2010
DOI: 10.1109/tpami.2009.153
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Abstract: Abstract-We present a generic detection/localization algorithm capable of searching for a visual object of interest without training. The proposed method operates using a single example of an object of interest to find similar matches, does not require prior knowledge (learning) about objects being sought, and does not require any preprocessing step or segmentation of a target image. Our method is based on the computation of local regression kernels as descriptors from a query, which measure the likeness of a … Show more

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Cited by 110 publications
(10 citation statements)
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“…Besides, LSSM is compared with other general target detection method [34,35], which uses a robust LARK feature to implement entire matching with a single template, and has good capability to detect human faces and targets with simple shape and compact structure.…”
Section: Resultsmentioning
confidence: 99%
See 2 more Smart Citations
“…Besides, LSSM is compared with other general target detection method [34,35], which uses a robust LARK feature to implement entire matching with a single template, and has good capability to detect human faces and targets with simple shape and compact structure.…”
Section: Resultsmentioning
confidence: 99%
“…Thus, we not only measure among the LSc/KSc/LSPSc/K-LSPSc-based LSSM models and the part-based sparse representation method [52] by using the simple car template set in Fig 14(B), but also compare LSSM against the training-free method [34,35] by using a single template of the side view of car. Fig 15(B) shows the results of car detection.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…Therefore, various image texture descriptors can be employed for finding which of the remaining candidate object ROIs is most similar to the object appearance model. In this paper, we have chosen to use Local Steering Kernel (LSK) descriptors, which were employed successfully in [16] for generic object detection and in [17] for monocular object tracking, due to their robustness in the appearance changes that the object undergoes between successive frames. LSKs are local image texture descriptors which measure the similarity of an image pixel to its surrounding ones.…”
Section: Use Of Object Texture In Trackingmentioning
confidence: 99%
“…In this paper, we present an appearance-based tracking algorithm, which exploits stereo information obtained from the disparity maps of the left and right channels acquired by an uncalibrated stereo camera. The proposed framework combines a representation for the object texture based on Local Steering Kernel (LSK) descriptors [16], color information and disparity information. LSKs are local texture descriptors, which fit a Gaussian function over a local image region around an image pixel by elongating and steering the function according to the direction and intensity of the image edges.…”
Section: Introductionmentioning
confidence: 99%