不同行业的企业形象要素体系的共性与个性

In the early part of this century, with the change from the seller's market to the buyer market, the competition between companies changed from product competition, selling competition to corporate image competition, and companies began to consciously build corporate reputation through fast developed mass media. As a result, a series of methods to build corporate image were created, such as advertising, public relations and corporate identify system(CIS), which ,in turn, promoted the development of the research of corporate image. The factors of corporate image have been the central issue of the corporate image research, for the probe of this issue is of great significance to both the development of corporate image theory and the practice of corporate image building. As far as the literature we have gathered is concerned, the exiting research on this topic either remains at the level of qualitative investigating and induction, or is limited in some particular industry. Therefore. There bean no commonly accepted corporate image theory so far. In the recent years, with the introduction of competition mechanism and the establishment of the company. As subject position in the market, the building of corporate image gas been developed quickly in our country, and the development of practice imperatively requires the guide of scientific theory. On the basis of the analysis and summarization of the research of the predecessors, the present dissertation attempts to do some investigation and research work on the common and individual characteristics of corporate image factors of the companies in different industries in our country. The method of questionnaire survey is used in the present research. The subject sample is gathered on the basis of convenience and feasibility, and at the mean time, some consideration is also given to straticulate randomization principles. The subjects are asked to select one of their most familiar companies, and determine the important of even item in the questionnaire to the selected company(i.e. the importance assessment), and then, determine the grades the selected company gains on every item(i.e. the image assessment). The discriminant analysis of corporate image of different industries. The selected sample is grouped and coded according to the standard of industry classification. The discriminant analysis is done with the selected companies as the sample and the grades of image assessment as the variables. The result indicates that industry variable is an important standard of the classification of corporate image, and the companies in the same industry are more similar in corporate image. The analysis of the common and individual characteristics of corporate image of different industries. Firstly, in every industry, the items are sieved according to the grades of importance assessment, and exploratory factor analysis is done with grades of image assessment on the selected items as the variables. Secondly, the factors drawn from every industry in arranged in order according to their importance. The result indicates that the corporate image of different industries shares some common characteristics, for there exist common factors among different industries. In the mean while, the corporate image of different industries has its individual characteristics, that is, there is some difference in the domain of the factors, and in the order of the factors(including the difference of the principle factor).

Structural Saliency: The Detection of Globally Salient Structures Using a Locally Connected Network

Certain salient structures in images attract our immediate attention without requiring a systematic scan. We present a method for computing saliency by a simple iterative scheme, using a uniform network of locally connected processing elements. The network uses an optimization approach to produce a "saliency map," a representation of the image emphasizing salient locations. The main properties of the network are: (i) the computations are simple and local, (ii) globally salient structures emerge with a small number of iterations, and (iii) as a by-product of the computations, contours are smoothed and gaps are filled in.

Recognition by Linear Combinations of Models

Visual object recognition requires the matching of an image with a set of models stored in memory. In this paper we propose an approach to recognition in which a 3-D object is represented by the linear combination of 2-D images of the object. If M = {M1,...Mk} is the set of pictures representing a given object, and P is the 2-D image of an object to be recognized, then P is considered an instance of M if P = Eki=aiMi for some constants ai. We show that this approach handles correctly rigid 3-D transformations of objects with sharp as well as smooth boundaries, and can also handle non-rigid transformations. The paper is divided into two parts. In the first part we show that the variety of views depicting the same object under different transformations can often be expressed as the linear combinations of a small number of views. In the second part we suggest how this linear combinatino property may be used in the recognition process.

Extracting Salient Curves from Images: An Analysis of the Saliency Network

The Saliency Network proposed by Shashua and Ullman is a well-known approach to the problem of extracting salient curves from images while performing gap completion. This paper analyzes the Saliency Network. The Saliency Network is attractive for several reasons. First, the network generally prefers long and smooth curves over short or wiggly ones. While computing saliencies, the network also fills in gaps with smooth completions and tolerates noise. Finally, the network is locally connected, and its size is proportional to the size of the image. Nevertheless, our analysis reveals certain weaknesses with the method. In particular, we show cases in which the most salient element does not lie on the perceptually most salient curve. Furthermore, in some cases the saliency measure changes its preferences when curves are scaled uniformly. Also, we show that for certain fragmented curves the measure prefers large gaps over a few small gaps of the same total size. In addition, we analyze the time complexity required by the method. We show that the number of steps required for convergence in serial implementations is quadratic in the size of the network, and in parallel implementations is linear in the size of the network. We discuss problems due to coarse sampling of the range of possible orientations. We show that with proper sampling the complexity of the network becomes cubic in the size of the network. Finally, we consider the possibility of using the Saliency Network for grouping. We show that the Saliency Network recovers the most salient curve efficiently, but it has problems with identifying any salient curve other than the most salient one.

Automatically Recovering Geometry and Texture from Large Sets of Calibrated Images

Three-dimensional models which contain both geometry and texture have numerous applications such as urban planning, physical simulation, and virtual environments. A major focus of computer vision (and recently graphics) research is the automatic recovery of three-dimensional models from two-dimensional images. After many years of research this goal is yet to be achieved. Most practical modeling systems require substantial human input and unlike automatic systems are not scalable. This thesis presents a novel method for automatically recovering dense surface patches using large sets (1000's) of calibrated images taken from arbitrary positions within the scene. Physical instruments, such as Global Positioning System (GPS), inertial sensors, and inclinometers, are used to estimate the position and orientation of each image. Essentially, the problem is to find corresponding points in each of the images. Once a correspondence has been established, calculating its three-dimensional position is simply a matter of geometry. Long baseline images improve the accuracy. Short baseline images and the large number of images greatly simplifies the correspondence problem. The initial stage of the algorithm is completely local and scales linearly with the number of images. Subsequent stages are global in nature, exploit geometric constraints, and scale quadratically with the complexity of the underlying scene. We describe techniques for: 1) detecting and localizing surface patches; 2) refining camera calibration estimates and rejecting false positive surfels; and 3) grouping surface patches into surfaces and growing the surface along a two-dimensional manifold. We also discuss a method for producing high quality, textured three-dimensional models from these surfaces. Some of the most important characteristics of this approach are that it: 1) uses and refines noisy calibration estimates; 2) compensates for large variations in illumination; 3) tolerates significant soft occlusion (e.g. tree branches); and 4) associates, at a fundamental level, an estimated normal (i.e. no frontal-planar assumption) and texture with each surface patch.

Image Chunking: Defining Spatial Building Blocks for Scene Analysis

Rapid judgments about the properties and spatial relations of objects are the crux of visually guided interaction with the world. Vision begins, however, with essentially pointwise representations of the scene, such as arrays of pixels or small edge fragments. For adequate time-performance in recognition, manipulation, navigation, and reasoning, the processes that extract meaningful entities from the pointwise representations must exploit parallelism. This report develops a framework for the fast extraction of scene entities, based on a simple, local model of parallel computation.sAn image chunk is a subset of an image that can act as a unit in the course of spatial analysis. A parallel preprocessing stage constructs a variety of simple chunks uniformly over the visual array. On the basis of these chunks, subsequent serial processes locate relevant scene components and assemble detailed descriptions of them rapidly. This thesis defines image chunks that facilitate the most potentially time-consuming operations of spatial analysis---boundary tracing, area coloring, and the selection of locations at which to apply detailed analysis. Fast parallel processes for computing these chunks from images, and chunk-based formulations of indexing, tracing, and coloring, are presented. These processes have been simulated and evaluated on the lisp machine and the connection machine.

Shape from Contour

The problem of using image contours to infer the shapes and orientations of surfaces is treated as a problem of statistical estimation. The basis for solving this problem lies in an understanding of the geometry of contour formation, coupled with simple statistical models of the contour generating process. This approach is first applied to the special case of surfaces known to be planar. The distortion of contour shape imposed by projection is treated as a signal to be estimated, and variations of non-projective origin are treated as noise. The resulting method is then extended to the estimation of curved surfaces, and applied successfully to natural images. Next, the geometric treatment is further extended by relating countour curvature to surface curvature, using cast shadows as a model for contour generation. This geometric relation, combined with a statistical model, provides a measure of goodness-of-fit between a surface and an image contour. The goodness-of-fit measure is applied to the problem of establishing registration between an image and a surface model. Finally, the statistical estimation strategy is experimentally compared to human perception of orientation: human observers' judgements of tilt correspond closely to the estimates produced by the planar strategy.

Cellular automata and non-static image processing for embodied robot systems on a massively parallel processor array

Huelse, M, Barr, D R W, Dudek, P: Cellular Automata and non-static image processing for embodied robot systems on a massively parallel processor array. In: Adamatzky, A et al. (eds) AUTOMATA 2008, Theory and Applications of Cellular Automata. Luniver Press, 2008, pp. 504-510. Sponsorship: EPSRC

Teaching Crossroads: 11th IPB Erasmus Week

In the wake of the latest news regarding IPB’s award for best Polytechnic Institute in Portugal, we would like to congratulate the IPB community who has always striven for the quality of the institution within the national and international academic milieu. We are, undoubtedly, bowled over by the 1st place in the national context (out of the 27 national institutions under evaluation) and the 7th position in the international scope. In fact, it is worth mentioning that the IPB has won this award, three years in a row, being in a leading position in the ranking promoted and sponsored by the European Union. This year’s edition has selected and evaluated over 1300 world higher education institutions. Teaching Crossroads intends thus to give a watershed contribution to the IPB’s successful and most valued pathway. Numbers indicate that Teaching Crossroads has had over 2550 downloads since it first came out. But let’s cut to the chase. Once again, we are delighted to present the 5th number of Teaching Crossroads. This wholly calculated and well-sustained editorial adventure started in 2012 when the first number of Teaching Crossroads first saw the light. This year’s publication includes the areas of Human Geography/anthropology, Information Technology and Forensics and Language and culture, focusing on minority languages. Alongside this, as in the two last previous numbers, we’ve included the specific area for International mobility, Intensive Programmes and Erasmus+ Mobility of Individuals, being the latter financed by the European Union within the Erasmus+ Programme, whereas the former is sponsored uniquely by the foreign partner institution, in this case, Lillebaelt Academy in Denmark. These types of programmes convey very positive and overarching ideas, resonant in cross-cultural and educational benefits, valuable for all the involved partners. We would very kindheartedly like to thank the authors for having contributed with much enlightening and serious articles on a wide array of areas. Pablo M. Orduna Pórtus’s article focuses on border culture and heritage management. The author’s study is placed on two borderlands of the Iberian Peninsula: Roncal Valley (Navarre) and Riverbanks of Douro. Going beyond the linear or physical conception of the border, the author centres his study on the metaphysical and symbolic ideas of the frontier that sustain his anthropological analysis. Michal Popdora manages to find evidence for his proposal of a new conception of teaching Image Processing, based on the student-centered approach. A hands-on experience on a Project-based Learning methodology sustains the teaching project. Grounded on “a forensic-flavored style”, using the author’s own words, he shows how students can become engaged in a highly effective learning process. Cláudia Martins is already a confirmed habitué of this publication, as in every Erasmus Week she delivers a lecture on Portuguese language and culture to visiting teaching and non-teaching staff. This time, the author decided to delve into a Portugal’s official language, Mirandese, spoken in a small designated area in the northeastern part of Portugal, Miranda do Douro and its surrounding area. The author gives account of some thought-provoking facts about the language, from the origin and the survival of the language, however still a minority language, up to the moment when it was acknowledged Portugal’s second official language, together with the challenges that nowadays faces. Luís Frölen Ribeiro, João Eduardo Ribeiro, Carlos Casimiro Costa, António Duarte, Carlos Andrade from the Polytechnic Institute and Arne Svinth, John Madsen, Morten Thomsen, Kent Smidstrup, Carsten John Jacobsen from the Lillebaelt Academy, in Denmark, participate in a joint project which they describe, outlining the main goals and gains of the project. To overcome teaching difficulties regarding the engineering degrees, a 12-ECTS joint course from Lillebaelt Academy and Polytechnic Institute of Bragança was created based on the Danish model. The course Product Development and Industrial Processing was hence created. Rui Pedro Lopes presents an insightful and acute account of the Internationalization programmes in Higher Education in Europe. At one go, the author describes his own experience as a visiting lecturer, within the Erasmus+ programme, to Università Politecnica delle Marche in Ancona, Italy, in a Master’s degree in computer science, bringing to light a personal reflection on the goals and benefits of the mobility for both students and teachers. Finally, the author puts forth some suggestions that would improve the whole mobility process. We sincerely hope to have stimulated you to keep on reading, upholding the belief that these texts can represent valuable sources for both teachers and students in their research work.

Estimating 3D Hand Pose from a Cluttered Image

A method is proposed that can generate a ranked list of plausible three-dimensional hand configurations that best match an input image. Hand pose estimation is formulated as an image database indexing problem, where the closest matches for an input hand image are retrieved from a large database of synthetic hand images. In contrast to previous approaches, the system can function in the presence of clutter, thanks to two novel clutter-tolerant indexing methods. First, a computationally efficient approximation of the image-to-model chamfer distance is obtained by embedding binary edge images into a high-dimensional Euclide an space. Second, a general-purpose, probabilistic line matching method identifies those line segment correspondences between model and input images that are the least likely to have occurred by chance. The performance of this clutter-tolerant approach is demonstrated in quantitative experiments with hundreds of real hand images.

Structural Analysis of Network Traffic Flows

Network traffic arises from the superposition of Origin-Destination (OD) flows. Hence, a thorough understanding of OD flows is essential for modeling network traffic, and for addressing a wide variety of problems including traffic engineering, traffic matrix estimation, capacity planning, forecasting and anomaly detection. However, to date, OD flows have not been closely studied, and there is very little known about their properties. We present the first analysis of complete sets of OD flow timeseries, taken from two different backbone networks (Abilene and Sprint-Europe). Using Principal Component Analysis (PCA), we find that the set of OD flows has small intrinsic dimension. In fact, even in a network with over a hundred OD flows, these flows can be accurately modeled in time using a small number (10 or less) of independent components or dimensions. We also show how to use PCA to systematically decompose the structure of OD flow timeseries into three main constituents: common periodic trends, short-lived bursts, and noise. We provide insight into how the various constituents contribute to the overall structure of OD flows and explore the extent to which this decomposition varies over time.

Analysis of OD Flows (Raw Data)

In a recent paper, Structural Analysis of Network Traffic Flows, we analyzed the set of Origin Destination traffic flows from the Sprint-Europe and Abilene backbone networks. This report presents the complete set of results from analyzing data from both networks. The results in this report are specific to the Sprint-1 and Abilene datasets studied in the above paper. The following results are presented here: 1 Rows of Principal Matrix (V) 2 1.1 Sprint-1 Dataset ................................ 2 1.2 Abilene Dataset.................................. 9 2 Set of Eigenflows 14 2.1 Sprint-1 Dataset.................................. 14 2.2 Abilene Dataset................................... 21 3 Classifying Eigenflows 26 3.1 Sprint-1 Dataset.................................. 26 3.2 Abilene Datase.................................... 44

Integrated Chest Image Analysis System "BU-MIA"

We introduce "BU-MIA," a Medical Image Analysis system that integrates various advanced chest image analysis methods for detection, estimation, segmentation, and registration. BU-MIA evaluates repeated computed tomography (CT) scans of the same patient to facilitate identification and evaluation of pulmonary nodules for interval growth. It provides a user-friendly graphical user interface with a number of interaction tools for development, evaluation, and validation of chest image analysis methods. The structures that BU-MIA processes include the thorax, lungs, and trachea, pulmonary structures, such as lobes, fissures, nodules, and vessels, and bones, such as sternum, vertebrae, and ribs.

Non-Rigid Shape from Image Streams

We present a framework for estimating 3D relative structure (shape) and motion given objects undergoing nonrigid deformation as observed from a fixed camera, under perspective projection. Deforming surfaces are approximated as piece-wise planar, and piece-wise rigid. Robust registration methods allow tracking of corresponding image patches from view to view and recovery of 3D shape despite occlusions, discontinuities, and varying illumination conditions. Many relatively small planar/rigid image patch trackers are scattered throughout the image; resulting estimates of structure and motion at each patch are combined over local neighborhoods via an oriented particle systems formulation. Preliminary experiments have been conducted on real image sequences of deforming objects and on synthetic sequences where ground truth is known.

Isoperimetric Graph Partitioning for Data Clustering and Image Segmentation

Spectral methods of graph partitioning have been shown to provide a powerful approach to the image segmentation problem. In this paper, we adopt a different approach, based on estimating the isoperimetric constant of an image graph. Our algorithm produces the high quality segmentations and data clustering of spectral methods, but with improved speed and stability.