Unimodal Multi-Feature Fusion and one-dimensional Hidden Markov Models for Low-Resolution Face Recognition

The objective of low-resolution face recognition is to identify faces from small size or poor quality images with varying pose, illumination, expression, etc. In this work, we propose a robust low face recognition technique based on one-dimensional Hidden Markov Models. Features of each facial image are extracted using three steps: firstly, both Gabor filters and Histogram of Oriented Gradients (HOG) descriptor are calculated. Secondly, the size of these features is reduced using the Linear Discriminant Analysis (LDA) method in order to remove redundant information. Finally, the reduced features are combined using Canonical Correlation Analysis (CCA) method. Unlike existing techniques using HMMs, in which authors consider each state to represent one facial region (eyes, nose, mouth, etc), the proposed system employs 1D-HMMs without any prior knowledge about the localization of interest regions in the facial image. Performance of the proposed method will be measured using the AR database

Offline Face Recognition System Based on GaborFisher Descriptors and Hidden Markov Models

This paper presents a new offline face recognition system. The proposed system is built on one dimensional left-to- right Hidden Markov Models (1D-HMMs). Facial image features are extracted using Gabor wavelets. The dimensionality of these features is reduced using the Fisher’s Discriminant Analysis method to keep only the most relevant information. Unlike existing techniques using 1D-HMMs, in classification step, the proposed system employs 1D-HMMs to find the relationship between reduced features components directly without any additional segmentation step of interest regions in the face image. The performance evaluation of the proposed method was performed with AR database and the proposed method showed a high recognition rate for this database

Système automatique de reconnaissance de la langue de signes arabe basé sur les CNNs

International audienceLa réalisation d'un système précis de reconnaissance des langues de signes arabe (RLSAr) a un large impact social. Un tel système rendra la communication facile entre les sourds-muets et les citoyens ordinaires dans le monde arabe. Cependant, la réalisation d'un système (RLSAr) reste très difficile à réaliser puisque la (LSA) présente de nombreux détails et caractéristiques en raison des grandes variations dans les actions des mains. Dans ce travail, nous proposons un système de (RLSAr) basé sur les réseaux de neurones convolutionnels qui extrait automatiquement les caractéristiques discriminantes des signes représentant les chiffres et les caractères. Nous validons le système proposé sur un jeu de données réel et nous démontrons son efficacité par rapport aux approches traditionnelles basés sur les k-plus proches voisins (KNN) machines à vecteurs de support (SVM

Colposcopic image registration using opponentSIFT descriptor

This work presents a colposcopic image registration system able to help physicians for cervical cancer diagnosis. The goal is to make registration between the cervical tissue throughout the whole temporal sequence. Recent digital images processing works, suggested using feature points to compute the tissue displacement. These methods achieve good results, because they are fast and do not need any segmentation, but to date, all methods based on feature points are sensitive to light change and reflections which are frequently current in colposcopic images. To solve this problem, we propose to apply the opponentSIFT descriptor which describes features point in the opponent color space. Experimental results show the robustness of this descriptor in colposcopic images registration in comparison with other descriptors

Improving bag of visual words image retrieval: a fuzzy weighing scheme for efficient indexation

Peer reviewed: YesNRC publication: Ye

Fuzzy indexing for bag of features scene categorization

This paper presents a novel Bag of Features (BoF) method for image classification. The BoF approach describes an image as a set of local descriptors using a histogram, where each bin represents the importance of a visual word. This indexing approach has been frequently used for image classification, and we have seen several implementations, but crucial representation choices \u2013 such as the weighting schemes \u2013 have not been thoroughly studied in the literature. In our work, we propose a Fuzzy model as an alternative to known weighting schemes in order to create more representative image signatures. Furthermore, we use the Fuzzy signatures to train the Gaussian Na\uefve Bayesian Network and classify images. Experiments with Corel-1000 dataset demonstrate that our method outperforms the known implementations.Cet article pr\ue9sente une nouvelle m\ue9thode de \uab sac de caract\ue9ristiques \ubb [BoF pour Bag of Features] pour la classification des images. La m\ue9thode BoF d\ue9crit une image comme un ensemble de caract\ue9ristiques locales au moyen d\u2019un histogramme, o\uf9 chaque compartiment repr\ue9sente l\u2019importance d\u2019un mot visuel. On utilise fr\ue9quemment cette m\ue9thode d\u2019indexation pour la classification des images, et nous avons vu plusieurs applications de cette m\ue9thode, mais des choix de repr\ue9sentation cruciaux, comme les syst\ue8mes de pond\ue9ration, n\u2019ont pas fait l\u2019objet d\u2019\ue9tudes approfondies dans la documentation. Dans cet article, nous proposons un mod\ue8le flou (Fuzzy model) comme solution de rechange aux syst\ue8mes de pond\ue9ration connus pour cr\ue9er des signatures d\u2019image plus repr\ue9sentatives. De plus, nous employons les signatures floues pour entra\ueener le r\ue9seau bay\ue9sien na\ueff gaussien et classer des images. Des exp\ue9riences effectu\ue9es en utilisant la base de donn\ue9es Corel-1000 d\ue9montrent que notre m\ue9thode est sup\ue9rieure aux applications connues.Peer reviewed: YesNRC publication: Ye

Efficient image recognition using local feature and fuzzy triangular number based similarity measures

Image local scale invariant features are of great importance for object recognition. Among various local scale invariant feature descriptors, Scale Invariant Feature Transform (SIFT) descriptor has been shown to be the most descriptive one and thus widely applied to image retrieval, object recognition and computer vision. By SIFT descriptor, an image may be described by hundreds of key points with each point depicted by a 128-element feature vector; this representation makes the subsequent feature matching very computationally demanding. In this paper, we propose to incorporate the fuzzy set concepts into SIFT features and define fuzzy similarity between images. The proposed approach is applied to image recognition. Experimental results with the coil-100 image database are provided to show the superiority of the proposed approach.Peer reviewed: YesNRC publication: Ye

Estimation of large-amplitude motion and disparity fields: Application to intermediate view reconstruction

This paper describes a method for establishing dense correspondence between two images in a video sequence (motion) or in a stereo pair (disparity) in case of large displacements. In order to deal with large-amplitude motion or disparity fields, multi-resolution techniques such as blocks matching and optical flow have been used in the past. Although quite successful, these techniques cannot easily cope with motion/disparity discontinuities as they do not explicitly exploit image structure. Additionally, their computational complexity is high; block matching requires examination of numerous vector candidates while optical flow-based techniques are iterative. In this paper, we propose a new approach that addresses both issues. The approach combines feature matching with Delaunay triangulation, and thus reliable long-range correspondences result while the computational complexity is not high (sparse representation). In the proposed approach, feature points are found first using a simple intensity corner detector. Then, correspondence pairs between two images are found by maximizing cross-correlation over a small window. Finally, the Delaunay triangulation is applied to the resulting points, and a dense vector field is computed by planar interpolation over Delaunay triangles. The resulting vector field is continuous everywhere, and thus does not reflect motion or depth discontinuities at object boundaries. In order to improve the rendition of such discontinuities, we propose to further divide Delaunay triangles whenever the displacement vectors within a triangle do not allow good intensity match. The approach has been extensively tested on stereoscopic images in the context of intermediate view reconstruction where the quality of estimated disparity fields is critical for f..

A hierarchical clustering based heuristic for automatic clustering

Determining an optimal number of clusters and producing reliable results are two challenging and critical tasks in cluster analysis. We propose a clustering method which produces valid results while automatically determining an optimal number of clusters. Our method achieves these results without user input pertaining directly to a number of clusters. The method consists of two main components: splitting and merging. In the splitting phase, a divisive hierarchical clustering method (based on the DIANA algorithm) is executed and interrupted by a heuristic function once the partial result is considered to be "adequate". This partial result, which is likely to have too many clusters, is then fed into the merging method which merges clusters until the final optimal result is reached. Our method's effectiveness in clustering various data sets is demonstrated, including its ability to produce valid results on data sets presenting nested or interlocking shapes. The method is compared with cluster validity analysis to other methods to which a known optimal number of clusters is provided and to other automatic clustering methods. Depending on the particularities of the data set used, our method has produced results which are roughly equivalent or better than those of the compared methods.Peer reviewed: YesNRC publication: Ye