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Different from traditional kernel methods for object recognition, the proposed method does not need to reserve the training samples.

Deep learning has been reported to significantly outperform classical machine learning methods for object detection and classification and has been increasingly used for medical image analysis [23].

Typical tracking methods and the proposed tracking algorithm are described in Sections 3. The experimental results and the tracking performances are in Section 4. Finally, conclusions are given in Section 5. Most methods for object detection are based on per-pixel background models [9 12].

There are two main methods for object identification that use boundary information.

The first contribution is that the model's setup makes it computationally highly efficient in comparison with existing window-sliding methods for object detection, while retaining an acceptable detection performance.

Although the gaze-control models mentioned earlier have contributed to the understanding of gaze control, none of them are competitive yet with existing window-sliding methods for object detection.

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Figures 4a, b, and c compare the accuracy of quantification achieved with the three segmentation methods for objects scanned in the hot background.

Some methods for objecting to something you disagree with can do more harm than good.

We present an efficient method for object image classification.

This paper presents a novel computational imaging method for object tracking.

This paper proposes a new Local Kernel Feature Analysis (LKFA) method for object recognition.

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