License plates detection is widely considered a solved problem, with many systems already in operation. However, the existing algorithms or systems work well only under some controlled conditions. ...There are still many challenges for license plate detection in an open environment, such as various observation angles, background clutter, scale changes, multiple plates, uneven illumination, and so on. In this paper, we propose a novel scheme to automatically locate license plates by principal visual word (PVW), discovery and local feature matching. Observing that characters in different license plates are duplicates of each other, we bring in the idea of using the bag-of-words (BoW) model popularly applied in partial-duplicate image search. Unlike the classic BoW model, for each plate character, we automatically discover the PVW characterized with geometric context. Given a new image, the license plates are extracted by matching local features with PVW. Besides license plate detection, our approach can also be extended to the detection of logos and trademarks. Due to the invariance virtue of scale-invariant feature transform feature, our method can adaptively deal with various changes in the license plates, such as rotation, scaling, illumination, etc. Promising results of the proposed approach are demonstrated with an experimental study in license plate detection.
The study aims to develop and validate the Curriculum-related Physical Activity Recall questionnaire (CUPAR) as a measure of physical activity in adolescents. 83 middle-school students (13.23 ± 0.74 ...yrs) completed the CUPAR and whore ActiGraph accelerometers for seven consecutive days. Correlations and Bland-Altman plots were to examine the agreement between these two measures. Significant correlations were observed between the CUPAR and ActiGraph accelerometer for 5-day MPA (r = 0.29, p < 0.01), and for both 5-day and 7-day VPA (r = 0.47 and 0.79, ps < 0.01), and MVPA (r = 0.79 and 0.42, ps < 0.01). Plots showed reasonable agreement between the CUPAR and ActiGraph estimates of VPA and MVPA. The agreement between CUPAR and ActiGraph was higher for in-school VPA (r = 0.58, p < 0.01) and MVPA (r = 0.44, p < 0.01) as compared to the out-school VPA (r = 0.22, p < 0.05) and MVPA (r = 0.26, p < 0.05). The CUPAR can reduce respondents' burden, representing a reliable and efficient measure of physical activity among adolescents, especially for PA occurred during in-school sessions and at vigorous intensity.
Bag-of-Words (BoWs) model based on Scale Invariant Feature Transform (SIFT) has been widely used in large-scale image retrieval applications. Feature quantization by vector quantization plays a ...crucial role in BoW model, which generates visual words from the high- dimensional SIFT features, so as to adapt to the inverted file structure for the scalable retrieval. Traditional feature quantization approaches suffer several issues, such as necessity of visual codebook training, limited reliability, and update inefficiency. To avoid the above problems, in this paper, a novel feature quantization scheme is proposed to efficiently quantize each SIFT descriptor to a descriptive and discriminative bit-vector, which is called binary SIFT (BSIFT). Our quantizer is independent of image collections. In addition, by taking the first 32 bits out from BSIFT as code word, the generated BSIFT naturally lends itself to adapt to the classic inverted file structure for image indexing. Moreover, the quantization error is reduced by feature filtering, code word expansion, and query sensitive mask shielding. Without any explicit codebook for quantization, our approach can be readily applied in image search in some resource-limited scenarios. We evaluate the proposed algorithm for large scale image search on two public image data sets. Experimental results demonstrate the index efficiency and retrieval accuracy of our approach.
The strong feature representation ability of deep learning enables content-based image retrieval (CBIR) to achieve higher retrieval accuracy, while there are still some challenges for CBIR such as ...high requirements of training labels and retrieve efficiency. In this paper, we propose an unsupervised adversarial image retrieval (UAIR) framework by breaking the limitation of training labels. The framework is composed of two opposite parts and is linked by an adversarial loss function. For each input image, a generative model is used to select “well-matched” images from the database; a discriminative model is used to distinguish whether the selected images are similar enough to the input image. During training, the generative model tries to convince the discriminative model that the selected images are similar and the discriminative model always challenges the results of the generative model. The performances of the UAIR have been compared with other state-of-the-art image retrieval methods, including recently reported GAN-based methods. Extensive experiments show that the UAIR achieves significant improvement in CBIR with unsupervised adversarial training.
Introduction Children with Attention Deficit Hyperactivity Disorder (ADHD) exhibit deficits in working memory (WM) and cardiorespiratory fitness (CRF), both of which are closely associated with the ...core symptoms of ADHD. This study aimed to investigate the effects of rope skipping exercise (RSE) on the WM and CRF of children with ADHD, to provide a theoretical foundation for the optimization of exercise intervention programs tailored to children with ADHD. Methods This study recruited 55 children (age range 6–12 years) and randomly assigned them into three groups: the ADHD with RSE (AWRSE, n=22, mean age: 10.18 ± 1.10 years), the ADHD with sports game (SG) (AWSG, n=16, mean age: 9.38 ± 0.96 years), and the typically developing (TD) control group (CG, n=17, mean age: 8.94 ± 0.56 years). The AWRSE underwent a RSE intervention, while the other two groups participated in SG. The exercise intervention lasted for 8 weeks, with sessions held twice a week for 60 minutes each, at a moderate-to-vigorous-intensity (64–95% HRmax). All children in each group underwent pre-test and post-test, including height, weight, BMI, n-back, and 20mSRT. One-way analysis of variance (Ony-way ANOVA) and paired sample t-test were used to analyze inter- and intra-group differences respectively. Results Before the intervention, children with ADHD exhibited a significantly lower VO 2 max compared to the TD children (p<0.05), and there was no significant difference in the other indicators between the groups (p>0.05). After the intervention, no significant inter-group differences were found across all indices for the three groups of children (p > 0.05). The AWRSE had significant improvements in the accuracy of 1-back task, Pacer (laps), and VO 2 max (p<0.05), with the level of CRF approaching that of TD children. A significant decrease in response time for the 1-back task was observed in the CG. Conclusion An 8-week RSE intervention is an effective therapeutic approach for children with ADHD, significantly enhancing their WM and CRF.
Most large-scale image retrieval systems are based on the bag-of-visual-words model. However, the traditional bag-of-visual-words model does not capture the geometric context among local features in ...images well, which plays an important role in image retrieval. In order to fully explore geometric context of all visual words in images, efficient global geometric verification methods have been attracting lots of attention. Unfortunately, current existing methods on global geometric verification are either computationally expensive to ensure real-time response, or cannot handle rotation well. To solve the preceding problems, in this article, we propose a novel geometric coding algorithm, to encode the spatial context among local features for large-scale partial-duplicate Web image retrieval. Our geometric coding consists of geometric square coding and geometric fan coding, which describe the spatial relationships of SIFT features into three geo-maps for global verification to remove geometrically inconsistent SIFT matches. Our approach is not only computationally efficient, but also effective in detecting partial-duplicate images with rotation, scale changes, partial-occlusion, and background clutter.
Experiments in partial-duplicate Web image search, using two datasets with one million Web images as distractors, reveal that our approach outperforms the baseline bag-of-visual-words approach even following a RANSAC verification in mean average precision. Besides, our approach achieves comparable performance to other state-of-the-art global geometric verification methods, for example, spatial coding scheme, but is more computationally efficient.
•Build a large-scale 3D shape retrieval benchmark that supports multi-modal queries.•Evaluate the 26 3D shape retrieval methods using 3 types of metrics.•Solicit and identify state-of-the-art methods ...and promising related techniques.•Perform detailed analysis on diverse methods w.r.t accuracy and efficiency.•Make benchmark and evaluation tools freely available to the community.
Large-scale 3D shape retrieval has become an important research direction in content-based 3D shape retrieval. To promote this research area, two Shape Retrieval Contest (SHREC) tracks on large scale comprehensive and sketch-based 3D model retrieval have been organized by us in 2014. Both tracks were based on a unified large-scale benchmark that supports multimodal queries (3D models and sketches). This benchmark contains 13680 sketches and 8987 3D models, divided into 171 distinct classes. It was compiled to be a superset of existing benchmarks and presents a new challenge to retrieval methods as it comprises generic models as well as domain-specific model types. Twelve and six distinct 3D shape retrieval methods have competed with each other in these two contests, respectively. To measure and compare the performance of the participating and other promising Query-by-Model or Query-by-Sketch 3D shape retrieval methods and to solicit state-of-the-art approaches, we perform a more comprehensive comparison of twenty-six (eighteen originally participating algorithms and eight additional state-of-the-art or new) retrieval methods by evaluating them on the common benchmark. The benchmark, results, and evaluation tools are publicly available at our websites (http://www.itl.nist.gov/iad/vug/sharp/contest/2014/Generic3D/, 2014, http://www.itl.nist.gov/iad/vug/sharp/contest/2014/SBR/, 2014).
•Build a small scale and a large scale sketch-based 3D model retrieval benchmark.•Evaluate 15 best sketch-based 3D model retrieval algorithms on the two benchmarks.•Solicit and identify the ...state-of-the-art methods and promising related techniques.•Incisive analysis on diverse methods w.r.t scalability and efficiency performance.•The benchmarks and evaluation tools provide good reference to the related community.
Sketch-based 3D shape retrieval has become an important research topic in content-based 3D object retrieval. To foster this research area, two Shape Retrieval Contest (SHREC) tracks on this topic have been organized by us in 2012 and 2013 based on a small-scale and large-scale benchmarks, respectively. Six and five (nine in total) distinct sketch-based 3D shape retrieval methods have competed each other in these two contests, respectively. To measure and compare the performance of the top participating and other existing promising sketch-based 3D shape retrieval methods and solicit the state-of-the-art approaches, we perform a more comprehensive comparison of fifteen best (four top participating algorithms and eleven additional state-of-the-art methods) retrieval methods by completing the evaluation of each method on both benchmarks. The benchmarks, results, and evaluation tools for the two tracks are publicly available on our websites 1,2.
3D sketching for 3D object retrieval Li, Bo; Yuan, Juefei; Ye, Yuxiang ...
Multimedia tools and applications,
03/2021, Letnik:
80, Številka:
6
Journal Article
Recenzirano
Sketching provides the most natural way to provide a visual search query for visual object search. However, how to draw 3D sketches in a three-dimensional space and how to use a hand-drawn 3D sketch ...to search similar 3D models are not only interesting and novel, but also challenging research topics. In this paper, we try to answer them by initiating a novel study on 3D sketching and build a 3D sketching system which allows users to freely draw 3D sketches in the air and demonstrate its promising potentials in related applications such as collecting 3D sketch data and conducting 3D sketch-based 3D model retrieval. By utilizing the 3D sketching system, we collect a 3D sketch dataset, build a 3D sketch-based 3D model retrieval benchmark, and organize a Eurographics Shape Retrieval Contest (SHREC) track on 3D sketch-based shape retrieval based on the benchmark. We investigate 3D sketch and model matching problems and propose a novel 3D sketch-based model retrieval algorithm CNN-SBR based on Convolutional Neural Networks (CNNs) and achieve the best performance in the SHREC track. We wish that the 3D sketching system, the 3D sketch-based model retrieval benchmark, and the proposed 3D sketch-based model retrieval algorithm CNN-SBR will further promote sketch-based shape retrieval and its applications. We have made all of these publicly available on the project homepage:
http://orca.st.usm.edu/~bli/SBR16/project.html
.
This study is cross-sectional in nature and aims to investigate and track sedentary behavior (SB) and physical activity among student (aged 9-23 years) for seven consecutive days using an ...accelerometer. It also intends to analyze the current status of the daily activities of students using age and school-segment differences. The study recruits a total of 384 students age: 14.41 ± 3.52 years; body mass index (BMI): 19.66 ± 3.67 from four schools out of which 180 (46.88%) were male. The study uses the means and standard deviations for statistical analysis and independent sample
-tests to determine gender differences. Analysis of covariance is used to determine whether or not daily SB and physical activity were statistically significant students according to gender and school segment followed by LSD
tests for multiple comparisons. The results demonstrate that students were less physically active moderate- to vigorous-intensity physical activity (MVPA):60.4 ± 23.48 min/day and more sedentary (598.47 ± 162.63 min/day). The sedentary time of the students displays an inverted U-trend, and their participation in MVPA exhibits a W-shape. After controlling for BMI, the results of ANCOVA point to a significant school-segment effect (
< 0.001) for SB (
= 83,
= 0.4) and physical activity (low-intensity physical activity:
= 108.61,
= 0.47; MPA:
= 401.65,
= 0.76; high-intensity physical activity:
= 88.43,
= 0.42; MVPA:
= 118.42,
= 0.49). Based on the behavioral characteristics of students across school segments, this study concluded that interventions targeting students' physical activity and physical health should be school segment specific. The results of the study provide a basis for future analysis of factors influencing students' physical activity behaviors across school segments and for proposing targeted intervention strategies for the future.
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