A human-machine cooperative path planning model based on cloud model is proposed in this paper. The system enables the planner take part in the A* searching process and the cloud model integrates fuzziness with random...
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ISBN:
(纸本)9780819485779
A human-machine cooperative path planning model based on cloud model is proposed in this paper. The system enables the planner take part in the A* searching process and the cloud model integrates fuzziness with randomness of the qualitative concept. In the process of human-computer cooperation, the position of the leading field is figured out based on cloud model;it effectively guides the A* searching process and avoids the drawback of the algorithm. Experiment results demonstrated the validity and the feasibility of the model. It's much more efficient than either a human or a computer algorithm in the path planning tasks.
Preventing accidental injuries of toddlers requires thorough, consistent supervision, but this isn't always practical. A proposed vision-based system detects three fall risk factors in the home environment to help...
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Preventing accidental injuries of toddlers requires thorough, consistent supervision, but this isn't always practical. A proposed vision-based system detects three fall risk factors in the home environment to help caregivers supervise nearby toddlers when they can't give continuous attention to the toddlers. The crucial technical challenge is to differentiate a human from other foreground objects in the images. Unlike previous systems, this one uses multiple dynamic motion cues for human detection, employing cues related to human appearance.
This paper is an extended abstract of a tutorial devoted to bio-inspired computervision neural models. It includes a description of the Human Visual System, the BCS/FCS model, and neural architectures for color image...
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Infrared small target detection is an important research area of computervision and often a key technique in Infrared Search and Track (IRST) systems. Many algorithms have been reported for this purpose. The facet-ba...
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ISBN:
(纸本)9780819485779
Infrared small target detection is an important research area of computervision and often a key technique in Infrared Search and Track (IRST) systems. Many algorithms have been reported for this purpose. The facet-based method is one of novel algorithms and is shown as robust and efficient, but it does not perform well in target preservation. The method cannot detect peripheral pixel of target, which causes information loss of target intensity distribution and affects post processing of detection, such as target tracking and recognition. In this paper an improved algorithm is developed for solving this shortcoming. The detection behavior of the facet model is further analyzed. Small target is surrounded by background, so local image edge that indicates target contour can be represented by zero-crossings of the second partial derivatives. The improved algorithm uses facet model to fit local intensity surface and detect potential targets using extremum theory, then the zero-crossings of the second partial derivatives of the fitting function in each potential target's neighborhood are found and the pixels inside the zero-crossing contour are restored to the potential target. In experiments involving typical infrared images target intensity distribution information is well preserved by proposed algorithm and its execution time is also acceptable.
We argue that to make robust computervision algorithms for face analysis and recognition, these should be based on configural and shape features. In this model, the most important task to be solved by computervision...
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Optimization is a powerful paradigm for expressing and solving problems in a wide range of areas, and has been successfully applied to many vision problems. Discrete optimization techniques are especially interesting ...
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Optimization is a powerful paradigm for expressing and solving problems in a wide range of areas, and has been successfully applied to many vision problems. Discrete optimization techniques are especially interesting since, by carefully exploiting problem structure, they often provide nontrivial guarantees concerning solution quality. In this paper, we review dynamic programming and graph algorithms, and discuss representative examples of how these discrete optimization techniques have been applied to some classical vision problems. We focus on the low-level vision problem of stereo, the mid-level problem of interactive object segmentation, and the high-level problem of model-based recognition.
Template matching is widely used in patternrecognition and computervision. However, the performance of traditional template matching approaches is often sensitive to large intraclass variance, occlusion, minor varie...
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We propose to detect abnormal events via a sparse reconstruction over the normal bases. Given an over-complete normal basis set (e.g., an image sequence or a collection of local spatio-temporal patches), we introduce ...
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ISBN:
(纸本)9781457703935
We propose to detect abnormal events via a sparse reconstruction over the normal bases. Given an over-complete normal basis set (e.g., an image sequence or a collection of local spatio-temporal patches), we introduce the sparse reconstruction cost (SRC) over the normal dictionary to measure the normalness of the testing sample. To condense the size of the dictionary, a novel dictionary selection method is designed with sparsity consistency constraint. By introducing the prior weight of each basis during sparse reconstruction, the proposed SRC is more robust compared to other outlier detection criteria. Our method provides a unified solution to detect both local abnormal events (LAE) and global abnormal events (GAE). We further extend it to support online abnormal event detection by updating the dictionary incrementally. Experiments on three benchmark datasets and the comparison to the state-of-the-art methods validate the advantages of our algorithm.
This paper proposes an approach to complete the group of images that make up a target visual pattern. Completion is realized with images derived by autoregressive models of variability. The derived images possess occa...
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