Nicola Ancona

A ball detection algorithm for real soccer image sequences

A large number of methods for circle detection has been studied in the last years for numerous image processing applications. The application domain considered in this paper is the soccer game. To identify the ball in soccer images is very important in order to evaluate the goal event. This domain is challenging as a great number of problems has to be managed, such as occlusions, shadows, objects similar to the ball, real time processing. The aim of this work is to present the results of a number of experiments obtained by using a modified version of the directional circle Hough transform. Different lighting conditions have been considered since they introduce strong modifications on the appearance of the ball in the image: when the image sequences are taken with natural light the ball appears as a spherical cap then the search of the ball has been modified in order to manage those situations. A large number of experiments has been carried out showing that the proposed method obtains an high detection score.

Ball detection in static images with Support Vector Machines for classification

We present a general method for detecting balls in images at the aim of automatically detecting goals during a soccer match. The detector learns the object to detect by using a supervised learning scheme called Support Vector Machines, in which the examples are views of the object. Due to the attitude of the camera with respect to football ground, the system can be thought of as an electronic linesman which helps the referee in establishing the occurrence of a goal during a soccer match. Numerous theoretical and practical issues are addressed in the paper. The first one concerns the determination of negative examples relevant for the problem at hand and the training of a reference classifier in the case of an unbalanced number of positive and negative examples. The second one focuses on the reduction of the computational complexity of the reference classifier during the test phase, without increasing its generalization error. The third issue regards the problem of parameter selection, which is equivalent, in our context, to the problem of selecting, among the classifiers the machine implements, the one having performances similar to the reference classifier. Experimental results on real images show the performances of the proposed detection scheme.

Rail corrugation detection by Gabor filtering

Inspection of the rail state in order to detect defects is one of the basic tasks in railway maintenance. Rail defects exhibit different properties and are divided in various categories relating to the type and position of flaws on the rail. We propose a technique, based on texture analysis of the rail surface, to detect and classify a particular class of defects: corrugation.

Ballast 3D reconstruction by a matching pursuit based stereo matcher

In the last years the railway association spent much money to check the railway infrastructure. The railway infrastructure is a particular field in which the periodical surface inspection of rolling plane can help the operator to prevent critical situations. The maintenance and monitoring of this infrastructure is an important aspect for railway association. In this paper we describe a visual system able to detect defect where the ballast bed has an anomalous behaviour. The proposed system is a stereo rig, based on two high resolution line scanner TV cameras, installed under the train. To detect defect of the layer ballast we developed stereo vision techniques use matching pursuit method to extract features from images and the similarity function to execute the correspondence between left and right images. Visual inspection can help to increase the control quality and reduce costs to maintenance.

Object detection in images: run-time complexity and parameter selection of support vector machines

We address two aspects related to the exploitation of support vector machines (SVM) for classification in real application domains, such as the detection of objects in images. The first one concerns the reduction of the run-time complexity of a reference classifier without increasing its generalization error. We show that the complexity in test phase can be reduced by training SVM classifiers on a new set of features obtained by using principal component analysis (PCA). Moreover due to the small number of features involved, we explicitly map the new input space in the feature space induced by the adopted kernel function. Since the classifier is simply a hyperplane in the feature space, then the classification of a new pattern involves only the computation of a dot product between the normal to the hyperplane and the pattern. The second issue concerns the problem of parameter selection. In particular we show that the receiver operating characteristic curves, measured on a suitable validation set, are effective for selecting, among the classifiers the machine implements, the one having performances similar to the reference classifier. We address these two issues for the particular application of detecting goals during a football match.

Visual recognition of hexagonal headed bolts by comparing ICA to wavelets

In this paper we present vision-based techniques to automatically detect the absence of the fastening bolts that secure the rails to the sleepers. The inspection system uses images from a digital line scan camera installed under a train. This application is part of the most general problem of object recognition. In object recognition as in supervised learning, we often extract new features from original ones for the purpose of reducing the feature space dimensions and achieving better performances. The goal of this paper is to compare two techniques within the context of the hexagonal-headed bolts recognition in railway maintenance. The first technique is Wavelets Transform (WT), the second technique is Independent Component Analysis (ICA), a new method that produces spatially localized and statistically independent basis vector. The coefficients of the new representation in the ICA and WT subspace are supplied as input to a Support Vector Machine (SVM). A SVM classifier analyses the images in order to evaluate the pre-processing technique which could give the highest rate in detecting the presence of the bolts. Results in terms of detection rate and false positive rate are given in the paper.

Texture analysis for shadow removing in video-surveillance systems

The paper presents a new approach for detecting and removing shadows from objects and pedestrians, since shadow removing is a fundamental step in video-surveillance systems for accurate object detection. In order to precisely remove the unwanted shadows, a novel approach is proposed, focused on the problem of representing texture information in terms of redundant systems of functions (frame). The method for discriminating shadows is based on the matching pursuit (MP) algorithm using an over-complete dictionary: the basic idea is to use MP for selecting the best little set of atoms (dictionary functions) of 2D Gabor dictionary and representing texture as linear combination of frame elements. The approach proves how MP is a powerful scheme able to compactly capture detailed textural information of little regions of the image, so MP decomposition coefficients can be used as an exhaustive features in the shadow points detection process. Experimental results validate the algorithms performance.

3D object recognition based on a viewpoint-analysis

Abstract An approach for the recognition of 3D objects is proposed. The approach is based on view-analysis and compilation of those aspects related to each of the objects being recognized, i.e. the expectations of appearance of the objects related to each viewpoint are generated and analyzed so as to discard viewpoints related to similar surfaces. For each object a 3D multiview description is introduced; these descriptions are integrated in a tree-structure data in order to simplify matching of the unknown object against every possible model.

Door detection in images based on learning by components

In this paper we present a vision-based technique for detecting targets of the environment which has to be reached by an autonomous mobile robot during its navigational task. The targets the robot has to reach are the doors of our office building. Color and shape information are used as identifying features for detecting principal components of the door. In fact in images the door can appear of different dimensions depending on the attitude of the robot with respect to the door, therefore detection of the door is performed by detecting its most significant components in the image. Positive and negative examples, in form of image patterns, are manually selected from real images for training two neural classifiers in order to recognize the single components. Each classifier has been realized by a feed-forward neural network with one hidden layer and sigmoid activation function. Moreover for selecting negative examples, relevant for the problem at hand, a bootstrap technique has been used during the training process. Finally the detecting system has been applied to several test real images for evaluating its performance.