Recognition Of Graphics Research Articles

Assessment of Chinese primary school mathematics teachers’ knowledge of students’ misconceptions regarding space and shape

ABSTRACT The study aimed to establish an assessment model for mathematics teachers’ knowledge of students’ misconceptions in the Space and Shape domain, develop the testing tool, investigate and analyse the overall and differences in performance, and propose suggestions for improvement. The assessment model included content knowledge and performance standards. The content knowledge standard included cognition and content dimensions. The cognition dimension was subdivided into predicting the misconceptions, interpreting the misconceptions, analysing the reasons and teaching strategies to correct misconceptions. According to the division of Space and Shape in official curriculum standard, the content dimension was subdivided into Recognition of graphics, Measurement, Motion, and Location. 701 Chinese mathematics teachers from 8 provinces were involved to verify the assessment model. Item response theory was used to estimate teachers’ performance, and combinations of response categories was used to divide the teachers’ performance into three response levels: Low, Medium and High. 81% of teachers were performing at Medium and High response levels. Teachers performed the worst in Recognition of graphics and the best in Measurement. The worst performance occurred in analysing the reasons, the best in interpreting the misconceptions. These results have provided evidences for adjusting teacher activities, improving teacher education courses and teachers training.

A Spiking Artificial Vision Architecture Based on Fully Emulating the Human Vision.

Intelligent vision necessitates the deployment of detectors that are always-on and low-power, mirroring the continuous and uninterrupted responsiveness characteristic of human vision. Nonetheless, contemporary artificial vision systems attain this goal by the continuous processing of massive image frames and executing intricate algorithms, thereby expending substantial computational power and energy. In contrast, biological data processing, based on event-triggered spiking, has higher efficiency and lower energy consumption. Here, this work proposes an artificial vision architecture consisting of spiking photodetectors and artificial synapses, closely mirroring the intricacies of the human visual system. Distinct from previously reported techniques, the photodetector is self-powered and event-triggered, outputting light-modulated spiking signals directly, thereby fulfilling the imperative for always-on with low-power consumption. With the spiking signals processing through the integrated synapse units, recognition of graphics, gestures, and human action has been implemented, illustrating the potent image processing capabilities inherent within this architecture. The results prove the 90% accuracy rate in human action recognition within a mere five epochs utilizing a rudimentary artificial neural network. This novel architecture, grounded in spiking photodetectors, offers a viable alternative to the extant models of always-on low-power artificial vision system.

The Chinese Character as an Interface for Artistic Creation: Re-Cognition of Graphics and Phonetics

ABSTRACT This article examines two modern artistic explorations of the Chinese character. It takes Xu Bing’s Square Word Calligraphy and Jonathan Stalling’s Sinophonic English Poetry as examples and investigate how contemporary art reimagines and reconstructs the “potential” of Chinese characters and what ideological and political implications these processes may have for transcultural communication in today’s world. Situating the production of both works in Xu’s and Stalling’s respective creative experiences and larger sociocultural conditions, this article shows how both works, which deploy the graphics and the phonetics of the Chinese character as interfaces, engage in the politics of transcultural art creation and communication, and achieve their generative effects. Through this investigation, it hopes to illuminate the critical power of the Chinese character used as an interface in challenging linguistic, ideological, and cultural “norms” and questioning racial, national, and political prejudice.

Design of a Classification Recognition Model for Bone and Muscle Anatomical Imaging Based on Convolutional Neural Network and 3D Magnetic Resonance.

In this paper, we use convolutional neural networks to conduct in-depth research and analysis on the classification and recognition of bone and muscle anatomical imaging graphics of 3D magnetic resonance and design corresponding models for practical applications. A series of medical image segmentation models based on convolutional neural networks is proposed. In this paper, firstly, a separated attention mechanism is introduced in the model, which divides the input data into multiple paths, applies self-attention weights to adjacent data paths, and finally fuses the weighted values to form the basic convolutional block. This structure has multiple parallel data paths, which increases the width of the network and therefore improves the feature extraction capability of the model. Then, this paper proposes a bidirectional feature pyramid for medical image segmentation task, which has top-down and bottom-up data paths, and, together with jump connections, can fully interact with feature maps at different scales. After that, a new activation function Mish is introduced, and its advantages over other activation functions are experimentally demonstrated. Finally, for the situation that medical image annotations are not easy to obtain, a semisupervised learning method is introduced in the model training process, and the effectiveness of this method is experimentally demonstrated. The joint network first denoises the input image, then super-resolution mapping is performed on the noise-removed feature map, and finally, the super-resolution 3D-MR image is obtained. We update the network by combining the denoising loss and super-resolution loss during the joint network training process. The experimental results show that the joint network with denoising first and then super-resolution outperforms the joint network with other task order and outperforms the method that performs the two tasks separately and the proposed method in this paper has the optimal performance.

Application of Deep Convolutional Neural Network Under Region Proposal Network in Patent Graphic Recognition and Retrieval

To improve the security of product technology and design efficiency, the recognition and retrieval methods of patent graphics are explored. First, for low detection accuracy of images in traditional detection methods, the deep convolutional neural network (CNN) with stronger feature extraction capabilities is selected for feature extraction. Second, the region proposal algorithm is used to improve patent graphic recognition accuracy and reduce the probability of image feature missed detection. Finally, the detection model is compressed by the deep separable CNN, and the convolution kernel performs the separation operation. The results show that the deep CNN has a more vital ability to extract features. The region proposal algorithm can accurately select the regions to be recognized and detected, which reduces the recognition workload. Simultaneously, through the multi-layer feature fusion strategy, the neural network’s ability to express patent graphics is improved, and the probability of missed detection of image features is reduced. The deep separable CNN can allow the convolution kernel to perform the separation operation, effectively reduce the model parameter amount, and shorten the model retrieval time. The investigation finds that the deep CNN based on the region proposal network (RPN) improves the accuracy and precision of the recognition and retrieval of patent graphics, as well as the detection time of the algorithm. The method of this investigation can be applied to the recognition and detection of actual patent graphics, thereby improving the security of patent information and the innovation of patent design.

A general framework for the recognition of online handwritten graphics

We revisit graph grammar and graph parsing as tools for recognizing graphics. A top-down approach for parsing families of handwritten graphics containing different kinds of symbols and of structural relations is proposed. It has been tested on two distinct domains, namely the recognition of handwritten mathematical expressions and of handwritten flowcharts. In the proposed approach, a graphic is considered as a labeled graph generated by a graph grammar. The recognition problem is translated into a graph parsing problem: Given a set of strokes (input data), a parse tree which represents the best interpretation is extracted. The graph parsing algorithm generates multiple interpretations (consistent with the grammar) that can be ranked according to a global cost function that takes into account the likelihood of symbols and structures. The parsing algorithm consists in recursively partitioning the stroke set according to rules defined in the graph grammar. To constrain the number of partitions to be evaluated, we propose the use of a hypothesis graph, built from data-driven machine learning techniques, to encode the most likely symbol and relation hypotheses. Within this approach, it is easy to relax the stroke ordering constraint allowing interspersed symbols, as opposed to some previous works. Experiments show that our method obtains accuracy comparable to methods specifically developed to recognize domain-dependent data.

Graphical Topology Autonomic Identification Method for Human-Machine Interface Modelling Based on Quadratic Space Mapping

To realize a computerized autonomous recognition of human-computer interface graphics and autonomously interface an interface graphic model with a simulation analysis program of a systems operation, a human-computer interface topology identification algorithm based on quadratic space mapping (QSM) is proposed, which includes three mapping spaces and two mapping methods. The three mapping spaces are defined as the interface modelling graphics space, the primitive information space and the abstract topology space. The first mapping method uses hierarchical topology mapping based on coordinate properties, and the second uses abstract topology mapping based on breadth first search. The QSM algorithm converts a visual graphical physical model on a human-machine interface into a computer-recognizable abstract model. Then, a simulation analysis of the system is performed automatically by a computer using the converted abstract model. Finally, taking the distribution network system as an example, the proposed topology recognition algorithm realizes the conversion of the interface modelling graph to the abstract model of the distribution network. Furthermore, the simulation of the power flow algorithm is performed by interfacing perfectly with the abstract model, and the feasibility and practicability of the algorithm are verified.

Research on Synchronized Recognition System of Literary Works Based on Tesseract-OCR Model

Using mobile terminal for image and character recognition can solve the dilemma of the traditional large-scale scanning equipment, such as complicated steps and inconvenience in moving, and provide great convenience for mobile learning and office. As a direct and concrete manifestation of objective things, images are rich in information, which is incomparable in other ways. Therefore, the importance of images is self-evident. Recognition of graphics and text information of terminal equipment can solve the disadvantage of large-scale equipment, such as difficult to move and complicated use steps, and can make office and mobile learning more smoothly. Tesseract-OCR is powerful in open source OCR. Based on Tesseract-OCR model, this paper puts forward the corresponding improvement scheme for the defects such as irregular graphics and characters, and inconspicuous image and text recognition system, and carries out synchronous preview and recognition of image and text as much as possible. The graphic publication of literary works reflects readers’ impetuous reading mentality, the popular trend of literature trend and the management crisis of publishing houses.

Scene-Awareness Based Single Image Dehazing Technique via Automatic Estimation of Sky Area

The occurrence of fog, mist, smog, or haze significantly reduces the visibility of the scenes and images, resulting in limited recognition of computer vision and computer graphics. So, removing haze from images is a must. In this paper, we regard image dehazing as a mathematical inversion process and image restoration based on atmospheric scattering models. The atmospheric light can be accurately estimated by combining the gray threshold segmentation and the skyline method. The improved least squares filtering method is used to optimize the transmittance map so that the edge details can be highlighted and the halo effect can be alleviated. A large number of test images show that our algorithm can achieve better experimental results than the other seven most advanced dehazing strategies.

Pierwsze rozważania na temat koloru we Francji przełomu XVII i XVIII wieku

A dispute concerning the importance of various artistic devices arose unexpectedly as a result of meetings held in the Royal Academy of Painting and Sculpture in Paris at the end of the 17th century. Its consequences, however, reiterated 100 years later in considerations on philosophical aesthetics in works by authors such as Denis Diderot, Johann Wolfgang Goethe, or even Immanuel Kant. In 1667, in the Royal Academy of Painting and Sculpture, a wide-ranging program of meetings was initiated in order to discuss theoretical aspects of art as well as comment on particular works later prepared for printing by Andre Felibien. For the Academy, one of the main aims was to prove that the status which painters, graphic artists, and sculptors enjoyed was comparable to that achieved by poets and musicians and that all the above mentioned artists are more than mere craftsmen. Drawing is the common point to all these fields of art and as s composition can justify the recognition of painting, graphics, and sculpture as art rather than craft since these primarily require intellectual work, then skills. The situation drastically changed in 1673 due to Roger de Piles who claimed that in art colouration (or a well-developed colour-matching skill) not colour itself is the key issue. Owing to that he was able to differentiate between a craftsman and an artist without referring to a drawing.

Las habilidades fonológicas, su organización neurofisiológica y su aplicación en la educación

For some authors phonological awareness is a prerequisite for reading, that has to be understood as a conscious activity that involves the activation of a set of skills that integrate higher brain functions, such as praxis, gnosis and language, and whose organization defines new neurophysiological components of reading acquisition, particularly, the phonemic stereotypes. The skill of reading, particularly, the early recognition of graphics and spelling is acquired through childhood in the interaction with the environment, especially, through recreational activities, as well as through biological maturation. This is why, both writing and reading require an accurate phonemic ear (brain mechanism that supports the development of phonological skills), so to identify the lexical units of language, allowing the comparison of the orthographic form of a written word (sequence of letters in an engram) with its mental representation. Once the word is recognized there is access to the semantic system, its sense and its corresponding articulatory production. For this reason, the best name for this phenomenon is that of phonological skills, because the processes involving reading are simultaneous and multiple and depend on the knowledge and development that the reader has acquired in this learning process.

Scene Reconstruction and Visualization from Internet Photo Collections: A Survey

The Internet has become an unprecedented source of visual information about our world, with millions of people uploading photos and videos to media-sharing sites at staggering rates. Virtually all of the world's famous landmarks and cities (and many not-so-famous ones) have been photographed hundreds of thousands or millions of times, and billions of these photos can be found on photo-sharing websites. This richness and variety make such Internet photo collections extremely attractive as a source of data for applications ranging from mapping and visualization to social science. However, a prerequisite to many such applications is recovering structure — often in the form of 3D geometry — from these massive, unorganized collections of imagery. This ever-growing collection of visual data opens up fundamental new questions in computer vision and computer graphics, where traditional techniques designed for small, controlled sets of images cannot be readily applied. This article surveys recent work on applying geometric computer vision to large, unstructured photo collections, as well as applications enabled by these new techniques in scene visualization, location recognition, image editing, and other areas of computer vision and graphics.