判别式
计算机科学
人工智能
水准点(测量)
神经编码
模式识别(心理学)
探测器
钥匙(锁)
集合(抽象数据类型)
编码(社会科学)
词汇
图像(数学)
灵活性(工程)
编码(内存)
计算机视觉
数学
大地测量学
哲学
统计
电信
语言学
计算机安全
程序设计语言
地理
标识
DOI:10.1109/ism.2020.00029
摘要
We present a novel Sparsity-Aware Keypoint detector (SAKD) to localize a set of discriminative keypoints via optimization of group-sparse coding. Unlike most of current handcrafted keypoint detectors that are limited by the manually defined local structures, the proposed method has the capacity to allow flexibility for exploiting diverse structures with the combination of visual atoms from a vocabulary. Another key valuable attribute is that its group-sparsity nature concentrates on discovering sharable structural patterns across keypoints within an image jointly. This main merit facilitates to localize repeatable keypoints and resists against distractors when image undergoes various transformations. Extensive experiments on four challenging benchmark datasets demonstrate that the proposed method achieves favorable performances compared with state-of-the-art in literature.
科研通智能强力驱动
Strongly Powered by AbleSci AI