8640545
Refining edge detection within spiral architecture
Detection of edge points of 3-dimensional physical object in a 2-dimensional image is one of the main research areas of computer vision. Object contour detection and object recognition rely heavily on edge detection. The Spiral Architecture, a relatively new image structure, possesses powerful computational features that are pertinent to the vision process. In this paper, we present an edge detection scheme using Gaussian Multi-resolution Theory. The detection algorithm reduces noise and unnecessary details of the image. This algorithm is applied to obtain the edge maps of some well-known images for image processing.
949912
Replicated Shared Object Model for Edge Detection with Spiral Architecture
Edge detection in computer vision and image processing is a process which detects one kind of significant features appearing as discontinuities in intensities. A parallel edge detection algorithm based on Spiral Architeture is designed in this paper by a Replicated Shared Object Model (RSOM). We use this approach to meet the need of replicated object management, update propagation, underlying communication and consistency maintenance among replicated objects.
40658443
A Parallel Algorithm for Object Contour Extraction Within a Spiral Architecture
5723803
Incremental semi-supervised subspace learning for image retrieval
Subspace learning techniques are widespread in pattern recognition research. They include Principal Component Analysis (PCA), Locality Preserving Projection (LPP), etc. These techniques are generally unsupervised which allows them to model data in the absence of labels or categories. In relevance feedback driven image retrieval system, the user provided information can be used to better describe the intrinsic semantic relationships between images. In this paper, we propose a semi-supervised subspace learning algorithm which incrementally learns an adaptive subspace by preserving the semantic structure of the image space, based on user interactions in a relevance feedback driven query-by-example system. Our algorithm is capable of accumulating knowledge from users, which could result in new feature representations for images in the database so that the system's future retrieval performance can be enhanced. Experiments on a large collection of images have shown the effectiveness and efficiency of our proposed algorithm.
14218377
A Methodology for Constructing Predicate Transition Net Specifications
In this paper, a methodology for constructing hierarchical and structured predicate transition net specifications is developed, which includes new systematic notation extensions for supporting various transformation techniques upon predicate transition nets and several rules for applying such transformation techniques. The levelling technique in data-flow diagrams is adapted in the refinement and the abstraction techniques, and the state decomposition idea in state-charts is employed in designing various label formulation operators. The methodology is illustrated through the specification of a lift system. The methodology can significantly reduce the constructing complexity and enhance the comprehensibility of large predicate transition net specifications.
13365148
Orthogonal Laplacianfaces for Face Recognition
Following the intuition that the naturally occurring face data may be generated by sampling a probability distribution that has support on or near a submanifold of ambient space, we propose an appearance-based face recognition method, called orthogonal Laplacianface. Our algorithm is based on the locality preserving projection (LPP) algorithm, which aims at finding a linear approximation to the eigenfunctions of the Laplace Beltrami operator on the face manifold. However, LPP is nonorthogonal, and this makes it difficult to reconstruct the data. The orthogonal locality preserving projection (OLPP) method produces orthogonal basis functions and can have more locality preserving power than LPP. Since the locality preserving power is potentially related to the discriminating power, the OLPP is expected to have more discriminating power than LPP. Experimental results on three face databases demonstrate the effectiveness of our proposed algorithm
14477095
Learning and Incorporating Top-Down Cues in Image Segmentation
Bottom-up approaches, which rely mainly on continuity principles, are often insufficient to form accurate segments in natural images. In order to improve performance, recent methods have begun to incorporate top-down cues, or object information, into segmentation. In this paper, we propose an approach to utilizing category-based information in segmentation, through a formulation as an image labelling problem. Our approach exploits bottom-up image cues to create an over-segmented representation of an image. The segments are then merged by assigning labels that correspond to the object category. The model is trained on a database of images, and is designed to be modular: it learns a number of image contexts, which simplify training and extend the range of object classes and image database size that the system can handle. The learning method estimates model parameters by maximizing a lower bound of the data likelihood. We examine performance on three real-world image databases, and compare our system to a standard classifier and other conditional random field approaches, as well as a bottom-up segmentation method.
14516443
Learning a semantic space from user's relevance feedback for image retrieval
As current methods for content-based retrieval are incapable of capturing the semantics of images, we experiment with using spectral methods to infer a semantic space from user's relevance feedback, so that our system will gradually improve its retrieval performance through accumulated user interactions. In addition to the long-term learning process, we also model the traditional approaches to query refinement using relevance feedback as a short-term learning process. The proposed short- and long-term learning frameworks have been integrated into an image retrieval system. Experimental results on a large collection of images have shown the effectiveness and robustness of our proposed algorithms.