| 1. |
- Delic, Marija, et al.
(författare)
-
αLBP – a novel member of the Local Binary Pattern family based on α-cutting
- 2015
-
Ingår i: Proc. 9th International Symposium on Image and Signal Processing and Analysis. - : IEEE. - 9781467380324 ; , s. 13-18
-
Konferensbidrag (refereegranskat)abstract
- Local binary pattern (LBP) descriptors have been popular in texture classification in recent years. They were introduced as descriptors of local image texture and their histograms are shown to be well performing texture features. In this paper we introduce two new LBP descriptors, αLBP and its improved variant IαLBP. We evaluate their performance in classification by comparing them with some of the existing LBP descriptors - LBP, ILBP, shift LBP (SLBP) and with one ternary descriptor - LTP. The texture descriptors are evaluated on three datasets - KTH-TIPS2b, UIUC and Virus texture dataset. The novel descriptor outperforms the other descriptors on two datasets, KTH-TIPS2b and Virus, and is tied for first place with ILBP on the UIUC dataset.
|
|
| 2. |
- Gottschlich, Carsten, et al.
(författare)
-
Improving Fingerprint Alteration Detection
- 2015
-
Ingår i: 2015 9th International Symposium on Image and Signal Processing and Analysis (ISPA). - : IEEE. - 9781467380324 - 9781467380317 ; , s. 83-86
-
Konferensbidrag (refereegranskat)abstract
- Fingerprint alteration is a type of presentation attack in which the attacker strives to avoid identification, e.g. at border control or in forensic investigations. As a countermeasure, fingerprint alteration detection aims to automatically discover the occurrence of such attacks by classifying fingerprint images as ’normal’ or ’altered’. In this paper, we propose four new features for improving the performance of fingerprint alteration detection modules. We evaluate the usefulness of these features on a benchmark and compare them to four existing features from the literature. © Copyright 2015 IEEE - All rights reserved.
|
|
