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In order to improve accuracy of the delay time estimate and the performance of noise ratio, complete the task of location and communication at the same time, a new method of pulse shaped modulation (PSM) is introduced in this paper. This method uses different orthogonal waveform to show the data. It is presented on the basis of orthogonaity and no direct component characteristics of modified Hermite...
In this paper, we propose a novel strategy based on spherical feature extraction and fast polar image correlation to recognize number ball which has arbitrary rotation pose just using single calibrated camera. The multiple view instances of a number ball are collected as a prior knowledge of a class and a simple distance measurement is applied to decide which class the candidate ball belongs to. The...
In this paper, a methodology for facial feature identification and localization approach is proposed based on binary neural network algorithms. We present a head pose and facial expression invariant 3D shape descriptor called mesh-like multi circle curvature descriptor (MMCCD), which provides more 3D curvature attributes than other similar approaches. To search and match the feature patterns with...
The brassiere-wearing effect is produced by the combined action of body shape and brassiere. The process is so complicated that it is an issue for people to realize what functions the different factors perform in the change of bust appearance. In this research, an artificial neural network model was applied, in which the input neurons contained 11 possible factors including body measurements and brassiere...
In this study, we confirmed proprioception training effect of patients with hemiplegia by manipulating visual feedback. Six patients with hemiplegia were participated in the experiment. Patients have trained with the reaching task with visual feedback without visual feedback for two weeks. Patients were evaluated with pre-, middle test and post-test with the task with and without visual feedback....
In this paper, the principal component analysis (PCA) for multi-view shape recognition is proposed. Our algorithm presents the signed enclosed area signature as the shape representation. In our method, the barycenter contour is used for decomposing the shape boundary into multiscale level. At each scale level, the signed enclosed area signatures are obtained. After that, the principal component analysis...
This paper provides a novel content-based image retrieval algorithm based on ROI (Region Of Interest) by using SIFT (Scale Invariant Feature Transform) feature matching. SIFT descriptors, which are invariant to image scaling and transformation and rotation, and partially invariant to illumination changes and affine, present the local features of an image. Therefore, feature keypoints can be extracted...
Many object categories are better characterized by the shape of their contour than by local appearance properties like texture or color. Multi-local features are designed in order to capture the global discriminative structure of an object while at the same time avoiding the drawbacks with traditional global descriptors such as sensitivity to irrelevant image properties. The specific structure of...
A novel touching cells splitting algorithm by using concave points and ellipse fitting is proposed to split circle-like or ellipse-like touching cells in this paper. The algorithm is divided into two parts. The first part is contour pre-processing, whose purpose is to find the concave points of the contour and separate the contour into different segments using the concave points. The second part is...
We present a new shape descriptor, the differential profile, for shape representation and recognition, which is derived from differential and geometric quantities evaluated at points on a shape. We model differential profiles from a class of shapes as a finite mixture of Gaussians, and use an expectation-maximization (EM) procedure for class-wise model learning. Experiments on handwritten digit and...
We present a shape matching algorithm based on the chamfer distance transform which can be easily integrated into the well-known SIFT framework. The shape matching was designed to overcome the limitations of SIFT matching for objects which lack texture and have the majority of their features located on the object boundary.
This paper introduces a 3-d representation of vehicles as a space of scale and orientation transformations that define the shape of individual vehicle instances. This shape space forms a group, where the similarity of different vehicle observations can be evaluated using a distance measure defined by Lie group theory. A generic class of vehicles (e.g. SUV) is represented by a set of curves on the...
Object detection in clutter or occlusion is a hard problem in computer vision. We propose an object detection method based on contour grouping. Two stages are included: a novel distance transform is applied to match templates to the test image so that candidates and locations of the object are obtained; verification using shape manifold is performed to preclude outliers and identify the prior. We...
This paper proposes a set of methods for building informative and robust feature point representations, used for accurately labeling points in a 3D point cloud, based on the type of surface the point is lying on. The feature space comprises a multi-value histogram which characterizes the local geometry around a query point, is pose and sampling density invariant, and can cope well with noisy sensor...
In this paper an appearance based, compositional approach to the recognition of deformable objects is presented. First, a hierarchical object model is proposed. On different levels of abstraction the model represents object categories, different views of an object, the parts of an object and basic feature vectors. Then, a training method based on multiple clustering steps is described. This paper...
We present a hierarchical method for segmenting text areas in natural images. The method assumes that the text is written with a contrasting color on a more or less uniform background. No assumption is made regarding the language or character set used to write the text. In particular, the text can contain simple graphics or symbols. The key feature of our approach is that we first concentrate on finding...
Kernel PCA has received a lot of attention over the past years and showed usefull for many image processing problems. In this paper we analyse the issue of normalization in Kernel PCA for the pre-image problem. We present a geometric interpretation of the normalization process for the gaussian kernel. As a consequence, we could formulate a correct normalization criterion in centered feature space...
Detecting and recognizing pedestrians in video footages are two essential and significant tasks in many automatic video understanding systems. In this paper, we propose an efficient approach to moving pedestrian detection and recognition in video. The testing process of this approach involves two main steps: moving edge detection and hypotheses generation. Moving edges are firstly extracted by comparing...
We present a method for recognizing individuals from their ldquostyle of actionrdquo. Two forms of human recognition can be useful: the determination that an object is from the class of humans (which is called human detection), and the determination that an object is a particular individual from this class (this is called individual recognition). This paper focuses on the latter problem. A periodicity...
We have developed a high-precision method that selects an appropriate model of a video image in order to track an unknown face in front of a large display. Currently, Active Appearance Models (AAMs) are used to track non-rigid objects, such as a faces, because the models efficiently learn the correlation between shape and texture. The problem with an AAM is that when it tracks an unknown face, excessive...
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