Representation Of Real World Objects Research Articles

Recognition and implementation of the smart manufacturing systems in industrial sectors for evolving industry 4.0

The digital twin is referred to as a virtual representation of the physical object and its representation of real-world objects in a virtual manner. The recognition of the digital twin is obtained from real-time data with artificial intelligence techniques. Virtual reality is a three-dimensional environment that enables the users to utilize and explore the virtual environment and is achieved through the combination of hardware and software systems. Augmented reality is enabled in the form of text, audio, and video format representations. It only gets in touch with a combination of the physical and the real world whereas virtual reality proceeds in a frictional world. The increasing usage of smart manufacturing systems using digital twin is providing versatile usages. This incorporates physical and virtual environments and is enabled with the obtained data that helps in the modification of the physical counterpart. Incorporating huge amounts is a bigger challenge, therefore augmented and virtual reality is implemented that helps the users to utilize digital data with higher interest through augmented reality.

Visual Perception of Photographs of Rotated 3D Objects in Goldfish (Carassius auratus).

Simple SummaryThe ability to visually recognize objects at different viewpoints is known as object constancy and is very important to animals, including humans. By presenting six goldfish with photographs of plastic turtles and frogs at different viewing angles, we were able to better understand object constancy abilities in goldfish. All the fish had successful performance, showing that they were able to distinguish between the turtle and frog photographs, which is evidence of object constancy. This study was the first to look at fish’s ability to identify photographs of objects they had previously seen, known as object-picture recognition.This study examined goldfishes’ ability to recognize photographs of rotated 3D objects. Six goldfish were presented with color photographs of a plastic model turtle and frog at 0° in a two-alternative forced-choice task. Fish were tested with stimuli at 0°, 90°, 180°, and 270° rotated in the picture plane and two depth planes. All six fish performed significantly above chance at all orientations in the three rotation planes tested. There was no significant difference in performance as a function of aspect angle, which supported viewpoint independence. However, fish were significantly faster at 180° than at +/−90°, so there is also evidence for viewpoint-dependent representations. These fish subjects performed worse overall in the current study with 2D color photographs (M = 88.0%) than they did in our previous study with 3D versions of the same turtle and frog stimuli (M = 92.6%), although they performed significantly better than goldfish in our two past studies presented with black and white 2D stimuli (M = 67.6% and 69.0%). The fish may have relied on color as a salient cue. This study was a first attempt at examining picture-object recognition in fish. More work is needed to determine the conditions under which fish succeed at object constancy tasks, as well as whether they are capable of perceiving photographs as representations of real-world objects.

2D or not 2D? An fMRI study of how dogs visually process objects.

Given humans' habitual use of screens, they rarely consider potential differences when viewing two-dimensional (2D) stimuli and real-world versions of dimensional stimuli. Dogs also have access to many forms of screens and touchpads, with owners even subscribing to dog-directed content. Humans understand that 2D stimuli are representations of real-world objects, but do dogs? In canine cognition studies, 2D stimuli are almost always used to study what is normally 3D, like faces, and may assume that both 2D and 3D stimuli are represented in the brain the same way. Here, we used awake fMRI in 15 dogs to examine the neural mechanisms underlying dogs' perception of two- and three-dimensional objects after the dogs were trained on either two- or three-dimensional versions of the objects. Activation within reward processing regions and parietal cortex of the dog brain to 2D and 3D versions of objects was determined by their training experience, as dogs trained on one dimensionality showed greater differential activation within the dimension on which they were trained. These results show that dogs do not automatically generalize between two- and three-dimensional versions of object stimuli and suggest that future research consider the implicit assumptions when using pictures or videos.

Web-of-Objects Based User-Centric Semantic Service Composition Methodology in the Internet of Things

The general goal of the Web-of-Objects (WoO) is to simplify object and application deployment, maintenance, and operation of IoT infrastructures. WoO also aim to provide user-centric IoT service by enabling object virtualization and semantic ontology based service composition. In WoO, semantic modeling of objects plays a distinguished role in achieving interoperability of device and service through semantic ontology model. In this paper, we propose a semantic functional module for user centric service composition in WoO platform. We design an ontology model for virtual object (ViO); it is the physical representation of real world objects. Consequently, we present a user-demand based service composition by showing an use case of WoO. In the proposed semantic based service composition approach, virtual object and composite virtual object are the key entity; this class of objects allows composing services from heterogeneous objects by following a service composition algorithm. This algorithm creates composite service along with the semantic description of that service. The proposed approach of service composition enhances the collaboration of objects as well as services. In the proposed system, user can search a service using natural language through the user interface; on the other hand, the service composition module creates the service by selecting the required objects dynamically.

Meaningful image objects for object-oriented image analysis

Image objects obtained by segmentation usually provide a much more reliable representation of real world objects than individual pixels. However, in regions with high quality spatial information, image analysis should focus on objects of interest rather than artificial image objects. A simple method for applying this object-oriented approach consists of converting existing vector geographic information system (GIS) data into raster objects. A problem with this method is that it may produce image objects with saw-toothed edges which barely match measured objects boundaries. In order to address such a problem, a method for adjusting boundaries of image objects is proposed. The new method uses a vector square grid for pixel representation. Vector-based image objects exhibit boundaries which better reproduce the shape and appearance of GIS objects. The proposed approach was applied to extract geometric and biophysical properties of agricultural plots from remotely sensed imagery. Results suggest that vector-based image objects provide much more accurate values than raster-based image objects.

Scalable Iterative Graph Duplicate Detection

Duplicate detection determines different representations of real-world objects in a database. Recent research has considered the use of relationships among object representations to improve duplicate detection. In the general case where relationships form a graph, research has mainly focused on duplicate detection quality/effectiveness. Scalability has been neglected so far, even though it is crucial for large real-world duplicate detection tasks. We scale-up duplicate detection in graph data (DDG) to large amounts of data and pairwise comparisons, using the support of a relational database management system. To this end, we first present a framework that generalizes the DDG process. We then present algorithms to scale DDG in space (amount of data processed with bounded main memory) and in time. Finally, we extend our framework to allow batched and parallel DDG, thus further improving efficiency. Experiments on data of up to two orders of magnitude larger than data considered so far in DDG show that our methods achieve the goal of scaling DDG to large volumes of data.

Self-efficacy and mental models in learning to program

Learning to program is a unique experience for each student, and it is not fully understood why one person in an introductory programming course learns to program better and more quickly than the next. Self-efficacy is an individual's judgment of his or her ability to perform a task within a specific domain [1]. A mental model is a person's internal (mental) representation of real world objects and systems [9]. Research has shown that high self-efficacy and a good mental model are important to knowledge acquisition and transfer. This research investigates the effects of students' self-efficacy and mental models of programming on learning to program. The results show that self-efficacy for programming is influenced by previous programming experience and increases as a student progresses through an introductory programming course. The results also show that the student's mental model of programming influences self-efficacy and that both the mental model and self-efficacy affect course performance.

A Wavelet-Based Sprite Codec

The International Standards Organization (ISO) has proposed a family of standards for compression of image and video sequences, including the JPEG, MPEG-1, and MPEG-2. The latest MPEG-4 standard has many new dimensions to coding and manipulation of visual content. A video sequence usually contains a background object and many foreground objects. Portions of this background may not be visible in certain frames due to the occlusion of the foreground objects or camera motion. MPEG-4 introduces the novel concepts of video object planes (VOPs) and Sprites. A VOP is a visual representation of real world objects with shapes that need not be rectangular. Sprite is a large image composed of pixels belonging to a video object visible throughout a video segment. Since a sprite contains all parts of the background that were at least visible once, it can be used for direct reconstruction of the background VOP. Sprite reconstruction is dependent on the mode in which it is transmitted. In the static sprite mode, the entire sprite is decoded as an Intra VOP before decoding the individual VOPs. Since sprites consist of the information needed to display multiple frames of a video sequence, they are typically much larger than a single frame of video. Therefore, a static sprite can be considered as a large static image. In this paper, a novel solution to address the problem of spatial scalability has been proposed, where the sprite is encoded in discrete wavelet transform (DWT). A lifting kernel method of DWT implementation has been used for encoding and decoding sprites. Modifying the existing lifting scheme while maintaining it to be shape-adaptive results in a reduced complexity. The proposed scheme has the advantages of: 1) avoiding the need for any extensions to image or tile border pixels and is hence superior to the discrete cosine transform-based low latency scheme (used in the current MPEG-4 verification model) and 2) mapping the in place computed wavelet coefficients into a zero-tree structure without actually rearranging them, thereby saving allocation of additional memory. The proposed solutions provide efficient implementation of the sprite decoder, making possible a VLSI realization with a reduced real estate.