An iterative algorithm for color space optimization on image segmentation

This paper proposes, a novel hybrid color component (HCC) issued from amounts number of color space with iterative manner, in fact traditional images obtained by RGB sensor weren’t the effective way in image processing applications, for this purpose we have propose a supervised algorithm to substitute RGB level by hybrid and suitable color space at the aim to make well representation of the handled amounts of data, this step is extremely important because the obtained results it will be injected in many future studies like tracking, classification, steganography and cryptography. The second part of this paper consists to segment image coded in hybrid color space already selected, the used algorithm is inspired from kernel function where statistical distribution was used to model background and Bayes rule to make decision of the membership of each pixel, in this research topics we have extended this algorithm in the aim to improve compactness of these distribution. Cauchy background modeling and subtraction is used, and shows the high accuracy of automatic player detection

Motion Detection and Clustering Using PCA and NN in Color Image Sequence

This paper presents a motion detection method with the use of the Principal Component Analysis. This method is able to detect and track moving objects in a sequence of images. The tested sequence is segmented within the meaning of movement. In this paper, the concept of extracting significant information from a large number of data is adopted to provide an effective method for tracking moving objects on the video image. The principal components are different in term of getting significant information, the nature of motion (the nature of information) is responsible of this difference, the algorithm in this paper distinguish the motion nature and choose the appropriate components to give a best segmentation

Objects Detection by Singular Value Decomposition Technique in Hybrid Color Space: Application to Football Images

In this paper, we present an improvement non-parametric background modeling and foreground segmentation. This method is important; it gives the hand to check many states kept by each background pixel. In other words, generates the historic for each pixel, indeed on certain computer vision applications the background can be dynamic; several intensities were projected on the same pixel. This paper describe a novel approach which integrate both Singular Value Decomposition (SVD) of each image to increase the compactness density distribution and hybrid color space suitable to this case constituted by the three relevant chromatics levels deduced by histogram analysis. In fact the proposed technique presents the efficiency of SVD and color information to subtract background pixels corresponding to shadows pixels. This method has been applied on colour images issued from soccer video. In the other hand to achieve some statistics information about players ongoing of the match (football, handball, volley ball, Rugby...) as well as to refine their strategy coach and leaders need to have a maximum of technical-tactics information. For this reason it is prominent to elaborate an algorithm detecting automatically interests color regions (players) and solve the confusion problem between background and foreground every moment from images sequence

Face recognition using fractional coefficients and discrete cosine transform tool

Face recognition is a computer vision application based on biometric information for automatic person identification or verification from image sequence or a video frame. In this context DCT is the easy technique to determine significant parameters. Until now the main object is selection of the coefficients to obtain the best recognition. Many techniques rely on premasking windows to discard the high and low coefficients to enhance performance. However, the problem resides in the shape and size of premask. To improve discriminator ability in discrete cosine transform domain, we used fractional coefficients of the transformed images with discrete cosine transform to limit the coefficients area for a better performance system. Then from the selected bands, we use the discrimination power analysis to search for the coefficients having the highest power to discriminate different classes from each other. Feature selection algorithm is a key issue in all pattern recognition system, in fact this algorithm is utilized to define features vector among several ones, where these features are selected according a specified discrimination criterion. Many classifiers are used to evaluate our approach like, support vector machine and random forests. The proposed approach is validated with Yale and ORL Face databases. Experimental results prove the sufficiency of this method in face and facial expression recognition field

Comparative Study of Statistical background Modeling and Subtraction

Background subtraction methods are widely exploited for moving object detection in videos in many computer vision applications, such as traffic monitoring, human motion capture and video surveillance. The two most distinguishing and challenging aspects of such approaches in this application field are how to build correctly and efficiently the background model and how to prevent the false detection between; (1) moving background pixels and moving objects, (2) shadows pixel and moving objects. In this paper we present a new method for image segmentation using background subtraction. We propose an effective scheme for modelling and updating a background adaptively in dynamic scenes focus on statistical learning. We also introduce a method to detect sudden illumination changes and segment moving objects during these changes. Unlike the traditional color levels provided by RGB sensor aren’t the best choice, for this reason we propose a recursive algorithm that contributes to select very significant color space. Experimental results show significant improvements in moving object detection in dynamic scenes such as waving tree leaves and sudden illumination change, and it has a much lower computational cost compared to Gaussian mixture model

Evaluation method based on a tracing mechanism for adaptive user interfaces: application in intelligent transport systems

International audienceNowadays, Adaptive User Interfaces (AUI) are more and more present everywhere in our daily life activities (at home, at work, in public places, etc.). Moreover, they can have different adaptation capabilities, can be disseminated in the environment of the users, and take into account different user profiles. Many academic and industrial studies are conducted about user modelling, design methods and tools for User Interface (UI) generation. However, the evaluation of such user interfaces is difficult. In fact, there exist relatively few works in the literature about such AUI evaluation. To fill up this lack, it is necessary to envisage new evaluation methods focused on adaptation quality of UI. In this research work, we propose an evaluation method called MetTra (METhod based on a TRAcing system). This method has been validated by evaluating AUIs in the transportation field