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Object detection is challenging when the object class exhibits large within-class variations. In this work, we show that foreground-background classification (detection) and within-class classification of the foreground class (pose estimation) can be jointly learned in a multiplicative form of two kernel functions. Model training is accomplished via standard SVM learning. When the foreground object...
The paper presents an efficient and low-cost method for automatically detecting and tracking the moving object from astronomical CCD image sequences, using a combination of active contours and shape feature similarities. An object detection algorithm is firstly implemented following some image preprocessing steps, in order to locate all the major objects in each image. Next, an object tracking method...
Segmenting videos into meaningful real-world objects remains one of the most challenging image processing research topics. Frame-by-frame object tracking is especially challenging due to the limitations of existing image segmentation algorithms often resulting in inconsistent regions occurring between adjacent frames. This work addresses this problem by introducing an innovative region matching and...
Tracking and recognition of objects in video sequences suffer from difficulties in learning appropriate object models. Often a high degree of supervision is required, including manual annotation of many training images. We aim at unsupervised learning of object models and present a novel way to build models based on motion information extracted from video sequences. We require a coarse delineation...
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