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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.
Firstly, an automatic method for object tracking from the satellite image sequence is proposed, aiming at identification of the qualified MCSs, their characteristics and their moving trajectories.
This article shows a pattern recognition method for object classification using ultrasonic sensors and a dual knowledge base fuzzy expert system.
In this paper we propose a novel method for object grasping that aims to unify robot vision techniques for efficiently accomplishing the demanding task of autonomous object manipulation.
The main topic and contribution of this paper is a new method for object part segmentation, from constant-curvature contour primitives, that is suited to process images of objects in real scenes.
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In this paper, a new method for object-based image retrieval is proposed.
Different from traditional kernel methods for object recognition, the proposed method does not need to reserve the training samples.
We evaluate our method on the Stanford dataset by comparing it against state-of-the-art methods for object segmentation and detection.
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].
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