Compared to traditional integrated imaging, the one-dimensional integrated imaging system abandons the stereoscopic effect in the vertical direction, leaving only the stereoscopic effect in the horizontal direction. While satisfying the stereoscopic perception, it greatly reduces the storage space of data and alleviates the large attenuation of resolution caused by integrated imaging. However, at present, there is no 3D film source suitable for a one-dimensional integrated imaging system. In order to address this problem, we propose an array image generation and padding algorithm based on a one-dimensional integrated imaging system. First, based on the theory of geometric optics and DIBR(Depth-image Based Rendering), we use depth maps to simulate viewpoint images at arbitrary locations. In the process, we classify the points mapped to the viewpoint image to make the generated viewpoint image more accurate. Secondly, when conducting hole filling, we first use the optical flow method to fill the large holes inside the image. We extract the edge of the hole, compare the depth values on both sides of the edge, and estimate the depth value of the hole by taking the optical flow value on the larger side, so as to calculate the mapping block of the hole on the reference frame. Finally, the other holes are filled using the Criminisi image restoration algorithm.
This paper introduces an algorithm to solve the the anomaly behavior detection problem of surveillance video through an improved autoencoder with multimodal inputs. Using 3D convolution and 3D deconvolution, and the decoder adds a feature map corresponding to the encoder on a specific layer to enhance the image detail information. Taking the RGB frame and the optical flow as inputs, abnormality scores are calculated according to the reconstruction error for locating the abnormal segment. Experiments conducted in the CUHK Avenue dataset, the UCSD Pedestrian dataset and the Behave dataset, our approach works best compare to the original approach. While improving the AUC, due to the use of unsupervised learning, a lot of labeling time is saved, which is more in line with the diversity and contingency of abnormal behavior in real life.
An eye state analysis (i.e., open or closed) is an important step in the detection of fatigue driving. In this paper, a weighted color difference matrix algorithm is proposed for analyzing a driver’s eye state. First, an image of the driver’s eye is obtained from a face detection database. Two feature images are then constructed for the eye image, which are each gray-scale normalized. The feature image is then projected into a block feature matrix, and the feature value is calculated to construct a feature vector. Finally, a support vector machine is used to train and classify the extracted eigenvectors, and the state of the driver’s eye is judged to further analyze the driver’s state of fatigue. To evaluate the performance of the proposed algorithm, experiments are conducted with several publicly available databases, showing that our algorithm is efficient and reasonable.
We present the discrete separable shearlet transform (DSST) separability assessment system for recognizing facial expressions. DSST is an image multiscale geometric analysis method. We use a separability assessment to evaluate the separability of different scales and directions of the coefficients after DSST transformation. First, all test and training images are normalized and equalized. Then, all preprocessed images are DSST-transformed, and all low- and high-frequency coefficients are obtained. Next, the separability of different scales and directions of the coefficients is evaluated, and we use only those that have a large separability index. Then, we combine the low- and high-frequency coefficients for the best separability direction and scale as the extracted features. Finally, we use a support vector machine to classify seven expressions (i.e., happiness, sadness, surprise, disgust, fear, anger, and neutrality) from the Japanese Female Facial Expression, Extended Cohn–Kanade, MMI, and Psychological Image Collection at Stirling datasets. The experimental results show that the recognition rate of the proposed method is better than those of state-of-the-art methods.
Eye and mouth state analysis is an important step in fatigue detection. An algorithm that analyzes the state of the eye and mouth by extracting contour features is proposed. First, the face area is detected in the acquired image database. Then, the eyes are located by an EyeMap algorithm through a clustering method to extract the sclera-fitting eye contour and calculate the contour aspect ratio. In addition, an effective algorithm is proposed to solve the problem of contour fitting when the human eye is affected by strabismus. Meanwhile, the value of chromatism s is defined in the RGB space, and the mouth is accurately located through lip segmentation. Based on the color difference of the lip, skin, and internal mouth, the internal mouth contour can be fitted to analyze the opening state of mouth; at the same time, another unique and effective yawning judgment mechanism is considered to determine whether the driver is tired. This paper is based on the three different databases to evaluate the performance of the proposed algorithm, and it does not need training with high calculation efficiency.