Eye Localization Using the Discriminative Generalized Hough Transform

Hahmann, Ferdinand; Ruppertshofen, Heike; Böer, Gordon; Stannarius, Ralf; Schramm, Hauke

doi:10.1007/978-3-642-32717-9_16

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…This can be automated using the face detector in [12] and the eye detector in [13] which ensure high accuracies. However, the main contribution of this work is to present a Reversible De-Identification method which is independent from the obfuscation process.…”

Section: A Face Obfuscationmentioning

confidence: 99%

Reversible De-Identification for lossless image compression using Reversible Watermarking

Farrugia

2014

2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

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-De-Identification is a process which can be used to ensure privacy by concealing the identity of individuals captured by video surveillance systems. One important challenge is to make the obfuscation process reversible so that the original image/video can be recovered by persons in possession of the right security credentials. This work presents a novel Reversible De-Identification method that can be used in conjunction with any obfuscation process. The residual information needed to reverse the obfuscation process is compressed, authenticated, encrypted and embedded within the obfuscated image using a two-level Reversible Watermarking scheme. The proposed method ensures an overall single-pass embedding capacity of 1.25 bpp, where 99.8% of the images considered required less than 0.8 bpp while none of them required more than 1.1 bpp. Experimental results further demonstrate that the proposed method managed to recover and authenticate all images considered.

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Section: A Face Obfuscationmentioning

confidence: 99%

Reversible De-Identification for lossless image compression using Reversible Watermarking

Farrugia

2014

2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

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“…Object localization is a prerequisite for many subsequent automatic image processing algorithms, e.g. automatic segmentation of organ structures in medical images (Ecabert et al, 2008), automatic object classification (Hahmann et al, 2012;Hahmann et al, 2014), automatic object tracking (Andriluka et al, 2008) etc. Approaches to automatic object localization in still images can i.a.…”

Section: Introductionmentioning

confidence: 99%

Structured Edge Detection for Improved Object Localization using the Discriminative Generalized Hough Transform

Gabriel

Hahmann

Böer

et al. 2016

Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

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Automatic localization of target objects in digital images is an important task in Computer Vision. The Generalized Hough Transform (GHT) and its variant, the Discriminative Generalized Hough Transform (DGHT), are model-based object localization algorithms which determine the most likely object position based on accumulated votes in the so-called Hough space. Many automatic localization algorithms-including the GHT and the DGHT-operate on edge images, using e.g. the Canny or the Sobel Edge Detector. However, if the image contains many edges not belonging to the object of interest (e.g. from other objects, background clutter, noise etc.), these edges cause misleading votes which increase the probability of localization errors. In this paper we investigate the effect of a more sophisticated edge detection algorithm, called Structured Edge Detector, on the performance of a DGHT-based object localization approach. This method utilizes information on the shape of the target object to substantially reduce the amount of non-object edges. Combining this technique with the DGHT leads to a significant localization performance improvement for automatic pedestrian and car detection.

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Detection and Localization of Landmarks in the Lower Extremities Using an Automatically Learned Conditional Random Field

Mader

Lorenz²,

Bergtholdt³

et al. 2017

Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics

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Eye Localization Using the Discriminative Generalized Hough Transform

Cited by 4 publications

References 12 publications

Reversible De-Identification for lossless image compression using Reversible Watermarking

Reversible De-Identification for lossless image compression using Reversible Watermarking

Structured Edge Detection for Improved Object Localization using the Discriminative Generalized Hough Transform

Detection and Localization of Landmarks in the Lower Extremities Using an Automatically Learned Conditional Random Field

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