Mango Leaf Disease Recognition and Classification Using Novel Segmentation and Vein Pattern Technique

Mango fruit is in high demand. So, the timely control of mango plant diseases is necessary to gain high returns. Automated recognition of mango plant leaf diseases is still a challenge as manual disease detection is not a feasible choice in this computerized era due to its high cost and the non-availability of mango experts and the variations in the symptoms. Amongst all the challenges, the segmentation of diseased parts is a big issue, being the pre-requisite for correct recognition and identification. For this purpose, a novel segmentation approach is proposed in this study to segment the diseased part by considering the vein pattern of the leaf. This leaf vein-seg approach segments the vein pattern of the leaf. Afterward, features are extracted and fused using canonical correlation analysis (CCA)-based fusion. As a final identification step, a cubic support vector machine (SVM) is implemented to validate the results. The highest accuracy achieved by this proposed model is 95.5%, which proves that the proposed model is very helpful to mango plant growers for the timely recognition and identification of diseases.

Patient Data Security System Using Finger Vein Authentication

The technology of using finger vein patterns is a biometric system that has a high level of security. By using the identification of blood vessels found on the human finger, when this data is needed it can be accessed immediately, and there is no possibility of being lost or forgotten. The condition of the blood vessels in the human body, precisely in the fat tissue, also makes it difficult for data to be stolen. A person's health data is quite crucial data. So we need a good security system. By using this blood vessel authentication, the data can only be accessed by the person concerned. With a finger vein pattern that is not easy to duplicate so it is suitable for creating a security system. In this research, we take advantage of the advantages of the finished vein pattern to create a patient data security system in the hospital. The system is made by utilizing one of the algorithms of an artificial neural network. This algorithm is oriented towards changing the value of weights and biases in the training process. From the training process, a model of an artificial neural network system is generated. The best labeling is for one finger vein identity, one label. The average time required for image recognition is 2.4 seconds. The best rejection result is 100% and the best acceptance is 81.67%.

Finger Vein Image Enhancement Based on Guided Tri-Gaussian Filters

In the process of image acquisition, the contrast between veins and non-veins in finger vein images is not high due to the influence of the fuzzy light source, skin scattering and finger movement. To solve this problem, a finger vein image enhancement method is proposed (GTGFs), which enhances finger vein patterns by setting guided image as input image firstly. On this basis, the tri-Gaussian model is based on disinhibitory properties of the concentric receptive field used to locally enhancing the image. The parameters of the tri-Gaussian model are determined based on the finger vein width information. The experiment results show that the proposed enhancement method can significantly enhance the finger vein patterns and improve the recognition effect of the methods based on vein pattern segmentation.

A Novel Finger Vein Recognition Method Based on Aggregation of Radon-Like Features

Finger vein (FV) biometrics is one of the most promising individual recognition traits, which has the capabilities of uniqueness, anti-forgery, and bio-assay, etc. However, due to the restricts of imaging environments, the acquired FV images are easily degraded to low-contrast, blur, as well as serious noise disturbance. Therefore, how to extract more efficient and robust features from these low-quality FV images, remains to be addressed. In this paper, a novel feature extraction method of FV images is presented, which combines curvature and radon-like features (RLF). First, an enhanced vein pattern image is obtained by calculating the mean curvature of each pixel in the original FV image. Then, a specific implementation of RLF is developed and performed on the previously obtained vein pattern image, which can effectively aggregate the dispersed spatial information around the vein structures, thus highlight vein patterns and suppress spurious non-boundary responses and noises. Finally, a smoother vein structure image is obtained for subsequent matching and verification. Compared with the existing curvature-based recognition methods, the proposed method can not only preserve the inherent vein patterns, but also eliminate most of the pseudo vein information, so as to restore more smoothing and genuine vein structure information. In order to assess the performance of our proposed RLF-based method, we conducted comprehensive experiments on three public FV databases and a self-built FV database (which contains 37,080 samples that derived from 1030 individuals). The experimental results denoted that RLF-based feature extraction method can obtain more complete and continuous vein patterns, as well as better recognition accuracy.

Performance Evaluation of Hardware Designs, Thinning, and Segmentation Algorithms in M-Health Environments

In this chapter, the authors have described the experimental analysis steps required for converting original veins images into thinned veins images by applying resample, segmentation, filtering, and thinning algorithms in the cloud IoT-based m-health environments. It is a little bit difficult to make a distinction between the vein pattern and the surroundings particularly in the cases of unclear and thin veins images. However, after applying the resample, segmentation, median filters, and thinning algorithms in the cloud IoT-based m-health environment, the superior quality veins image patterns of a single line are obtained.