Analisis Algoritma Shi-Tomasi Dalam Pengujian Citra Senyum Pada Wajah Manusia

Problems in image processing to obtain the best smile are strongly influenced by the quality, background, position, and lighting, so it is very necessary to have an analysis by utilizing existing image processing algorithms to get a system that can make the best smile selection, then the Shi-Tomasi Algorithm is used. the algorithm that is commonly used to detect the corners of the smile region in facial images. The Shi-Tomasi angle calculation processes the image effectively from a target image in the edge detection ballistic test, then a corner point check is carried out on the estimation of translational parameters with a recreation test on the translational component to identify the cause of damage to the image, it is necessary to find the edge points to identify objects with remove noise in the image. The results of the test with the shi-Tomasi algorithm were used to detect a good smile from 20 samples of human facial images with each sample having 5 different smile images, with test data totaling 100 smile images, the success of the Shi-Tomasi Algorithm in detecting a good smile reached an accuracy value of 95% using the Confusion Matrix, Precision, Recall and Accuracy Methods.

SA-GAN: Stain Acclimation Generative Adversarial Network for Histopathology Image Analysis

Histopathological image analysis is an examination of tissue under a light microscope for cancerous disease diagnosis. Computer-assisted diagnosis (CAD) systems work well by diagnosing cancer from histopathology images. However, stain variability in histopathology images is inevitable due to the use of different staining processes, operator ability, and scanner specifications. These stain variations present in histopathology images affect the accuracy of the CAD systems. Various stain normalization techniques have been developed to cope with inter-variability issues, allowing standardizing the appearance of images. However, in stain normalization, these methods rely on the single reference image rather than incorporate color distributions of the entire dataset. In this paper, we design a novel machine learning-based model that takes advantage of whole dataset distributions as well as color statistics of a single target image instead of relying only on a single target image. The proposed deep model, called stain acclimation generative adversarial network (SA-GAN), consists of one generator and two discriminators. The generator maps the input images from the source domain to the target domain. Among discriminators, the first discriminator forces the generated images to maintain the color patterns as of target domain. While second discriminator forces the generated images to preserve the structure contents as of source domain. The proposed model is trained using a color attribute metric, extracted from a selected template image. Therefore, the designed model not only learns dataset-specific staining properties but also image-specific textural contents. Evaluated results on four different histopathology datasets show the efficacy of SA-GAN to acclimate stain contents and enhance the quality of normalization by obtaining the highest values of performance metrics. Additionally, the proposed method is also evaluated for multiclass cancer type classification task, showing a 6.9% improvement in accuracy on ICIAR 2018 hidden test data.

Automatic verification method of progressive image model matching information based on machine learning

In order to solve the problems of large error and low performance of traditional progressive image model matching information checking methods, an automatic progressive image model matching information checking method based on machine learning is proposed. The generation method of progressive image is analyzed, and the target image sample is obtained. On this basis, machine learning algorithm is used to segment progressive image samples. In each image segmentation part, crawler technology is used to automatically collect progressive image model matching information, and under the constraint of image model matching information checking standard, automatic checking of progressive image model matching information is realized from geometric structure, image content and other aspects. Experimental results show that the verification error of the design method is reduced by 0.687 Mb, and the quality of progressive image is improved.

Computer Vision in Analyzing the Propagation of a Gas–Gunpowder Jet

A method of mathematically processing the digital images of targets is developed. The theoretical and mathematical justification and the experimental validation of the possibility of estimating the amount of gunshot residue (GSR) and determining the GSR distribution over the target on the basis of its digital image is provided. The analysis of the optical density in selected concentric rings in the images reveals the radial dependence of soot distribution in the cross section of a gas–gunpowder jet. The analysis of the optical density in selected sectors of the circle reveals the angular dependence of the soot distribution in the gas–gunpowder jet cross section. It is shown that the integral optical density averaged over a selected area in the target image characterizes the mass of GSP deposited on it. It is possible to quantify the differences in the radial and angular distributions of the thickness of the GSR layer on various targets obtained both with the help of weapons of different types at the same distances and with the help of weapons of the same type at different distances, by calculating the distribution of optical density on their digital images.

Developmental change in children’s speech processing of auditory and visual cues: An eyetracking study

This study investigates how children aged two to eight years (N = 129) and adults (N = 29) use auditory and visual speech for word recognition. The goal was to bridge the gap between apparent successes of visual speech processing in young children in visual-looking tasks, with apparent difficulties of speech processing in older children from explicit behavioural measures. Participants were presented with familiar words in audio-visual (AV), audio-only (A-only) or visual-only (V-only) speech modalities, then presented with target and distractor images, and looking to targets was measured. Adults showed high accuracy, with slightly less target-image looking in the V-only modality. Developmentally, looking was above chance for both AV and A-only modalities, but not in the V-only modality until 6 years of age (earlier on /k/-initial words). Flexible use of visual cues for lexical access develops throughout childhood.

The Role of Emotional Content in the Control of Eye Movements

<p>The anti-saccade paradigm has been a favourite among researchers of attention and the control of eye movements. Most pro/anti-saccade studies have utilized meaningless stimuli, though stimulus meaning is known to have an impact on looking behaviour in free viewing conditions. Here, we explore the role of content in the control of pro/antisaccades by contrasting two alternative views on the impact of emotional stimuli. One view supports an "informativeness" hypothesis, where visual processing is directed towards threatening stimuli, suggesting that RT should be particularly large for negative, high arousal pictures in an antisaccade task. An alternative view emphasizes approach and withdrawal behaviours. Here negative images are thought to encourage avoidance behaviours, causing faster RTs for antisaccades; whereas positive pictures encourage approach behaviours, causing faster RTs for prosaccades. Participants performed an antisaccade task in which they were presented with an image to the left or right visual field and instructed to look at or away from the image. The experimental design included five groups of images, with a factorial combination of valence (positive or negative) and arousal (high or low), and a neutral condition. In Experiments one and two the instruction was given 200 ms before the picture was presented and did not produce any effects of emotional content. Thus, if participants are given advanced notice of the upcoming saccade, the initiation of that saccade is not influenced by the emotional content of the target image. In experiments three and four, the cue was presented 200 ms after the onset of the target image. This change of SOA provided an effect of emotional content was observed in experiments three and four which was illustrated by slowed RTs for both pro- and anti-saccades. However erotic images appeared to slow down latencies across both saccades which were accompanied by high error rates.</p>

The Role of Emotional Content in the Control of Eye Movements

<p>The anti-saccade paradigm has been a favourite among researchers of attention and the control of eye movements. Most pro/anti-saccade studies have utilized meaningless stimuli, though stimulus meaning is known to have an impact on looking behaviour in free viewing conditions. Here, we explore the role of content in the control of pro/antisaccades by contrasting two alternative views on the impact of emotional stimuli. One view supports an "informativeness" hypothesis, where visual processing is directed towards threatening stimuli, suggesting that RT should be particularly large for negative, high arousal pictures in an antisaccade task. An alternative view emphasizes approach and withdrawal behaviours. Here negative images are thought to encourage avoidance behaviours, causing faster RTs for antisaccades; whereas positive pictures encourage approach behaviours, causing faster RTs for prosaccades. Participants performed an antisaccade task in which they were presented with an image to the left or right visual field and instructed to look at or away from the image. The experimental design included five groups of images, with a factorial combination of valence (positive or negative) and arousal (high or low), and a neutral condition. In Experiments one and two the instruction was given 200 ms before the picture was presented and did not produce any effects of emotional content. Thus, if participants are given advanced notice of the upcoming saccade, the initiation of that saccade is not influenced by the emotional content of the target image. In experiments three and four, the cue was presented 200 ms after the onset of the target image. This change of SOA provided an effect of emotional content was observed in experiments three and four which was illustrated by slowed RTs for both pro- and anti-saccades. However erotic images appeared to slow down latencies across both saccades which were accompanied by high error rates.</p>

Magnetic Resonance Imaging Segmentation on the Basis of Boundary Tracking Algorithm in Lung Cancer Surgery

This work was to study the guiding value of magnetic resonance imaging (MRI) based on the target region boundary tracking algorithm in lung cancer surgery. In this study, the traditional boundary tracking algorithm was optimized, and the target neighborhood point boundary tracking method was proposed. The iterative method was used to binarize the lung MRI image, which was applied to the MRI images of 50 lung cancer patients in hospital. The patients were divided into two groups as the progression-free survival (PFS) and overall survival (OS) of surgical treatment group (experimental group, n = 25) and nonsurgical treatment group (control group, n = 25). The experimental group received surgical resection, while the control group received systemic chemotherapy. The results showed that the traditional boundary tracking algorithm needed to manually rejudge whether the concave and convex parts of the image were missing. The target boundary tracking algorithm can effectively avoid the leakage of concave and convex parts and accurately locate the target image contour, fast operation, without manual intervention. The PFS time of the experimental group (325 days) was significantly higher than that of the control group (186 days) P < 0.05 . The OS time of the experimental group (697 days) was significantly higher than that of the control group (428 days) P < 0.05 . Fisher exact probability method was used to test the total survival time of patients in the two groups, and the tumor classification and treatment group had significant influence on the OS time P < 0.05 . The target boundary tracking algorithm in this study can effectively locate the contour of the target image, and the operation speed was fast. Surgical resection of lung cancer can improve the PFS and OS of patients.

StainNet: A Fast and Robust Stain Normalization Network

Stain normalization often refers to transferring the color distribution to the target image and has been widely used in biomedical image analysis. The conventional stain normalization usually achieves through a pixel-by-pixel color mapping model, which depends on one reference image, and it is hard to achieve accurately the style transformation between image datasets. In principle, this difficulty can be well-solved by deep learning-based methods, whereas, its complicated structure results in low computational efficiency and artifacts in the style transformation, which has restricted the practical application. Here, we use distillation learning to reduce the complexity of deep learning methods and a fast and robust network called StainNet to learn the color mapping between the source image and the target image. StainNet can learn the color mapping relationship from a whole dataset and adjust the color value in a pixel-to-pixel manner. The pixel-to-pixel manner restricts the network size and avoids artifacts in the style transformation. The results on the cytopathology and histopathology datasets show that StainNet can achieve comparable performance to the deep learning-based methods. Computation results demonstrate StainNet is more than 40 times faster than StainGAN and can normalize a 100,000 × 100,000 whole slide image in 40 s.

Intelligent Driving Detection System Based on unmanned Driving Background

Unmanned driving is the future development direction of the automobile industry, and intelligent driving detection is one of the important research topics. In order to improve driverless technology and traffic safety, this design is based on FPGA technology to design a driving detection system, combined with off-chip SDRAM’s high-speed, large-capacity cache and image processing related algorithms, and it will achieve the function of acquiring target image, cache, detection and display. Finally, the FPGA development board is used to test the driving detection system, which demonstrates that this driving detection system can better improve the safety monitoring problems in unmanned driving. At the same time, the system’s frame rate is close to 55pfs, which has practical significance and application value.