Established in 1997, the International Machine vision and Image Processing (IMVIP) conferences bring together theoreticians, practitioners, industrialists and academics, from the numerous related disciplines involved ...
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ISBN:
(数字)9781527550766
ISBN:
(纸本)9781443829625
Established in 1997, the International Machine vision and Image Processing (IMVIP) conferences bring together theoreticians, practitioners, industrialists and academics, from the numerous related disciplines involved in the processing and analysis of image-based information. These events provide a platform for communication and exchange between participants whereby cutting edge research and advances within the field can be communicated, discussed and information *** events are hosted annually by different universities on the island of Ireland. These proceedings reflect the manuscripts selected for oral presentation at the 14th instalment of the series hosted by the University of Limerick, Ireland in 2010 in association with the Irish patternrecognition and Classification Society (IPRCS), a member organisation of the International Association of Patten recognition (IAPR).
This paper proposes a new template matching method that is robust to outliers and fast enough for real-time operation. The template and image are densely transformed in binary code form by projecting and quantizing hi...
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We propose a novel method for recognition of structured images and demonstrate it on detection of windows in facade images. Given an ability to obtain local low-level data evidence on primitive elements of a structure...
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This paper addresses the independent assumption issue in fusion process. In the last decade, dependency modeling techniques were developed under a specific distribution of classifiers. This paper proposes a new framew...
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This paper presents a multi-agent solution for cooperative visual mapping using planar regions. Each agent is assumed to be equipped with a conventional camera and has limited communication capabilities. Our approach ...
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This paper presents a multi-agent solution for cooperative visual mapping using planar regions. Each agent is assumed to be equipped with a conventional camera and has limited communication capabilities. Our approach starts building topological maps from independent image sequences where natural landmarks extracted from conventional images are grouped to create a graph of planes. With this approach the features observed in several images belonging to the same planar region are stored only once, reducing the size of the individual maps. In a distributed scenario this is very important because smaller maps can be transmitted faster, which makes our approach better suited for cooperative mapping. The later fusion of the individual maps is obtained via distributed consensus without any initial information about the relations between the different maps. Experiments with real images in complex scenarios show the good performance of our proposal. (c) 2011 Elsevier Ltd. All rights reserved.
The automatic processing of handwritten historical documents is considered a hard problem in patternrecognition. In addition to the challenges given by modern handwritten data, a lack of training data as well as effe...
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Unsupervised categorization of objects is a fundamental problem in computervision. While appearance-based methods have become popular recently, other important cues like functionality are largely neglected. Motivated...
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Human action recognition and annotation is an active research topic in computervision. How to model various actions, varying with time resolution, visual appearance, and others, is a challenging task. In this paper, ...
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Human action recognition and annotation is an active research topic in computervision. How to model various actions, varying with time resolution, visual appearance, and others, is a challenging task. In this paper, we propose a boosted exemplar learning (BEL) approach to model various actions in a weakly supervised manner, i.e., only action bag-level labels are provided but action instance level ones are not. The proposed BEL method can be summarized as three steps. First, for each action category, amount of class-specific candidate exemplars are learned through an optimization formulation considering their discrimination and co-occurrence. Second, each action bag is described as a set of similarities between its instances and candidate exemplars. Instead of simply using a heuristic distance measure, the similarities are decided by the exemplar-based classifiers through the multiple instance learning, in which a positive (or negative) video or image set is deemed as a positive (or negative) action bag and those frames similar to the given exemplar in Euclidean Space as action instances. Third, we formulate the selection of the most discriminative exemplars into a boosted feature selection framework and simultaneously obtain an action bag-based detector. Experimental results on two publicly available datasets: the KTH dataset and Weizmann dataset, demonstrate the validity and effectiveness of the proposed approach for action recognition. We also apply BEL to learn representations of actions by using images collected from the Web and use this knowledge to automatically annotate action in YouTube videos. Results are very impressive, which proves that the proposed algorithm is also practical in unconstraint environments.
Many state-of-the-art face recognition algorithms use image descriptors based on features known as Local Binary patterns (LBPs). While many variations of LBP exist, so far none of them can automatically adapt to the t...
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