We examine how to identify video shots with at least two humans using only detected face information. While face detection is much more reliable than shape based people classification in broadcast video, one particula...
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The proceedings contains 38 papers from the conference on the proceedings 10th working conference on reverse engineering. The topics discussed include: extracting an explicitly data-parallel representation of image-pr...
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The proceedings contains 38 papers from the conference on the proceedings 10th working conference on reverse engineering. The topics discussed include: extracting an explicitly data-parallel representation of image-processing.programs;a comparative evaluation of dynamic visualization tools;hierarchical reflexion models detecting merging and splitting using origin analysis;improving fact extraction of framework-based software systems and algorithm recognition based on demand-driven data-flow analysis.
The following topics are discussed: computer vision; illumination and appearance-based matching; tracking; shape recognition; image segmentation; medical image analysis; 3D reconstruction; face recognition; motion est...
The following topics are discussed: computer vision; illumination and appearance-based matching; tracking; shape recognition; image segmentation; medical image analysis; 3D reconstruction; face recognition; motion estimation; illumination and image restoration; fingerprint recognition; image registration; image retrieval; and reflectance model.
The paper presents a novel fast matching algorithm between airborne and satellite-borne SAR images so as to efficiently integrate SAR images into GPSISINS navigation system. Because the character such as gray level an...
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In this paper, we propose an automatic model based image segmentation system, where the instantiated model is refined incrementally using the domain knowledge combined by Fuzzy Logic. The Fuzzy Inference System (FIS) ...
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ISBN:
(纸本)0819448141
In this paper, we propose an automatic model based image segmentation system, where the instantiated model is refined incrementally using the domain knowledge combined by Fuzzy Logic. The Fuzzy Inference System (FIS) combines several different image features, which are used by experts to detect prostates in noisy ultrasound images. We use the Discrete Dynamic Contour (DDC) model because of its favorable performances in both open and closed contour models. The FIS governs the automatic open DDC model initialization and the following incremental growing process on a low-resolution image. At this stage, the initial open contour model grows by tracking the coarse edge details until it closes. The resulting closed contour model is then refined incrementally up to the original image resolution, incorporating finer edge details on to the model. The algorithm developed here is a general tool for object detection in an image analysis system, which employs a flexible framework designed to support multiple decision tools to collaborate in forming a solution. The FIS in our tool retrieves the domain knowledge it needs from the framework, to govern the model refinement process. The proposed algorithm can be used to detect the boundary of any object on an image, if the knowledge of the dominant image features is stored in the system. We have included results of the algorithm successfully applied to several ultrasound images to define the boundary of the prostate.
We address the problem of vector-valued image regularization with variational methods and PDEs. From the study of existing formalisms, we propose a unifying framework based on a very local interpretation of the regula...
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We address the problem of vector-valued image regularization with variational methods and PDEs. From the study of existing formalisms, we propose a unifying framework based on a very local interpretation of the regularization processes. The resulting equations are then specialized into new regularization PDEs and corresponding numerical schemes that respect the local geometry of vector-valued images. They are finally applied on a wide variety of imageprocessing.problems, including color image restoration, in-painting, magnification and flow visualization.
This paper presents an object detection framework applied to cinematographic post-processing.of video sequences. Post-processing.is done after production and before editing. At the beginning of each shot of a video, a...
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ISBN:
(纸本)0819450235
This paper presents an object detection framework applied to cinematographic post-processing.of video sequences. Post-processing.is done after production and before editing. At the beginning of each shot of a video, a slate (also called clapperboard) is shown. The slate contains notably an electronic audio timecode that is necessary for audio-visual synchronization. This paper presents an object detection framework to detect slates in video sequences for automatic indexing and post-processing. It is based on five steps. The first two steps aim to reduce drastically the video data to be analyzed. They ensure high recall rate but have low precision. The first step detects images at the beginning of a shot possibly showing up a slate while the second step searches in these images for candidates regions with color distribution similar to slates. The objective is to not miss any slate while eliminating long parts of video without slate appearance. The third and fourth steps are statistical classification and pattern matching to detected and precisely locate slates in candidate regions. These steps ensure high recall rate and high precision. The objective is to detect slates with very little false alarms to minimize interactive corrections. In a last step, electronic timecodes are read from slates to automize audio-visual synchronization. The presented slate detector has a recall rate of 89% and a precision of 97,5%. By temporal integration, much more than 89% of shots in dailies are detected. By timecode coherence analysis, the precision can be raised too. Issues for future work are to accelerate the system to be faster than real-time and to extend the framework for several slate types.
Color image segmentation has been extensively applied to a lot of applications such as patternrecognition, image compression and matching. In the literature, conventional k-means (MacQueen 1967) is one common algorit...
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Color image segmentation has been extensively applied to a lot of applications such as patternrecognition, image compression and matching. In the literature, conventional k-means (MacQueen 1967) is one common algorithm used in pixel-based image segmentation. However, it needs to pre-assign an appropriate cluster number before performing clustering, which is an intractable problem from a practical viewpoint. In contrast, the recently proposed Rival Penalization Controlled Competitive Learning (RPCCL) approach (Cheung 2002) can perform correct clustering without knowing the exact cluster number in analog with the RPCL (Xu et al. 1993). The RPCCL penalizes the rivals with a strength control such that extra seed points are automatically driven far away from the input data set, but without the de-learning rate selecting problem as the RPCL. In this paper, we further investigate the RPCCL on color image segmentation in comparison with the k-means and RPCL algorithms.
A two-stage face recognition algorithm is proposed. In the first stage, the mutual information match is applied to reduce the candidate pattern amount in the database. In the second stage, the principal component anal...
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