The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Images are usually represented by different groups of features, such as color, shape and texture attributes. In this paper, we propose a classification approach that integrates multiple features, such as spectral and spatial information. We refer this approach to multiple feature learning via rotation (MFL-R) strategy, which adopt a rotation-based ensemble method by using a data transformation approach...
In this paper we are interesting in knowing which features provide useful information for detecting a fall and how the set of selected characteristics impact the performance of detection. Then we define a large set of possible features, which are extracted from a cloud of points of a person by the kinect device, some of features were used in previous work, and we propose to add and evaluate the effect...
Over the past few decades, a considerable amount of literature has been published on shape classification. Since classification of well-segmented shapes has become easy to achieve, a number of recent studies have emphasized the importance of robustness to noise and deformations. So in this paper, we undertake the task of classifying similar & noisy binary shape images, using a biologically inspired...
Technological advancement had replaced humans with machines in almost every field. Banking automation have reduced human workload by introducing machines. Tedious task like currency handling that require more care are simplified by banking automation. When machines are handling currency they should recognize it. In this paper a method for currency recognition using principal component analysis is...
To recognize objects within narrow categories, it is important to extract effective features from small number of training samples. In this paper, first we discuss several depth features to improve object recognition accuracy. After that, we also discuss feature dimension reduction when we have insufficient training samples.
In this study, we have experimented with different image and shape descriptors on the automatic leaf recognition problem. We have studied the effects of gross shape descriptors, Fourier descriptors, multiscale distance descriptors, and the combination of these on the leaf recognition performance using two different datasets. We have achieved 94.62% recognition performance on Flavia, comparable to...
In resent time there is no such system by which doctors can relate their qualitative result of Histopathological image, which varies person to person. Histopathological images are the images of diseased tissues of any section of the body. All these are microscopic digital images. To analyze these images by naked eye, it is very difficult. So we develop such a black-box that analyzes the quality of...
This paper describes an improved system for locating facial features in images using constrained local models (CLM). CLM links a set of local patch classifiers via a PCA shape model for non-rigid alignment and tracking. The convex quadratic fitting (CQF) approach to CLM approximates the patch responses with quadratic functions, allowing the parameter updates to be calculated directly. The Bayesian...
This paper presents a novel traffic sign recognition system comprising of: (i) Color/shape classification, (ii) Pictogram extraction, (iii) Features selection and, (iv) Lyapunov Theory-based Radial Basis Function neural network (RBFNN). In the proposed system, traffic signs are first segmented and classified with regard to its unique color and shape in order to partition a large set of data into smaller...
In this paper, we address the shape classification problem by proposing a new integrating approach for shape classification that gains both local and global image representation using Histogram of Oriented Gradient (HOG). In both local and global feature extraction steps, we use PCA to make this method invariant to shapes rotation. Moreover, by using a learning algorithm based on Adaboost we improve...
This paper presents an efficient method for Persian signature recognition based on Fuzzy RBF neural network (FRBF). A new training method will be presented which had a very low error rates in Persian signature recognition. In this training algorithm, connection weights, centers, width and number of RBF units will be determined during training phase. FCM algorithm will be used for initializing parameters...
Cursive scripts such as Urdu, Pashto and Arabic contain large number of unique shapes called ligatures. Recognition of thousands of ligatures is challenging due to variations of various kinds including scaling, orientation, font style, spatial location/registration of ligatures and limited number of samples available for training. Accurate segmentation is a key challenge for analytic approaches, whereas...
Activity recognition has been applied to many varied applications ranging from surveillance to medical analysis. Interpreting human actions is often a complex problem for computer vision. Actions can be classified through shape, motion or region based algorithms. While all have their distinct advantages, we consider a feature extraction approach using convexity defects. This algorithmic approach offers...
In this paper, we present a graph based face representation for efficient age invariant face recognition. The graph contains information on the appearance and geometry of facial feature points. An age model is learned for each individual and a graph space is built using the set of feature descriptors extracted from each face image. A two-stage method for matching is developed, where the first stage...
This paper describes a method for traffic signs detection, tracking and recognition. Color and shape are combined to detect signs. Hue and saturation are used to detect the red color of the sign. Circles are detected through the improved round-degree method of extracting area feature parameters. An improved Kalman filter is introduced to track multiple targets in the next frames. A feature extraction...
Since the medical training samples are very limited, it is difficult to construct a statistical shape model with good generalization using few samples. In this paper, we propose a novel statistical shape modeling method using 2D PCA. The 3D shape is represented as a matrix by spherical parameterization. The experiments showed that our proposed method can reconstruct statistical shape model with good...
The aim of this work is to learn a shape prior model for an object class and to improve shape matching with the learned shape prior. Given images of example instances, we can learn a mean shape of the object class as well as the variations of non-affine and affine transformations separately based on the thin plate spline (TPS) parameterization. Unlike previous methods, for learning, we represent shapes...
Active shape model (ASM) has been widely accepted as one of the best methods for image understanding. In this paper, we propose to improve ASM by introducing Procrustes analysis technique in the matching of feature landmark points of a set of training images and strengthening the edge in searching face profile. Firstly, each landmark point labeled manually is matched by its local profile in its current...
This paper proposes a new human action recognition method which deals with recognition task in a quite different way when compared with traditional methods which use sequence matching scheme. Our method compresses a sequence of an action into a Motion History Image (MHI) on which low-dimensional features are extracted using subspace analysis methods. Unlike other methods which use a sequence consisting...
We have investigated a technique for recognising faces invariant of facial expressions. We apply multi-linear tensor algebra, which subsumes linear algebra, to analyse and recognise 3D face surfaces. This potent framework possesses a remarkable ability to deal with the shortcomings of principle component analysis in less constrained situations. A set of vector spaces can be used to represent the variation...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.