Recognition of partially occluded objects using neural network based indexing
TL;DR: A new neural network based indexing scheme has been proposed for recognition of planar shapes and object contours have been obtained using a new algorithm which combines advantages of region growing and edge detection.
About: This article is published in Pattern Recognition.The article was published on 1999-10-01. It has received 16 citations till now. The article focuses on the topics: Search engine indexing & Distance transform.
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TL;DR: A bibliography of nearly 1700 references related to computer vision and image analysis, arranged by subject matter is presented, including computational techniques; feature detection and segmentation; image and scene analysis; and motion.
39 citations
TL;DR: A new interpretation of the control parameter, order vector, used in synergetic neural net (SNN) is exploited and used as the basis of a similarity function for shape-based retrieval and an efficient affine invariant similarity measure has been developed for trademark images.
19 citations
13 Mar 2005
TL;DR: This paper presents the robust method for recognizing partially occluded objects based on symmetry properties, which is based on the contours of objects, and provides simple techniques to reconstruct Occluded regions via a region copy using the symmetry axis within an object.
Abstract: This paper discusses the problem of partial object recognition in image databases. We propose the method to reconstruct and estimate partially occluded shapes and regions of objects in images from overlapping and cutting. We present the robust method for recognizing partially occluded objects based on symmetry properties, which is based on the contours of objects. Our method provides simple techniques to reconstruct occluded regions via a region copy using the symmetry axis within an object. Based on the estimated parameters for partially occluded objects, we perform object recognition on the classification tree. Since our method relies on reconstruction of the object based on the symmetry rather than statistical estimates, it has proven to be remarkably robust in recognizing partially occluded objects in the presence of scale changes, rotation, and viewpoint changes.
16 citations
Cites background or methods from "Recognition of partially occluded o..."
...Even though there have been several efforts in object recognition with occlusion, currents methods have been highly sensitive to object pose, rotation, scaling, and visible portion of occluded objects [12] [9] [17] [3] [15]....
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...[17] introduced a method for partial object recognition using neural network based indexing....
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TL;DR: It is shown that a predefined set of visual keywords as prototype patterns stored with the SNN can provide the framework for image indexing or retrieval which is scalable, robust and efficient for web-based search.
Abstract: Feature extraction and similarity measure are two basic key issues in image retrieval. Combining the advantages of SNN in image recognition and selective attention for image retrieval, a novel visual keywords-driven image retrieval approach based on these properties has been proposed. By using a predefined set of visual keywords as prototype patterns stored with the SNN and then measuring the degree of similiarity of the stored images to the visual keywords, we show that such a visual keyword driven SNN can provide the framework for image indexing or retrieval which is scalable, robust and efficient for web-based search.
7 citations
Cites background from "Recognition of partially occluded o..."
...• Local invariants, on the other hand, such as geometric invariants (Rivlin and Weiss, 1995), dominant points (Rajpal, Chaudhury, and Banerjee, 1999) and boundary segments (Mehrotra and Gary, 1995), defined at each point of a shape seperately, can handle images containing partially visible,…...
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09 Jul 2010
TL;DR: The strategies of YCbCr skin-color filtering and multi-template matching are introduced to get more accurate ear location under occasions of insufficient training and bad initial ear position and results show the proposed method is robust and effective not only in static image but also under dynamic environments.
Abstract: As an essential stage of human ear recognition, Ear detection has a direct and important impact on final recognition performance. Traditional Adaboost algorithm based human ear detection method has some inherent drawbacks will lead to imperfect ear detection, such as the long time training, overly dependent on ear samples quality, etc. Therefore, to overcome such problems partially, the strategies of YCbCr skin-color filtering and multi-template matching are introduced to get more accurate ear location under occasions of insufficient training and bad initial ear position. The proposed method can eliminate most cases of false positing and multi-location or incomplete selection. Experimental results show the proposed method is robust and effective not only in static image but also under dynamic environments.
7 citations
References
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Book•
01 Jan 1991TL;DR: This book is a detailed, logically-developed treatment that covers the theory and uses of collective computational networks, including associative memory, feed forward networks, and unsupervised learning.
Abstract: From the Publisher:
This book is a comprehensive introduction to the neural network models currently under intensive study for computational applications. It is a detailed, logically-developed treatment that covers the theory and uses of collective computational networks, including associative memory, feed forward networks, and unsupervised learning. It also provides coverage of neural network applications in a variety of problems of both theoretical and practical interest.
7,518 citations
"Recognition of partially occluded o..." refers background or methods in this paper
...Features extracted from these orientations have not been used in training phase of two types of neural networks and also not stored in the hash table....
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...Number of mappings for which response was more than 0.5 after neural networks-based indexing, for both types of networks are given in the third and fourth row of the table....
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...Similarity invariant feature vectors extracted from triplets of these images, were given as inputs to both types of neural networks and the output response corresponding to each model triplet was stored in the table....
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...This 20-dimensional feature vector is used as input for neural network-based indexing technique explained in the next section....
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2,411 citations
Book•
01 Jan 1989TL;DR: This is a book that will show you even new to old thing, and when you are really dying of adaptive pattern recognition and neural networks, just pick this book; it will be right for you.
Abstract: It's coming again, the new collection that this site has. To complete your curiosity, we offer the favorite adaptive pattern recognition and neural networks book as the choice today. This is a book that will show you even new to old thing. Forget it; it will be right for you. Well, when you are really dying of adaptive pattern recognition and neural networks, just pick it. You know, this book is always making the fans to be dizzy if not to find.
2,166 citations
"Recognition of partially occluded o..." refers background or methods in this paper
...Features extracted from these orientations have not been used in training phase of two types of neural networks and also not stored in the hash table....
[...]
...Number of mappings for which response was more than 0.5 after neural networks-based indexing, for both types of networks are given in the third and fourth row of the table....
[...]
...Similarity invariant feature vectors extracted from triplets of these images, were given as inputs to both types of neural networks and the output response corresponding to each model triplet was stored in the table....
[...]
...This 20-dimensional feature vector is used as input for neural network-based indexing technique explained in the next section....
[...]
Book•
01 Aug 1981
TL;DR: This chapter discusses Graphics, Image Processing, and Pattern Recognition, and the Reconstruction techniques used in this program, as well as some of the problems faced in implementing this program.
Abstract: 1: Introduction.- 1.1 Graphics, Image Processing, and Pattern Recognition.- 1.2 Forms of Pictorial Data.- 1.2.1 Class 1: Full Gray Scale and Color Pictures.- 1.2.2 Class 2: Bilevel or "Few Color" pictures.- 1.2.3 Class 3: Continuous Curves and Lines.- 1.2.4 Class 4: Points or Polygons.- 1.3 Pictorial Input.- 1.4 Display Devices.- 1.5 Vector Graphics.- 1.6 Raster Graphics.- 1.7 Common Primitive Graphic Instructions.- 1.8 Comparison of Vector and Raster Graphics.- 1.9 Pictorial Editor.- 1.10 Pictorial Transformations.- 1.11 Algorithm Notation.- 1.12 A Few Words on Complexity.- 1.13 Bibliographical Notes.- 1.14 Relevant Literature.- 1.15 Problems.- 2: Digitization of Gray Scale Images.- 2.1 Introduction.- 2.2 A Review of Fourier and other Transforms.- 2.3 Sampling.- 2.3.1 One-dimensional Sampling.- 2.3.2 Two-dimensional Sampling.- 2.4 Aliasing.- 2.5 Quantization.- 2.6 Bibliographical Notes.- 2.7 Relevant Literature.- 2.8 Problems.- Appendix 2.A: Fast Fourier Transform.- 3: Processing of Gray Scale Images.- 3.1 Introduction.- 3.2 Histogram and Histogram Equalization.- 3.3 Co-occurrence Matrices.- 3.4 Linear Image Filtering.- 3.5 Nonlinear Image Filtering.- 3.5.1 Directional Filters.- 3.5.2 Two-part Filters.- 3.5.3 Functional Approximation Filters.- 3.6 Bibliographical Notes.- 3.7 Relevant Literature.- 3.8 Problems.- 4: Segmentation.- 4.1 Introduction.- 4.2 Thresholding.- 4.3 Edge Detection.- 4.4 Segmentation by Region Growing.- 4.4.1 Segmentation by Average Brightness Level.- 4.4.2 Other Uniformity Criteria.- 4.5 Bibliographical Notes.- 4.6 Relevant Literature.- 4.7 Problems.- 5: Projections.- 5.1 Introduction.- 5.2 Introduction to Reconstruction Techniques.- 5.3 A Class of Reconstruction Algorithms.- 5.4 Projections for Shape Analysis.- 5.5 Bibliographical Notes.- 5.6 Relevant Literature.- 5.7 Problems.- Appendix 5.A: An Elementary Reconstruction Program.- 6: Data Structures.- 6.1 Introduction.- 6.2 Graph Traversal Algorithms.- 6.3 Paging.- 6.4 Pyramids or Quad Trees.- 6.4.1 Creating a Quad Tree.- 6.4.2 Reconstructing an Image from a Quad Tree.- 6.4.3 Image Compaction with a Quad Tree.- 6.5 Binary Image Trees.- 6.6 Split-and-Merge Algorithms.- 6.7 Line Encodings and the Line Adjacency Graph.- 6.8 Region Encodings and the Region Adjacency Graph.- 6.9 Iconic Representations.- 6.10 Data Structures for Displays.- 6.11 Bibliographical Notes.- 6.12 Relevant Literature.- 6.13 Problems.- Appendix 6.A: Introduction to Graphs.- 7: Bilevel Pictures.- 7.1 Introduction.- 7.2 Sampling and Topology.- 7.3 Elements of Discrete Geometry.- 7.4 A Sampling Theorem for Class 2 Pictures.- 7.5 Contour Tracing.- 7.5.1 Tracing of a Single Contour.- 7.5.2 Traversal of All the Contours of a Region.- 7.6 Curves and Lines on a Discrete Grid.- 7.6.1 When a Set of Pixels is not a Curve.- 7.6.2 When a Set of Pixels is a Curve.- 7.7 Multiple Pixels.- 7.8 An Introduction to Shape Analysis.- 7.9 Bibliographical Notes.- 7.10 Relevant Literature.- 7.11 Problems.- 8: Contour Filling.- 8.1 Introduction.- 8.2 Edge Filling.- 8.3 Contour Filling by Parity Check.- 8.3.1 Proof of Correctness of Algorithm 8.3.- 8.3.2 Implementation of a Parity Check Algorithm.- 8.4 Contour Filling by Connectivity.- 8.4.1 Recursive Connectivity Filling.- 8.4.2 Nonrecursive Connectivity Filling.- 8.4.3 Procedures used for Connectivity Filling.- 8.4.4 Description of the Main Algorithm.- 8.5 Comparisons and Combinations.- 8.6 Bibliographical Notes.- 8.7 Relevant Literature.- 8.8 Problems.- 9: Thinning Algorithms.- 9.1 Introduction.- 9.2 Classical Thinning Algorithms.- 9.3 Asynchronous Thinning Algorithms.- 9.4 Implementation of an Asynchronous Thinning Algorithm.- 9.5 A Quick Thinning Algorithm.- 9.6 Structural Shape Analysis.- 9.7 Transformation of Bilevel Images into Line Drawings.- 9.8 Bibliographical Notes.- 9.9 Relevant Literature.- 9.10 Problems.- 10: Curve Fitting and Curve Displaying.- 10.1 Introduction.- 10.2 Polynomial Interpolation.- 10.3 Bezier Polynomials.- 10.4 Computation of Bezier Polynomials.- 10.5 Some Properties of Bezier Polynomials.- 10.6 Circular Arcs.- 10.7 Display of Lines and Curves.- 10.7.1 Display of Curves through Differential Equations.- 10.7.2 Effect of Round-off Errors in Displays.- 10.8 A Point Editor.- 10.8.1 A Data Structure for a Point Editor.- 10.8.2 Input and Output for a Point Editor.- 10.9 Bibliographical Notes.- 10.10 Relevant Literature.- 10.11 Problems.- 11: Curve Fitting with Splines.- 11.1 Introduction.- 11.2 Fundamental Definitions.- 11.3 B-Splines.- 11.4 Computation with B-Splines.- 11.5 Interpolating B-Splines.- 11.6 B-Splines in Graphics.- 11.7 Shape Description and B-splines.- 11.8 Bibliographical Notes.- 11.9 Relevant Literature.- 11.10 Problems.- 12: Approximation of Curves.- 12.1 Introduction.- 12.2 Integral Square Error Approximation.- 12.3 Approximation Using B-Splines.- 12.4 Approximation by Splines with Variable Breakpoints.- 12.5 Polygonal Approximations.- 12.5.1 A Suboptimal Line Fitting Algorithm.- 12.5.2 A Simple Polygon Fitting Algorithm.- 12.5.3 Properties of Algorithm 12.2.- 12.6 Applications of Curve Approximation in Graphics.- 12.6.1 Handling of Groups of Points by a Point Editor.- 12.6.2 Finding Some Simple Approximating Curves.- 12.7 Bibliographical Notes.- 12.8 Relevant Literature.- 12.9 Problems.- 13: Surface Fitting and Surface Displaying.- 13.1 Introduction.- 13.2 Some Simple Properties of Surfaces.- 13.3 Singular Points of a Surface.- 13.4 Linear and Bilinear Interpolating Surface Patches.- 13.5 Lofted Surfaces.- 13.6 Coons Surfaces.- 13.7 Guided Surfaces.- 13.7.1 Bezier Surfaces.- 13.7.2 B-Spline Surfaces.- 13.8 The Choice of a Surface Partition.- 13.9 Display of Surfaces and Shading.- 13.10 Bibliographical Notes.- 13.11 Relevant Literature.- 13.12 Problems.- 14: The Mathematics of Two-Dimensional Graphics.- 14.1 Introduction.- 14.2 Two-Dimensional Transformations.- 14.3 Homogeneous Coordinates.- 14.3.1 Equation of a Line Defined by Two Points.- 14.3.2 Coordinates of a Point Defined as the Intersection of Two Lines.- 14.3.3 Duality.- 14.4 Line Segment Problems.- 14.4.1 Position of a Point with respect to a Line.- 14.4.2 Intersection of Line Segments.- 14.4.3 Position of a Point with respect to a Polygon.- 14.4.4 Segment Shadow.- 14.5 Bibliographical Notes.- 14.6 Relevant Literature.- 14.7 Problems.- 15: Polygon Clipping.- 15.1 Introduction.- 15.2 Clipping a Line Segment by a Convex Polygon.- 15.3 Clipping a Line Segment by a Regular Rectangle.- 15.4 Clipping an Arbitrary Polygon by a Line.- 15.5 Intersection of Two Polygons.- 15.6 Efficient Polygon Intersection.- 15.7 Bibliographical Notes.- 15.8 Relevant Literature.- 15.9 Problems.- 16: The Mathematics of Three-Dimensional Graphics.- 16.1 Introduction.- 16.2 Homogeneous Coordinates.- 16.2.1 Position of a Point with respect to a Plane.- 16.2.2 Intersection of Triangles.- 16.3 Three-Dimensional Transformations.- 16.3.1 Mathematical Preliminaries.- 16.3.2 Rotation around an Axis through the Origin.- 16.4 Orthogonal Projections.- 16.5 Perspective Projections.- 16.6 Bibliographical Notes.- 16.7 Relevant Literature.- 16.8 Problems.- 17: Creating Three-Dimensional Graphic Displays.- 17.1 Introduction.- 17.2 The Hidden Line and Hidden Surface Problems.- 17.2.1 Surface Shadow.- 17.2.2 Approaches to the Visibility Problem.- 17.2.3 Single Convex Object Visibility.- 17.3 A Quad Tree Visibility Algorithm.- 17.4 A Raster Line Scan Visibility Algorithm.- 17.5 Coherence.- 17.6 Nonlinear Object Descriptions.- 17.7 Making a Natural Looking Display.- 17.8 Bibliographical Notes.- 17.9 Relevant Literature.- 17.10 Problems.- Author Index.- Algorithm Index.
1,395 citations
TL;DR: A method that combines region growing and edge detection for image segmentation is presented and is thought that the success in the tool images is because the objects shown occupy areas of many pixels, making it is easy to select parameters to separate signal information from noise.
Abstract: A method that combines region growing and edge detection for image segmentation is presented. The authors start with a split-and merge algorithm wherein the parameters have been set up so that an over-segmented image results. Region boundaries are then eliminated or modified on the basis of criteria that integrate contrast with boundary smoothness, variation of the image gradient along the boundary, and a criterion that penalizes for the presence of artifacts reflecting the data structure used during segmentation (quadtree in this case). The algorithms were implemented in the C language on a Sun 3/160 workstation running under the Unix operating system. Simple tool images and aerial photographs were used to test the algorithms. The impression of human observers is that the method is very successful on the tool images and less so on the aerial photograph images. It is thought that the success in the tool images is because the objects shown occupy areas of many pixels, making it is easy to select parameters to separate signal information from noise. >
567 citations