Image Based Hair Segmentation Algorithm for the Application of Automatic Facial Caricature Synthesis

Hair is a salient feature in human face region and are one of the important cues for face analysis. Accurate detection and presentation of hair region is one of the key components for automatic synthesis of human facial caricature. In this paper, an automatic hair detection algorithm for the application of automatic synthesis of facial caricature based on a single image is proposed. Firstly, hair regions in training images are labeled manually and then the hair position prior distributions and hair color likelihood distribution function are estimated from these labels efficiently. Secondly, the energy function of the test image is constructed according to the estimated prior distributions of hair location and hair color likelihood. This energy function is further optimized according to graph cuts technique and initial hair region is obtained. Finally, K-means algorithm and image postprocessing techniques are applied to the initial hair region so that the final hair region can be segmented precisely. Experimental results show that the average processing time for each image is about 280 ms and the average hair region detection accuracy is above 90%. The proposed algorithm is applied to a facial caricature synthesis system. Experiments proved that with our proposed hair segmentation algorithm the facial caricatures are vivid and satisfying

Norm-Constrained Least-Squares Solutions to the Matrix Equation A

An iterative method to compute the least-squares solutions of the matrix AXB=C over the norm inequality constraint is proposed. For this method, without the error of calculation, a desired solution can be obtained with finitely iterative step. Numerical experiments are performed to illustrate the efficiency and real application of the algorithm

Iterative methods to solve the constrained Sylvester equation

In this paper, the multiple constraint least squares solution of the Sylvester equation $AX+XB = C$ is discussed. The necessary and sufficient conditions for the existence of solutions to the considered matrix equation are given. Noting that the alternating direction method of multipliers (ADMM) is a one-step iterative method, a multi-step alternating direction method of multipliers (MSADMM) to solve the considered matrix equation is proposed and some convergence results of the proposed algorithm are proved. Problems that should be studied in the near future are listed. Numerical comparisons between MSADMM, ADMM and ADMM with Anderson acceleration (ACADMM) are included

Positive definite solution of two kinds of nonlinear matrix equations

Abstract. Based on the elegant properties of the Thompson metric, we prove that the following two kinds of nonlinear matrix equations always have a unique positive definite solution. Iterative methods are proposed to compute the unique positive definite solution. We show that the iterative methods are more effective as δ = max{|δi|, i = 1, 2, · · · , m} decreases. Perturbation bounds for the unique positive definite solution are derived in the end

Shear Resistance Capacity of Interface of Plate-Studs Connection between CFST Column and RC Beam

The combination of a concrete-filled steel tube (CFST) column and reinforced concrete (RC) beam produces a composite structural system that affords good structural performance, functionality, and workability. The effective transmission of moments and shear forces from the beam to the column is key to the full exploitation of the structural performance. The studs of the composite beam transfer the interfacial shear force between the steel beam and the concrete slab, with the web bearing most of the vertical shear force of the steel beam. In this study, the studs and vertical steel plate were welded to facilitate the transfer of the interfacial shear force between the RC beam and CFST column. Six groups of a total of 18 specimens were used to investigate the shear transfer mechanism and failure mode of the plate-studs connection, which was confirmed to effectively transmit the shear forces between the beam and column. The results of theoretical calculations were also observed to be in good agreement with the experimental measurements

Enhancing Logical Reasoning of Large Language Models through Logic-Driven Data Augmentation

Combining large language models with logical reasoning enhance their capacity to address problems in a robust and reliable manner. Nevertheless, the intricate nature of logical reasoning poses challenges to gathering reliable data from web for building comprehensive training datasets, subsequently affecting the performance on downstream tasks. To address this, we introduce a novel logic-driven data augmentation approach, AMR-LDA. AMR-LDA converts the original text into an Abstract Meaning Representation (AMR) graph, a structured semantic representation that encapsulates the logic structure of the sentence, upon which operations are performed to generate logically modified AMR graphs. The modified AMR graphs are subsequently converted back into texts to create augmented data. Notably, our methodology is architecture-agnostic and enhances generative large language models, such as GPT-3.5 and GPT-4, through prompt augmentation, and fine-tuning discriminative large language models through contrastive learning with logic-driven data augmentation. Empirical evidence underscores the efficacy of our proposed method with improvement in performance across seven downstream tasks, such as logical reasoning reading comprehension, textual entailment, and natural language inference. Furthermore, our method ranked first on the ReClor leaderboard \url{https://eval.ai/web/challenges/challenge-page/503/leaderboard/1347}. The source code and data are publicly available \url{https://github.com/Strong-AI-Lab/Logical-Equivalence-driven-AMR-Data-Augmentation-for-Representation-Learning}.Comment: Accepted for oral presentation at the LLM@IJCAI 2023 non-archival symposiu

Point Cloud Deep Learning Network Based on Balanced Sampling and Hybrid Pooling

The automatic semantic segmentation of point cloud data is important for applications in the fields of machine vision, virtual reality, and smart cities. The processing capability of the point cloud segmentation method with PointNet++ as the baseline needs to be improved for extremely imbalanced point cloud scenes. To address this problem, in this study, we designed a weighted sampling method based on farthest point sampling (FPS), which adjusts the sampling weight value according to the loss value of the model to equalize the sampling process. We also introduced the relational learning of the neighborhood space of the sampling center point in the feature encoding process, where the feature importance is distinguished by using a self-attention model. Finally, the global–local features were aggregated and transmitted using the hybrid pooling method. The experimental results of the six-fold crossover experiment showed that on the S3DIS semantic segmentation dataset, the proposed network achieved 9.5% and 11.6% improvement in overall point-wise accuracy (OA) and mean of class-wise intersection over union (MIoU), respectively, compared with the baseline. On the Vaihingen dataset, the proposed network achieved 4.2% and 3.9% improvement in OA and MIoU, respectively, compared with the baseline. Compared with the segmentation results of other network models on public datasets, our algorithm achieves a good balance between OA and MIoU

Multi-cue Based Face and Facial Feature Detection On Video Segments ∗

Abstract: An approach is presented to detect faces and facial features on a video segment based on multi-cues, including gray-level distribution, color, motion, templates, algebraic features and so on. Faces are first detected across the frames by using color segmentation, template matching and artificial neural network. A PCA-based (Principle Component Analysis, PCA) feature detector for still images is then used to detect facial features on each single frame until the resulting features of three adjacent frames, named as base frames, are consistent to each other. The features of frames neighbor hooded to the base frames are first detected by the still-image feature detector, then verified and corrected according to the smoothness constraint and the planar surface motion constraint. Experiments have been performed on video segments captured under different environments, and prove the presented method to be robust and accurate over variable poses, ages and illumination conditions