The three-dimensional shape analysis problem is a very demanding test of shape analysis algorithms. Previous approaches to the problem have employed global features such as moments and Fourier descriptors. Global feat...
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The three-dimensional shape analysis problem is a very demanding test of shape analysis algorithms. Previous approaches to the problem have employed global features such as moments and Fourier descriptors. Global features lack the capacity for solving the partial shape recognition problem, in which only part of the unknown shape is available. Previous approaches to local shape analysis have employed structural (syntactic) methods, but these methods have so far failed to solve the three-dimensional problem. This study describes a hybrid structural/statistical local shape analysis algorithm which is applied to the three-dimensional problem.
A new and fast way to find local image correspondences for wide baseline image matching is described. The targeted application is visual navigation, e.g. of a semi-automatic wheelchair. Such applications pose some add...
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ISBN:
(纸本)0769521584
A new and fast way to find local image correspondences for wide baseline image matching is described. The targeted application is visual navigation, e.g. of a semi-automatic wheelchair. Such applications pose some additional requirements, like the need to work with natural landmarks rather than artificial markers, and the need to recognize locations fast. The restricted motion of the camera can be exploited to simplify the feature extraction. These features should support their identification from different, but nevertheless restricted viewing directions, and under variable illumination conditions. The paper proposes a specialization of so-called affine invariant regions for these particular conditions, which in this case simplifies to column segments. Their applicability is wider than robot navigation, and includes localization for wearable computing and scene recognition for automatic movie indexing.
In this paper, we discuss an appearance matching technique for the interpretation of color scenes containing occluded objects. Dealing with occlusions is very difficult, and we have explored the use of an iterative, c...
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ISBN:
(纸本)0818678224;0780342364
In this paper, we discuss an appearance matching technique for the interpretation of color scenes containing occluded objects. Dealing with occlusions is very difficult, and we have explored the use of an iterative, coarse-to-fine correlation-based method that uses hypothesized occlusion events to modify the scene-to-template similarity measure at run-time. Specifically, a binary mask is used to adaptively exclude regions of the template image from the correlation computation. At each iteration, these masks are adjusted based on higher resolution scene data and the occluding interactions between multiple object hypotheses. We present results which demonstrate the technique is reasonably robust over a large database of color test scenes containing objects at a variety of scales, and tolerates minor object rotations and global illumination variations.
Sketch-based image retrieval (SBIR) has undergone an increasing interest in the community of computer vision bringing high impact in real applications. For instance, SBIR brings an increased benefit to eCommerce searc...
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ISBN:
(纸本)9781665448994
Sketch-based image retrieval (SBIR) has undergone an increasing interest in the community of computer vision bringing high impact in real applications. For instance, SBIR brings an increased benefit to eCommerce search engines because it allows users to formulate a query just by drawing what they need to buy. However, current methods showing high precision in retrieval work in a high dimensional space, which negatively affects aspects like memory consumption and time processing. Although some authors have also proposed compact representations, these drastically degrade the performance in a low dimension. Therefore in this work, we present different results of evaluating methods for producing compact embeddings in the context of sketch-based image retrieval. Our main interest is in strategies aiming to keep the local structure of the original space. The recent unsupervised local-topology preserving dimension reduction method UMAP fits our requirements and shows outstanding performance, improving even the precision achieved by SOTA methods. We evaluate six methods in two different datasets. We use Flickr15K and eCommerce datasets;the latter is another contribution of this work. We show that UMAP allows us to have feature vectors of 16 bytes improving precision by more than 35%.
Shape description and recognition is an important and interesting problem in scene analysis. The authors approach to shape description is a formal model of a shape consisting of a set of primitives, their properties, ...
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Shape description and recognition is an important and interesting problem in scene analysis. The authors approach to shape description is a formal model of a shape consisting of a set of primitives, their properties, and their interrelationships. The primitives are the simple parts and intrusions of the shape which can be derived through the graph-theoretic clustering procedure previously described. The interrelationships are two ternary relations on the primitives: the intrusion relation which relates two simple parts that join to the intrusion they surround and the protrusion relation which relates two intrusions to the protrusion between them. Using this model, a shape matching procedure that uses a tree search with look-ahead to find mappings from a prototype shape to a candidate shape has been developed.
The k-syntactic similarity approach is couched in graphical representation terms and its ability to provide global recognition capability while retaining a low time complexity is explored. One potential application do...
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The k-syntactic similarity approach is couched in graphical representation terms and its ability to provide global recognition capability while retaining a low time complexity is explored. One potential application domain, that of composite shape decomposition into approximately convex subshapes, is described. This is shown to be equivalent to finding cycles within a particular graph. The approach yields valid decompositions in many cases where additional semantic considerations are necessary for proper analysis. The permissible graph structures representing composite shapes given a reasonable set of relations are determined. Experimental results on non-ideal data are given.
Large images are becoming more and more common in earth resources monitoring, medical diagnosis and other applications. Often it would be helpful to work with only a subset of a large image since less space and time w...
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Large images are becoming more and more common in earth resources monitoring, medical diagnosis and other applications. Often it would be helpful to work with only a subset of a large image since less space and time would be required to process it. Subsets extracted according to semantic attributes have irregular shapes and as such are awkward to store and process. Irregular subsets can be covered with rectangular regions to simplify the regions to be stored and processed. Then the rectangular regions must be organized with an index. Here several covering methods are compared and indexing methods suggested. A surprizing result is that the sequential-greatest coverage heuristic can lead to arbitrarily bad coverings in some situations. However, this disadvantage can be overcome by combination with a tiling approach.
The proceedings contains 165 papers on computer vision and patternrecognition. Topics discussed include recognition systems, imageprocessing. computational methods, algorithms and information use.
ISBN:
(纸本)0818658274
The proceedings contains 165 papers on computer vision and patternrecognition. Topics discussed include recognition systems, imageprocessing. computational methods, algorithms and information use.
We present a semantic segmentation algorithm for RGB remote sensing images. Our method is based on the Dilated Stacked U-Nets architecture. This state-of-the-art method has been shown to have good performance in other...
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ISBN:
(数字)9781538661000
ISBN:
(纸本)9781538661000
We present a semantic segmentation algorithm for RGB remote sensing images. Our method is based on the Dilated Stacked U-Nets architecture. This state-of-the-art method has been shown to have good performance in other applications. We perform additional post-processing.by blending image tiles and degridding the result. Our method gives competitive results on the DeepGlobe dataset.
The growing use and analysis of images by computer presents many problems in managing images and image information. Solutions to these problems can be specific, with file structures to represent the needed information...
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The growing use and analysis of images by computer presents many problems in managing images and image information. Solutions to these problems can be specific, with file structures to represent the needed information, or general, such as a general Data Base Management System. A presentation is made of a compromise solution, a Raster image File Format (RIFF), that uses image file headers for the storage of specific image format information, as well as very general Name-Value pair information. This latter structure enables varying kinds of information to be represented, including links to other kinds of image data structures as the need arises.
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