Simplification Of Objects Research Articles

A hierarchical shape description approach and its application in similarity measurement of polygon entities

Spatial similarity provides an important basis for geographic information processing and is widely applied in multi-source data fusion and update, data retrieval and query, and cartographic generalization. To address the shape description and similarity measurement of polygon entities, this study presents a new hierarchical shape description approach and examines its application in similarity measurement of polygon entities. Using the rotation and segmentation methods, we first constructed a hierarchical shape description model for target polygon entities, followed by measurement of global and hierarchical shape description of polygon entities, respectively, using the farthest-point-distance and geometric feature description methods. Finally, we constructed a comprehensive similarity measurement model through a weighted integration of position, size, direction, and shape. The hierarchical shape description approach proposed in this paper can be applied to the shape similarity measurement of polygon elements, similarity measurement after spatial object simplification, and multi-scale polygon entity matching. The experimental results showed that the hierarchical shape description approach and similarity measurement model are able to effectively measure spatial similarity between different polygon entities, and have obtained good application results.

An approximate representation of objects underlies physical reasoning.

People make fast and reasonable predictions about the physical behavior of everyday objects. To do so, people may use principled mental shortcuts, such as object simplification, similar to models developed by engineers for real-time physical simulations. We hypothesize that people use simplified object approximations for tracking and action (the body representation), as opposed to fine-grained forms for visual recognition (the shape representation). We used three classic psychophysical tasks (causality perception, time-to-collision, and change detection) in novel settings that dissociate body and shape. People's behavior across tasks indicates that they rely on coarse bodies for physical reasoning, which lies between convex hulls and fine-grained shapes. Our empirical and computational findings shed light on basic representations people use to understand everyday dynamics, and how these representations differ from those used for recognition. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

PVGAN: a generative adversarial network for object simplification in prosthetic vision

Objective. By means of electrical stimulation of the visual system, visual prostheses provide promising solution for blind patients through partial restoration of their vision. Despite the great success achieved so far in this field, the limited resolution of the perceived vision using these devices hinders the ability of visual prostheses users to correctly recognize viewed objects. Accordingly, we propose a deep learning approach based on generative adversarial networks (GANs), termed prosthetic vision GAN (PVGAN), to enhance object recognition for the implanted patients by representing objects in the field of view based on a corresponding simplified clip art version. Approach. To assess the performance, an axon map model was used to simulate prosthetic vision in experiments involving normally-sighted participants. In these experiments, four types of image representation were examined. The first and second types comprised presenting phosphene simulation of real images containing the actual high-resolution object, and presenting phosphene simulation of the real image followed by the clip art image, respectively. The other two types were utilized to evaluate the performance in the case of electrode dropout, where the third type comprised presenting phosphene simulation of only clip art images without electrode dropout, while the fourth type involved clip art images with electrode dropout. Main results. The performance was measured through three evaluation metrics which are the accuracy of the participants in recognizing the objects, the time taken by the participants to correctly recognize the object, and the confidence level of the participants in the recognition process. Results demonstrate that representing the objects using clip art images generated by the PVGAN model results in a significant enhancement in the speed and confidence of the subjects in recognizing the objects. Significance. These results demonstrate the utility of using GANs in enhancing the quality of images perceived using prosthetic vision.

Evaluating preprocessing and interpolation strategies to create moving regions from real-world observations

Spatio-temporal data may be used to represent the evolution of real-world objects and phenomena. Such data can be represented in discrete time, which associates spatial information (like position and shape) to time instants, or in continuous time, in which the representation of the evolution of the phenomena is decomposed into slices and interpolation functions are used to estimate the intermediate position and shape at any time. The use of a discrete model may seem more straightforward, but a continuous representation provides potential gains in terms of data management, including in compression and spatio-temporal operations. In this work, we study the use of the continuous model to represent deformable moving regions captured at discrete snapshots. We use a dissimilarity distance-based strategy to select the observations that should be used to define the time slices of the continuous representation, thus transforming data acquired at discrete steps into a continuous model. We also study how the use of geometry simplification algorithms and simplification levels may impact on moving regions interpolation quality. We evaluate our proposals using a dataset composed by thousands of aerial bush-fires images. After applying object simplification and slice decomposition, we use two region interpolation algorithms to generate in-between observations and compare them with geometries representing real images. The results prove the effectiveness of our proposals and their importance in terms of interpolation accuracy.

The use of cartographic control points in the harmonization and revision of MRDBs

The main purpose of the research is to achieve fully automated approach for supplying multi-resolution databases with linear objects in each scale. Moreover, the proposed solutions maintain the repeatability and accuracy of output data wherever possible according to the input scale. These properties are achieved by keeping the minimal object dimensions as well as the appropriate data pre-processing, based on the classification of source points. The classification distinguishes three classes of points: constant (unchangeable), temporary, and inherited. These classes build a structure of cartographic control points. Based on these solutions, the authors proposed an algorithm for linear object simplification based on minimal object dimensions and cartographic control points. It was also confirmed that the simplification between constant points does not cause geometry discrepancies in relation to the global simplification of the whole line.

Innovation and Practice Guidance and Training Pattern of the Modern Economic Management Specialty

In the face of the increasingly prominent problems in the practice of economic management, as well as the urgent employment situation, we explore a practical approach to the cultivation of entrepreneurial oriented talents in the field of economic management. In this paper, the author analyzes the characteristics and requirements of innovative and entrepreneurial talents with broad and solid theoretical foundation, multiple thinking ability, entrepreneurial spirit, and puts forward the mode of cultivating innovative talents. The traditional teaching evaluation of economic management emphasizes on the screening function of evaluation, leading to the evaluation of the content of knowledge, quantitative evaluation methods, evaluation of the subject and object simplification. To construct the teaching evaluation mode of economic management specialty, we must first establish the concept of evaluation focusing on the development of students.

Packing Subsets of 3D Parts for Layered Manufacturing

The efficiency of layered manufacturing processes, the dominant form of rapid prototyping (RP), can be dramatically improved by building multiple, arbitrarily shaped, three-dimensional (3D) parts simultaneously in the available working volume. Given a large set of parts waiting to be manufactured, the goal is to arrange a subset of the parts to effectively use the available working volume. Generating good solutions for this problem is difficult, since the number of possible subsets grows exponentially, and since it is very computationally expensive to evaluate potential packing arrangements. This paper documents a comprehensive solution to this problem, based on a hybrid heuristic-optimization algorithm and an object simplification technique to reduce computation times. The optimization algorithm combines a heuristic to select the ordering of the parts being packed, and a simulated annealing algorithm to optimize the position of each part. The successful placement of individual parts relies on a unique metric that was developed to compare different packing arrangements of the same parts. Test cases based on real-world problems demonstrate the effectiveness of the overall approach.

Volumetric virtual environments

Driven by fast development of both virtual reality and volume visualization, we discuss some critical techniques towards building a volumetric VR system, specifically the modeling, rendering, and manipulations of a volumetric scene. Techniques such as voxel-based object simplification, accelerated volume rendering, fast stereo volume rendering, and volumetric “collision detection” are introduced and improved, with the idea of demonstrating the possibilities and potential benefits of incorporating volumetric models into VR systems.

Controlled topology simplification

We present a simple, robust, and practical method for object simplification for applications where gradual elimination of high frequency details is desired. This is accomplished by converting an object into multi resolution volume rasters using a controlled filtering and sampling technique. A multiresolution triangle mesh hierarchy can then be generated by applying the Marching Cubes algorithm. We further propose an adaptive surface generation algorithm to reduce the number of triangles generated by the standard Marching Cubes. Our method simplifies the topology of objects in a controlled fashion. In addition, at each level of detail, multilayered meshes can be used for an efficient antialiased rendering.

Simplification of objects rendered by polygonal approximations

Current technology provides a means to obtain sampled data that digitally describes three-dimensional surfaces and objects. Three-dimensional digitizing cameras can be used to obtain sampled data that maps the surface of three-dimensional figures and models. Data obtained from such sources enable accurate renderings of the original surface. However, the digitizing process often provides much more data than is needed to accurately recreate the surface or object. In order to use such data in real-time visual simulators, a significant reduction in the data needed to accurately render the sampled surfaces is required. The techniques presented were developed to drastically reduce the number of data points required to depict an object without sacrificing the detail and accuracy inherent in the digitizing process.