Light field (LF) depth estimation plays a crucial role in many LF-based applications. Existing LF depth estimation methods consider depth estimation as a regression problem, where a pixel-wise L1 loss is employed to supervise the training process. However, the disparity map is only a sub-space projection (i.e., an expectation) of the disparity distribution, which is essential for models to learn. In this paper, we propose a simple yet effective method to learn the sub-pixel disparity distribution by fully utilizing the power of deep networks, especially for LF of narrow baselines. We construct the cost volume at the sub-pixel level to produce a finer disparity distribution and design an uncertainty-aware focal loss to supervise the predicted disparity distribution toward the ground truth. Extensive experimental results demonstrate the effectiveness of our method. Our method significantly outperforms recent state-of-the-art LF depth algorithms on the HCI 4D LF Benchmark in terms of all four accuracy metrics (i.e., BadPix 0.01, BadPix 0.03, BadPix 0.07, and MSE ×100). The code and model of the proposed method are available at https://github.com/chaowentao/SubFocal .

Facial expressions are an essential part of human emotional communication, and micro-expressions (MEs), as transient and imperceptible non-verbal signals, can potentially reveal real human emotions. However, subtle motion variations, limited and unbalanced samples make micro-expression recognition (MER) challenging. In this paper, we design a novel dual-branch learning framework of multi-level flow-driven attention for micro-expression recognition (MFDAN), which innovatively integrates optical flow prior to guide the attention learning in the image encoding branch, enabling the model to focus on the most discriminative facial regions for subtle motion patterns. Firstly, we extract optical flow information by an optical flow encoding module. Then, in the image coding module, we construct a Transformer structure containing an optical flow-driven attention mechanism, which can effectively locate the interest region of micro-expressions in the image according to the position information of optical flow to capture more sensitive and fine-grained micro-expressions. By interoperating prior knowledge with data learning, and introducing the Dropkey operation and Focal Loss, our method can handle subtle micro-expression features on small imbalanced datasets. Through extensive experiments on three independent datasets and a composite database, including SMIC-HS, SAMM, and CASME II, robust leave-one-subject-out (LOSO) evaluation results show that our method outperforms state-of-the-art methods especially on the composite database.

Sex determination from skeletons is an important research subject in forensic medicine. Previous skeletal sex assessments are through subjective visual analysis by anthropologists or metric analysis of sexually dimorphic features. In this work, we present an automatic sex determination method for 3D digital skulls, in which a statistical shape model for skulls is constructed, which projects the high-dimensional skull data into a low-dimensional shape space, and Fisher discriminant analysis is used to classify skulls in the shape space. This method combines the advantages of metrical and morphological methods. It is easy to use without professional qualification and tedious manual measurement. With a group of Chinese skulls including 127 males and 81 females, we choose 92 males and 58 females to establish the discriminant model and validate the model with the other skulls. The correct rate is 95.7% and 91.4% for females and males, respectively. Leave-one-out test also shows that the method has a high accuracy.

This paper proposes a vessel active contour model based on local intensity weighting and a vessel vector field. Firstly, the energy function we define is evaluated along the evolving curve instead of all image points, and the function value at each point on the curve is based on the interior and exterior weighted means in a local neighborhood of the point, which is good for dealing with the intensity inhomogeneity. Secondly, a vascular vector field derived from a vesselness measure is employed to guide the contour to evolve along the vessel central skeleton into thin and weak vessels. Thirdly, an automatic initialization method that makes the model converge rapidly is developed, and it avoids repeated trails in conventional local region active contour models. Finally, a speed-up strategy is implemented by labeling the steadily evolved points, and it avoids the repeated computation of these points in the subsequent iterations. Experiments using synthetic and real vessel images validate the proposed model. Comparisons with the localized active contour model, local binary fitting model, and vascular active contour model show that the proposed model is more accurate, efficient, and suitable for extraction of the vessel tree from different medical images.