International Journal of Applied Pattern Recognition (3 papers in press)
Zebra Crossing Detection Based on HSV Colour Model and Projective Invariant for Driver Assistance System
by Muhammad Kamal Hossen, Mohammad Obaidur Rahman, Anika Sadia, Md. Shahnur Azad Chowdhury
Abstract: This paper implements a colour and shape based Zebra crossings detection model. As Zebra crossings are white coloured, a robust binarization based on the V channel of the input image is used to find the large rectangle sized white stripes of Zebra crossing. After the morphological open operation, boundary tracing is done using Moore-neighbourhood tracing algorithm along with Jacobs stopping criterion. For every region, the bounding quadrilateral with the minimum area is computed and the candidate regions are then filtered out using some criteria e.g., aspect ratio. The y-coordinates of the lower vertices of the white stripes are considered as cluster and the optimal number of clusters is determined using the Calinski-Harabasz criterion. These clusters are now checked to determine whether they represent crosswalks and then validate using projective invariant. Various Zebra crossing images are used to test the proposed framework and the results are presented to prove its effectiveness.
Keywords: Zebra crossing; crosswalks; intelligent transport systems; HSV; region of interest; Moore-neighbourhood; bounding quadrilateral; K-means clustering; Calinski-Harabasz; projective invariant.
Using Empirical Mode Decomposition and KullbackLeibler distance for online handwritten signature verification
by Toufik Hafs, Layachi Bennacer, Hanene Brahmia, Mohamed Boughazi, Amine Naitali
Abstract: The signature is one of the most accepted biometric modalities in the world. In this paper, we present a new method for online handwritten signature based on the empirical mode decomposition (EMD).After extracting each of the signature coordinates, a phase of pre-processing and normalisation is carried out. Then, the features of the signatures are extracted by using the EMD. After that, three similarity measures are used to match the signatures between them. The database used in our work is the one used in Signature Verification Competition (SVC 2004). Experimental results confirm the effectiveness of our approach and show the level of its reliability. Finally, the proposed method gives an EER of 2.03 % and allows high rates of recognition compared to other approaches.
Keywords: Online signature; Verification; Biometrics; Kullback–Leibler divergence; Empirical Mode Decomposition (EMD); Hilbert transform.
Influence of noise, light and shadows on image segmentation algorithms
by Pedro Furtado, Pedro Martins, Jose Cecílio
Abstract: Image segmentation is a required step in many image processing and object recognition tasks, where regions obtained by segmentation are analyzed to classify as specific objects. In all those procedures, image segmentation tech- niques are used to divide the image into multiple regions or clusters. Each image pixel will be assigned to one of the clusters using different metrics such as pixel color value, gray-scale intensity, edges, shapes, among others. The most diverse image segmentation algorithms have already been proposed and are currently used. It is important to analyze the robustness of those algorithms, taking into consideration diverse types of noise and difficult con- ditions. Both the types of noise and difficult conditions and the number of algorithms makes this a difficult task. In this paper we choose a small set of frequently-used algorithms and analyze their behavior in those robustness- testing conditions. In order to do that, we inject difficulties (noise, shadows, various degrees of illumination) and compare the quality of the segmentation with those algorithms against a ground truth. The objective is to analyze how differences in illumination, shadows or noise influence the output of the algorithms, and how they compare on those metrics. Based on those results we conclude about the quality of the approaches tested.
Keywords: Image segmentation; image clustering; algorithms.