Ontology-based topological representation of remote-sensing images

This article proposes an ontology-based topological representation of remote-sensing images. Semantics, especially related to the topological relationships between the objects represented, are not explicit in remote-sensing images and this fact limits spatial analysis. Our aim is to provide an explicit ontological definition of the topological relations between objects in the image using the Quadtree data structure for spatial indexing. This structure is explicitly defined in an ontology allowing the automatic interpretation of the representations obtained, taking into account the topological relations and increasing the spatial analytical capabilities. This representation has been validated by a case study of semantic retrieval based on the normalized difference vegetation index (NDVI), taking into account the topological relations between NDVI regions in images. In the experiments, we compare the effectiveness of results from eight queries using four traditional supervised image classification algorithms and the proposal representation. The experimental results show the feasibility of the proposal, supporting the concept of the image retrieval process providing a semantic complement to remote-sensing images. The proposed representation contributes to incorporation of semantics into geographical data, especially to remote-sensing images, and it can be used to develop applications in the Geospatial Semantic Web.     

Wigner distribution representation of digital images

The Wigner Distribution (WD) for discrete images is computed for different test images with gray level and spatial frequency information contents. The most relevant characteristics of the Wigner Distribution are analyzed from 2-D displays of the original 4-D distribution of the test images. This representation through the WD is shown to be specially adequate for processing of textured information and of spatially variant degraded images.     

A generalized chain-scattering representation and its algebraicsystem properties

Presents a generalization of the chain-scattering representation to the case of general plants. Through the notion of input-output consistency, the conditions under which the generalized chain-scattering representation (GCSR) and the dual-generalized chain-scattering representation (DGCSR) exist are proposed. The generalized chain-scattering matrices are formulated into a general parameterized form by using the generalized inverse of matrices. Some algebraic system properties, such as the cascade structure property, the symmetry (duality) of the GCSR's and DGCSR's, are studied     

Accurate and scalable surface representation and reconstruction from images

We introduce a new surface representation method, called patchwork, to extend three-dimensional surface reconstruction capabilities from multiple images. A patchwork is the combination of several patches that are built one by one. This design potentially allows for the reconstruction of an object with arbitrarily large dimensions while preserving a fine level of detail. We formally demonstrate that this strategy leads to a spatial complexity independent of the dimensions of the reconstructed object and to a time complexity that is linear with respect to the object area. The former property ensures that we never run out of storage and the latter means that reconstructing an object can be done in a reasonable amount of time. In addition, we show that the patchwork representation handles equivalently open and closed surfaces, whereas most of the existing approaches are limited to a specific scenario, an open or closed surface, but not both. The patchwork concept is orthogonal to the method chosen for surface optimization. Most of the existing optimization techniques can be cast into this framework. To illustrate the possibilities offered by this approach, we propose two applications that demonstrate how our method dramatically extends a recent accurate graph technique based on minimal cuts. We first revisit the popular carving techniques. This results in a well-posed reconstruction problem that still enjoys the tractability of voxel space. We also show how we can advantageously combine several image-driven criteria to achieve a finely detailed geometry by surface propagation. These two examples demonstrate the versatility and flexibility of patchwork reconstruction. They underscore other properties inherited from patchwork representation: Although some min-cut methods have difficulty in handling complex shapes (e.g., with complex topologies), they can naturally manipulate any geometry through the patchwork representation while preserving their intrinsic qualities. The above properties of patchwork representation and reconstruction are demonstrated with real image sequences.     

Modeling generalized cylinders using direction map representation

For generalized cylinders (GC) defined by contours of discrete curves, we propose two algorithms to generate GC surfaces (1) in polygonal meshes and (2) in developable surface patches of the cylindrical type. To solve the contour blending problem of generalized cylinder, the presented algorithms have adopted the algorithms and properties of linear interpolation by direction map (LIDM) that interpolate geometric shapes based on direction map merging and group scaling operations. Moreover, we propose an algorithm to develop generated developable surface patches on a plane. Proposed algorithms are fast to compute and easy to implement.     

Stability investigation of the sign representation of images

The stability notion of the sign representation of images is introduced. Group properties of the invariant transformations on sign representations of images are investigated. Analytical estimates are obtained of the stability measure of the sign representation of images noise-affected by the normal additive noise. The relation is set up between the stability of the complete sign representation and the stability of the window sign representation. The results of the statistical modeling that confirm the validity of theoretical estimates are presented.     

A novel quantum representation for log-polar images

The power of quantum mechanics has been extensively exploited to meet the high computational requirement of classical image processing. However, existing quantum image models can only represent the images sampled in Cartesian coordinates. In this paper, quantum log-polar image (QUALPI), a novel quantum image representation is proposed for the storage and processing of images sampled in log-polar coordinates. In QUALPI, all the pixels of a QUALPI are stored in a normalized superposition and can be operated on simultaneously. A QUALPI can be constructed from a classical image via a preparation whose complexity is approximately linear in the image size. Some common geometric transformations, such as symmetry transformation, rotation, etc., can be performed conveniently with QUALPI. Based on these geometric transformations, a fast rotation-invariant quantum image registration algorithm is designed for log-polar images. Performance comparison with classical brute-force image registration method reveals that our quantum algorithm can achieve a quartic speedup.     

A new representation and associated algorithms for generalized planning

Constructing plans that can handle multiple problem instances is a longstanding open problem in AI. We present a framework for generalized planning that captures the notion of algorithm-like plans and unifies various approaches developed for addressing this problem. Using this framework, and building on the TVLA system for static analysis of programs, we develop a novel approach for computing generalizations of classical plans by identifying sequences of actions that will make measurable progress when placed in a loop. In a wide class of problems that we characterize formally in the paper, these methods allow us to find generalized plans with loops for solving problem instances of unbounded sizes and also to determine the correctness and applicability of the computed generalized plans. We demonstrate the scope and scalability of the proposed approach on a wide range of planning problems.     

Graph-based representation for similarity retrieval of symbolic images

Image retrieval from an image database by the image objects and their spatial relationships has emerged as an important research subject in these decades. To retrieve images similar to a given query image, retrieval methods must assess the similarity degree between a database image and the query image by the extracted features with acceptable efficiency and effectiveness. This paper proposes a graph-based model SRG (spatial relation graph) to represent the semantic information of the contained objects and their spatial relationships in an image with no file annotation. In an SRG graph, the image objects are symbolized by the predefined class names as vertices and the spatial relations between object pairs are represented as arcs. The proposed model assesses the similarity degree between two images by calculating the maximum common subgraph of two corresponding SRG’s through intersection, which has quadratic time complexity owing to the characteristics of SRG. Its efficiency remains quadratic regardless of the duplication rate of the object symbols. The extended model SRG_T is also proposed, with the same time complexity, for the applications that need to consider the topological relations among objects. A synthetic symbolic image database and an existing image dataset are used in the conducted experiments to verify the performance of the proposed models. The experimental results show that the proposed models have compatible retrieval quality with remarkable efficiency improvements compared with three well-known methods LCS_Clique, SIM_R, and 2D Be-string, where LCS_Clique utilizes the number of objects in the maximum common subimage as its similarity function, SIM_R uses accumulation-based similarity function of similar object pairs, and 2D Be-string calculates the similarity of 2D patterns by the linear combination of two 1D similarities.     

A novel quantum representation of color digital images

In this paper, we propose a novel quantum representation of color digital images (NCQI) in quantum computer. The freshly proposed quantum image representation uses the basis state of a qubit sequence to store the RGB value of each pixel. All pixels are stored into a normalized superposition state and can be operated simultaneously. Comparison results with the latest multi-channel representation for quantum image reveal that NCQI can achieve a quadratic speedup in quantum image preparation. Meanwhile, some NCQI-based image processing operations are discussed. Analyses and comparisons demonstrate that many color operations can be executed conveniently based on NCQI. Therefore, the proposed NCQI model is more flexible and better suited to carry out color quantum image processing.     

Palmprint authentication using a symbolic representation of images

A new branch of biometrics, palmprint authentication, has attracted increasing amount of attention because palmprints are abundant of line features so that low resolution images can be used. In this paper, we propose a new texture based approach for palmprint feature extraction, template representation and matching. An extension of the SAX (Symbolic Aggregate approXimation), a time series technology, to 2D data is the key to make this new approach effective, simple, flexible and reliable. Experiments show that by adopting the simple feature of grayscale information only, this approach can achieve an equal error rate of 0.3%, and a rank one identification accuracy of 99.9% on a 7752 palmprint public database. This new approach has very low computational complexity so that it can be efficiently implemented on slow mobile embedded platforms. The proposed approach does not rely on any parameter training process and therefore is fully reproducible. What is more, besides the palmprint authentication, the proposed 2D extension of SAX may also be applied to other problems of pattern recognition and data mining for 2D images.     

(λ,μ)Vague商环

引入了[(λ,μ)]Vague环,[(λ,μ)]Vague理想的概念,利用截集给出了它们的等价刻画,研究了同态映射下它们的像与原像的问题,在此基础上通过构造陪集的方法引入了[(λ,μ)]Vague商环,给出了[(λ,μ)]Vague商环的同构定理,初步探索了[(λ,μ)]Vague环的理论。     

结合KSVD和分类稀疏表示的图像压缩感知

由于传统稀疏字典训练方法不能充分利用图像细节信息,提出一种分类稀疏字典训练方法。根据待训练样本的特性,将其划分为平滑、边缘和纹理三类,用KSVD算法分别训练出适合三类图像块特性的冗余字典,利用构造的冗余字典分别稀疏表示三类图像块。同时根据每类图像块所含信息量,自适应地分配测量率。实验结果表明,和单一正交基、冗余字典相比,该算法的稀疏系数更加稀疏,在低图像测量率时,重构效果更好,对边缘信息丰富的图像重构效果改善尤为明显。     

Shape representation using a generalized potential field model

This paper is concerned with efficient derivation of the medial axis transform of a 2D polygonal region. Instead of using the shortest distance to the region border, a potential field model is used for computational efficiency. The region border is assumed to be charged and the valleys of the resulting potential field are used to estimate the axes for the medial axis transform. The potential valleys are found by following the force field, thus, avoiding 2D search. The potential field is computed in closed form using equations of the border segments. The simple Newtonian potential is shown to be inadequate for this purpose. A higher order potential is defined which decays faster with distance than the inverse of distance. It is shown that as the potential order becomes arbitrarily large, the axes approach those computed using the shortest distance to the border. Algorithms are given for the computation of axes, which can run in linear parallel time for part of the axes having initial guesses. Experimental results are presented for a number of examples