Search results for: mental image

Authors: Ismail Haddad, Djamel Herbadji, Aissa Belmeguenai, Selma Boumerdassi

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in this paper, we provide a novel approach for image encryption that employs the Fibonacci matrix and an enhanced fractional order chaotic map. The enhanced map overcomes the drawbacks of the classical map, especially the limited chaotic range and non-uniform distribution of chaotic sequences, resulting in a larger encryption key space. As a result, this strategy improves the encryption system's security. Our experimental results demonstrate that our proposed algorithm effectively encrypts grayscale images with exceptional efficiency. Furthermore, our technique is resistant to a wide range of potential attacks, including statistical and entropy attacks.

Keywords: image encryption, logistic map, fibonacci matrix, grayscale images

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Authors: Yuan-Hsiang Chang, Zhong-Xian Peng, Li-Der Jeng

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Music has always been an integral part of human’s daily lives. But, for the most people, reading musical score and turning it into melody is not easy. This study aims to develop an Automatic music score recognition system using digital image processing, which can be used to read and analyze musical score images automatically. The technical approaches included: (1) staff region segmentation; (2) image preprocessing; (3) note recognition; and (4) accidental and rest recognition. Digital image processing techniques (e.g., horizontal /vertical projections, connected component labeling, morphological processing, template matching, etc.) were applied according to musical notes, accidents, and rests in staff notations. Preliminary results showed that our system could achieve detection and recognition rates of 96.3% and 91.7%, respectively. In conclusion, we presented an effective automated musical score recognition system that could be integrated in a system with a media player to play music/songs given input images of musical score. Ultimately, this system could also be incorporated in applications for mobile devices as a learning tool, such that a music player could learn to play music/songs.

Keywords: connected component labeling, image processing, morphological processing, optical musical recognition

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Authors: Md Shohidul Islam, Jongmyon Kim, Ui-pil Chong

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Medical image is an integral part of e-health care and e-diagnosis system. Medical image watermarking is widely used to protect patients’ information from malicious alteration and manipulation. The watermarked medical images are transmitted over the internet among patients, primary and referred physicians. The images are highly prone to corruption in the wireless transmission medium due to various noises, deflection, and refractions. Distortion in the received images leads to faulty watermark detection and inappropriate disease diagnosis. To address the issue, this paper utilizes error correction code (ECC) with (8, 4) Hamming code in an existing watermarking system. In addition, we implement the high complex ECC on a graphics processing units (GPU) to accelerate and support real-time requirement. Experimental results show that GPU achieves considerable speedup over the sequential CPU implementation, while maintaining 100% ECC efficiency.

Keywords: medical image watermarking, e-health system, error correction, Hamming code, GPU

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Authors: Lauren McGillivray, Michelle Torok, Alison Calear

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Suicide is the leading cause of death for Australians aged 15-24 years, with rates increasing over the past decade. As young people can be particularly vulnerable to mental health problems and suicidal behavior, they are an essential and obvious target for suicide prevention efforts. This study investigates the effectiveness of the universal mental health promotion and suicide prevention program, Youth Aware of Mental Health (YAM), to reduce suicidal ideation and attempts and increase help-seeking in young people. This trial took place in Australian schools across four regions in New South Wales that form part of LifeSpan, a larger multilevel suicide prevention research trial. The YAM program was delivered to Year 9 students in up to 78 schools over two years (from January 2017 to December 2019). All schools were invited to participate in YAM's evaluation, which included completing a student questionnaire at three time-points: baseline, 3-month post-intervention, and 6-month follow-up. The primary outcome is suicidal ideation severity. Secondary outcomes are new reports of suicide attempts, stigma towards suicide, knowledge about suicide, help-seeking intentions and behaviors, and depressive symptoms. Results from pre-post and follow-up data will be presented. These research findings are promising and will contribute to the evidence-based for YAM and suicide prevention programs in Australian schools. These findings are also expected to promote YAM's value and sustainability to be more widely delivered in Australian secondary schools.

Keywords: adolescent mental health, suicidal ideation, suicide prevention, universal program

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Authors: M. Nadeem, Y. K. Chang, C. Diallo, U. Venkatadri, P. Havard, T. Nguyen-Quang

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Uniform distribution of agro-chemicals is highly important because there is a significant loss of agro-chemicals, for example from pesticide, during spraying due to non-uniformity of droplet and off-target drift. Improving the efficiency of spray pattern for different cropping systems would reduce energy, costs and to minimize environmental pollution. In this paper, we examine the water jet patterns in order to study the performance and uniformity of water distribution during the spraying process. We present a method to quantify the water amount from a sprayer jet by using the Particle Image Velocimetry (PIV) system. The results of the study will be used to optimize sprayer or nozzles design for chemical application. For this study, ten sets of images were acquired by using the following PIV system settings: double frame mode, trigger rate is 4 Hz, and time between pulsed signals is 500 µs. Each set of images contained different numbers of double-framed images: 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100 at eight different pressures 25, 50, 75, 100, 125, 150, 175 and 200 kPa. The PIV images obtained were analysed using custom-made image processing software for droplets and volume calculations. The results showed good agreement of both manual and PIV measurements and suggested that the PIV technique coupled with image processing can be used for a precise quantification of flow through nozzles. The results also revealed that the method of measuring fluid flow through PIV is reliable and accurate for sprayer patterns.

Keywords: image processing, PIV, quantifying the water volume from nozzle, spraying pattern

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Authors: Ouafa Benaida, Abdelhamid Loukil, Adda Ali Pacha

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In recent years, the growing interest of scientists in the field of image processing and analysis of underwater images and videos has been strengthened following the emergence of new underwater exploration techniques, such as the emergence of autonomous underwater vehicles and the use of underwater image sensors facilitating the exploration of underwater mineral resources as well as the search for new species of aquatic life by biologists. Indeed, underwater images and videos have several defects and must be preprocessed before their analysis. Underwater landscapes are usually darkened due to the interaction of light with the marine environment: light is absorbed as it travels through deep waters depending on its wavelength. Additionally, light does not follow a linear direction but is scattered due to its interaction with microparticles in water, resulting in low contrast, low brightness, color distortion, and restricted visibility. The improvement of the underwater image is, therefore, more than necessary in order to facilitate its analysis. The research presented in this paper aims to implement and evaluate a set of classical techniques used in the field of improving the quality of underwater images in several color representation spaces. These methods have the particularity of being simple to implement and do not require prior knowledge of the physical model at the origin of the degradation.

Keywords: underwater image enhancement, histogram normalization, histogram equalization, contrast limited adaptive histogram equalization, single-scale retinex

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Authors: Styliani Kotrotsiou, Evagelia Kotrotsiou, Fani Mokia, Theodosis Paralikas, Konstantinos Tsaras

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Objective: The objective of this study was the investigation of the mental health of patients undergoing chronic or transient hemodialysis at Artificial Kidney Unit, as well as its relationship to the demographic characteristic of patients. Material and Method: The study took place in Larisa during the month of December in 2016 and the sample was composed of 60 patients undergoing in chronic or transient hemodialysis at Artificial Kidney Unit of the University General Hospital of Larisa. For the investigation of the physical and mental health of patients who participated in the study, the tool measurement << General Health Questionnaire- 28 >> (GHQ-28) was used. The questionnaires were administered with the interview method during the hemodialysis. This survey is designed for the existence or not of a mental disorder. It examines four factors (physical symptoms, anxiety, social dysfunction and depression). Results: The hemodialysis patients gave the following scores: -to the physical symptoms, women showed a higher average value than men (1,16 ± 1,26 against 0,49 ± 0,93), -at the anxiety scale, it seems that women are superior to men (1,68 ± 1,20 against 0,90 ± 1,22), -at the social dysfunction scale, the elderly patients ( > 65 years old) were presented a with higher average (2,59), and -at the depression scale, patients with a higher average value were those who lived in non-urban areas. The appearance of mental disorder, in relation to patient characteristics, did not show significant statistical correlation. The sex, the age and the place of residence affect more the assessment of mental health, while education did not seem to have any significant effect on the other. Conclusions: The hemodialysis process can significantly affect the patient’s Quality of Life and it can bring adverse changes in lifestyle, affecting the physical, social and psychological state of the individual. For that reason, hemodialysis should be aimed not only at extending life but in upgrading the Quality of Life.

Keywords: hemodialysis, chronic kidney disease, depression, social dysfunction, physical condition

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Authors: Sidney A. Lima, Hermann J. H. Kux, Elcio H. Shiguemori

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The Unmanned Aircraft Systems (UAS) usually navigate through the Global Navigation Satellite System (GNSS) associated with an Inertial Navigation System (INS). However, GNSS can have its accuracy degraded at any time or even turn off the signal of GNSS. In addition, there is the possibility of malicious interferences, known as jamming. Therefore, the image navigation system can solve the autonomy problem, because if the GNSS is disabled or degraded, the image navigation system would continue to provide coordinate information for the INS, allowing the autonomy of the system. This work aims to evaluate the accuracy of the positioning though photogrammetry concepts. The methodology uses orthophotos and Digital Surface Models (DSM) as a reference to represent the object space and photograph obtained during the flight to represent the image space. For the calculation of the coordinates of the perspective center and camera attitudes, it is necessary to know the coordinates of homologous points in the object space (orthophoto coordinates and DSM altitude) and image space (column and line of the photograph). So if it is possible to automatically identify in real time the homologous points the coordinates and attitudes can be calculated whit their respective accuracies. With the methodology applied in this work, it is possible to verify maximum errors in the order of 0.5 m in the positioning and 0.6º in the attitude of the camera, so the navigation through the image can reach values equal to or higher than the GNSS receivers without differential correction. Therefore, navigating through the image is a good alternative to enable autonomous navigation.

Keywords: autonomy, navigation, security, photogrammetry, remote sensing, spatial resection, UAS

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Authors: Ying-Lun Tseng Chiu-Ying Chen

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Taiwan's Mental Health Act was amended at the end of 2022; they added regulations regarding refusing compulsory treatment by patients with mental illnesses. In addition, not only by an examination committee, the judge must also assess the patient's need for compulsory treatment. Additionally, the maximum of compulsory hospitalization has been reduced from an unlimited period to a maximum of 60 days. They aim to promote the healthcare autonomy of individuals with mental illnesses in Taiwan and prevent their silenced voice in medical decision-making while they still possess rationality. Furthermore, they plan to use community support and social care networks to replace the current practice of compulsory treatment in Taiwan. This study uses qualitative research methodology, utilizing interview guidelines to inquire about the experiences of Taiwanese who have undergone compulsory hospitalization, compulsory community treatment, and compulsory medical care. The interviews aimed to explore their feelings when they were subjected to compulsory medical intervention, the inside of their illness, their opinions after treatments, and whether alternative medical interventions proposed by them were considered. Additionally, participants also asked about their personal life history and their support networks in their lives. We collected 12 Taiwanese who had experienced compulsory medical interventions and were interviewed 14 times. The findings indicated that participants still possessed rationality during the onset of their illness. However, when they have other treatments to replace compulsory medical, they sometimes diverge from those of the doctors and their families. Finally, doctors prefer their professional judgment and patients' families' option. Therefore, Taiwanese mental health patients' power of decision-making still needs to improve. Because this research uses qualitative research, so difficult to find participants, and the sample size rate was smaller than Taiwan's population, it may have biases in the analysis. So, Taiwan still has significant progress in enhancing the decision-making rights of participants in the study.

Keywords: medical decision making, compulsory treatment, medical ethics, mental health act

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Authors: Andreas Kornmaaler Hansen, Carlos Gomez Cubero, Elizabeth Jochum

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The use of the arts in therapy and rehabilitation is well established, and there is growing recognition of the value of the arts for improving health and well-being across diverse populations. Combining arts with socially assistive robots is a relatively under-explored research area. This paper presents the results of a feasibility study conducted within an existing arts and health program to scope the possibility of combining visual arts with socially assistive robots to promote mental health and well-being. Using a participatory research design with participant-led perspectives, we present the results of our feasibility study with a collaborative drawing robot among an adult population with mild to severe mental illness. We identify key methodological challenges and advantages of working with participatory and human-centered approaches. Based on the results of three pilot workshops with participants and lay health workers, we outline suggestions for authentic engagement with real stakeholders toward the development of socially assistive robots in community health contexts. Working closely with a patient population at all levels of the research process is key for developing tools and interventions that center patient experience and priorities while minimizing the risks of alienating patients and communities.

Keywords: arts and health, visual art, health promotion, mental health, collaborative robots, creativity, socially assistive robots

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Authors: Trupti Chavan, Ramachandra Guda, Kameshwar Rao

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The lack of data is a pain problem in medical image analysis using a convolutional neural network (CNN). This work uses various spatial transformation techniques to address the medical image augmentation issue for knee detection and localization using an enhanced single shot detector (SSD) network. The spatial transforms like a negative, histogram equalization, power law, sharpening, averaging, gaussian blurring, etc. help to generate more samples, serve as pre-processing methods, and highlight the features of interest. The experimentation is done on the OpenKnee dataset which is a collection of knee images from the openly available online sources. The CNN called enhanced single shot detector (SSD) is utilized for the detection and localization of the knee joint from a given X-ray image. It is an enhanced version of the famous SSD network and is modified in such a way that it will reduce the number of prediction boxes at the output side. It consists of a classification network (VGGNET) and an auxiliary detection network. The performance is measured in mean average precision (mAP), and 99.96% mAP is achieved using the proposed enhanced SSD with spatial transformations. It is also seen that the localization boundary is comparatively more refined and closer to the ground truth in spatial augmentation and gives better detection and localization of knee joints.

Keywords: data augmentation, enhanced SSD, knee detection and localization, medical image analysis, openKnee, Spatial transformations

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Authors: Khader S. Al-Aidmat, Venus W. Samawi

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Blurred vision in the misty atmosphere is essential problem which needs to be resolved. To solve this problem, we developed a technique to restore foggy degraded image from its original version using Back-propagation neural network (BP-NN). The suggested technique is based on mapping between foggy scene and its corresponding original scene. Seven different approaches are suggested based on type of features used in image restoration. Features are extracted from spatial and spatial-frequency domain (using DCT). Each of these approaches comes with its own BP-NN architecture depending on type and number of used features. The weight matrix resulted from training each BP-NN represents a fog filter. The performance of these filters are evaluated empirically (using PSNR), and perceptually. By comparing the performance of these filters, the effective features that suits BP-NN technique for restoring foggy images is recognized. This system proved its effectiveness and success in restoring moderate foggy images.

Keywords: artificial neural network, discrete cosine transform, feed forward neural network, foggy image restoration

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Authors: Poojitha Vemula

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Understanding how we use image manipulation to communicate through an audience’s perceptions and conceive visual intelligence. With the use of many software and high-end skills, designers have developed a third eye to combine two different visuals and create the desired image by using photoshop and other software skills. The purpose of visual intelligence is to convey a message to the targeted audience. For instance, the images of models are retouched on their skin to make it more convincing and draw attention from the audience. There are many ways of manipulating an image, such as double exposure, retouching photography inks or paint airbrushing and piecing photos together, or enhancing the brightness and contrast. To understand visual intelligence, a questionnaire survey as well as research was conducted on how image manipulation is used by both the audience and the designers. This depends on the message that needs to be conveyed by the brands. For instance, Fair & Lovely, a brightening cream for ladies use a lot of retouching and effects to show the dramatic change the cream takes effect on dark or dusky faces. Thus the designer’s role is to use their third eye to incorporate the message into visuals. The research and questionnaire survey concludes the perceptions and manipulations used in visual communication. However this is all to make an effortless communication between the designer and the audience by using the skills of the designer and the features provided by the software. The objective of visual intelligence is to covet the message of the brands that advertise their products or services by using visuals through softwares. Conveying a message through visual intelligence requires an audiences perceptions and understanding from the visuals created by the artists or designers. Visual intelligence determines how we use our technical skills to retouch and manipulate an image for a better understanding to convey the message to the targeted audience. This also bridges the communication between the brand and the audience.

Keywords: graphic design, visual communication, convey messages, photoshop, image manipulation

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Authors: O. Boussoufi, K. Lamrini Uahabi, M. Atounti

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The main purpose of this paper is to calculate the fractal dimension of some Julia Sets and Mandelbrot Set in the Misiurewicz Points. Using Matlab to generate the Julia Sets images that match the Misiurewicz points and using a Fractal software, we were able to find different measures that characterize those fractals in textures and other features. We are actually focusing on fractal dimension and the error calculated by the software. When executing the given equation of regression or the log-log slope of image a Box Counting method is applied to the entire image, and chosen settings are available in a FracLAc Program. Finally, a comparison is done for each image corresponding to the area (boundary) where Misiurewicz Point is located.

Keywords: box counting, FracLac, fractal dimension, Julia Sets, Mandelbrot Set, Misiurewicz Points

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Authors: Saba Fahham, Supak Ngamsom, Suchaya Damrongsri

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Objective: The objective is to determine the optimal threshold of Romexis software for the airway volume and minimum cross-section area (MCA) analysis using Image J as a gold standard. Materials and Methods: A total of ten cone-beam computed tomography (CBCT) images were collected. The airway volume and MCA of each patient were analyzed using the automatic airway segmentation function in the CBCT DICOM viewer (Romexis). Airway volume and MCA measurements were conducted on each CBCT sagittal view with fifteen different threshold values from the Romexis software, Ranging from 300 to 1000. Duplicate DICOM files, in axial view, were imported into Image J for concurrent airway volume and MCA analysis as the gold standard. The airway volume and MCA measured from Romexis and Image J were compared using a t-test with Bonferroni correction, and statistical significance was set at p<0.003. Results: Concerning airway volume, thresholds of 600 to 850 as well as 1000, exhibited results that were not significantly distinct from those obtained through Image J. Regarding MCA, employing thresholds from 400 to 850 within Romexis Viewer showed no variance from Image J. Notably, within the threshold range of 600 to 850, there were no statistically significant differences observed in both airway volume and MCA analyses, in comparison to Image J. Conclusion: This study demonstrated that the utilization of Planmeca Romexis Viewer 6.4.3.3 within threshold range of 600 to 850 yields airway volume and MCA measurements that exhibit no statistically significant variance in comparison to measurements obtained through Image J. This outcome holds implications for diagnosing upper airway obstructions and post-orthodontic surgical monitoring.

Keywords: airway analysis, airway segmentation, cone beam computed tomography, threshold

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Authors: S. Khan, A. Khan, Abdul R. Soomrani, Raja F. Zafar, A. Waqas, G. Akbar

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This paper proposes a low-complexity image interpolation method. Image interpolation is used to convert a low dimension video/image to high dimension video/image. The objective of a good interpolation method is to upscale an image in such a way that it provides better edge preservation at the cost of very low complexity so that real-time processing of video frames can be made possible. However, low complexity methods tend to provide real-time interpolation at the cost of blurring, jagging and other artifacts due to errors in slope calculation. Non-linear methods, on the other hand, provide better edge preservation, but at the cost of high complexity and hence they can be considered very far from having real-time interpolation. The proposed method is a linear method that uses gradient orientation for slope calculation, unlike conventional linear methods that uses the contrast of nearby pixels. Prewitt edge detection is applied to separate uniform regions and edges. Simple line averaging is applied to unknown uniform regions, whereas unknown edge pixels are interpolated after calculation of slopes using gradient orientations of neighboring known edge pixels. As a post-processing step, bilateral filter is applied to interpolated edge regions in order to enhance the interpolated edges.

Keywords: edge detection, gradient orientation, image upscaling, linear interpolation, slope tracing

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Authors: RAM PAL SINGH, VIKASH CHAUDHARY, MONIKA VERMA

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In this paper, we proposed the implementation of optimization based Extreme Learning Machine (ELM) for watermarking of B-channel of color image in discrete wavelet transform (DWT) domain. ELM, a regularization algorithm, works based on generalized single-hidden-layer feed-forward neural networks (SLFNs). However, hidden layer parameters, generally called feature mapping in context of ELM need not to be tuned every time. This paper shows the embedding and extraction processes of watermark with the help of ELM and results are compared with already used machine learning models for watermarking.Here, a cover image is divide into suitable numbers of non-overlapping blocks of required size and DWT is applied to each block to be transformed in low frequency sub-band domain. Basically, ELM gives a unified leaning platform with a feature mapping, that is, mapping between hidden layer and output layer of SLFNs, is tried for watermark embedding and extraction purpose in a cover image. Although ELM has widespread application right from binary classification, multiclass classification to regression and function estimation etc. Unlike SVM based algorithm which achieve suboptimal solution with high computational complexity, ELM can provide better generalization performance results with very small complexity. Efficacy of optimization method based ELM algorithm is measured by using quantitative and qualitative parameters on a watermarked image even though image is subjected to different types of geometrical and conventional attacks.

Keywords: BER, DWT, extreme leaning machine (ELM), PSNR

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Authors: Shiau-Fang Chao, Yu-Chih Chen

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Owing to physical limitations and restrained lifestyle, older long-term care (LTC) residents are more likely to be affected by their environment than their community-dwelling counterparts. They also participate fewer activities and experience worse mental health than healthy older adults. This study adopts the ICF model to determine the extent to which the clustered patterns of LTC environment and activity participation are associated with older residents’ mental health. Method: Data were collected from a stratified equal probability sample of 634 older residents in 155 LTC institutions in Taiwan. Latent profile analysis (LPA) and latent class analysis (LCA) were conducted to explore the profiles for environment and activity participation. Multilevel modeling was performed to elucidate the relationships among environment profiles, activity profiles, and mental health. Results: LPA identified three mutually exclusive environment profiles (Low-, Moderate-, and High-Support Environment) based on the physical, social, and attitudinal environmental domains, consolidated from 12 environmental measures. LCA constructed two distinct activity profiles (Low- and High-Activity Participation) across seven activity domains (outdoor, volunteer-led leisure, spiritual, household chores, interpersonal exchange, social, and sedentary activity) that were factored from 20 activities. Compared to the Low-Support Environment class, older adults in the Moderate- and High-Support Environment classes had better mental health. Older residents in the Moderate- and High-Support Environment classes were more likely to be in the “High Activity” class, which in turn, exhibited better mental health. Conclusion: This study advances the current knowledge through rigorous methods and study design. The study findings lead to several conclusions. First, this study supports the use of ICF framework to institutionalized older individuals with functional limitations and demonstrates that both measures of environment and activity participation can be refined from multiple indicators. Second, environmental measures that encompass the physical, social, and attitudinal domains would provide a more comprehensive assessment on the place where an older individual embeds. Third, simply counting activities in which an older individual participates or considering a certain type of activity may not capture his or her way of life. Practitioners should not only focus on group or leisure activities within the institutions; rather, more efforts should be made to consider residents’ preferences for everyday life and support their remaining ability by encouraging continuous participation in activities they still willing and capable to perform. Fourth, environment and activity participation are modifiable factors which have greater potential to strengthen older LTC residents’ mental health, and activity participation should be considered in the link between environment and mental health. A combination of enhanced physical, social, and attitudinal environments, and continual engagement in various activities may optimize older LTC residents’ mental health.

Keywords: activity, environment, mental health, older LTC residents

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Authors: Dacheng Li, Bo Huang, Qinjin Han, Ming Li

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Remotely sensed imagery with the high spatial and temporal characteristics, which it is hard to acquire under the current land observation satellites, has been considered as a key factor for monitoring environmental changes over both global and local scales. On a basis of the limited high spatial-resolution observations, challenged studies called spatiotemporal fusion have been developed for generating high spatiotemporal images through employing other auxiliary low spatial-resolution data while with high-frequency observations. However, a majority of spatiotemporal fusion approaches yield to satisfactory assumption, empirical but unstable parameters, low accuracy or inefficient performance. Although the spatiotemporal fusion methodology via sparse representation theory has advantage in capturing reflectance changes, stability and execution efficiency (even more efficient when overcomplete dictionaries have been pre-trained), the retrieval of high-accuracy dictionary and its response to fusion results are still pending issues. In this paper, we employ additional image pairs (here each image-pair includes a Landsat Operational Land Imager and a Moderate Resolution Imaging Spectroradiometer acquisitions covering the partial area of Baotou, China) only into the coupled dictionary training process based on K-SVD (K-means Singular Value Decomposition) algorithm, and attempt to improve the fusion results of two existing sparse representation based fusion models (respectively utilizing one and two available image-pair). The results show that more eligible image pairs are probably related to a more accurate overcomplete dictionary, which generally indicates a better image representation, and is then contribute to an effective fusion performance in case that the added image-pair has similar seasonal aspects and image spatial structure features to the original image-pair. It is, therefore, reasonable to construct multi-dictionary training pattern for generating a series of high spatial resolution images based on limited acquisitions.

Keywords: spatiotemporal fusion, sparse representation, K-SVD algorithm, dictionary learning

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Authors: Geetika Barman, B. S. Daya Sagar

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In this article, we proposed a pixel-wise classification of hyperspectral images using a mathematical morphology operator-based distance metric called “dilation distance” and “erosion distance”. This method involves measuring the spatial distance between the spectral features of a hyperspectral image across the bands. The key concept of the proposed approach is that the “dilation distance” is the maximum distance a pixel can be moved without changing its classification, whereas the “erosion distance” is the maximum distance that a pixel can be moved before changing its classification. The spectral signature of the hyperspectral image carries unique class information and shape for each class. This article demonstrates how easily the dilation and erosion distance can measure spatial distance compared to other approaches. This property is used to calculate the spatial distance between hyperspectral image feature vectors across the bands. The dissimilarity matrix is then constructed using both measures extracted from the feature spaces. The measured distance metric is used to distinguish between the spectral features of various classes and precisely distinguish between each class. This is illustrated using both toy data and real datasets. Furthermore, we investigated the role of flat vs. non-flat structuring elements in capturing the spatial features of each class in the hyperspectral image. In order to validate, we compared the proposed approach to other existing methods and demonstrated empirically that mathematical operator-based distance metric classification provided competitive results and outperformed some of them.

Keywords: dilation distance, erosion distance, hyperspectral image classification, mathematical morphology

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Authors: Gracie Tredget, Raymond McGrath, Karen Ang, Julie Williams, Nick Sevdalis, Fiona Gaughran, Jorge Aria de la Torre, Ioannis Bakolis, Andy Healey, Zarnie Khadjesari, Euan Sadler, Natalia Stepan

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Background: Preventable physical health problems have been found to increase morbidity rates amongst adults living with serious mental illness (SMI). Community mental health clinicians have a role in identifying, and preventing physical health problems worsening, and supporting primary care services to administer routine physical health checks for their patients. However, little is known about how mental health staff perceive and approach their role when providing physical healthcare amongst patients with SMI, or the impact these attitudes have on routine practice. Methods: The present study involves a prospective service evaluation specific to Adult Community Mental Health Services at South London and Maudsley NHS Foundation Trust (SLaM). A qualitative methodology will use semi-structured interviews, focus groups and observations to explore attitudes, perceptions and experiences of staff, patients, and carers (n=64) towards physical healthcare, and barriers or facilitators that impact upon it. 1South London and Maudsley NHS Foundation Trust, London, SE5 8AZ, UK 2 Centre for Implementation Science, King’s College London, London, SE5 8AF, UK 3 Psychosis Studies, King's College London, London, SE5 8AF, UK 4 Department of Biostatistics and Health Informatics, King’s College London, London, SE5 8AF, UK 5 Kings Health Economics, King's College London, London, SE5 8AF, UK 6 Behavioural and Implementation Science (BIS) research group, University of East Anglia, Norwich, UK 7 Department of Nursing, Midwifery and Health, University of Southampton, Southampton, UK 8 Mind and Body Programme, King’s Health Partners, Guy’s Hospital, London, SE1 9RT *[email protected] Analysis: Data from across qualitative tasks will be synthesised using Framework Analysis methodologies. Staff, patients, and carers will be invited to participate in co-development of recommendations that can improve routine physical healthcare within Adult Community Mental Health Teams at SLaM. Results: Data collection is underway at present. At the time of the conference, early findings will be available to discuss. Conclusions: An integrated approach to mind and body care is needed to reduce preventable deaths amongst people with SMI. This evaluation will seek to provide a framework that better equips staff to approach physical healthcare within a mental health setting.

Keywords: severe mental illness, physical healthcare, adult community mental health, nursing

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Authors: Shangdong Zhu, Yunzhou Zhang, Jie Zhang, Hang Hu, Yazhou Zhang

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Image stitching is a very important branch in the field of computer vision, especially for panoramic map. In order to eliminate shape distortion, a novel stitching method is proposed based on gradually changing matrix when images are horizontal. For images captured horizontally, this paper assumes that there is only translational operation in image stitching. By analyzing each parameter of the homography matrix, the global homography matrix is gradually transferred to translation matrix so as to eliminate the effects of scaling, rotation, etc. in the image transformation. This paper adopts matrix approximation to get the minimum value of the energy function so that the shape distortion at those regions corresponding to the homography can be minimized. The proposed method can avoid multiple horizontal images stitching failure caused by accumulated shape distortion. At the same time, it can be combined with As-Projective-As-Possible algorithm to ensure precise alignment of overlapping area.

Keywords: image stitching, gradually changing matrix, horizontal direction, matrix approximation, homography matrix

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Authors: Anderson Rocha, Pedro Miguel de Figueiredo Dinis Oliveira Gaspar

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Machine vision has been widely used in recent years in agriculture, as a tool to promote the automation of processes and increase the levels of productivity. The aim of this work is the development of a path recognition algorithm based on image processing to guide a terrestrial robot in-between tree rows. The proposed algorithm was developed using the software MATLAB, and it uses several image processing operations, such as threshold detection, morphological erosion, histogram equalization and the Hough transform, to find edge lines along tree rows on an image and to create a path to be followed by a mobile robot. To develop the algorithm, a set of images of different types of orchards was used, which made possible the construction of a method capable of identifying paths between trees of different heights and aspects. The algorithm was evaluated using several images with different characteristics of quality and the results showed that the proposed method can successfully detect a path in different types of environments.

Keywords: agricultural mobile robot, image processing, path recognition, hough transform

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Authors: Shamsundar Kulkarni

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Video capturing by non-professionals will lead to unanticipated effects. Such as image distortion, image blurring etc. Hence, many researchers study such drawbacks to enhance the quality of videos. In this paper, an algorithm is proposed to stabilize jittery videos .A stable output video will be attained without the effect of jitter which is caused due to shaking of handheld camera during video recording. Firstly, salient points from each frame from the input video are identified and processed followed by optimizing and stabilize the video. Optimization includes the quality of the video stabilization. This method has shown good result in terms of stabilization and it discarded distortion from the output videos recorded in different circumstances.

Keywords: video stabilization, point feature matching, salient points, image quality measurement

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Authors: Maciej Bialorudzki, Zbigniew Izdebski, Alicja Kozakiewicz, Joanna Mazur

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The quality of sleep is extremely important for physical and mental health, especially among professional groups exposed to the suffering of the people they serve. The aim of the study is to assess sleep quality and various aspects of physical and mental health. A nationwide cross-sectional survey conducted in the first quarter of 2022 included 2227 healthcare professionals from 114 Polish hospitals and specialized outpatient clinics. The following distribution for each professional group was obtained (22% doctors; 52.6% nurses; 7.3% paramedics; 10.1% other medical professionals; 7.9% other non-medical professionals). The mean age of the respondents was 46.24 (SD=11.53). The Jenkins Sleep Scale with four items (JSS-4) was used to assess sleep quality, yielding a mean value of 5.35 (SD=5.20) in the study group and 13.7% of subjects with poor sleep quality using the cutoff point of the mean JSS-4 sum score as >11. More often, women than men reported poorer sleep quality (14,8% vs. 9,1% p=0,002). Respondents with poor sleep quality were more likely to report occupational burnout as measured by the BAT-12 (43.1% vs. 12.9% p<0.001) and high levels of stress as measured by the PSS-4 (72.5% vs. 27.5% p<0.001). In addition, those who declare experiencing a traumatic event compared to those who have not experienced it has an almost two times higher risk of poorer sleep quality (OR:1.958; 95% CI:1.509-2.542; p<0.001). In contrast, those with occupational burnout had more than five times the risk of those without occupational burnout (OR:5.092; 95% CI: 3.763-6.889; p<0.001). Sleep quality remains an important predictor of stress levels, job burnout, and quality of life assessment.

Keywords: quality of sleep, medical staff, mental health, physical health, occupational burnout, stress

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Authors: Nikolaos Papadakis, Constantinos Condaxakis, Konstantinos Savvakis

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The aim of this paper is to determine the elastic properties (elastic modulus and Poisson ratio) of a composite material based on noncontacting imaging methods. More specifically, the significantly reduced cost of digital cameras has given the opportunity of the high reliability of low-cost strain measurement. The open source platform Ncorr is used in this paper which utilizes the method of digital image correlation (DIC). The use of digital image correlation in measuring strain uses random speckle preparation on the surface of the gauge area, image acquisition, and postprocessing the image correlation to obtain displacement and strain field on surface under study. This study discusses technical issues relating to the quality of results to be obtained are discussed. [0]8 fabric glass/epoxy composites specimens were prepared and tested at different orientations 0[o], 30[o], 45[o], 60[o], 90[o]. Each test was recorded with the camera at a constant frame rate and constant lighting conditions. The recorded images were processed through the use of the image processing software. The parameters of the test are reported. The strain map output which is obtained through strain measurement using Ncorr is validated by a) comparing the elastic properties with expected values from Classical laminate theory, b) through finite element analysis.

Keywords: composites, Ncorr, strain map, videoextensometry

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Authors: Gemma Reynolds, Deborah Bailey Rodriguez, Maria Paula Valdivieso Rueda

Abstract:

In recent years, the mental health of Higher Education (HE) students has been a growing concern. This has been further exacerbated by the stresses associated with the Covid-19 pandemic, placing students at even greater risk of developing mental health issues. Support available to students in HE tends to follow an established and traditional route. The demands for counselling services have grown, not only with the increase in student numbers but with the number of students seeking support for mental health issues. One way of improving well-being and mental health in HE students is through the use of brief interventions, such as expressive writing (EW). This intervention involves encouraging individuals to write continuously for at least 15-20 minutes for three to five sessions (often on consecutive days) about their deepest thoughts and feelings to explore significant personal experiences in a meaningful way. Given the brevity, simplicity and cost-effectiveness of EW, this intervention has considerable potential as an intervention for HE populations. The current study, therefore, employed a mixed-methods design to explore the effectiveness of EW in reducing anxiety, general stress, academic stress and depression in HE students while improving well-being. HE students at MDX were randomly assigned to one of three conditions: (1) The UniExp-EW group were required to write about their emotions and thoughts about any stressors they have faced that are directly relevant to their university experience (2) The NonUniExp-EW group were required to write about their emotions and thoughts about any stressors that are NOT directly relevant to their university experience, and (3) The Control group were required to write about how they spent their weekend, with no reference to thoughts or emotions, and without thinking about university. Participants were required to carry out the EW intervention for 15minutes per day for four consecutive days. Baseline mental health and wellbeing measures were taken before the intervention via a battery of standardised questionnaires. Following completion of the intervention on day four, participants were required to complete the questionnaires a second time and again one week later. Participants were also invited to attend focus groups to discuss their experience of the intervention. This will allow an in-depth investigation into students’ perceptions of EW as an effective intervention to determine whether they would choose to use this intervention in the future. The quantitative findings will be discussed at the conference as well as a discussion of the important implications of the findings. The study is fundamental because if EW is an effective intervention for improving mental health and well-being in HE students, its brevity and simplicity means it can be easily implemented and can be freely-available to students. Improving the mental health and well-being of HE students can have knock-on implications for improving academic skills and career development.

Keywords: mental health, wellbeing, higher education students, expressive writing

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Authors: Christoph Linse, Thomas Martinetz

Abstract:

Recent findings have shown that Neural Networks generalize also in over-parametrized regimes with zero training error. This is surprising, since it is completely against traditional machine learning wisdom. In our empirical study we fortify these findings in the domain of fine-grained image classification. We show that very large Convolutional Neural Networks with millions of weights do learn with only a handful of training samples and without image augmentation, explicit regularization or pretraining. We train the architectures ResNet018, ResNet101 and VGG19 on subsets of the difficult benchmark datasets Caltech101, CUB_200_2011, FGVCAircraft, Flowers102 and StanfordCars with 100 classes and more, perform a comprehensive comparative study and draw implications for the practical application of CNNs. Finally, we show that VGG19 with 140 million weights learns to distinguish airplanes and motorbikes with up to 95% accuracy using only 20 training samples per class.

Keywords: convolutional neural networks, fine-grained image classification, generalization, image recognition, over-parameterized, small data sets

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Authors: S. Zith Dey Babu, S. Kour, S. Verma, C. Verma, V. Pathania, A. Agrawal, V. Chaudhary, A. Manoj Puthur, R. Goyal, A. Pal, T. Danti Dey, A. Kumar, K. Wadhwa, O. Ved

Abstract:

Background: Skin cancer is now is the buzzing button in the field of medical science. The cyst's pandemic is drastically calibrating the body and well-being of the global village. Methods: The extracted image of the skin tumor cannot be used in one way for diagnosis. The stored image contains anarchies like the center. This approach will locate the forepart of an extracted appearance of skin. Partitioning image models has been presented to sort out the disturbance in the picture. Results: After completing partitioning, feature extraction has been formed by using genetic algorithm and finally, classification can be performed between the trained and test data to evaluate a large scale of an image that helps the doctors for the right prediction. To bring the improvisation of the existing system, we have set our objectives with an analysis. The efficiency of the natural selection process and the enriching histogram is essential in that respect. To reduce the false-positive rate or output, GA is performed with its accuracy. Conclusions: The objective of this task is to bring improvisation of effectiveness. GA is accomplishing its task with perfection to bring down the invalid-positive rate or outcome. The paper's mergeable portion conflicts with the composition of deep learning and medical image processing, which provides superior accuracy. Proportional types of handling create the reusability without any errors.

Keywords: computer-aided system, detection, image segmentation, morphology

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Authors: Shannon L. Stewart, Ashley Toohey, Natalia Lapshina

Abstract:

There is a well-established relationship between childhood maltreatment and negative outcomes (e.g., physical and mental health problems, social skill deficits, poor quality of life). The goal of this study was to examine the relationship between polyvictimization (multiple types of trauma) and risk of harm to self and others, taking into account possible age and sex differences. A total of 8980 children and youth were recruited from over 50 mental health facilities across Ontario, Canada. Among this sample, 29% of children and youth had experienced polyvictimization. Results showed that female children and youth who had experienced trauma were at greater risk of harm to themselves, while their male counterparts were at greater risk of harming others. Further, findings from this study highlight that experiencing polyvictimization, regardless of age or sex, increased the risk of harm to self and others. These findings add to extant literature as to the cumulative relationship between polyvictimization and risk in relation to harming oneself or others. Further, results from this study have significant implications for assessment and care-planning for those children and youth presenting with a trauma background.

Keywords: children's mental health, polyvictimization, risk of harm, sex differences

Procedia PDF Downloads 116