A novel curvature-based shape Fourier Descriptor

Content Based Image Retrieval (CBIR) is important in computer aided plant species recognition. Texture and shape information have been the primitive image descriptors in content based image retrieval systems. This paper presents a novel framework for combining both the features, texture and shape information, and achieve higher retrieval efficiency. The study provides a methodology for retrieving medicinal plants images from a database of medicinal plant images based on shape and texture features. The shape descriptors include Zernike moment, Fourier descriptor (FD), Generic fourier Descriptor(GFD) and for texture descriptors gabor filters are used. The similarity measures, euclidean distance of each medicinal plant image from the database to query image is used. The images are sorted based on similarity of Euclidean distance. The retrieval experiments are carried on different training and test medicinal plant images. The effectiveness of different descriptors is confirmed by the experimental results. We have investigated shape and texture features for medicinal plant retrieval by successively combining the different transforms. The retrieval efficiency is reported through precision and recall rate. Experimental results by combining Gabor and Zernike transform outperforms the all other methods.

We propose a method for enhancing the accuracy of shape descriptors. The concept of connectivity to obtain discriminating shape descriptors, is introduced. We show how connectivity is applied to two popular shape descriptors. Experiments are performed to test the effect of using connectivity with generic Fourier descriptors and distance histograms. Item S8 within the MPEG-7 still images content set is used for performing experiments. This dataset consists of 3621 still images. The experimental results show that connectivity enhances the performance of the methods significantly.

The Fourier descriptors (FDs) is a classical but still popular method for contour matching. The key idea is to apply the Fourier transform to a periodic representation of the contour, which results in a shape descriptor in the frequency domain. FDs are most commonly used to compare object silhouettes and object contours; the authors instead use this well-established machinery to describe local regions to be used in an object-recognition framework. Many approaches to matching FDs are based on the magnitude of each FD component, thus ignoring the information contained in the phase. Keeping the phase information requires us to take into account the global rotation of the contour and shifting of the contour samples. The authors show that the sum-of-squared differences of FDs can be computed without explicitly de-rotating the contours. The authors compare correlation-based matching against affine-invariant Fourier descriptors (AFDs) and WARP-matched FDs and demonstrate that correlation-based approach outperforms AFDs and WARP on real data. As a practical application the authors demonstrate the proposed correlation-based matching on a road sign recognition task.

Fourier descriptors (FDs) is a classical but still popular method for contour matching. The key idea is to apply the Fourier transform to a periodic representation of the contour, which results in a shape descriptor in the frequency domain. Fourier descriptors have mostly been used to compare object silhouettes and object contours; we instead use this well established machinery to describe local regions to be used in an object recognition framework. We extract local regions using the Maximally Stable Extremal Regions (MSER) detector and represent the external contour by FDs. Many approaches to matching FDs are based on the magnitude of each FD component, thus ignoring the information contained in the phase. Keeping the phase information requires us to take into account the global rotation of the contour and shifting of the contour samples. We show that the sum-of-squared differences of FDs can be computed without explicitly de-rotating the contours. We compare our correlation based matching against affine-invariant Fourier descriptors (AFDs) and demonstrate that our correlation based approach outperforms AFDs on real world data.

There are various possible approaches to the task of automatic identification of football players on video sequences. In the paper the numbers on jerseys are analysed and investigated. Firstly, the algorithm for the localisation of sport shirts and consecutively numbers is described. Secondly, the method for the recognition of the extracted numbers is proposed and experimentally investigated. The approach analysed in the paper applies the Generic Fourier Descriptor for the representation of extracted binary shapes of numbers.

The use of region shape descriptors was investigated for categorisation of textile design images. Images were segmented using MRF pixel labelling and the shapes of regions obtained were described with generic Fourier descriptors. Each image was represented as a bag of shapes. A simple yet competitive classification scheme based on nearest neighbour class-based matching was used. Classification performance was compared to that obtained when using bags of SIFT features.

In this paper, we propose a method for indexing and retrieval of images based on shapes of objects. The concept of connectivity is introduced. 3D models are used to represent 2D images. 2D images are decomposed a priori using connectivity which is followed by 3D model construction. 3D model descriptors are obtained for 3D models and used to represent the underlying 2D shapes. We have used spherical harmonics descriptors as the 3D model descriptors. Difference between two images is computed as the Euclidean distance between their descriptors. Experiments are performed to test the effectiveness of spherical harmonics for retrieval of 2D images. The proposed method is compared with methods based on principal components analysis (PCA) and generic Fourier descriptors (GFD). It is found that the proposed method is effective. Item S8 within the MPEG-7 still images content set is used for performing experiments.

Automated determination of poplar chip size distribution based on combined image and multivariate analyses

The use of Millimetre wave images has been proposed recently in the biometric field to overcome certain limitations when using images acquired at visible frequencies. In this paper, several body shape-based techniques were applied to model the silhouette of images of people acquired at 94 GHz. We put forward several methods for the parameterization and classification stage with the objective of finding the best configuration in terms of biometric recognition performance. Contour coordinates, shape contexts, Fourier descriptors and silhouette landmarks were used as feature approaches and for classification we utilized Euclidean distance and a dynamic programming method. Results showed that the dynamic programming algorithm improved the performance of the system with respect to the baseline Euclidean distance and the necessity of a minimum resolution of the contour to achieve promising equal error rates. The use of the contour coordinates is the most suitable feature to use in the system regarding the performance and the computational cost involved when having at least 3 images for model training. Besides, Fourier descriptors are more robust against rotations, which may be of interest when dealing with few training images.

Sign languages are used all over the world as a primary means of communication by deaf people. Sign language translation is a promising application for vision-based gesture recognition methods. Therefore, it is need such a tool that can translate sign language directly. This paper aims to create a system that can translate static sign language into textual form automatically based on computer vision. The method contains three phases, i.e. segmentation, feature extraction, and recognition. We used Generic Fourier Descriptor (GFD) as feature extraction method and K-Nearest Neighbour (KNN) as classification approach to recognize the signs. The system was applied to recognize each 120 stored images in database and 120 images which is captured real time by webcam. We also translated 5 words in video sequences. The experiment revealed that the system can recognized the signs with about 86 % accuracy for stored images in database and 69 % for testing data which is captured real time by webcam.

Face detection is an important part of any face recognition system and it is a difficult task to detect faces of arbitrary size, orientation and location. Fusion is a technique which combines the results from two or more distinct methods in such a way that better results are obtained. We propose two different fusion techniques, data fusion and sensor fusion, that combine statistical and shape based methods. Four different features, eigenweights from Principal Component Analysis (PCA), Legendre Moments (LM), Zernike Moments (ZM) and Generic Fourier Descriptor (GFD), are used for our fusion methods. In data fusion technique, the calculated eigenweights, moments, and descriptors are concatenated together to form a feature vector. This combined feature is then classified as face or non-face pattern using Support Vector Machine (SVM). In the case of sensor fusion technique, each of the feature vectors obtained by individual methods are first classified using individual SVMs. Then the class label obtained from the individual SVMs are concatenated together to form a feature vector. This feature vector is then classified using another SVM. Both the techniques produced better results compared with the individual methods. For a database containing 300 face images and 200 non-face images, both of our methods achieved 0% false rejection rate. 99.75% accuracy was achieved for data fusion method, while sensor fusion method produced 99.9% accuracy.

Decomposing Strain Maps Using Fourier-Zernike Shape Descriptors

This work explores automatic object recognition and semantic capture in vector graphics through shape description. The low-level graphical content of graphical documents, such as a map or architectural drawing, are often captured manually and the encoding of the semantic content seen as an extension of this. The large quantity of new and archived graphical data available on paper makes automatic structuring of such graphical data desirable. Contour shape description techniques, such as Fourier descriptors, moment invariants play an important role in systems for object recognition and representation. However, most work carried out in this area has concentrated on categories of object boundaries representing very specific shapes (for example, a particular type of aircraft). Two classifiers were implemented and proved accurate in their automatic recognition of objects from drawings in different domains. Classical classifier combination techniques were used to improve performance. Further work will employ more complex fusion techniques and it is envisaged they will be used in combination with recognition based on object context using various modelling methods. A demonstration system has been constructed using all these techniques.

Fourier descriptor is widely used for shape analysis and shape matching. Generally, the Euclid distance from boundary point to shape centroid is used in constructing Fourier descriptor. This kind of shape descriptor, however, is sensitive for rigid-transform. In this paper, we proposed a new kind of shape descriptor, namely geodesic Fourier descriptor. It remains robust under rigid transform. We first define a reference point by Poisson equation, which remains almost invariant under rigid transform. Then, the geodesic distance from shape boundary to reference point is used to construct GFD. Geodesic distance shows distinct advantage over the Euclid distance due to its robustness under rigid transformation. An algorithm based on two-scan dilating operation is presented to compute the geodesic distance efficiently in discrete image fields. Finally, experiments are carried out to show that geodesic Fourier descriptor can achieve better matching precision than Euclid distance based Fourier descriptor.

In the present study, we investigated the detection of contours defined by constant curvature and the statistics of curved contours in natural scenes. In Experiment 1, we examined the degree to which human sensitivity to contours is affected by changing the curvature angle and disrupting contour curvature continuity by varying the orientation of end elements. We find that (1) changing the angle of contour curvature decreased detection performance, while (2) end elements oriented in the direction (i.e., clockwise) of curvature facilitated contour detection regardless of the curvature angle of the contour. In Experiment 2 we further established that the relative effect of end—element orientation on contour detection was not only dependent on their orientation (collinear or cocircular), but also their spatial separation from the contour, and whether the contour shape was curved or not (i.e., C-shaped or S-shaped). Increasing the spatial separation of end-elements reduced contour detection performance regardless of their orientation or the contour shape. However, at small separations, cocircular end-elements facilitated the detection of C-shaped contours, but not S-shaped contours. The opposite result was observed for collinear end-elements, which improved the detection of S- shaped, but not C-shaped contours. These dissociative results confirmed that the visual system specifically codes contour curvature, but the association of contour elements occurs locally. Finally, we undertook an analysis of natural images that mapped contours with a constant angular change and determined the frequency of occurrence of end elements with different orientations. Analogous to our behavioral data, this image analysis revealed that the mapped end elements of constantly curved contours are likely to be oriented clockwise to the angle of curvature. Our findings indicate that the visual system is selectively sensitive to contours defined by constant curvature and that this might reflect the properties of curved contours in natural images.