Search

The main purpose of this study is to observe the importance of machine vision (MV) approach for the identification of five types of skin cancers, namely, actinic-keratosis, benign, solar-lentigo, malignant, and nevus. The 1000 (200 × 5) benchmark image datasets of skin cancers are collected from the International Skin Imaging Collaboration (ISIC). The acquired ISIC image datasets were transformed into texture feature dataset that was a combination of first-order histogram and gray level co-occurrence matrix (GLCM) features. For the skin cancer image, a total of 137,400 (229 × 3 x 200) texture features were acquired on three nonover-lapping regions of interest (ROIs). Principal component analysis (PCA) clustering approach was employed for reducing the dimension of feature dataset. Each image acquired twenty most discriminate features based on two different approaches of statistical features such as average correlation coefficient plus probability of error (ACC + POE) and Fisher (Fis). Furthermore, a correlation-based feature selection (CFS) approach was employed for feature reduction, and optimized 12 features were acquired. Furthermore, a classification algorithm naive bayes (NB), Bayes Net (BN), LMT Tree, and multilayer perception (MLP) using 10 K-fold cross-validation approach were employed on optimized feature datasets and the overall accuracy achieved by MLP is 97.1333%.

This paper describes a design of fast recognition of road information based on mobile terminal. Firstly, based on the HOG algorithm, we study and verify the effects of different parameters on the performance of the algorithm. Secondly, we test 800 images randomly selected from the INRIA pedestrian dataset to obtain the optimal parameters for the mobile terminal and the proportion of video resolution and detection window. Then, under the same test conditions, the time overheads of the SVMLight and the LibSVM are recorded and SVMLight training time is significantly less than LibSVM. Thirdly, we design and implement a real-time road information recognition and warning system on the Windows platform and Android platform. Features include real-time pedestrians detection, voice warning, and road signs recognition. When the vehicle speed is less than 30 km/h, the video resolution is less than 720 × 576 and the detection window/image ratio is less than 1 : 50; the system can guarantee low delay and high recognition rate (97.2%).