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2d object recognition based on curvature functions obtained from local histograms of the contour chain code

PATTERN recognition LETTERS 1999年第1期20卷 49-55页

作者： Bandera, A Urdiales, C Arrebola, F Sandoval, F Univ Malaga ETSI Telecomunicac Dpto Tecnol Elect E-29071 Malaga Spain

In this paper a real time 2d object recognition algorithm is proposed. Contours are represented by their curvature functions, decomposed in the Fourier domain as linear combination of a set of representative objects. ... 详细信息

关键词： 2d object recognition clustering algorithm chain code local histograms curvature function hierarchical classification architecture

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COMPARISON OF LOCAL VS GLOBAL APPROACHES TO 2d object recognition

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 1994年第3期7卷 299-310页

作者： BAdAL, dZ STECK, UJ UNIV COLORADO DEPT COMP SCICOLORADO SPRINGSCO 80919

This paper compares global and local approaches to object recognition in 2d images. In the global approach the network is trained on atomic images which contain only one object in selected positions of the whole image. These can be viewed as transparencies, each containing a single object in a selected position. The network is then tested on images that are generated by overlaying these transparencies to produce composite images, i.e. images containing several objects. The local approach utilizes a ''flying window'', or a window that is passed over the input image in a raster scan manner. The network looks only at what is in the window as the window passes over the whole image. It is trained on a set of atomic images the size of the window. The recognition rates for the two methods are shown, and the advantages and disadvantages of the two methods are discussed.

关键词： 2d object recognition COMPOSITE IMAGES WINdOWING LOCAL VS GLOBAL APPROACH

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A Comparative Analysis for 2d object recognition: A Case Study with Tactode Puzzle-Like Tiles

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JOURNAL OF IMAGING 2021年第4期7卷 65-65页

作者： Silva, daniel Sousa, Armando Costa, Valter Inst Syst & Comp Engn Technol & Sci INESC TEC P-4200465 Porto Portugal Univ Tras Os Montes & Alto Douro UTAD Dept Engn P-5000801 Vila Real Portugal Univ Porto FEUP Fac Engn P-4200465 Porto Portugal Inst Sci & Innovat Mech & Ind Engn INEGI P-4200465 Porto Portugal Campus FEUP Rua Dr Roberto Frias 400 P-4200465 Porto Portugal

object recognition represents the ability of a system to identify objects, humans or animals in images. Within this domain, this work presents a comparative analysis among different classification methods aiming at Tactode tile recognition. The covered methods include: (i) machine learning with HOG and SVM;(ii) deep learning with CNNs such as VGG16, VGG19, ResNet152, MobileNetV2, SSd and YOLOv4;(iii) matching of handcrafted features with SIFT, SURF, BRISK and ORB;and (iv) template matching. A dataset was created to train learning-based methods (i and ii), and with respect to the other methods (iii and iv), a template dataset was used. To evaluate the performance of the recognition methods, two test datasets were built: tactode_small and tactode_big, which consisted of 288 and 12,000 images, holding 2784 and 96,000 regions of interest for classification, respectively. SSd and YOLOv4 were the worst methods for their domain, whereas ResNet152 and MobileNetV2 showed that they were strong recognition methods. SURF, ORB and BRISK demonstrated great recognition performance, while SIFT was the worst of this type of method. The methods based on template matching attained reasonable recognition results, falling behind most other methods. The top three methods of this study were: VGG16 with an accuracy of 99.96% and 99.95% for tactode_small and tactode_big, respectively;VGG19 with an accuracy of 99.96% and 99.68% for the same datasets;and HOG and SVM, which reached an accuracy of 99.93% for tactode_small and 99.86% for tactode_big, while at the same time presenting average execution times of 0.323 s and 0.232 s on the respective datasets, being the fastest method overall. This work demonstrated that VGG16 was the best choice for this case study, since it minimised the misclassifications for both test datasets.

关键词： computer vision machine learning 2d object recognition HOG SVM VGG ResNet MobileNet YOLO SSd

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On using functions to describe the shape

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JOURNAL OF MATHEMATICAL IMAGING ANd VISION 2003年第3期18卷 225-245页

作者： Kindratenko, VV Univ Illinois Natl Ctr Supercomp Applicat Urbana IL 61801 USA

In this paper, a systematic review of various contour functions and methods of their analysis, as applied in the field of shape description and characterization, is presented. Contour functions are derived from planar object outlines and are used as an intermediate representation from which various shape properties can be obtained. All the functions are introduced and analyzed following the same scheme, thus making it possible to compare various representations. Although only a small subset of contour functions is included in the survey (cross-section, radius-vector, support, width, parametric, complex, tangent-angle, curvature, polynomial, and parametric cubic), the paper demonstrates a multitude of techniques for shape description that are based on this approach. Several analysis tools, such as statistics, line moments and invariants, Fourier and other series expansions, curvature scale space image, wavelet, and Radon transform are described.

关键词： contour functions shape shape analysis 2d object recognition

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A Hidden Markov Model object recognition technique for incomplete and distorted corner sequences

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IMAGE ANd VISION COMPUTING 2003年第10期21卷 879-889页

作者： de Trazegnies, C Urdiales, C Bandera, A Sandoval, F Univ Malaga ETSI Telecommun Dpto Tecnol Elect E-29071 Malaga Spain

This paper presents a new technique for planar object recognition based on Hidden Markov Models. First, the contour of the object is processed to extract a sequence of high curvature points. These points are extracted from a new adaptively extracted curvature function which is resistant against noise and transformations. Each corner is characterized by its subtended angle and its distance to the next corner. Then, corner sequences are analyzed by using HMMs. The method has been successfully tested for different databases. Its main advantage is that it can deal with incomplete and distorted corner sequences. (C) 2003 Elsevier B.V. All rights reserved.

关键词： 2d object recognition Hidden Markov Models

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A Hidden Markov Model object recognition technique for incomplete and distorted corner sequences

A Hidden Markov Model object recognition technique for incom...

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Symposium on Probabilistic Models in Computer Vision

作者： de Trazegnies, C Urdiales, C Bandera, A Sandoval, F Univ Malaga ETSI Telecommun Dpto Tecnol Elect E-29071 Malaga Spain

关键词： 2d object recognition Hidden Markov Models

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Enhancing Haptic Representation for 2d objects

Enhancing Haptic Representation for 2D Objects

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International Conference on Computer, Electrical and Electronics Engineering (ICCEEE)

作者： Bedri, Abdelkareem Harwin, William Univ Reading Sch Syst Engn Reading Berks England

ISBN: (纸本)9781467362313;9781467362320

This paper considers two dimensional object tracking and recognition using combined texture and pressure cues. Tactile object recognition plays a major role in several HCI applications especially scientific visualization for the visually impaired. A tactile display based on a pantograph mechanism and 2x2 vibrotactile unit array was used to represent haptic virtual objects. Two experiments were carried out to evaluate the efficiency and reliability of this novel method. The first experiment studied how the combined texture and pressure cues improves the haptic interaction compared to using pressure cues only. The second experiment examined users' capability to track and recognize objects with two dimensional shapes using this method. Results obtained indicate an improvement in boundary recognition and tracking accuracy by the combined stimulation over the sole pressure stimulation. Results also demonstrate the capability of users to readily define shapes for most of the virtually represented objects.

关键词： HCI haptics texture cues 2d object recognition

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Invariant object recognition using eigenvalues of covariance matrices and autocorrelation

Invariant object recognition using eigenvalues of covariance...

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IASTEd International Conference on Artificial Intelligence and Applications

作者： Sun, T. H. Tien, F. C. Department of Industrial Engineering and Management Chaoyang University of Technology Taipei 106 1 Section 3 Chung-Hsiao E. Rd. Taiwan Department of Industrial Engineering and Management National Taipei University of Technology Taipei 106 1 Section 3 Chung-Hsiao E. Rd. Taiwan

ISBN: (纸本)9780889866294

This paper presents a new boundary-based part recognition method for two-dimensional part. The proposed method adopts the eigenvalues of covariance matrix, re-sampling and transformation of autocorrelation coefficient for feature extraction and the simple minimum Euclidean distance for object classification. The boundary of the binary digital object is represented into the form of the eigenvalues of covariance matrix over a given region of support, and then is further transformed with autocorrelation function. The derived features are unique and invariant to translation, rotation, and scaling. Finally, the minimum Euclidean distance is used for pattern recognition for simplicity. Twenty-five standard patterns are acquired, and for each object ten extra images using different positions, orientation and scales are digitized for system verification. The experimental results show that the proposed system achieves the high recognition rate.

关键词： 2d object recognition eigenvalues of covariance matrix autocorrelation coefficient feature extraction

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A Robust Edge-Based Corner detector (EBCd)

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INTERNATIONAL JOURNAL OF IMAGE ANd GRAPHICS 2014年第4期14卷 1450018-1450018页

作者： Al Nachar, Rabih Inaty, Elie Bonnin, Patrick J. Alayli, Yasser Univ Balamand Dept Comp Engn POB 100Elkoura North POB 33 Beirut Lebanon Lab Ingn Syst Versailles LISV F-78140 Vlizy France

This paper presents a novel and robust edge-based corner detector (EBCd) which finds corners that are considered stable interest points in the framework of 2d object recognition especially for robot navigation. Using the EBCd, the corners are defined as intersection points of non-collinear straight image edges. The straight edge detector plus the corner detector will localize the corner positions. The detected corners have special features which are their angles and their sides length ratios. These features are invariant parameters which make the corners perfect for 2d object recognition. In addition, these corners are shown to be very robust against various image transformations like image scaling, rotation, translation and viewpoint illumination changes. Some updates are applied on the linking edge step in order to extract edges and their intersections that in turn construct the searched corners. Experiments conducted on synthetic and real images show that the proposed EBCd is able to achieve a very good performance in terms of accuracy, stability and especially computational efficiency in comparison with existing algorithms on interest points.

关键词： Corners image transformations edge detector straight edge 2d object recognition

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