Detection of fingertips based on the combination of color information and circle detection

Fingertips of human hand play an important role in hand-based interaction with computers. Therefore, identification of fingertips' positions on hand image is vital for developing a human computer interaction system. All most all of the research works for fingertips detection, initially isolate hand image from the background image. Most of these techniques develop color based segmentation methods because human skin color possess an exceptional characterises that can be used to isolate hand from the rest of the image quite easily. Sometimes color image segmentation becomes difficult due to illumination and background variations. To make it simple and reliable, this paper proposes a robust method for detecting fingertips of a hand image based on the combination of color segmentation and circle detection. Due to the characteristics of circularity of fingertips regions of hand boundary, any existing circle detection algorithms can be applied to detect circles at fingertips region. It is difficult to detect fingertips solely based on the circle detection method. For this reason, initially the proposed method detects all the circular regions on the image applying Circle Hough Transformation (CHT) then the fingertips are selected based on the color characteristics of the fingertips regions. Experimental results show that the proposed approach is promising.

A new color space based on K-medoids clustering for fire detection

Pixel color has proven to be a useful and robust cue for detection of most objects of interest like fire. In this paper, a hybrid intelligent algorithm is proposed to detect fire pixels in the background of an image. The proposed algorithm is introduced by the combination of a computational search method based on a swarm intelligence technique and the Kemdoids clustering method in order to form a Fire-based Color Space (FCS), in fact, the new technique converts RGB color system to FCS through a 3*3 matrix. This algorithm consists of five main stages:(1) extracting fire and non-fire pixels manually from the original image. (2) using K-medoids clustering to find a Cost function to minimize the error value. (3) applying Particle Swarm Optimization (PSO) to search and find the best W components in order to minimize the fitness function. (4) reporting the best matrix including feature weights, and utilizing this matrix to convert the all original images in the database to the new color space. (5) using Otsu threshold technique to binarize the final images. As compared with some state-of-the-art techniques, the experimental results show the ability and efficiency of the new method to detect fire pixels in color images.

The use of auricular examination for screening heaptic disorders

Researches on auricular acupuncture (AA) have examined mainly its treatment effects. This study aimed to investigate the accuracy and precision of using auricular examination (AE) as a complementary diagnostic tool for screening hepatic disorders. Twenty patients suffering from liver dysfunction and 25 controls aged 18–60 years were recruited from an acute hospital. Participants were examined using three AE methods including visual inspection, electrical skin resistance measurement, and tenderness testing on the liver AA zone of both ears. Significant differences were found in visual inspection and electrical skin resistance on the AA zones between the two groups. Patients suffering from liver dysfunction tended to have at least one abnormality in skin color, appearance, presence of papules, abundance of capillary and desquamation on the ear (Relative Risk—Right ear: RR = 2.9, 95% confidence interval (CI) 1.4, 6.2; Left: RR = 1.8, 95% CI, 1.01, 3.1). The sensitivity for visual inspection was 0.7 for both ears; specificity was 0.76 for the (R) and 0.6 for the (L) ear. The mean difference in electrical skin resistance was 4.3 MΩ (95% CI, 1.7, 6.9) for the (L) ear; 4.5 MΩ (95% CI, 1.5, 7.6) for the (R) ear. Our results suggest that malfunction of the liver appeared to be reflected by the presence of morphological changes on the liver AA zone. Visual inspection and electrical skin resistance on the liver AA zone are potentially sensitive to screen hepatic disorders.

Age, class and race discrimination: their interactions and associations with mental health among Brazilian university students

Although research on discrimination and health has progressed significantly, it has tended to focus on racial discrimination and US populations. This study explored different types of discrimination, their interactions and associations with common mental disorders among Brazilian university students, in Rio de Janeiro in 2010. Associations between discrimination and common mental disorders were examined using multiple logistic regression models, adjusted for confounders. Interactions between discrimination and socio-demographics were tested. Discrimination attributed to age, class and skin color/race were the most frequently reported. In a fully adjusted model, discrimination attributed to skin color/race and class were both independently associated with increased odds of common mental disorders. The simultaneous reporting of skin color/race, class and age discrimination was associated with the highest odds ratio. No significant interactions were found. Skin color/race and class discrimination were important, but their simultaneous reporting, in conjunction with age discrimination, were associated with the highest occurrence of common mental disorders.

Fuzzy clustering of color and texture features for image segmentation: a study on satellite image retrieval

In this paper, a new image segmentation approach that integrates color and texture features using the fuzzy c-means clustering algorithm is described. To demonstrate the applicability of the proposed approach to satellite image retrieval, an interactive region-based image query system is designed and developed. A database comprising 400 multispectral satellite images is used to evaluate the performance of the system. The results are analyzed and discussed, and a performance comparison with other methods is included. The outcomes reveal that the proposed approach is able to improve the quality of the segmentation results as well as the retrieval performance.

Factors influencing skin colour in cultured Australian snapper Pagrus auratus

Sea cage production of Australian snapper has been largely constrained during the past decade by abnormal skin pigmentation which has negatively impacted marketability. The research described in this study identified dietary, environmental and harvesting techniques to successfully alter skin colour to potentially improve the viability of snapper aquaculture in Australia.

Hybrid image segmentation using fuzzy clustering algorithms satellite imagery searching and retrieval

Satellite image processing is a complex task that has received considerable attention from many researchers. In this paper, an interactive image query system for satellite imagery searching and retrieval is proposed. Like most image retrieval systems, extraction of image features is the most important step that has a great impact on the retrieval performance. Thus, a new technique that fuses color and texture features for segmentation is introduced. Applicability of the proposed technique is assessed using a database containing multispectral satellite imagery. The experiments demonstrate that the proposed segmentation technique is able to improve quality of the segmentation results as well as the retrieval performance.

A fuzzy clustering approach to content-based image retrieval

In this study, an interactive Content-Based Image Retrieval (CBIR) system that allows searching and retrieving images from databases is designed and developed. Based on the fuzzy c-means clustering algorithm, the CBIR system fuses color and texture features in image segmentation. A technique to form compound queries based on the combined features of different images is devised. This technique allows users to have a better control on the search criteria, thus a higher retrieval performance can be achieved. A database consisting of skin cancer imagery is used to demonstrate the applicability of the CBIR system.

Developing an empirical algorithm for protecting text-based CAPTCHAs against segmentation attacks

In this paper, we aim to provide an effective and efficient method to generate text-based Captchas which are resilient against segmentation attack. Different to the popular industry practice of using very simple color schemes, we advocate to use multiple colors in our Captchas. We adopt the idea of brush and canvas when coloring our Captchas. Furthermore, we choose to use simple accumulating functions to achieve diffusion on painted colors and DES encryption to achieve a good level of confusion on the brush pattern. To facilitate ordinary users and developers, we propose an empirical algorithm with support of Taguchi method to guarantee the quality of the chosen color schemes. Our proposed methodology has at least three advantages — 1) the settings of color schemes can be fully customized by the user or developer; 2) the quality of selected colors have desirable statistical features that are ensured by Taguchi method; 3) the algorithm can be fully automated into computer programs. Moreover, our included examples and experiments prove the practicality and validity of our algorithm.