scholarly journals Hand Detection Algorithm: Pre-processing Stage

2020 ◽  
Author(s):  
Raissa Likhonina
Keyword(s):  
Detection Algorithm ◽  
Hand Detection ◽  
Processing Stage

scholarly journals Study on the extraction of the text region from natural scene images by an analysis of the edge-oriented pattern

2018 ◽  
Vol 7 (2.12) ◽  
pp. 29
Author(s):  
Jae Ho Yang ◽  
Gang Seong Lee ◽  
Young Pyo Hong ◽  
Sang Hun Lee
Keyword(s):  
Light Exposure ◽  
Ground Truth ◽  
Histogram Equalization ◽  
Detection Algorithm ◽  
Text Detection ◽  
Natural Scene ◽  
Processing Stage ◽  
Edge Based ◽  
Candidate Regions ◽  
Natural Scene Images

Background/Objectives: In this paper, we propose a hybrid scene-detection method using an edge and textural analysis in natural scene images, and finally, we detect the text regions by removing the non-text regions through a pattern analysis of each region.Methods/Statistical analysis: The proposed algorithm is divided into the pre-processing stage and the extraction processing stage to perform the text detection. The lost texts that are improved through a histogram equalization for the minimization of the loss of the text parts that is due to light exposure are detected before the edge detection. After that, the edge is detected using the Canny operator. The detected edge is obtained in the step of applying the SWT algorithm to detect the text candidate regions. The extraction processing step is the step of removing the noise region that is detected by the pixel analysis of the SWT algorithm, and it analyzes the pattern of the text regions and then removes the non-text regions to finally detect the text regions. For the quantitative comparison of the proposed algorithm, our results are compared with the ground-truth image using the precision, recall, and F-measure.Findings: One of the existing text-detection algorithms, the edge-based method, is problematic, as, in addition to the text, the complex backgrounds and textures are detected as the edges in natural scene images. The connected component-based method is also problematic, as the non-text region is included in the text region in the process of finding the connection component.Improvements/Applications: The proposed method shows an effective text-detection result regardless of the light exposure in natural scene images compared with the conventional text-detection algorithm.  

Read, Understand, Learn, & Excel: Development and Testing of an Automated Reading Strategy Detection Algorithm for Postsecondary Students

2019 ◽  
Vol 28 (3) ◽  
pp. 1257-1267 ◽  
Author(s):  
Priya Kucheria ◽  
McKay Moore Sohlberg ◽  
Jason Prideaux ◽  
Stephen Fickas
Keyword(s):  
Academic Success ◽  
Undergraduate Students ◽  
Expository Text ◽  
Reading Strategy ◽  
Detection Algorithm ◽  
Expository Texts ◽  
Human Observer ◽  
Behavioral Strategies ◽  
Periodic Review ◽  
Wide Range

PurposeAn important predictor of postsecondary academic success is an individual's reading comprehension skills. Postsecondary readers apply a wide range of behavioral strategies to process text for learning purposes. Currently, no tools exist to detect a reader's use of strategies. The primary aim of this study was to develop Read, Understand, Learn, & Excel, an automated tool designed to detect reading strategy use and explore its accuracy in detecting strategies when students read digital, expository text.MethodAn iterative design was used to develop the computer algorithm for detecting 9 reading strategies. Twelve undergraduate students read 2 expository texts that were equated for length and complexity. A human observer documented the strategies employed by each reader, whereas the computer used digital sequences to detect the same strategies. Data were then coded and analyzed to determine agreement between the 2 sources of strategy detection (i.e., the computer and the observer).ResultsAgreement between the computer- and human-coded strategies was 75% or higher for 6 out of the 9 strategies. Only 3 out of the 9 strategies–previewing content, evaluating amount of remaining text, and periodic review and/or iterative summarizing–had less than 60% agreement.ConclusionRead, Understand, Learn, & Excel provides proof of concept that a reader's approach to engaging with academic text can be objectively and automatically captured. Clinical implications and suggestions to improve the sensitivity of the code are discussed.Supplemental Materialhttps://doi.org/10.23641/asha.8204786

Age-Related Decline in Emotional Prosody Discrimination

2014 ◽  
Vol 61 (3) ◽  
pp. 215-223 ◽  
Author(s):  
Rachel L. C. Mitchell ◽  
Rachel A. Kingston
Keyword(s):  
Older Adults ◽  
Auditory Perception ◽  
Duration Discrimination ◽  
Pitch Perception ◽  
Sensory Level ◽  
Emotional Prosody ◽  
Processing Stage ◽  
Acoustic Characteristics ◽  
Age Related ◽  
Level Of Processing

It is now accepted that older adults have difficulty recognizing prosodic emotion cues, but it is not clear at what processing stage this ability breaks down. We manipulated the acoustic characteristics of tones in pitch, amplitude, and duration discrimination tasks to assess whether impaired basic auditory perception coexisted with our previously demonstrated age-related prosodic emotion perception impairment. It was found that pitch perception was particularly impaired in older adults, and that it displayed the strongest correlation with prosodic emotion discrimination. We conclude that an important cause of age-related impairment in prosodic emotion comprehension exists at the fundamental sensory level of processing.

On the Nature of Gender Categorization

2012 ◽  
Vol 43 (1) ◽  
pp. 14-27 ◽  
Author(s):  
Silvia Tomelleri ◽  
Luigi Castelli
Keyword(s):  
Early Stage ◽  
Processing Stage ◽  
Brain Potentials ◽  
Ongoing Process ◽  
Gender Categorization

In the present paper, relying on event-related brain potentials (ERPs), we investigated the automatic nature of gender categorization focusing on different stages of the ongoing process. In particular, we explored the degree to which gender categorization occurs automatically by manipulating the semantic vs. nonsemantic processing goals requested by the task (Study 1) and the complexity of the task itself (Study 2). Results of Study 1 highlighted the automatic nature of categorization at an early (N170) and on a later processing stage (P300). Findings of Study 2 showed that at an early stage categorization was automatically driven by the ease of extraction of category-based knowledge from faces while, at a later stage, categorization was more influenced by situational constrains.

Sign in / Sign up

Export Citation Format

Share Document