Gesture image segmentation with Otsu’s method based on noise adaptive angle threshold

Ouyang, Haibin; Fan, Chaodong; Umer, Tariq; Poonia, Ramesh Chandra; Wan, Shaohua

doi:10.1007/s11042-019-08544-7

Cited by 21 publications

(11 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…43 While there are a variety of thresholding methods present in the literature, Otsu's method is the most accurate and most widely used. [44][45][46][47] In the program, Otsu's method determines a threshold that distinguishes the background from the region of interest. The determined threshold is then used to segment each image, removing background noise and displaying the red stain.…”

Section: Automated Image Segmentationmentioning

confidence: 99%

“…Horkowitz et al human bias. 43,[45][46][47] Area of stain, which corresponds to the total number of pixels occupied by fluorescent signal from any one image, was evaluated by region and compared between infected HC3-treated groups and infected vehicle control groups ( Figure 7B). Area of stain measurements from infected HC3-treated groups were greater than those observed in infected vehicle control groups (whole lung; p < 0.01, BF UL > 6).…”

Section: Dovepressmentioning

confidence: 99%

See 1 more Smart Citation

<p>Acetylcholine Regulates Pulmonary Pathology During Viral Infection and Recovery</p>

Horkowitz

Schwartz

Álvarez

et al. 2020

ITT

View full text Add to dashboard Cite

Introduction This study was designed to explore the role of acetylcholine (ACh) in pulmonary viral infection and recovery. Inflammatory control is critical to recovery from respiratory viral infection. ACh secreted from non-neuronal sources, including lymphocytes, plays an important, albeit underappreciated, role in regulating immune-mediated inflammation. Methods ACh and lymphocyte cholinergic status in the lungs were measured over the course of influenza infection and recovery. The role of ACh was examined by inhibiting ACh synthesis in vivo. Pulmonary inflammation was monitored by Iba1 immunofluorescence, using a novel automated algorithm. Tissue repair was monitored histologically. Results Pulmonary ACh remained constant through the early stage of infection and increased during the peak of the acquired immune response. As the concentration of ACh increased, cholinergic lymphocytes appeared in the BAL and lungs. Cholinergic capacity was found primarily in CD4 T cells, but also in B cells and CD8 T cells. The cholinergic CD4 + T cells bound to influenza-specific tetramers and were retained in the resident memory regions of the lung up to 2 months after infection. Histologically, cholinergic lymphocytes were found in direct physical contact with activated macrophages throughout the lung. Inflammation was monitored by ionized calcium-binding adapter molecule 1 (Iba1) immunofluorescence, using a novel automated algorithm. When ACh production was inhibited, mice exhibited increased tissue inflammation and delayed recovery. Histologic examination revealed abnormal tissue repair when ACh was limited. Conclusion These findings point to a previously unrecognized role for ACh in the transition from active immunity to recovery and pulmonary repair following respiratory viral infection.

show abstract

Section: Automated Image Segmentationmentioning

confidence: 99%

Section: Dovepressmentioning

confidence: 99%

<p>Acetylcholine Regulates Pulmonary Pathology During Viral Infection and Recovery</p>

Horkowitz

Schwartz

Álvarez

et al. 2020

ITT

View full text Add to dashboard Cite

show abstract

“…A noise adaptive angle threshold method that uses Otsu's method for gesture image segmentation is proposed in [13]. Gray mean is truncated from a two-dimensional histogram of gray value-neighborhood to evade the interference of excessive noise by removing the extremes of the identified region and improve the algorithm's antinoise ability, according to the dissimilarity between pixels and their neighbors; the probability of noise is adaptively calculated so that it could deal automatically with noise different.…”

Section: Survey On Hand Gesture Segmentationmentioning

confidence: 99%

A Study on Hand Gesture Segmentation Approaches

Sunuwar

Borah

Agarwal

et al. 2021

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

Computer vision is the study of making machines capable of understanding the input image and video using some algorithms or techniques. As for humans, understanding a scene given in an image or video is not a difficult task. Humans are blessed to understand images and videos effortlessly without knowing the complexities behind the process of image and video understanding. Making machines also capable of understanding their input image and video is not that simple. With ongoing research for making human-computer interaction more natural and realistic, lots of advancements have been done in different components of computer vision. The paper focuses on the image segmentation process used in computer vision and image processing which is a very crucial component while processing images and video. Image segmentation is the process of extracting only relevant content or information while removing irreverent content or information. Image segmentation is the most researched topic in image processing and computer vision. It is one of the major activities that will help the later process to ease its operation by filtering unnecessary and irrelevant contents. The paper aims to describe the segmentation process and highlight different types of segmentation approaches used in recent trends comparing their performances.

show abstract

“…Preprocessing filters noise and differentiates various areas in input images acquired by a camera in an industrial setting. The Otsu threshold algorithm is a kind of common segmentation technique in image processing [60] and widely used in character classification [61,62]. It is applied during image binarization.…”

Section: ) Image Preprocessingmentioning

confidence: 99%

A Hybrid Character Recognition Approach Using Fuzzy Logic and Stroke Bayesian Program Learning With Naïve Bayes in Industrial Environments

Liang

Wang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Automated character recognition is critical for reading and tracking data in a variety of fields. It is particularly challenging in industrial settings since information may be printed on the surface of various materials with complex and uneven shapes, causing overlapping, obstructing, and distorting characters. We propose a hybrid character recognition approach using fuzzy logic and stroke Bayesian program learning with naï ve Bayes. During character segmentation, touching characters are separated using support vector machines and a three-feature fuzzy segmentation strategy that uses particle swarm optimization. This approach includes a new methodology for stroke presentation and extraction using a prebuilt primitive-stroke library containing prior knowledge. During character recognition, a conceptual character model is constructed using stroke Bayesian program learning. Monte Carlo Markov chain sampling is used to produce a fitting model for each character. This model predicts character classification by calculating the probability that a target sample belongs to a training set. To this end, naï ve Bayes effectively discerns extremely similar characters. We evaluate our method experimentally using a database of industrial images and the NIST dataset. Our method outperforms existing state-of-the art methods.

show abstract

Gesture image segmentation with Otsu’s method based on noise adaptive angle threshold

Cited by 21 publications

References 13 publications

<p>Acetylcholine Regulates Pulmonary Pathology During Viral Infection and Recovery</p>

<p>Acetylcholine Regulates Pulmonary Pathology During Viral Infection and Recovery</p>

A Study on Hand Gesture Segmentation Approaches

A Hybrid Character Recognition Approach Using Fuzzy Logic and Stroke Bayesian Program Learning With Naïve Bayes in Industrial Environments

Contact Info

Product

Resources

About