Shape-based invariant features extraction for object recognition

International audienceThe emergence of new technologies enables generating large quantity of digital information including images; this leads to an increasing number of generated digital images. Therefore it appears a necessity for automatic systems for image retrieval. These systems consist of techniques used for query specification and re-trieval of images from an image collection. The most frequent and the most com-mon means for image retrieval is the indexing using textual keywords. But for some special application domains and face to the huge quantity of images, key-words are no more sufficient or unpractical. Moreover, images are rich in content; so in order to overcome these mentioned difficulties, some approaches are pro-posed based on visual features derived directly from the content of the image: these are the content-based image retrieval (CBIR) approaches. They allow users to search the desired image by specifying image queries: a query can be an exam-ple, a sketch or visual features (e.g., colour, texture and shape). Once the features have been defined and extracted, the retrieval becomes a task of measuring simi-larity between image features. An important property of these features is to be in-variant under various deformations that the observed image could undergo. In this chapter, we will present a number of existing methods for CBIR applica-tions. We will also describe some measures that are usually used for similarity measurement. At the end, and as an application example, we present a specific ap-proach, that we are developing, to illustrate the topic by providing experimental results

Contour Based 3D Biological Image Reconstruction and Partial Retrieval

Image segmentation is one of the most difficult tasks in image processing. Segmentation algorithms are generally based on searching a region where pixels share similar gray level intensity and satisfy a set of defined criteria. However, the segmented region cannot be used directly for partial image retrieval. In this dissertation, a Contour Based Image Structure (CBIS) model is introduced. In this model, images are divided into several objects defined by their bounding contours. The bounding contour structure allows individual object extraction, and partial object matching and retrieval from a standard CBIS image structure. The CBIS model allows the representation of 3D objects by their bounding contours which is suitable for parallel implementation particularly when extracting contour features and matching them for 3D images require heavy computations. This computational burden becomes worse for images with high resolution and large contour density. In this essence we designed two parallel algorithms; Contour Parallelization Algorithm (CPA) and Partial Retrieval Parallelization Algorithm (PRPA). Both algorithms have considerably improved the performance of CBIS for both contour shape matching as well as partial image retrieval. To improve the effectiveness of CBIS in segmenting images with inhomogeneous backgrounds we used the phase congruency invariant features of Fourier transform components to highlight boundaries of objects prior to extracting their contours. The contour matching process has also been improved by constructing a fuzzy contour matching system that allows unbiased matching decisions. Further improvements have been achieved through the use of a contour tailored Fourier descriptor to make translation and rotation invariance. It is proved to be suitable for general contour shape matching where translation, rotation, and scaling invariance are required. For those images which are hard to be classified by object contours such as bacterial images, we define a multi-level cosine transform to extract their texture features for image classification. The low frequency Discrete Cosine Transform coefficients and Zenike moments derived from images are trained by Support Vector Machine (SVM) to generate multiple classifiers

DTW-Radon-based Shape Descriptor for Pattern Recognition

International audienceIn this paper, we present a pattern recognition method that uses dynamic programming (DP) for the alignment of Radon features. The key characteristic of the method is to use dynamic time warping (DTW) to match corresponding pairs of the Radon features for all possible projections. Thanks to DTW, we avoid compressing the feature matrix into a single vector which would otherwise miss information. To reduce the possible number of matchings, we rely on a initial normalisation based on the pattern orientation. A comprehensive study is made using major state-of-the-art shape descriptors over several public datasets of shapes such as graphical symbols (both printed and hand-drawn), handwritten characters and footwear prints. In all tests, the method proves its generic behaviour by providing better recognition performance. Overall, we validate that our method is robust to deformed shape due to distortion, degradation and occlusion

Assessment of Mould Growth on Building Materials using Spatial and Frequency Domain Analysis Techniques

The phenomenon of Sick Building Syndrome (SBS), Building Related Illness (BRI) and some other indoor related diseases have been attributed to mould and fungi exposure in the indoor environment. Despite the growing concern over mould and fungi infestations on building materials, little has been reported in the literature on the development of an objective tool and criteria for measuring and characterizing the shape and the level of severity of such parasitic phenomenon. In this paper, an objective based approach of mould and fungi growth assessment using spatial and frequency domain information is proposed. The spatial domain analysis of the acquired Mould Infested Images (MII) is achieved using Ratio Test (RT), Compactness Test (CT) and Visual Test (VT) while the frequency domain analysis uses the popular Discrete Fourier Transform (DFT) implemented in the form of Fast Fourier Transform (FFT) in analyzing the boundary pixel sequence. The resulting frequency components (Fourier Descriptors (FD)) can now be analyzed or stored for reconstruction purposes. Application of structural similarity measures on the reconstructed MII in spatial domain shows that the use of relative low number of FD is sufficient for analyzing, characterizing and reconstruction of the original spatial domain boundary pixels