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Journal articles on the topic 'Object matching'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Li, Stan Z. "Bayesian object matching." Journal of Applied Statistics 25, no. 3 (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, and Alexandre Gazola. "Matching object catalogues." Innovations in Systems and Software Engineering 4, no. 4 (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, no. 2-3 (2013): 225–50. http://dx.doi.org/10.1007/s10994-013-5357-4.

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Koning, Arno, and Johan Wagemans. "Detection of Symmetry and Repetition in One and Two Objects." Experimental Psychology 56, no. 1 (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 segment

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Auclair-Ouellet, Noémie, Marion Fossard, Joël Macoir, and 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, no. 2 (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 individu

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Guo, Lei, Ningdong Song, Jindong Hu, Huiyan Han, Xie Han, and Fengguang Xiong. "Focusing 3D Small Objects with Object Matching Set Abstraction." Applied Sciences 15, no. 8 (2025): 4121. https://doi.org/10.3390/app15084121.

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Currently, 3D object detection methods fail to detect small objects due to the fewer effective points of small objects. It is a significant challenge to reduce the loss of information of points in representation learning. To this end, we propose an effective 3D detection method with object matching set abstraction (OMSA). We observe that key points are lost during feature learning with multiple set abstraction layers, especially for downsampling and queries. Therefore, we present a novel sampling module named focus-based sampling, which raises the sampling probability of small objects. In addi

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Ulrich, Markus, Patrick Follmann, and Jan-Hendrik Neudeck. "A comparison of shape-based matching with deep-learning-based object detection." tm - Technisches Messen 86, no. 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-bas

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Zhu, Daoye, Chengqi Cheng, Weixin Zhai, Yihang Li, Shizhong Li, and Bo Chen. "Multiscale Spatial Polygonal Object Granularity Factor Matching Method Based on BPNN." ISPRS International Journal of Geo-Information 10, no. 2 (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 datas

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Flusser, Jan. "Object matching by means of matching likelihood coefficients." Pattern Recognition Letters 16, no. 9 (1995): 893–900. http://dx.doi.org/10.1016/0167-8655(95)00032-c.

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BHANDARKAR, SUCHENDRA M. "A SURFACE FEATURE ATTRIBUTED HYPERGRAPH REPRESENTATION FOR 3-D OBJECT RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 09, no. 06 (1995): 869–909. http://dx.doi.org/10.1142/s0218001495000365.

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A surface feature hypergraph (SFAHG) representation is proposed for the recognition and localization of three-dimensional objects. The hypergraph representation is shown to be viewpoint independent thus resulting in substantial savings in terms of memory for the object model database. The resulting hypergraph matching algorithm integrates both, relational and the rigid pose constraint in a consistent unified manner. The matching algorithm is also shown to have a polynomial order of complexity even in multiple-object scenes with instances of objects partially occluding each other. An algorithm

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Yang, Yingpeng. "A Stereo Matching System." International Journal of Advanced Pervasive and Ubiquitous Computing 5, no. 2 (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.

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Loghin, Mihai-Adrian. "3D Deformable Object Matching Using Graph Neural Networks." Studia Universitatis Babeș-Bolyai Informatica 69, no. 1 (2024): 21–40. http://dx.doi.org/10.24193/subbi.2024.1.02.

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Considering the current advancements in computer vision it can be observed that most of it is focused on two-dimensional imagery. This includes problems such as classification, regression, and the lesser-known object matching problem. While object matching ca be viewed as a solved problem in a two-dimensional space, for a three-dimensional space there is a long way to go, especially for non-rigid objects. The problem is focused on matching a given object to a target object. We propose a solution based on Graph Neural Networks that tries to generalize over multiple objects at once, based on sel

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Iwata, Tomoharu, Tsutomu Hirao, and Naonori Ueda. "Unsupervised Cluster Matching via Probabilistic Latent Variable Models." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (2013): 445–51. http://dx.doi.org/10.1609/aaai.v27i1.8558.

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We propose a probabilistic latent variable model for unsupervised cluster matching, which is the task of finding correspondences between clusters of objects in different domains. Existing object matching methods find one-to-one matching. The proposed model finds many-to-many matching, and can handle multiple domains with different numbers of objects. The proposed model assumes that there are an infinite number of latent vectors that are shared by all domains, and that each object is generated using one of the latent vectors and a domain-specific linear projection. By inferring a latent vector

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Newell, F. N. "Searching for Objects in the Visual Periphery: Effects of Orientation." Perception 25, no. 1_suppl (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

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Cui, Zhoujuan, Yuqi Dai, Yiping Duan, and Xiaoming Tao. "Joint Object Detection and Multi-Object Tracking Based on Hypergraph Matching." Applied Sciences 14, no. 23 (2024): 11098. http://dx.doi.org/10.3390/app142311098.

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Addressing the challenges in online multi-object tracking algorithms under complex scenarios, where the independence among feature extraction, object detection, and data association modules leads to both error accumulation and the difficulty of maintaining visual consistency for occluded objects, we have proposed an end-to-end multi-object tracking method based on hypergraph matching (JDTHM). Initially, a feature extraction and object detection module is introduced to achieve preliminary localization and description of the objects. Subsequently, a deep feature aggregation module is designed to

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Karma, I. Gede Made, and I. Ketut Darma. "Application of feature-based image matching method as an object recognition method." Bulletin of Electrical Engineering and Informatics 14, no. 2 (2025): 1073–79. https://doi.org/10.11591/eei.v14i2.8803.

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In everyday life, objects are recognized based on the suitability of their characteristics to familiar objects. A feature matching process occurs when recognizing objects. This concept is what we want to apply and test in this research. Because various factors can influence the level of accuracy and success of an image matching method, the first step taken is to improve the accuracy level of the image matching method used. There are three feature-based image matching methods, which are implemented as object recognition methods. These three methods are the result of modifications of the image m

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Koning, Arno, and Rob Van Lier. "Object-based connectedness facilitates matching." Perception & Psychophysics 65, no. 7 (2003): 1094–102. http://dx.doi.org/10.3758/bf03194836.

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Jain, A. K., Yu Zhong, and S. Lakshmanan. "Object matching using deformable templates." IEEE Transactions on Pattern Analysis and Machine Intelligence 18, no. 3 (1996): 267–78. http://dx.doi.org/10.1109/34.485555.

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Yang, Cong, Oliver Tiebe, Kimiaki Shirahama, and Marcin Grzegorzek. "Object matching with hierarchical skeletons." Pattern Recognition 55 (July 2016): 183–97. http://dx.doi.org/10.1016/j.patcog.2016.01.022.

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Nassar, Ahmed Samy, Sébastien Lefèvre, and Jan Dirk Wegner. "Multi-View Instance Matching with Learned Geometric Soft-Constraints." ISPRS International Journal of Geo-Information 9, no. 11 (2020): 687. http://dx.doi.org/10.3390/ijgi9110687.

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We present a new approach for matching urban object instances across multiple ground-level images for the ultimate goal of city-scale mapping of objects with high positioning accuracy. What makes this task challenging is the strong change in view-point, different lighting conditions, high similarity of neighboring objects, and variability in scale. We propose to turn object instance matching into a learning task, where image-appearance and geometric relationships between views fruitfully interact. Our approach constructs a Siamese convolutional neural network that learns to match two views of

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Lawson, Rebecca, Glyn W. Humphreys, and Derrick G. Watson. "Object Recognition under Sequential Viewing Conditions: Evidence for Viewpoint-Specific Recognition Procedures." Perception 23, no. 5 (1994): 595–613. http://dx.doi.org/10.1068/p230595.

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In many computational approaches to vision it has been emphasised that object recognition involves the encoding of view-independent descriptions prior to matching to a stored object model, thus enabling objects to be identified across different retinal projections. In contrast, neurophysiological studies suggest that image descriptions are matched to less abstract, view-specific representations, resulting in more efficient access to stored object knowledge for objects presented from a view similar to a stored viewpoint. Evidence favouring a primary role for view-specific object descriptions in

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Nishina, S., and T. Inui. "Differential Effect of Object Complexity on 2-D and 3-D Matching Processes." Perception 26, no. 1_suppl (1997): 290. http://dx.doi.org/10.1068/v970308.

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Previously, we found that two matching processes work in parallel when an object is recognised from unknown viewpoints: the 2-dimensional (2-D) and the 3-dimensional (3-D) matching process. These processes were shown to differ in several respects, including recognition speed, generalisation range, and learning ability. We have now examined the effect of the complexity of an object on these two matching processes. We performed a recognition experiment where the subjects had to compare two sequentially presented images. The stimuli were objects that had different numbers of segments, presented f

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Li, Yongdong, Liang Qu, Guiyan Cai, et al. "Video Object Counting With Scene-Aware Multi-Object Tracking." Journal of Database Management 34, no. 3 (2023): 1–13. http://dx.doi.org/10.4018/jdm.321553.

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The critical challenge of video object counting is to avoid counting the same object multiple times in different frames. By comparing the appearance and motion feature information of the detection results, the authors use the multi-object tracking method to assign an independent ID number to each object. From the time the ID tag is obtained until the end of the video, each object is counted only once. However, even minor amounts of image noise can cause irreversible changes in feature information, resulting in severe tracking drifts. This paper introduces the concept of scene awareness and add

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Tan, Qifan, Xuqi Yang, Cheng Qiu, et al. "Graph-Based Target Association for Multi-Drone Collaborative Perception Under Imperfect Detection Conditions." Drones 9, no. 4 (2025): 300. https://doi.org/10.3390/drones9040300.

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Multi-drone collaborative perception aims to address single-drone viewpoint limitations. The existing matching and association methods based on visual features and spatial topology rely heavily on detection, making it challenging to associate targets under imperfect detection conditions. To address this issue, a Graph-Based Target Association Network (GTA-Net) is proposed to utilize graph matching to associate the key objects before affine transforming and matching both detected and undetected targets. The Key Object Detection Network (KODN) finds the key object that is more likely to be a Tru

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Wang, Yanli, Zhipeng Dong, Mi Wang, and Yi Ding. "Adaptive Orientation Object-Detection Method for Large-scale Remote Sensing Images Based on Multi-scale Block Fusion." Photogrammetric Engineering & Remote Sensing 91, no. 1 (2025): 27–34. https://doi.org/10.14358/pers.24-00036r1.

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Object detection is crucial to extracting and analyzing information autonomously from high-resolution remote sensing images (HRSIs). To address ideal blocking for large-scale HRSI object detection, this study uses a novel adaptive orientation object-detection method for large-scale HRSIs based on multi-scale block fusion. An adaptive orientation object-detection framework based on a convolutional neural network is applied to detect diverse objects of large-scale HRSIs through different block scales; average precision (AP) values of diverse object-detection results are calculated at different b

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Yu, Hang, Jiulu Gong, and Derong Chen. "Object Detection Using Multi-Scale Balanced Sampling." Applied Sciences 10, no. 17 (2020): 6053. http://dx.doi.org/10.3390/app10176053.

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Detecting small objects and objects with large scale variants are always challenging for deep learning based object detection approaches. Many efforts have been made to solve these problems such as adopting more effective network structures, image features, loss functions, etc. However, for both small objects detection and detecting objects with various scale in single image, the first thing should be solve is the matching mechanism between anchor boxes and ground-truths. In this paper, an approach based on multi-scale balanced sampling(MB-RPN) is proposed for the difficult matching of small o

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Ko, K. H., T. Maekawa, N. M. Patrikalakis, H. Masuda, and F. E. Wolter. "Shape Intrinsic Properties for Free-Form Object Matching." Journal of Computing and Information Science in Engineering 3, no. 4 (2003): 325–33. http://dx.doi.org/10.1115/1.1633277.

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Despite extensive research and rapid increase of computing power, free-form object matching still remains a challenging problem in CAD/CAM areas. In this paper, various object features are discussed, and matching methods which use these features are introduced along with robust computational algorithms for umbilical points and intrinsic wireframes. The similarity of matched objects is assessed with three proposed tests. Each algorithm is demonstrated with examples.

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Bethmann, F., and T. Luhmann. "Semi-Global Matching in Object Space." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W2 (March 10, 2015): 23–30. http://dx.doi.org/10.5194/isprsarchives-xl-3-w2-23-2015.

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Semi-Global Matching (SGM) is a widespread algorithm for image matching which is used for very different applications, ranging from real-time applications (e.g. for generating 3D data for driver assistance systems) to aerial image matching. Originally developed for stereo-image matching, several extensions have been proposed to use more than two images within the matching process (multi-baseline matching, multi-view stereo). These extensions still perform the image matching in (rectified) stereo images and combine the pairwise results afterwards to create the final solution. This paper propose

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Zhang, Peng, Qingyang Jing, Xinlei Zhao, et al. "A Multi-Object Tracking Approach Combining Contextual Features and Trajectory Prediction." Electronics 12, no. 23 (2023): 4720. http://dx.doi.org/10.3390/electronics12234720.

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Aiming to solve the problem of the identity switching of objects with similar appearances in real scenarios, a multi-object tracking approach combining contextual features and trajectory prediction is proposed. This approach integrates the motion and appearance features of objects. The motion features are mainly used for trajectory prediction, and the appearance features are divided into contextual features and individual features, which are mainly used for trajectory matching. In order to accurately distinguish the identities of objects with similar appearances, a context graph is constructed

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S., S., Vibu Krishnan S., Mathan Raj Kumar, Ashok .., and M. Janakiraman. "Object Detection Using Deep Learning." Journal of Cognitive Human-Computer Interaction 6, no. 1 (2023): 32–38. http://dx.doi.org/10.54216/jchci.060103.

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Object recognition is an important task in computer vision that involves identifying the objects such as digital images or videos. This research paper provides a comprehensive review of the different techniques and applications of object recognition. The paper first discusses the basic concepts of object recognition, including feature extraction and matching, classification, and detection. Next, the paper reviews the different techniques for object recognition, such as template matching, PCA-based recognition, and deep learning-based recognition. The paper then presents an overview of the diff

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Kang, Seok-Hoon, and Joon-Sang Park. "Aligned Matching: Improving Small Object Detection in SSD." Sensors 23, no. 5 (2023): 2589. http://dx.doi.org/10.3390/s23052589.

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Although detecting small objects is critical in various applications, neural network models designed and trained for generic object detection struggle to do so with precision. For example, the popular Single Shot MultiBox Detector (SSD) tends to perform poorly for small objects, and balancing the performance of SSD across different sized objects remains challenging. In this study, we argue that the current IoU-based matching strategy used in SSD reduces the training efficiency for small objects due to improper matches between default boxes and ground truth objects. To address this issue and im

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Van Brandt, Seppe, Kamil Yavuz Kapusuz, Joryan Sennesael, et al. "Reliability Analysis and Optimization of a Reconfigurable Matching Network for Communication and Sensing Antennas in Dynamic Environments through Gaussian Process Regression." Sensors 24, no. 9 (2024): 2689. http://dx.doi.org/10.3390/s24092689.

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During the implementation of the Internet of Things (IoT), the performance of communication and sensing antennas that are embedded in smart surfaces or smart devices can be affected by objects in their reactive near field due to detuning and antenna mismatch. Matching networks have been proposed to re-establish impedance matching when antennas become detuned due to environmental factors. In this work, the change in the reflection coefficient at the antenna, due to the presence of objects, is first characterized as a function of the frequency and object distance by applying Gaussian process reg

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Djuric, Nemanja, Mihajlo Grbovic, and Slobodan Vucetic. "Convex Kernelized Sorting." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 893–99. http://dx.doi.org/10.1609/aaai.v26i1.8314.

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Kernelized sorting is a method for aligning objects across two domains by considering within-domain similarity, without a need to specify a cross-domain similarity measure. In this paper we present the Convex Kernelized Sorting method where, unlike in the previous approaches, the cross-domain object matching is formulated as a convex optimization problem, leading to simpler optimization and global optimum solution. Our method outputs soft alignments between objects, which can be used to rank the best matches for each object, or to visualize the object matching and verify the correct choice of

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Harley, Heidi E. "WAU! Identifying complex objects with an acoustic flashlight?" Journal of the Acoustical Society of America 151, no. 4 (2022): A107. http://dx.doi.org/10.1121/10.0010802.

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A Hawaiian bunker, a bearded and barefoot entrepreneur, some dolphins, and an electrical engineer: A starter recipe for one of the most productive dolphin biosonar researchers in world history? Sound unlikely? Overstated? Apocryphal? And yet… Thirty years with Whit inspired us to investigate a dolphin’s use of wholistic shape matching versus “flashlight” object-parts-focused matching. Our stimuli were 3 3-object sets (Sets W, A,U). Objects within sets varied on shape alone made with identical PVC parts, but PVC parts varied between sets. In Condition Shape, experienced dolphin Calvin matched o

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Noda, Mitsuru. "Imagery and Perceptual Basis of Matching Tasks in Young Children." Perceptual and Motor Skills 107, no. 2 (2008): 419–38. http://dx.doi.org/10.2466/pms.107.2.419-438.

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Object identification in perceptual tests may include constituents of mental rotation. A matching-to-sample task was conducted with same or different objects to look for evidence of rotation. Elementary schoolchildren (6 to 8 years old) in Grades 1 to 4 ( N = 264) participated, using the inclined Flags Test and the Water Level Test to ensure that children can use kinematic imagery for the Flags Test even if they used static imagery for the Water Level Test. Performance on the inclined Flags Test varied by age group. Use of implicit mental rotation of the inclined object was inferred in recogni

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Doi, Kento, Ryuhei Hamaguchi, Yusuke Iwasawa, Masaki Onishi, Yutaka Matsuo, and Ken Sakurada. "Detecting Object-Level Scene Changes in Images with Viewpoint Differences Using Graph Matching." Remote Sensing 14, no. 17 (2022): 4225. http://dx.doi.org/10.3390/rs14174225.

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We developed a robust object-level change detection method that could capture distinct scene changes in an image pair with viewpoint differences. To achieve this, we designed a network that could detect object-level changes in an image pair. In contrast to previous studies, we considered the change detection task as a graph matching problem for two object graphs that were extracted from each image. By virtue of this, the proposed network more robustly detected object-level changes with viewpoint differences than existing pixel-level approaches. In addition, the network did not require pixel-le

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Peng, Mengkang, and Narendra K. Gupta. "Invariant and Occluded Object Recognition Based on Graph Matching." International Journal of Electrical Engineering & Education 32, no. 1 (1995): 31–38. http://dx.doi.org/10.1177/002072099503200104.

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Invariant and occluded object recognition based on graph matching The paper describes an algorithm for object recognition using neural network. The objects may be occluded and may have any changes in position, orientation and scale. The paper aims to generate project ideas for the final year students at the university and also to provide a basis for further research.

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Chantara, Wisarut, Ji-Hun Mun, Dong-Won Shin, and Yo-Sung Ho. "Object Tracking using Adaptive Template Matching." IEIE Transactions on Smart Processing and Computing 4, no. 1 (2015): 1–9. http://dx.doi.org/10.5573/ieiespc.2015.4.1.001.

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Robeson, Brendan, Mohammadreza Javanmardi, and Xiaojun Qi. "Object tracking using temporally matching filters." IET Computer Vision 15, no. 4 (2021): 245–57. http://dx.doi.org/10.1049/cvi2.12040.

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Ke, Wei, and Ka-Hou Chan. "Pattern Matching Based on Object Graphs." IEEE Access 9 (2021): 159313–25. http://dx.doi.org/10.1109/access.2021.3128575.

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Dryden, Ian L., and Gary Walker. "Highly Resistant Regression and Object Matching." Biometrics 55, no. 3 (1999): 820–25. http://dx.doi.org/10.1111/j.0006-341x.1999.00820.x.

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Zhu, Jie, Shufang Wu, Xizhao Wang, Guoqing Yang, and Liyan Ma. "Multi‐image matching for object recognition." IET Computer Vision 12, no. 3 (2018): 350–56. http://dx.doi.org/10.1049/iet-cvi.2017.0261.

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Zhou, Chuan, and Gurdeep S. Hura. "Matching Object Structure Using Petri Nets." IFAC Proceedings Volumes 31, no. 15 (1998): 65–70. http://dx.doi.org/10.1016/s1474-6670(17)40530-1.

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Penn, Michal, and Moshe Tennenholtz. "Constrained multi-object auctions and -matching." Information Processing Letters 75, no. 1-2 (2000): 29–34. http://dx.doi.org/10.1016/s0020-0190(00)00073-9.

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Bunke, Horst. "Graph matching for visual object recognition." Spatial Vision 13, no. 2-3 (2000): 335–40. http://dx.doi.org/10.1163/156856800741153.

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Su, Ching-Liang. "Manufacture Automation: Model and Object Recognition by Using Object Position Auto Locating Algorithm and Object Comparison Model." JALA: Journal of the Association for Laboratory Automation 5, no. 2 (2000): 61–65. http://dx.doi.org/10.1016/s1535-5535-04-00062-0.

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This research uses the geometry matching technique to identify the different objects. The object is extracted from the background. The second moment 6 is used to find the orientation and the center point of the extracted object. Since the second moment can find the orientations and the center point of the object, the perfect object and the test object can be aligned to the same orientation. Furthermore, these two images can be shifted to the same centroid. After this, the perfect object can be subtracted from the test face. By using the subtracted result, the objects can be classified. The tec

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Lawson, Rebecca. "An Investigation into the Cause of Orientation-Sensitivity in Haptic Object Recognition." Seeing and Perceiving 24, no. 3 (2011): 293–314. http://dx.doi.org/10.1163/187847511x579052.

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AbstractObject orientation influences visual and haptic recognition differently. This could be caused by the two modalities accessing different object representations or it could be due to differences in how each modality acquires information. These two alternatives were investigated using sequential haptic matching tasks. Matches presented the same object twice. Mismatches presented two similarly-shaped objects. Objects were either both placed at the same orientation or were rotated 90° in depth from each other. Experiment 1 manipulated exploration time to test if longer durations weakened or

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Li, Wen-Jing, and Tong Lee. "Object recognition and articulated object learning by accumulative Hopfield matching." Pattern Recognition 35, no. 9 (2002): 1933–48. http://dx.doi.org/10.1016/s0031-3203(01)00158-3.

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Mettes, Pascal, William Thong, and Cees G. M. Snoek. "Object Priors for Classifying and Localizing Unseen Actions." International Journal of Computer Vision 129, no. 6 (2021): 1954–71. http://dx.doi.org/10.1007/s11263-021-01454-y.

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AbstractThis work strives for the classification and localization of human actions in videos, without the need for any labeled video training examples. Where existing work relies on transferring global attribute or object information from seen to unseen action videos, we seek to classify and spatio-temporally localize unseen actions in videos from image-based object information only. We propose three spatial object priors, which encode local person and object detectors along with their spatial relations. On top we introduce three semantic object priors, which extend semantic matching through w

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Eckes, Christian, Jochen Triesch, and Christoph von der Malsburg. "Analysis of Cluttered Scenes Using an Elastic Matching Approach for Stereo Images." Neural Computation 18, no. 6 (2006): 1441–71. http://dx.doi.org/10.1162/neco.2006.18.6.1441.

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We present a system for the automatic interpretation of cluttered scenes containing multiple partly occluded objects in front of unknown, complex backgrounds. The system is based on an extended elastic graph matching algorithm that allows the explicit modeling of partial occlusions. Our approach extends an earlier system in two ways. First, we use elastic graph matching in stereo image pairs to increase matching robustness and disambiguate occlusion relations. Second, we use richer feature descriptions in the object models by integrating shape and texture with color features. We demonstrate th

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