Image fusion is the combination of two or more different images by using suitable algorithms to form an output image. It provides a useful tool to integrate multiple images into a composite image. In this paper, we present an approach that uses the principle component transform along with the selection of maximum pixel intensity to perform pixel level fusion. The entropy, mutual information and the universal index based measure are used to evaluate the performance of this fusion algorithm. Keywords: Wavelet transformation, pixel level image fusion, PCA

Female breast cancer is a major cause of death in occidental countries. CAD/CADx systems can aidradiologists in detection and diagnostic of lesions in mammograms. In this work, we present a methodologyto detect masses from mammograms. The K-means clustering algorithm is used to split the mammogramsin regions. Each region is then classified through a Support Vector Machine (SVM) as mass or non-massregion. SVM is a machine-learning method, based on the principle of structural risk minimization, whichperforms well when applied to data outside the training set. We use a set of textural and shape measures todetect suspicious regions, as bening and malignant masses. Each textural measure (contrast, homogeneity,inverse difference moment, entropy and energy) is computed through the co-ocurrence matrix technique.The methodology obtained an accuracy of 93.11% discriminate mass from non-mass elements.

We present a linear algorithm for the computation of the illuminant change occurring between two colorpictures of a scene. We model the light variations with the von Kries diagonal transform and we estimate itby minimizing a dissimilarity measure between the piecewise inversions of the cumulative color histogramsof the considered images. We also propose a method for illuminant invariant image recognition based onour von Kries transform estimate.

This article describes a new method and approch of texture characterization. Using complex network representation of an image, classical and derived (hierarchical) measurements, we presente how to have good performance in texture classification. Image is represented by a complex networks : one pixel as a node. Node degree and clustering coefficient, using with traditionnal and extended hierarchical measurements, are used to characterize ”organisation” of textures.

This paper presents a new scheme for acquiring 3D kinematic structure and motion from time series volume data. Our basic strategy is to first represent the shape structure of the target in each frame by Reeb graph which we compute by using geodesic distance of target’s surface, and then estimate the kinematic structure of the target which is consistent with these shape structures. Although the shape structures can be very different from frame to frame, we propose to derive a unique kinematic structure by way of clustering some nodes of graph, based on the fact that they are partly coherent to a certain extent of time series. Once we acquire a unique kinematic structure, we fit it to other Reeb graphs in the remaining frames, and describe the motion throughout the entire time series. The only assumption we make is that human body can be approximated by an articulated body with certain numbers of end-points and branches. We demonstrate the efficacy of the proposed scheme through some experiments.

In this paper, we present an effective hierarchical shot classification scheme for broadcast soccer video. We first partition a video into replay and non-replay shots with replay logo detection. Then, non-replay shots are further classified into Long, Medium, Close-up or Out-field types with color and texture features based on a decision tree. We tested the method on real broadcast FIFA soccer videos, and the experimental results demonstrate its effectiveness..

In this paper, we systematically review recent advances in surface inspection using computer vision andimage processing techniques, particularly those based on texture analysis methods. The aim is to reviewthe state-of-the-art techniques for the purposes of visual inspection and decision making schemes that areable to discriminate the features extracted from normal and defective regions. This field is so vast that itis impossible to cover all the aspects of visual inspection. This paper focuses on a particular but importantsubset which generally treats visual surface inspection as texture analysis problems. Other topics related tovisual inspection such as imaging system and data acquisition are out of the scope of this survey.The surface defects are loosely separated into two types. One is local textural irregularities which is themain concern for most visual surface inspection applications. The other is global deviation of colour and/ortexture, where local pattern or texture does not exhibit abnormalities. We refer this type of defects as shadeor tonality problem. The second type of defects have been largely neglected until recently, particularly whencolour imaging system has been widely used in visual inspection and where chromatic consistency plays animportant role in quality control. The emphasis of this survey though is still on detecting local abnormalities,given the fact that majority of the reported works are dealing with the first type of defects.The techniques used to inspect textural abnormalities are discussed in four categories, statistical approaches,structural approaches, filter based methods, and model based approaches, with a comprehensivelist of references to some recent works. Due to rising demand and practice of colour texture analysis inapplication to visual inspection, those works that are dealing with colour texture analysis are discussedseparately. It is also worth noting that processing vector-valued data has its unique challenges, which conventionalsurface inspection methods have often ignored or do not encounter.We also compare classification approaches with novelty detection approaches at the decision makingstage. Classification approaches often require supervised training and usually provide better performancethan novelty detection based approaches where training is only carried out on defect-free samples. However,novelty detection is relatively easier to adapt and is particularly desirable when training samples areincomplete

A novel technique on shape coding of an object in a binary digital image, based on the tight isotheticpolygonal covers of the object in a multiresolution background, is proposed. This technique would be useful in various analyses and applications related with digital images. To demonstrate the power and usefulness of such shape codes, we have also proposed an image retrieval scheme based on shape codes. The elegance of the scheme on shape codes lies in capturing the shape of the object(s) present in an image from its gross appearance to its finer details by a set of isothetic polygons, in a hierarchical manner. The inherent tolerance present in such a cast of isothetic polygonal shape enables the shape codes of two objects resemble closely as the grid resolution becomes finer and finer, provided the objects are of similar shapes. The method is very fast because it does not involve any complex computations, and requires only integer-based comparisons and additions. Experimental results demonstrate the strength and efficiency of the proposed scheme.

This paper presents a novel solution of vehicle occlusion and 3D measurement for traffic monitoring by data fusion from multiple stationary cameras. Comparing with single camera based conventional methods in traffic monitoring, our approach fuses video data from different viewpoints into a common probability fusion map (PFM) and extracts targets. The proposed PFM concept is efficient to handle and fuse data in order to estimate the probability of vehicle appearance, which is verified to be more reliable than single camera solution by real outdoor experiments. An AMF based shadowing modeling algorithm is also proposed in this paper in order to remove shadows on the road area and extract the proper vehicle regions.

Since the inception of successful rasterization of curves and objects in the digital space, several algorithms have been proposed for approximating a given digital curve. All these algorithms, however, resort to thinning as preprocessing before approximating a digital curve with changing thickness. Described in this paper is a novel thinning-free algorithm for polygonal approximation of an arbitrarily thick digital curve, using the concept of “cellular envelope”, which is newly introduced in this paper. The cellular envelope, defined as the smallest set of cells containing the given curve, and hence bounded by two tightest (inner and outer) isothetic polygons, is constructed using a combinatorial technique. This envelope, in turn, is analyzed to determine a polygonal approximation of the curve as a sequence of cells using certain attributes of digital straightness. Since a real-world curve=curve-shaped object with varying thickness, unexpected disconnectedness, noisy information, etc., is unsuitable for the existing algorithms on polygonal approximation, the curve is encapsulated by the cellular envelope to enable the polygonal approximation. Owing to the implicit Euclidean-free metrics and combinatorial properties prevailing in the cellular plane, implementation of the proposed algorithm involves primitive integer operations only, leading to fast execution of the algorithm. Experimental results that include output polygons for different values of the approximation parameter corresponding to several real-world digital curves, a couple of measures on the quality of approximation, comparative results related with two other well-referred algorithms, and CPU times, have been presented to demonstrate the elegance and efficacy of the proposed algorithm.