An Android based Mobile Application for Color Recognition Assistance for Colorblind Individual Through Color Segmentation Using Color Threshold Algorithm

In the traditional setting, the only way to identify color-blindness is if an individual has a severe color deficiency which make it easy to identify due to individuals have difficulty in identifying colours. However, this is not the case for those suffering from mild color-blindness, as the only way to ensure that a person is truly color-blind with a mild case is to have a check up with an eye care professional. The proponents developed an Android mobile application that provides an Ishihara test, a standardized eye test which allows users to take a color-blind test to determine if an individual is color-blind. Existing adaptation tools, the color-blind uses an eyeglass or contact lenses modified for color vision deficiency to help distinguish colors. These types of products. Due to limited resources of supply in the Philippines, specialized type of eyeglasses is not readily available and there are people that cannot afford or are reluctant to purchase this type of device. The researchers developed an Android mobile application that aids with color-blind people using color segmentation and the Color Thresholding Algorithm

Computer Vision Based Early Intraocular Pressure Assessment From Frontal Eye Images

Intraocular Pressure (IOP) in general, refers to the pressure in the eyes. Gradual increase of IOP and high IOP are conditions or symptoms that may lead to certain diseases such as glaucoma, and therefore, must be closely monitored. While the pressure in the eye increases, different parts of the eye may become affected until the eye parts are damaged. An effective way to prevent rise in eye pressure is by early detection. Exiting IOP monitoring tools include eye tests at clinical facilities and computer-aided techniques from fundus and optic nerves images. In this work, a new computer vision-based smart healthcare framework is presented to evaluate the intraocular pressure risk from frontal eye images early-on. The framework determines the status of IOP by analyzing frontal eye images using image processing and machine learning techniques. A database of images from the Princess Basma Hospital was used in this work. The database contains 400 eye images; 200 images with normal IOP and 200 high eye pressure case images. This study proposes novel features for IOP determination from two experiments. The first experiment extracts the sclera using circular hough transform, after which four features are extracted from the whole sclera. These features are mean redness level, red area percentage, contour area and contour height. The pupil/iris diameter ratio feature is also extracted from the frontal eye image after a series of pre-processing techniques. The second experiment extracts the sclera and iris segment using a fully conventional neural network technique, after which six features are extracted from only part of the segmented sclera and iris. The features include mean redness level, red area percentage, contour area, contour distance and contour angle along with the pupil/iris diameter ratio. Once the features are extracted, classification techniques are applied in order to train and test the images and features to obtain the status of the patients in terms of eye pressure. For the first experiment, neural network and support vector machine algorithms were adopted in order to detect the status of intraocular pressure. The second experiment adopted support vector machine and decision tree algorithms to detect the status of intraocular pressure. For both experiments, the framework detects the status of IOP (normal or high IOP) with high accuracies. This computer vison-based approach produces evidence of the relationship between the extracted frontal eye image features and IOP, which has not been previously investigated through automated image processing and machine learning techniques from frontal eye images

Enhancing camera surveillance using computer vision: a research note

$\mathbf{Purpose}$ - The growth of police operated surveillance cameras has out-paced the ability of humans to monitor them effectively. Computer vision is a possible solution. An ongoing research project on the application of computer vision within a municipal police department is described. The paper aims to discuss these issues. $\mathbf{Design/methodology/approach}$ - Following the demystification of computer vision technology, its potential for police agencies is developed within a focus on computer vision as a solution for two common surveillance camera tasks (live monitoring of multiple surveillance cameras and summarizing archived video files). Three unaddressed research questions (can specialized computer vision applications for law enforcement be developed at this time, how will computer vision be utilized within existing public safety camera monitoring rooms, and what are the system-wide impacts of a computer vision capability on local criminal justice systems) are considered. $\mathbf{Findings}$ - Despite computer vision becoming accessible to law enforcement agencies the impact of computer vision has not been discussed or adequately researched. There is little knowledge of computer vision or its potential in the field. $\mathbf{Originality/value}$ - This paper introduces and discusses computer vision from a law enforcement perspective and will be valuable to police personnel tasked with monitoring large camera networks and considering computer vision as a system upgrade

Image Content Enhancement Through Salient Regions Segmentation for People With Color Vision Deficiencies

Color vision deficiencies affect visual perception of colors and, more generally, color images. Several sciences such as genetics, biology, medicine, and computer vision are involved in studying and analyzing vision deficiencies. As we know from visual saliency findings, human visual system tends to fix some specific points and regions of the image in the first seconds of observation summing up the most important and meaningful parts of the scene. In this article, we provide some studies about human visual system behavior differences between normal and color vision-deficient visual systems. We eye-tracked the human fixations in first 3 seconds of observation of color images to build real fixation point maps. One of our contributions is to detect the main differences between the aforementioned human visual systems related to color vision deficiencies by analyzing real fixation maps among people with and without color vision deficiencies. Another contribution is to provide a method to enhance color regions of the image by using a detailed color mapping of the segmented salient regions of the given image. The segmentation is performed by using the difference between the original input image and the corresponding color blind altered image. A second eye-tracking of color blind people with the images enhanced by using recoloring of segmented salient regions reveals that the real fixation points are then more coherent (up to 10%) with the normal visual system. The eye-tracking data collected during our experiments are in a publicly available dataset called Eye-Tracking of Color Vision Deficiencies

Live Video and Image Recolouring for Colour Vision Deficient Patients

Colour Vision Deficiency (CVD) is an important issue for a significant population across the globe. There are several types of CVD\u27s, such as monochromacy, dichromacy, trichromacy, and anomalous trichromacy. Each of these categories contain specific other subtypes. The aim of this research is to device a scheme to address CVD by using variations in pixel plotting of colours to capture colour disparities and perform colour compensation. The proposed scheme recolours the video and images by colour contrast variation of each colour for CVD patients, and depending on the type of deficiency, it is able to provide live results. Different types of CVD’s can be identified and cured by changing the particular colour related to it and based upon the type of diseases, it performs RGB (Red, Green, and Blue) to LMS (Long, Medium, and Short) transformation. This helps in colour identification and also adjustments of colour contrasts. The processing and rendering of recoloured video and images, allows the affected patients with CVD to see perfect shades in the recoloured frames of video or images and other modes of files. In this thesis, we propose an efficient recolouring algorithm with a strong focus on real-time applications that is capable of providing different recoloured outputs based on specific types of CVD

WormAssay: A Novel Computer Application for Whole-Plate Motion-based Screening of Macroscopic Parasites

Lymphatic filariasis is caused by filarial nematode parasites, including Brugia malayi. Adult worms live in the lymphatic system and cause a strong immune reaction that leads to the obstruction of lymph vessels and swelling of the extremities. Chronic disease leads to the painful and disfiguring condition known as elephantiasis. Current drug therapy is effective against the microfilariae (larval stage) of the parasite, but no drugs are effective against the adult worms. One of the major stumbling blocks toward developing effective macrofilaricides to kill the adult worms is the lack of a high throughput screening method for candidate drugs. Current methods utilize systems that measure one well at a time and are time consuming and often expensive. We have developed a low-cost and simple visual imaging system to automate and quantify screening entire plates based on parasite movement. This system can be applied to the study of many macroparasites as well as other macroscopic organisms