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Communication between devices and data through networks have been increasing drastically in the past few decades. Encryption of data provides high level of security. This paper presents a median based technique for selective encryption where partial data of image is encrypted based on pixel values. An encrypted mask of the image is also appended with the image that specifies the pixels that are encrypted...
Learning based hashing plays a pivotal role in large-scale visual search. However, most existing hashing algorithms tend to learn shallow models that do not seek representative binary codes. In this paper, we propose a novel hashing approach based on unsupervised deep learning to hierarchically transform features into hash codes. Within the heterogeneous deep hashing framework, the autoencoder layers...
This document describes a system to gather information from a stationary camera to identify moving objects. The proposed solution makes only use of motion vectors between adjacent frames, obtained from any algorithm. Starting from them, the system is able to retrieve clusters of moving objects in a scene acquired by an image sensor device. Since all the system is only based on optical flow, it is...
Face spoofing detection nowadays has attracted attentions regarding the biometrics authentication issue. Inspired by the observation that face spoofing detection is highly relevant with the inherent image quality which also strongly depends on the properties of the capturing devices and conditions, in this paper, we tackle the spoofing detection problem based on a two-stage learning approach. Firstly,...
It is generally well known that the overall performance of the most widely used types of unsupervised change detection methods, based on the luminance pixel-wise difference, is mainly relied on the quality of the so-called difference image and the accuracy of the classification method. In order to address these two issues, this work proposes to first estimate, a new and robust similarity feature map,...
Multiple instance learning (MIL) is a form of weakly supervised learning for problems in which training instances are arranged into bags, and a label is provided for whole bags but not for individual instances. Most proposed MIL algorithms focus on bag classification, but more recently, the classification of individual instances has attracted the attention of the pattern recognition community. While...
Securing face recognition systems against replay attacks has been recognized as a real challenge. In this work, the problem of fake face detection is addressed by modelling radiometric distortions involved in the recapturing process. The originality of our approach is that the fake face detection process occurs after the face identification process. Having access to enrolment data of each client,...
Multi-spectral face recognition has been an active area of research over the past few decades. However, the vulnerability of multi-spectral face recognition systems is a growing concern that argues the need for Presentation Attack Detection (PAD) (or countermeasure or anti-spoofing) schemes to successfully detect targeted attacks. In this work, we present a novel feature descriptor LαMTiF that can...
We introduce a novel method of cell detection and segmentation based on a polar transformation. The method assumes that the seed point of each candidate is placed inside the nucleus. The polar representation, built around the seed, is segmented using k-means clustering into one candidate-nucleus cluster, one candidate-cytoplasm cluster and up to three miscellaneous clusters, representing background...
In this paper we propose a new method for segmenting retinal vessels in adaptive optics images. This method is particularly dedicated for segmenting vessels with significant morphological alterations due to vasculitis, but it is also accurate for vessels with moderate or without alteration. It relies on a pre-segmentation step which is crucial for the robustness and accuracy of the results. This step...
Head measurements are widely used in different fields as ergonomics, medicine and acoustics. In acoustics they are useful to create 3D virtual auditoriums, since the manipulation of the Head Related Transfer Function (HRTF) allows to virtually place sound sources. Despite the HRTF dependents on the physical characteristics of each person, a generic HRTF is frequently adopted in commercial systems...
Algorithms for facial landmark detection in real-world images require manually annotated training databases. However, the task of selecting or creating the images and annotating the data is extremely time-consuming, leaving researchers with the options of investing significant amounts of time for creating annotated images optimized for the given task or resigning from creating such hand-labeled databases...
This paper suggests a new method for detecting 2D translation between two images based on calculating three independent cross-correlations (CCs) on them. Such a method is conceptually different from other area based methods which generally perform only one CC or its variants for phase shift detection. The principle of traditional area based methods could be interpreted as a fast but simplified implementation...
The Walsh-Hadamard transform plays a major role in many image and video coding algorithms. In one hand, its intensive use in these algorithms makes its acceleration a challenge, in order to speed-up the algorithm execution. On the other hand, the available fast implementations are not efficient across different platforms. In this work, a parallel-based implementation of the WHT is proposed for CPU...
In this paper, we propose a mutual framework that combines two state-of-the-art visual object tracking algorithms. Both trackers benefit from each other's advantage leading to an efficient visual tracking approach. Many state-of-the-art trackers have poor performance due to rain, fog or occlusion in real-world scenarios. Often, after several frames, objects are getting lost, only leading to a short-term...
This paper approaches the problem of geometric multi-model fitting as a data segmentation problem which is solved by a sequence of sampling, model selection and clustering steps. We propose a sampling method that significantly facilitates solving the segmentation problem using the Normalized cut. The sampler is a novel application of Markov-Chain-Monte-Carlo (MCMC) method to sample from a distribution...
Diabetic Retinopathy (DR) is one of the leading causes of blindness amongst the working age population. The presence of microaneurysms (MA) in retinal images is a pathognomonic sign of DR. In this work we have presented a novel combination of algorithms applied to a public dataset for automated detection of MA in colour fundus images of the retina. The proposed technique first detects an initial set...
Palm ROI extraction is one of the most important processes in palmprint recognition. The core idea is to employ the valley points between the fingers to establish a coordinate system and then obtain the ROI of palmprints. However when extracting the keypoints, conventional methods have three problems: (i) they are so sensitive to parameters and background noise due to lack of joint optimization, (ii)...
Due to the wide applicability of pedestrian detection in surveillance and safety, this research topic has received much attention in computer vision literature. However, the focus of this research mainly lies in detecting and locating pedestrians individually as accurate as possible. In recent years, a number of datasets are captured using a forward looking camera from a car, which imposes the application...
In the last few years, visual privacy has become a major problem. Because of this, encrypted image processing has received a lot of attention within the scientific and business communities. Data hiding in encrypted images (DHEI) is an effective technique to embed data in the encrypted domain. The owner of an image encrypts it with a secret key and it is still possible to embed additional data without...
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