Head Pose Estimation (HPE) has a wide range of applications in computer vision, but still faces challenges: (1) Existing studies commonly use Euler angles or quaternions as pose labels, which may lead to discontinuity problems. (2) HPE does not effectively address regression via rotated matrices. (3) There is a low recognition rate in complex scenes, high computational requirements, etc. This paper presents an improved unconstrained HPE model to address these challenges. First, a rotation matrix form is introduced to solve the problem of unclear rotation labels. Second, a continuous 6D rotation matrix representation is used for efficient and robust direct regression. The RepVGG-A2 lightweight framework is used for feature extraction, and by adding a multi-level feature fusion module and a coordinate attention mechanism with residual connection, to improve the network’s ability to perceive contextual information and pay attention to features. The model’s accuracy was further improved by replacing the network activation function and improving the loss function. Experiments on the BIWI dataset 7:3 dividing the training and test sets show that the average absolute error of HPE for the proposed network model is 2.41. Trained on the dataset 300W_LP and tested on the AFLW2000 and BIWI datasets, the average absolute errors of HPE of the proposed network model are 4.34 and 3.93. The experimental results demonstrate that the improved network has better HPE performance.

Face recognition is widely used and is one of the most challenging tasks in computer vision. In recent years, many face recognition methods based on dictionary learning have been proposed. However, most methods only focus on the resolution of the original image, and the change of resolution may affect the recognition results when dealing with practical problems. Aiming at the above problems, a method of multi-resolution dictionary learning combined with sample reverse representation is proposed and applied to face recognition. First, the dictionaries associated with multiple resolution images are learnt to obtain the first representation error. Then different auxiliary samples are generated for each test sample, and a dictionary consisted of test sample, auxiliary samples, and other classes of training samples is established to sequentially represent all training samples at this resolution, and to obtain the second representation error. Finally, a weighted fusion scheme is used to obtain the ultimate classification result. Experimental results on four widely used face datasets show that the proposed method achieves better performance and is effective for resolution change.

The pulse-coupled neural network (PCNN) model is a third-generation artificial neural network without training that uses the synchronous pulse bursts of neurons to process digital images, but the lack of in-depth theoretical research limits its extensive application. By analyzing the working mechanism of the PCNN, we present an expression for the fire-extinguishing time of neurons that fire in the second iteration and an expression for the firing time of neurons that extinguish in the second iteration. In addition, we find a phenomenon of the PCNN and name it mathematically coupled fire extinguishing. Based on the above analysis, we propose a new working mode for the PCNN, where the refiring of fire-extinguishing neurons is only allowed when all firing neurons are extinguished. We also work out the constraint conditions of the parameter settings under this mode. Furthermore, we analyze the relationship between the network parameters and mathematically coupled fire extinguishing, the coupling of neighboring neurons, and the convergence rate of the PCNN, respectively. In addition, we demonstrate the essential regularity of extinguished neuron in the PCNN and then propose an optimal parameter setting to achieve the best comprehensive performance of the PCNN.

In many registration problems of an aerial video images, computational complexity and precision is of critical importance. In this paper an new aerial video images registration algorithm based on optimal derivative filters with Scene-Adaptive Corners is proposed. Firstly, the Harris detector based on optimal derivative filters is presented and scene-adaptation is used to control the number of feature points, Then through comparing the euclidean distance of the SURF(speed up robust feature) descriptors defined on the corner neighborhoods, the corresponding matches are established. Lastly, the transformation parameters are estimated using the invariant of five coplanar points, then the most "useful" matching points are used to register the frames. Experiment results illustrate that the proposed algorithm carries out accurate image registration and is robust to large image translation, scaling and rotation.