Object localization is a challenging problem due to variations in object's structure and illumination. Although existing part based models have achieved impressive progress in the past several years, their improve...
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We propose a method for vector field learning with outliers, called vector field consensus (VFC). It could distinguish inliers from outliers and learn a vector field fitting for the inliers simultaneously. A prior is ...
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The classic Bag-of-Features (BOF) model and its extensional work use a single value to represent a visual code. This strategy ignores the relation of visual codes. In this paper, we explore this relation and propose a...
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Effective similarity search indexing in general metric spaces has traditionally received special attention in several areas of interest like patternrecognition, computervision or information retrieval. A typical met...
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ISBN:
(纸本)9780889868656
Effective similarity search indexing in general metric spaces has traditionally received special attention in several areas of interest like patternrecognition, computervision or information retrieval. A typical method is based on the use of a distance as a dissimilarity function (not restricting to Euclidean distance) where the main objective is to speed up the search of the most similar object in a database by minimising the number of distance computations. Several types of search can be defined, being the k-nearest neighbour or the range search the most common. AESA is one of the most well known of such algorithms due to its performance (measured in distance computations). PiAESA is an AESA variant where the main objective has changed. Instead of trying to find the best nearest neighbour candidate at each step, it tries to find the object that contributes the most to have a bigger lower bound function, that is, a better estimation of the distance. In this paper we extend and test PiAESA to support several similarity queries. Our empirical results show that this approach obtains a significant improvement in performance when comparing with competing algorithms.
This paper presents inexpensive computervision techniques allowing to measure the texture characteristics of woven fabric, such as weave repeat and yarn counts, and the surface roughness. First, we discuss the automa...
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This paper presents a manifold based kernel optimizing algorithm for KPCA which has recently shown effectiveness for patternrecognition and systematic classification based on extracting nonlinear features. However, t...
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The current paper proposes a new parametric local color correction technique. Initially, several color transfer functions are computed from the output of the mean shift color segmentation algorithm. Secondly, color in...
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This paper demonstrates a system for the automatic extraction of novelty in images captured from a small video camera attached to a subject's chest, replicating his visual perspective, while performing activities ...
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In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database sig...
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We present a robust radiometric calibration method that capitalizes on the transform invariant low-rank structure of sensor irradiances recorded from a static scene with different exposure times. We formulate the radi...
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