Showing papers on "Object-oriented design published in 2016"

Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

Abstract: We study the problem of 3D object generation We propose a novel framework, namely 3D Generative Adversarial Network (3D-GAN), which generates 3D objects from a probabilistic space by leveraging recent advances in volumetric convolutional networks and generative adversarial nets The benefits of our model are three-fold: first, the use of an adversarial criterion, instead of traditional heuristic criteria, enables the generator to capture object structure implicitly and to synthesize high-quality 3D objects; second, the generator establishes a mapping from a low-dimensional probabilistic space to the space of 3D objects, so that we can sample objects without a reference image or CAD models, and explore the 3D object manifold; third, the adversarial discriminator provides a powerful 3D shape descriptor which, learned without supervision, has wide applications in 3D object recognition Experiments demonstrate that our method generates high-quality 3D objects, and our unsupervisedly learned features achieve impressive performance on 3D object recognition, comparable with those of supervised learning methods

Tree-structured reinforcement learning for sequential object localization

Abstract: Existing object proposal algorithms usually search for possible object regions over multiple locations and scales separately, which ignore the interdependency among different objects and deviate from the human perception procedure. To incorporate global interdependency between objects into object localization, we propose an effective Tree-structured Reinforcement Learning (Tree-RL) approach to sequentially search for objects by fully exploiting both the current observation and historical search paths. The Tree-RL approach learns multiple searching policies through maximizing the long-term reward that reflects localization accuracies over all the objects. Starting with taking the entire image as a proposal, the Tree-RL approach allows the agent to sequentially discover multiple objects via a tree-structured traversing scheme. Allowing multiple near-optimal policies, Tree-RL offers more diversity in search paths and is able to find multiple objects with a single feedforward pass. Therefore, Tree-RL can better cover different objects with various scales which is quite appealing in the context of object proposal. Experiments on PASCAL VOC 2007 and 2012 validate the effectiveness of the Tree-RL, which can achieve comparable recalls with current object proposal algorithms via much fewer candidate windows.

An object-oriented approach for multi-objective flexible job-shop scheduling problem

Abstract: Object-oriented technology contributes to building manufacturing control systems.Machine and operation associations are designed as software objects.Operation routing is represented by a class.Simulated annealing algorithm is used in the problem solution. Flexible manufacturing systems are very complex to control and it is difficult to generate controlling systems for this problem domain. Flexible job-shop scheduling problem (FJSP) is one of the instances in this domain. It is a problem which inherits the job-shop scheduling problem (JSP) characteristics. FJSP has additional routing sub-problem in addition to JSP. In routing sub-problem each operation is assigned to a machine out of a set of capable machines. In scheduling sub-problem the sequence of assigned operations is obtained while optimizing the objective function(s). In this paper an object-oriented (OO) approach is presented for multi-objective FJSP along with simulated annealing optimization algorithm. Solution approaches in the literature generally use two-string encoding scheme to represent this problem. However, OO analysis, design and programming methodology help to present this problem on a single encoding scheme effectively which result in a practical integration of the problem solution to manufacturing control systems where OO paradigm is frequently used. OO design of FJSP is achieved by using UML class diagram and this design reduces the problem encoding to a single data structure where operation object of FJSP could hold its data about alternative machines in its own data structure hierarchically. Many-to-many associations between operations and machines are transformed into two one-to-many associations by inserting a new class between them. Minimization of the following three objective functions are considered in this paper: maximum completion time, workload of the most loaded machine and total workload of all machines. Some benchmark sets are run in order to show the effectiveness of the proposed approach. It is proved that using OO approach for multi-objective FJSP contributes to not only building effective manufacturing control systems but also achieving effective solutions.

Quantile-based spectral analysis in an object-oriented framework and a reference implementation in R: The quantspec package

Abstract: Quantile-based approaches to the spectral analysis of time series have recently attracted a lot of attention. Several methods for estimation have been proposed in the literature and their statistical properties were analyzed. Yet, so far, neither a systematic method for computation nor a comprehensive software implementation are available to date. This paper contains two main contributions. First, an extensible framework for quantile-based spectral analysis of time series is developed and documented using object-oriented models. A comprehensive, open source reference implementation of this framework is provided in the R package quantspec, which is available from the Comprehensive R Archive Network. The second contribution of the present paper is to provide a detailed tutorial, with worked examples, for this R package. A reader who is already familiar with quantile-based spectral analysis and whose primary interest is not the design of the quantspec package, but how to use it, can read the tutorial and worked examples (Sections 3 and 4) independently.

Design Patterns Explained A New Perspective On Object Oriented Design

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Heuristic 3D object shape completion based on symmetry and scene context

Abstract: Object shape information is essential for robot manipulation tasks, in particular for grasp planning and collision-free motion planning. But in general a complete object model is not available, in particular when dealing with unknown objects. We propose a method for completing shapes that are only partially known, which is a common situation when a robot perceives a new object only from one direction. Our approach is based on the assumption that most objects used in service robotic setups have symmetries. We determine and rate symmetry plane candidates to estimate the hidden parts of the object. By finding possible supporting planes based on its immediate neighborhood, the search space for symmetry planes is restricted, and the bottom part of the object is added. Gaps along the sides in the direction of the view axis are closed by linear interpolation. We evaluate our approach with real-world experiments using the YCB object and model set [1].

Object Oriented Systems Analysis And Design Using Uml

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Learning to order objects using haptic and proprioceptive exploratory behaviors

Abstract: This paper proposes a novel framework that enables a robot to learn ordinal object relations. While most related work focuses on classifying objects into discrete categories, such approaches cannot learn object properties (e.g., weight, height, size, etc.) that are context-specific and relative to other objects. To address this problem, we propose that a robot should learn to order objects based on ordinal object relations. In our experiments, the robot explored a set of 32 objects that can be ordered by three properties: height, weight, and width. Next, the robot used unsupervised learning to discover multiple ways that the objects can be ordered based on the haptic and proprioceptive perceptions detected while exploring the objects. Following, the robot's model was presented with labeled object series, allowing it to ground the three ordinal relations in terms of how similar they are to the orders discovered during the unsupervised stage. Finally, the grounded models were used to recognize whether new object series were ordered by any of the three properties as well as to correctly insert additional objects into an existing series.

RotationNet: Learning Object Classification Using Unsupervised Viewpoint Estimation.

Abstract: The recent popularization of depth sensors and the availability of large-scale 3D model databases such as ShapeNet have drawn increased attention to 3D object recognition. Despite the convenience of using 3D models captured offline, we are allowed to observe only a single view of an object at once, with the exception of the use of special environments such as multi-camera studios. This impedes the recognition of diverse objects in a real environment. If a mechanical system (or a robot) has access to multi-view models of objects and is able to estimate the viewpoint of a currently observed object, it can rotate the object to a better view for classification. In this paper, we propose a novel method to learn a deep convolutional neural network that both classifies an object and estimates the rotation path to its best view under the predicted object category. We conduct experiments on a 3D model database as well as a real image dataset to demonstrate that our system can achieve an effective strategy of object rotation for category classification.

Software design pattern recognition using machine learning techniques

Abstract: Design patterns helpful for software development are the reusable abstract documents which provide acceptable solutions for the recurring design problems. But in the process of reverse engineering, it is often desired to identify as well as recognize design pattern from source code, as it improves maintainability and documentation of the source code. In this study, the process of software design pattern recognition is presented which is based on machine learning techniques. Firstly, a training dataset is developed which is based on software metrics. Subsequently, machine learning algorithms such as Layer Recurrent Neural Network and Decision Tree are applied for patterns detection process. In order to evaluate the proposed study, an open source software i.e., JHotDraw 7.0.6 has been used for the recognition of design patterns.

From passive to interactive object learning and recognition through self-identification on a humanoid robot

Abstract: Service robots, working in evolving human environments, need the ability to continuously learn to recognize new objects. Ideally, they should act as humans do, by observing their environment and interacting with objects, without specific supervision. Taking inspiration from infant development, we propose a developmental approach that enables a robot to progressively learn objects appearances in a social environment: first, only through observation, then through active object manipulation. We focus on incremental, continuous, and unsupervised learning that does not require prior knowledge about the environment or the robot. In the first phase, we analyse the visual space and detect proto-objects as units of attention that are learned and recognized as possible physical entities. The appearance of each entity is represented as a multi-view model based on complementary visual features. In the second phase, entities are classified into three categories: parts of the body of the robot, parts of a human partner, and manipulable objects. The categorization approach is based on mutual information between the visual and proprioceptive data, and on motion behaviour of entities. The ability to categorize entities is then used during interactive object exploration to improve the previously acquired objects models. The proposed system is implemented and evaluated with an iCub and a Meka robot learning 20 objects. The system is able to recognize objects with 88.5 % success and create coherent representation models that are further improved by interactive learning.

Dilemma between the Structured and Object-Oriented Approaches to Systems Analysis and Design

Abstract: In recent years, there has been a surge of interest in adapting object-oriented (OO) concepts, UML, and Unified Process of system development in the Systems Analysis and Design texts. However, there is a question of how to best fit these concepts with the existing coherent discussion of structured approach. This paper addresses some of the intricacies of OO concepts such as complexity of diagrams and models, weak links between phases, and lack of support for designing system components. We would like to recommend that there should be a separate text for the OO methodology and it should not present various OO models according to the phases of the traditional structured approach, rather it should focus on the evolution of the models leading to the design of the system components. Furthermore, there should be a standard set of models for the OO methodology as well as a clear definition of steps as an analyst moves from one set of models to the next.

Object Oriented Analysis And Design Understanding System Development With Uml 2 0

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Object Oriented Analysis And Design With The Unified Process

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Objectminer: a new approach for mining complex objects

Abstract: Keywords: Data-mining algorithms, Association rules, Complex objects. Abstract: Since their introduction in 1993, association rules have been successfully applied to the description and summarization of discovered relations between attributes in a large collection of objects. However, most of the research works in this area have focused on mining simple objects, usually represented as a set of binary variables. The proposed work presents a framework for mining complex objects, whose attributes can be of any data type (single and multi-valued). The mining process is guided by the semantics associated to each object feature, which is stated by users by providing both a comparison criterion and a similarity function over the object subdescriptions. Experimental results show the usefulness of the proposal.

Understanding object descriptions in robotics by open-vocabulary object retrieval and detection

Abstract: We address the problem of retrieving and detecting objects based on open-vocabulary natural language queries: given a phrase describing a specific object, for example ?the corn flakes box?, the task is to find the best match in a set of images containing candidate objects. When naming objects, humans tend to use natural language with rich semantics, including basic-level categories, fine-grained categories, and instance-level concepts such as brand names. Existing approaches to large-scale object recognition fail in this scenario, as they expect queries that map directly to a fixed set of pre-trained visual categories, for example ImageNet synset tags. We address this limitation by introducing a novel object retrieval method. Given a candidate object image, we first map it to a set of words that are likely to describe it, using several learned image-to-text projections. We also propose a method for handling open vocabularies, that is, words not contained in the training data. We then compare the natural language query to the sets of words predicted for each candidate and select the best match. Our method can combine category- and instance-level semantics in a common representation. We present extensive experimental results on several datasets using both instance-level and category-level matching and show that our approach can accurately retrieve objects based on extremely varied open-vocabulary queries. Furthermore, we show how to process queries referring to objects within scenes, using state-of-the-art adapted detectors. The source code of our approach will be publicly available together with pre-trained models at http://openvoc.berkeleyvision.org and could be directly used for robotics applications.

SHIFT-Sliding and DEPTH-POP for 3D Positioning

Abstract: Moving objects is an important task in 3D user interfaces. We describe two new techniques for 3D positioning, designed for a mouse, but usable with other input devices. The techniques enable rapid, yet easy-to-use positioning of objects in 3D scenes. With sliding, the object follows the cursor and moves on the surfaces of the scene. Our techniques enable precise positioning of constrained objects. Sliding assumes that by default objects stay in contact with the scene's front surfaces, are always at least partially visible, and do not interpenetrate other objects. With our new Shift-Sliding method the user can override these default assumptions and lift objects into the air or make them collide with other objects. Shift-Sliding uses the local coordinate system of the surface that the object was last in contact with, which is a new form of context-dependent manipulation. We also present Depth-Pop, which maps mouse wheel actions to all object positions along the mouse ray, where the object meets the default assumptions for sliding. For efficiency, both methods use frame buffer techniques. Two user studies show that the new techniques significantly speed up common 3D positioning tasks.

Analysis of saliency object detection algorithms for search and rescue operations

Abstract: The aim of saliency object detection algorithms is to find objects in image or video which draw attention of humans at the first sight. This very popular topic in robotics and computer vision research is useful in various areas and applications like object segmentation, adaptive compression, object recognition, visual surveillance and so on. In this paper, we will explore the possibilities of using these algorithms on the problem of detection of objects for Search and Rescue operations (SAR) in UAV images.

Detecting Design Patterns in Object-Oriented Design Models by Using a Graph Mining Approach

Abstract: Object-oriented design patterns are frequently used in real-world applications. Detection of design patterns is essential for comprehension of the intent and design of a software project. This paper presents a graph-mining approach for detecting design patterns. Our approach is based on searching input design patterns in the space of model graph of the source code by isomorphic sub-graph search method. We developed a tool called DesPaD to apply our pattern detection approach in an automated-way. We successfully detected 23 GoF design patterns in the demo source code of the Applied Java Patterns book and also obtained encouraging results out of our experiments that we conducted on JUnit 3.8, JUnit 4.1 and Java AWT open source projects.

Systems and methods for optimization of 3-d printed objects

Abstract: The present subject matter includes systems, methods, and devices for optimization of objects generated using 3-D printing A printed object may be optimized for performance, such as increasing the strength of the object while retaining the shape of the object For example, if the object is an object designed for a three-point bend, optimization may include removing material from regions within the object to change the relative densities and stiffness in each of the regions while retaining the original shape of the object Optimization of an object while retaining the object shape enables the object to function and to appear as it was originally designed, and to continue to interact with neighboring components in the same way

Reflective Approach for Software Design Decision Making

Abstract: Good software design practice is difficult to define and teach. Despite the many software design methods and processes that are available, the quality of software design relies on human factors. We notice from literature and our own experiments that some of these factors concern design reasoning and reflection. In this paper, we propose a reflective approach to software design decision making. The approach is built upon Two-Minds model and is enabled by a set of problem-generic reflective questions. We illustrate its usefulness in design sessions with an example taken from preliminary experimentation.

Knowledge-based open Internet of Things service provisioning architecture on beacon-enabled Web of Objects

Abstract: Web of Objects uses a semantic ontology to create and provide knowledge-based Internet of Things services in terms of virtual objects and composite virtual objects. The objects are created through ...

Visual Detection of Unknown Objects in Video Games Using Qualitative Stability Analysis

Abstract: Many current computer vision approaches for object detection can only detect objects that have been learned in advance. In this paper, we present a method that uses qualitative stability analysis to infer the existence of unknown objects in certain areas of the images based on gravity and stability of already detected objects. Our method recursively searches these areas for unknown objects until all detected objects form a stable structure or no new objects can be identified anymore. We evaluate our method using the popular video game Angry Birds . We only start with detecting the green pigs and are able to automatically identify and detect all essential game objects in all 400+ available levels. All objects can be accurately and reliably detected. Our method can be applied to other video games where objects obey gravity and are bound by polygons.

MUSE: A Framework for Measuring Object-Oriented Design Quality.

Abstract: Good object-oriented design facilitates the maintainability of a software product. While metric-based approaches and the idea of identifying design smells have been established, there still remains the gap of verifying the compliance of design best practices in source code. Thus, there is no comprehensive set of metrics or design best practices that does not only support design measurement and evaluation but can also guide the improvement process. This paper proposes a novel approach based on measuring design best practices that closes the gap between the identification of design flaws and the support for improvements. An expert group six researchers captured a set of 67 design best practices that are implemented by the framework MUSE (Muse Understand Script Engine). For a first validation of MUSE in this paper, its measuring result is compared with QMOOD, which is an established metric-based approach for measuring the quality of object-oriented design. The qualitative assessment based on data from six versions of the Java tool jEdit shows that MUSE is better suited to guide improvements than QMOOD, e.g., for the design property encapsulation QMOOD indicates no substantial changes in the design quality while the data provided by MUSE highlights that the encapsulation property of jEdit became worse over time. These first promising results of the application of MUSE have to be further validated and future work will concentrate on measuring object-oriented design principles.

Mathematical and software tools for modeling objects with distributed parameters

Abstract: In this paper the software system for constructing the mathematical macromodels for objects with distributed parameters using different methods of structure and parameters identification is considered.