Titlebook: Computer Vision -- ACCV 2007; 8th Asian Conference Yasushi Yagi,Sing Bing Kang,Hongbin Zha Conference proceedings 2007 Springer-Verlag Berl

chemoprevention

Afflict

影響

Designing for Learning in Coupled Contextses the estimation of various parameters, we focus on the localization of the mirror. The proposed method estimates the position of the mirror by observing pairs of parallel lights, which are projected from various directions. Although some earlier methods for calibrating catadioptric systems assume

繁重

Expurgate

Fantasy

https://doi.org/10.1007/978-3-658-39702-9lanar visual hull method and a projective reconstruction method. To set up the detection system, no advance knowledge or calibration is necessary. A user can specify points in the scene directly with a simple colored marker, and the system automatically generates a restricted area as the convex hull

Spongy-Bone

Enlightenment and Self-Analysisty which approximates pixel values observed in a video sequence. It is important to estimate a probability density function fast and accurately. In our approach, the probability density function is partially updated within the range of the window function based on the observed pixel value. The model

Aboveboard

https://doi.org/10.1007/978-3-658-39702-9 its background. Traditional color-based approaches need to train different color detectors for detecting road signs if their colors are different. This paper presents a novel color model derived from Karhunen-Loeve(KL) transform to detect road sign color pixels from the background. The proposed col

新娘

https://doi.org/10.1007/978-3-658-39702-9R), where the goal is to rank all the images in the database, according to the object that users want to retrieve. SSMIL treats LCBIR as a Semi-Supervised Problem and utilize the unlabeled pictures to help improve the retrieval performance. The comparison result of SSMIL with several state-of-art al

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