Improved eigen-value corner detection technique

Ipek, HL
Yardimci, Y
Eigenvalue based corner detection is known to be effective in detecting corners of objects in noise. In this paper, a comer detection technique based on including the orientation and the angle of the comer in addition to its eigenvalue is introduced. It is shown that both orientation and comer information improve the detectability of corners. Moreover, comers that have been selected via the new technique are more likely to be detected in subsequent frames and therefore improve the performance of an object tracker. This modification only adds a minor computational load to our tracking scheme. Real and synthetic images are used to evaluate the detection performance as well as their effect on tracking.


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Çakırgöz, Çağlayan Can; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2022-5-10)
Object detection aims for detecting objects of certain classes in an image by bounding them in rectangular boxes whereas instance segmentation tries to detect objects in pixel level. Deep learning techniques, which have shown great improvements over the last decade, are utilized in these topics as well, and a significant success is achieved against the traditional methods. Similar improvements can be observed in dense depth estimation which deals with deducing dense information of a scene from a single imag...
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Improvements on one-stage object detection by visual reasoning
Aksoy, Tolga; Halıcı, Uğur; Department of Electrical and Electronics Engineering (2022-5-09)
Current state-of-the-art one-stage object detectors are limited by treating each image region separately without considering possible relations of the objects. This causes dependency solely on high-quality convolutional feature representations for detecting objects successfully. However, this may not be possible sometimes due to some challenging conditions. In this thesis, a new architecture is proposed for one-stage object detection that reasons the relations of the image regions by using self-attention. T...
Citation Formats
H. Ipek and Y. Yardimci, “Improved eigen-value corner detection technique,” 2004, vol. 5429, p. 522, Accessed: 00, 2020. [Online]. Available: