Current Issue : April - June Volume : 2019 Issue Number : 2 Articles : 5 Articles
This paper presents a study carried out at Beijing Normal University with the\naim of investigating whether semi-finished products could affect liberal arts studentsâ??\nmastery of knowledge, mastery of operational skills and ICT self-efficacy\nin multimedia creation. The literature has argued that obstacles in creating\nmultimedia artifacts lead liberal arts students to have low ICT self-efficacy.\nSemi-finished products are used as a scaffolding to facilitate liberal arts studentsâ??\ncreation of multimedia artifacts, such as Flash animations and interactive\nweb-pages. However, empirical research on the effects of such scaffolding\nis lacking. We conducted a quasi-experiment in which we compared an experimental\nclass of 117 students majoring in History with a control class of 102\nstudents majoring in Chinese Language and Literature who took a Multimedia\nTechnology and Webpage Producing (MTWP) course. The experimental\nclass (revising condition) used semi-finished products to develop animations\nand web-pages while the control class (creating condition) developed\nanimations and web-pages from scratch. Data were collected through a\nKnowledge and Skill Test and a Scale on ICT self-efficacy. T-tests were used\nto compare outcomes of the two conditions. Results revealed that studentsâ??\nmastery of knowledge in the revising condition was significantly higher than\nstudents in the creation condition, but there were no significant differences\nbetween the two conditions in terms of studentsâ?? mastery of operational skills .\nResults also showed that there were significant differences between the two\nconditions in terms of studentsâ?? ICT self-efficacy . Further analysis indicated\nthat studentsâ?? ICT self-efficacy in the revising condition improved significantly\nfrom pre-test to post-test, while those in the creating condition declined,\nbut it was not significant. Implications for ICT teaching in higher education were discussed....
The height estimation of the target object is an important research direction in the field of computer vision. The three-dimensional\nreconstruction of structured light has the characteristics of high precision, noncontact, and simple structure and is widely used in\nmilitary simulation and cultural heritage protection. In this paper, the height of the target object is estimated by using the word\nstructure light. According to the height dictionary, the height under the offset is estimated by the movement of the structured light\nto the object. In addition, by effectively preprocessing the captured structured light images, such as expansion, seeking skeleton,\nand other operations, the flexibility of estimating the height of different objects by structured light is increased, and the height of\nthe target object can be estimated more accurately....
Perceptual hashing technique for tamper detection has been intensively investigated owing to the speed and memory efficiency.\nRecent researches have shown that leveraging supervised information could lead to learn a high-quality hashing code. However,\nmost existing methods generate hashing code by treating each region equally while ignoring the different perceptual saliency\nrelating to the semantic information. We argue that the integrity for salient objects is more critical and important to be verified,\nsince the semantic content is highly connected to them. In this paper, we propose a Multi-View Semi-supervised Hashing\nalgorithm with Perceptual Saliency (MV-SHPS),which explores supervised information andmultiple features into hashing learning\nsimultaneously. Our method calculates the image hashing distance by taking into account the perceptual saliency rather than\ndirectly considering the distance value between total images. Extensive experiments on benchmark datasets have validated the\neffectiveness of our proposed method....
Visualization provides an interactive investigation of details of interest and improves understanding the implicit information. There\nis a strong need today for the acquisition of high quality visualization result for various fields, such as biomedical or other scientific\nfield. Quality of biomedical volume data is often impacted by partial effect, noisy, and bias seriously due to the CT (Computed\nTomography) or MRI (Magnetic Resonance Imaging) devices, which may give rise to an extremely difficult task of specifying\ntransfer function and thus generate poor visualized image. In this paper, firstly a nonlinear neural network based denoising in\nthe preprocessing stage is provided to improve the quality of 3D volume data. Based on the improved data, a novel region space\nwith depth based 2D histogram construction method is then proposed to identify boundaries between materials, which is helpful\nfor designing the proper semiautomated transfer function. Finally, the volume rendering pipeline with ray-casting algorithm is\nimplemented to visualize several biomedical datasets. The noise in the volume data is suppressed effectively and the boundary\nbetween materials can be differentiated clearly by the transfer function designed via the modified 2D histogram....
Video segmentation into shots is the first step for video indexing and searching. Videos shots are mostly very small in duration\nand do not give meaningful insight of the visual contents. However, grouping of shots based on similar visual contents gives a\nbetter understanding of the video scene; grouping of similar shots is known as scene boundary detection or video segmentation\ninto scenes. In this paper, we propose a model for video segmentation into visual scenes using bag of visual word (BoVW) model.\nInitially, the video is divided into the shots which are later represented by a set of key frames. Key frames are further represented by\nBoVW feature vectors which are quite short and compact compared to classical BoVW model implementations. Two variations of\nBoVWmodel are used: (1) classical BoVWmodel and (2) Vector of LinearlyAggregated Descriptors (VLAD)which is an extension\nof classical BoVW model. The similarity of the shots is computed by the distances between their key frames feature vectors within\nthe sliding window of length....................
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