Accepted Papers

  • A Novel Background Subtraction Algorithm for Person Tracking Based on K-Nn
    Asmaa Ait Moulay and Aouatif Amine, University Compus, Morocco

    Object tracking can be defined as the process of detecting an object of interest from a video scene and keeping track of its motion, orientation, occlusion etc. in order to extract useful information. It is indeed a challenging problem and it’s an important task. Many researchers are getting attracted in the field of computer vision, specifically the field of object tracking in video surveillance. The main purpose of this paper is to give to the reader information of the present state of the art object tracking, together with presenting steps involved in Background Subtraction and their techniques. In related literature we found three main methods of object tracking: the first method is the optical flow; the second is related to the background subtraction, which is divided into two types presented in this paper, and the last one is temporal differencing. We present a novel approach to background subtraction that compare a current frame with the background model that we have set before, so we can classified each pixel of the image as a foreground or a background element, then comes the tracking step to present our object of interest, which is a person, by his centroid. The tracking step is divided into two different methods, the surface method and the K-NN method, both are explained in the paper. Our proposed method is implemented and evaluated using CAVIAR database.

  • Neural Networks For High Performance Time-Delay Estimation And Acoustic Source Localization
    Ludwig Houégnigan, Pooyan Safari, Climent Nadeu,Mike van der Schaar, Marta Solé and Michel André, Polytechnic University of Catalonia, Spain

    Time-delay estimation is an essential building block of many signal processing applications. This paper follows up on earlier work for acoustic source localization and time delay estimation using pattern recognition techniques in the adverse environment such as reverberant rooms or underwater; it presents unprecedented high performance results obtained with supervised training of neural networks which challenge the state of the art and compares its performance to that of well-known methods such as the Generalized Cross-Correlation or Adaptive Eigenvalue Decomposition.

    Madhumitha S and Manikandan M,Madras Institute of Technology, Chennai

    In this paper, various pupil localizations, segmentations, and pupil parameter measurements are studied. Study and Analysis is done on various algorithms based on the Hough Transform, radial symmetry transform, Otsu's algorithm, Region growing algorithm, Thresholding, Starburst algorithm which aids for accurate and effective localization and segmentation results. In this paper a brief comparison is made on the results that is obtained from different algorithms by analyzing the advantages and disadvantages.

  • Spontaneous Smile Detection with Application of Landmark Points Supported by Visual Indications
    Karolina Nurzynska and Bogdan Smolka, Silesian University of Technology, Poland

    When automatic recognition of emotion was achieved, a novel challenges has evolved. One of them is detection whether a presented emotion is genuine or not. In this work, a fully automated system for differentiation between spontaneous and posed smile is presented. This solution exploits information derived from landmark points, which track the movement of fiducial elements of face. Additionally, the smile intensity computed with SNiP system [1] is exploited to deliver additional descriptive data. The performed experiments reviled that when an image sequence describes all phases of smile the landmark points approach achieves almost 80% accuracy, but when only onset is exploited additional support from visual cues is necessary to obtain slightly weaker results.

    Yuqian ZHOU and Shuhao LU, Hong Kong University of Science and Technology, China

    Diabetic retinopathy (DR) is one of the retinal diseases due to long-term effect of diabetes. Early detection for diabetic retinopathy is crucial since timely treatment can prevent progressive loss of vision.The most common diagnosis technique of diabetic retinopathy is to screen abnormalities through retinal fundus images by clinicians. However, limited number of well-trained clinicians increase the possibilities of misdiagnosing. In this work, we propose a big-data-driven automatic computer-aided diagnosing (CAD) system for diabetic retinopathy severity regression based on transfer learning, which starts from a deep convolutional neural network pre-trained on generic images, and adapts it to large-scale DR datasets. From images in the training set, we also automatically segment the abnormal patches with an occlusion test, and model the transformations and deterioration process of DR. Our results can be wildly used for fast diagnosis of DR, medical education and public-level healthcare propagation.