Vehicle Type Classification using Hybrid Features and a Deep Neural Network

Currently, considerable research has been done in vehicle type classification, especially due to the success of deep learning in many image classification problems. In this research, a system incorporating hybrid features is proposed to improve the performance of vehicle type classification. The feature vectors are extracted from the pre-processed images using Gabor features, a histogram of oriented gradients and a local optimal oriented pattern. The hybrid set of features contains complementary information that could help discriminate between the classes better, further, an ant colony optimizer is utilized to reduce the dimension of the extracted feature vectors. Finally, a deep neural network is used to classify the types of vehicles in the images. The proposed approach was tested on the MIO vision traffic camera dataset and another more challenging real-world dataset consisting of videos of multiple lanes of a toll plaza. The proposed model showed an improvement in accuracy ranging from 0.28% to 8.68% in the MIO TCD dataset when compared to well-known neural network architectures.

Common Gabor Features for Image Watermarking Identification

Image watermarking is one of many methods for preventing unauthorized alterations to digital images. The major goal of the research is to find and identify photos that include a watermark, regardless of the method used to add the watermark or the shape of the watermark. As a result, this study advocated using the best Gabor features and classifiers to improve the accuracy of image watermarking identification. As classifiers, discriminant analysis (DA) and random forests are used. The DA and random forest use mean squared energy feature, mean amplitude feature, and combined feature vector as inputs for classification. The performance of the classifiers is evaluated using a variety of feature sets, and the best results are achieved. In order to assess the performance of the proposed method, we use a public database. VOC2008 is a public database that we use. The findings reveal that our proposed method’s DA classifier with integrated features had the greatest TPR of 93.71 and the lowest FNR of 6.29. This shows that the performance outcomes of the proposed approach are consistent. The proposed method has the advantages of being able to find images with the watermark in any database and not requiring a specific type or algorithm for embedding the watermark.

Shared and Independent Neural Representation Between Visual Perception and Mental Imagery

Visual mental imagery and visual perception have been shown to share a hierarchical topological visual structure of neural representation. Meanwhile, many studies have reported a dissociation of neural substrate between mental imagery and perception in function and structure. However, we have limited knowledge about how the visual hierarchical cortex involved into internally generated mental imagery and perception with visual input. Here we used a dataset from previous fMRI research (Horikawa & Kamitani, 2017), which included a visual perception and an imagery experiment with human participants. We trained two types of voxel-wise encoding models, based on Gabor features and activity patterns of high visual areas, to predict activity in the early visual cortex (EVC, i.e., V1, V2, V3) during perception, and then evaluated the performance of these models during mental imagery. Our results showed that during perception and imagery, activities in the EVC could be independently predicted by the Gabor features and activity of high visual areas via encoding models, which suggested that perception and imagery might share neural representation in the EVC. We further found that there existed a Gabor-specific and a non-Gabor-specific neural response pattern to stimuli in the EVC, which were shared by perception and imagery. These findings provide insight into mechanisms of how visual perception and imagery shared representation in the EVC.

Deep Learning for Face Recognition under Complex Illumination Conditions Based on Log-Gabor and LBP

The complex light conditions, and this is one of the most important and difficult problems in practical face recognition, in this paper, we propose a new deep learning-based method to solve the problem of the effect of the light of the changes in the facial recognition process. First, the primary treatment of the lighting can be used to improve the negative effects of intensive changes in the lighting of a photo of a face, and for a second, the Log-Gabor filters in order to get the images used in the Log-Gabor features at different scales and in different directions, and then, the LBP (Local Binary Pattern) features on the image subblock is obtained. Finally, the histogram of the texture of the features of the formation and the visual layer of the deep belief network (DBN) to drop, and then the classification and the recognition is done with a deep-learning-DBN. The experimental results show that superior performance can be obtained in the application of the strategy in comparison to some of the modern technology.