systems to larger areas and expanding the monitoring to more interesting behaviors. We show how statistical dependence estimation generalizes previous work and may have applications in other areas. The two problems represent different applications of our thesis that statistical dependence estimation underlies the learning of the structure of probabilistic models. First, we analyze the relationship between Bayesian, information-theoretic, and classical statistical methods for statistical dependence estimation. Then, we apply these ideas to formulate object interaction in terms of dependency structure model selection.
(cont.) We describe experiments on simulated and real interaction data to validate our approach. Second, we formulate the matching problem in terms of maximizing statistical dependence. This allows us to generalize previous work on matching, and we show improved results on simulated and real data for non-overlapping cameras. We also prove an intractability result on exact maximally dependent matching.
by Kinh Tieu.
Ph.D.
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Ko, Kwang Hee 1971. "Algorithms for three-dimensional free-form object matching". Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/29751.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering, 2003.
Includes bibliographical references (leaves 117-126).
This thesis addresses problems of free-form object matching for the point vs. NURBS surface and the NURBS surface vs. NURBS surface cases, and its application to copyright protection. Two new methods are developed to solve a global and partial matching problem with no a priori information on correspondence or initial transformation and no scaling effects, namely the KH and the umbilic method. The KH method establishes a correspondence between two objects by utilizing the Gaussian and mean curvatures. The umbilic method uses the qualitative properties of umbilical points to find correspondence information between two objects. These two methods are extended to deal with uniform scaling effects. The umbilic method is enhanced with an algorithm for scaling factor estimation using the quantitative properties of umbilical points. The KH method is used as a building block of an optimization scheme based on the golden section search which recovers iteratively an optimum scaling factor. Since the golden section search only requires an initial interval for the scaling factor, the solution process is simplified compared to iterative optimization algorithms, which require good initial estimates of the scaling factor and the rigid body transformation. The matching algorithms are applied to problems of copyright protection.
(cont.) A suspect model is aligned to an original model through matching methods so that similarity between two geometric models can be assessed to determine if the suspect model contains part(s) of the original model. Three types of tests, the weak, intermediate and strong tests, are proposed for similarity assessment between two objects. The weak and intermediate tests are performed at node points obtained through shape intrinsic wireframing. The strong test relies on isolated umbilical points which can be used as fingerprints of an object for supporting an ownership claim to the original model. The three tests are organized in two decision algorithms so that they produce systematic and statistical measures for a similarity decision between two objects in a hierarchical manner. Based on the systematic statistical evaluation of similarity, a decision can be reached whether the suspect model is a copy of the original model.
by Kwang Hee Ko.
Ph.D.
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Sangi, P. (Pekka). "Object motion estimation using block matching with uncertainty analysis". Doctoral thesis, Oulun yliopisto, 2013. http://urn.fi/urn:isbn:9789526200774.

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Abstract Estimation of 2-D motion is one of the fundamental problems in video processing and computer vision. This thesis addresses two general tasks in estimating projected motions of background and foreground objects in a scene: global motion estimation and motion based segmentation. The work concentrates on the study of the block matching method, and especially on those cases where the matching measure is based on the sum of squared or absolute displaced frame differences. Related techniques for performing the confidence analysis of local displacement are considered and used to improve the performance of the higher-level tasks mentioned. In general, local motion estimation techniques suffer from the aperture problem. Therefore, confidence analysis methods are needed which can complement motion estimates with information about their reliability. This work studies a particular form of confidence analysis which uses the evaluation of the match criterion for local displacement candidates. In contrast to the existing approaches, the method takes into account the local image gradient. The second part of the thesis presents a four-step feature based method for global motion estimation. For basic observations, it uses motion features which are combinations of image point coordinates, displacement estimates at those points, and representations of displacement uncertainty. A parametric form of uncertainty representation is computed exploiting the technique described in the first part of the thesis. This confidence information is used as a basis for weighting the features in motion estimation. Aspects of gradient based feature point selection are also studied. In the experimental part, the design choices of the method are compared, using both synthetic and real sequences. In the third part of the thesis, a technique for feature based extraction of background and foreground motions is presented. The new sparse segmentation algorithm performs competitive segmentation using both the spatial and temporal propagation of support information. The weighting of features exploits parametric uncertainty information which is experimentally shown to improve the performance of motion estimation. In the final part of the thesis, a novel framework for motion based object detection, segmentation, and tracking is developed. It uses a block grid based representation for segmentation and a particle filter based approach to motion estimation. Analysis techniques for obtaining the segmentation are described. Finally, the approach is integrated with the sparse motion segmentation and the combination of the methods is experimentally shown to increase both the efficiency of sampling and the accuracy of segmentation
Tiivistelmä Tässä väitöskirjassa tutkitaan yhtä videonkäsittelyn ja konenäön perusongelmaa, kaksiulotteisen liikkeen estimointia. Työ käsittelee kahta yleistä tehtävää taustan ja etualan kohteiden liikkeiden määrittämisessä: hallitsevan liikkeen estimointia ja liikepohjaista kuvan segmentointia. Tutkituissa ratkaisuissa lähtökohtana käytetään lohkosovitukseen perustuvaa paikallisen liikkeen määritystä, jossa sovituksen kriteerinä käytetään poikkeutettujen kehysten pikseliarvojen erotusta. Tähän liittyen tarkastellaan estimoinnin luotettavuuden analyysin tekniikoita ja näiden hyödyntämistä edellä mainittujen tehtävien ratkaisuissa. Yleensä ottaen paikallisen liikkeen estimointia vaikeuttaa apertuuriongelma. Tämän vuoksi tarvitaan analyysitekniikoita, jotka kykenevät antamaan täydentävää tietoa liike-estimaattien luotettavuudesta. Työn ensimmäisessä osassa kehitetty analyysimenetelmä käyttää lähtötietona lohkosovituksen kriteerin arvoja, jotka on saatu eri liikekandidaateille. Erotuksena aiempiin menetelmiin kehitetty ratkaisu ottaa huomioon kuvagradientin vaikutuksen. Työn toisessa osassa tutkitaan nelivaiheista piirrepohjaista ratkaisua hallitsevan liikkeen estimoimiseksi. Perushavaintoina mallissa käytetään liikepiirteitä, jotka koostuvat valittujen kuvapisteiden koordinaateista, näissä pisteissä lasketuista liike-estimaateista ja estimaattien epävarmuuden esityksestä. Jälkimmäinen esitetään parametrisessa muodossa käyttäen laskentaan työn ensimmäisessä osassa esitettyä menetelmää. Tätä epävarmuustietoa käytetään piirteiden painottamiseen hallitsevan liikkeen estimoinnissa. Lisäksi tutkitaan gradienttipohjaista piirteiden valintaa. Kokeellisessa osassa erilaisia suunnitteluvalintoja verrataan toisiinsa käyttäen synteettisiä ja todellisia kuvasekvenssejä. Väitöstyön kolmannessa osassa esitetään piirrepohjainen menetelmä taustan ja etualan kohteen liikkeiden erottamiseksi toisistaan. Algoritmi tekee analyysin kahta liikettä sisältävälle näkymälle käyttäen sekä spatiaalista että ajallista segmentointitiedon välittämistä. Piirteiden painotus hyödyntää epävarmuustietoa tässä yhteydessä, jonka osoitetaan kokeellisesti parantavan liike-estimoinnin suorituskykyä. Viimeisessä osassa kehitetään viitekehys liikepohjaisen kohteen ilmaisun, segmentoinnin ja seurannan toteutukselle. Se perustuu lohkopohjaiseen esitystapaan ja näytteistyksen soveltamiseen liikkeen estimoinnissa. Analyysitekniikka segmentoinnin määrittämiseksi esitellään. Lopuksi ratkaisu integroidaan työn kolmannessa osassa esitetyn menetelmän kanssa, ja menetelmien yhdistelmän osoitetaan kokeellisesti parantavan sekä näytteistyksen tehokkuutta että segmentoinnin tarkkuutta
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Staniaszek, Michal. "Feature-Feature Matching For Object Retrieval in Point Clouds". Thesis, KTH, Datorseende och robotik, CVAP, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-170475.

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In this project, we implement a system for retrieving instances of objects from point clouds using feature based matching techniques. The target dataset of point clouds consists of approximately 80 full scans of office rooms over a period of one month. The raw clouds are reprocessed to remove regions which are unlikely to contain objects. Using locations determined by one of several possible interest point selection methods, one of a number of descriptors is extracted from the processed clouds. Descriptors from a target cloud are compared to those from a query object using a nearest neighbour approach. The nearest neighbours of each descriptor in the query cloud are used to vote for the position of the object in a 3D grid overlaid on the room cloud. We apply clustering in the voting space and rank the clusters according to the number of votes they contain. The centroid of each of the clusters is used to extract a region from the target cloud which, in the ideal case, corresponds to the query object. We perform an experimental evaluation of the system using various parameter settings in order to investigate factors affecting the usability of the system, and the efficacy of the system in retrieving correct objects. In the best case, we retrieve approximately 50% of the matching objects in the dataset. In the worst case, we retrieve only 10%. We find that the best approach is to use a uniform sampling over the room clouds, and to use a descriptor which factors in both colour and shape information to describe points.
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Sim, Hak Chuah. "Invariant object matching with a modified dynamic link network". Thesis, University of Southampton, 1999. https://eprints.soton.ac.uk/256269/.

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Krupnik, Amnon. "Multiple-patch matching in the object space for aerotriangulation /". The Ohio State University, 1994. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487857546386844.

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Książki na temat "Object matching"

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Lamdan, Yehezkel. Object recognition by affine invariant matching. New York: Courant Institute of Mathematical Sciences, New York University, 1988.

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Bastuscheck, C. Marc. Object recognition by 3-dimensional curve matching. New York: Courant Institute of Mathematical Sciences, New York University, 1986.

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Dawson, K. M. Implicit model matching as an approach to three-dimensional object recognition. Dublin: Trinity College, Department of Computer Science, 1991.

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Lee, Raymond Shu Tak. Invariant object recognition based on elastic graph matching: Theory and applications. Amsterdam: IOS Press, 2003.

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Information routing, correspondence finding, and object recognition in the brain. Berlin: Springer-Verlag, 2010.

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Kao, Chang-Lung. Affine invariant matching of noisy objects. Monterey, Calif: Naval Postgraduate School, 1989.

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Abdelmalek, Nabih N. A computer vision system for matching 3-D range data objects. Ottawa, Ont: National Research Council Canada, Division of Electrical Engineering, 1987.

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Schwartz, Jacob T. Identification of partially obscured objects in two dimensions by matching of noisy 'characteristic curves,'. New York: Courant Institute of Mathematical Sciences, New York University, 1985.

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Yang, Cong. Object Shape Generation, Representation and Matching. Logos Verlag Berlin, 2016.

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Schonberg, Edith, C. Marc Bastuscheck i Jacob T. Schwartz. Object Recognition by 3-Dimensional Curve Matching. Creative Media Partners, LLC, 2015.

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Części książek na temat "Object matching"

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Bennamoun, M., i G. J. Mamic. "Object Representation and Feature Matching". W Object Recognition, 161–94. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-3722-1_4.

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Treiber, Marco. "Flexible Shape Matching". W An Introduction to Object Recognition, 117–43. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-235-3_6.

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Tiebe, Oliver, Cong Yang, Muhammad Hassan Khan, Marcin Grzegorzek i Dominik Scarpin. "Stripes-Based Object Matching". W Computer and Information Science, 59–72. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40171-3_5.

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Bennamoun, M., i G. J. Mamic. "Stereo Matching and Reconstruction of a Depth Map". W Object Recognition, 29–96. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-3722-1_2.

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Emir, Burak, Martin Odersky i John Williams. "Matching Objects with Patterns". W ECOOP 2007 – Object-Oriented Programming, 273–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73589-2_14.

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Berg, Alexander C., i Jitendra Malik. "Shape Matching and Object Recognition". W Toward Category-Level Object Recognition, 483–507. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11957959_25.

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Lammari, Nadira, Jacky Akoka i Isabelle Comyn-Wattiau. "Supporting Database Evolution: Using Ontologies Matching". W Object-Oriented Information Systems, 284–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45242-3_27.

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Cootes, Tim F. "Deformable Object Modelling and Matching". W Computer Vision – ACCV 2010, 1–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19315-6_1.

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Cho, Tai-Hoon. "Object Matching Using Generalized Hough Transform and Chamfer Matching". W Lecture Notes in Computer Science, 1253–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-36668-3_171.

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Shan, Y., H. S. Sawhney, B. Matei i R. Kumar. "Partial Object Matching with Shapeme Histograms". W Lecture Notes in Computer Science, 442–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24672-5_35.

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Streszczenia konferencji na temat "Object matching"

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Balikai, Anupriya, i Peter Hall. "Depiction Inviariant Object Matching". W British Machine Vision Conference 2012. British Machine Vision Association, 2012. http://dx.doi.org/10.5244/c.26.56.

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Zardetto, Diego, Monica Scannapieco i Tiziana Catarci. "Effective automated Object Matching". W 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010). IEEE, 2010. http://dx.doi.org/10.1109/icde.2010.5447904.

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Choe, Jaesung, Kyungdon Joo, Francois Rameau i In So Kweon. "Stereo Object Matching Network". W 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021. http://dx.doi.org/10.1109/icra48506.2021.9562027.

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Huang, Aiming, Zheng Gao, Bin Dai i Li Luo. "Real-time object matching". W SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, redaktor Andrew G. Tescher. SPIE, 1998. http://dx.doi.org/10.1117/12.323233.

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Pon, Alex D., Jason Ku, Chengyao Li i Steven L. Waslander. "Object-Centric Stereo Matching for 3D Object Detection". W 2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020. http://dx.doi.org/10.1109/icra40945.2020.9196660.

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Hu, Nan, Qixing Huang, Boris Thibert i Leonidas Guibas. "Distributable Consistent Multi-object Matching". W 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. http://dx.doi.org/10.1109/cvpr.2018.00261.

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Grove, T. D., i R. B. Fisher. "Attention in Iconic Object Matching". W British Machine Vision Conference 1996. British Machine Vision Association, 1996. http://dx.doi.org/10.5244/c.10.19.

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Arandjelovi, Ognjen. "Object Matching Using Boundary Descriptors". W British Machine Vision Conference 2012. British Machine Vision Association, 2012. http://dx.doi.org/10.5244/c.26.85.

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Celenk, Mehmet. "Three-dimensional object surface matching". W SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, redaktorzy Friedrich O. Huck i Richard D. Juday. SPIE, 1995. http://dx.doi.org/10.1117/12.211999.

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Ma, Thomas, Vijay Menon i Kate Larson. "Improving Welfare in One-Sided Matchings using Simple Threshold Queries". W Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/45.

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We study one-sided matching problems where each agent must be assigned at most one object. In this classic problem it is often assumed that agents specify only ordinal preferences over objects and the goal is to return a matching that satisfies some desirable property such as Pareto optimality or rank-maximality. However, agents may have cardinal utilities describing their preference intensities and ignoring this can result in welfare loss. We investigate how to elicit additional cardinal information from agents using simple threshold queries and use it in turn to design algorithms that return a matching satisfying a desirable matching property, while also achieving a good approximation to the optimal welfare among all matchings satisfying that property. Overall, our results show how one can improve welfare by even non-adaptively asking agents for just one bit of extra information per object.
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Raporty organizacyjne na temat "Object matching"

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Gyaourova, A., C. Kamath i S. Cheung. Block Matching for Object Tracking. Office of Scientific and Technical Information (OSTI), październik 2003. http://dx.doi.org/10.2172/15009731.

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Smith, David. Parallel approximate string matching applied to occluded object recognition. Portland State University Library, styczeń 2000. http://dx.doi.org/10.15760/etd.5608.

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Cass, Todd A. Feature Matching for Object Localization in the Presence of Uncertainty. Fort Belvoir, VA: Defense Technical Information Center, maj 1990. http://dx.doi.org/10.21236/ada231405.

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Luo, Ming, Daniel DeMenthon, Xiaodong Yu i David Doermann. SOFTCBIR: Object Searching in Videos Combining Keypoint Matching and Graduated Assignment. Fort Belvoir, VA: Defense Technical Information Center, maj 2006. http://dx.doi.org/10.21236/ada448477.

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Asari, Vijayan, Paheding Sidike, Binu Nair, Saibabu Arigela, Varun Santhaseelan i Chen Cui. PR-433-133700-R01 Pipeline Right-of-Way Automated Threat Detection by Advanced Image Analysis. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), grudzień 2015. http://dx.doi.org/10.55274/r0010891.

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A novel algorithmic framework for the robust detection and classification of machinery threats and other potentially harmful objects intruding onto a pipeline right-of-way (ROW) is designed from three perspectives: visibility improvement, context-based segmentation, and object recognition/classification. In the first part of the framework, an adaptive image enhancement algorithm is utilized to improve the visibility of aerial imagery to aid in threat detection. In this technique, a nonlinear transfer function is developed to enhance the processing of aerial imagery with extremely non-uniform lighting conditions. In the second part of the framework, the context-based segmentation is developed to eliminate regions from imagery that are not considered to be a threat to the pipeline. Context based segmentation makes use of a cascade of pre-trained classifiers to search for regions that are not threats. The context based segmentation algorithm accelerates threat identification and improves object detection rates. The last phase of the framework is an efficient object detection model. Efficient object detection �follows a three-stage approach which includes extraction of the local phase in the image and the use of local phase characteristics to locate machinery threats. The local phase is an image feature extraction technique which partially removes the lighting variance and preserves the edge information of the object. Multiple orientations of the same object are matched and the correct orientation is selected using feature matching by histogram of local phase in a multi-scale framework. The classifier outputs locations of threats to pipeline.�The advanced automatic image analysis system is intended to be capable of detecting construction equipment along the ROW of pipelines with a very high degree of accuracy in comparison with manual threat identification by a human analyst. �
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Love, N., i C. Kamath. An Empirical Study of Block Matching Techniques for the Detection of Moving Objects. Office of Scientific and Technical Information (OSTI), styczeń 2006. http://dx.doi.org/10.2172/898460.

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