Deep Stacked Autoencoder-Based Automatic Emotion Recognition Using an Efficient Hybrid Local Texture Descriptor

Extracting an effective facial feature representation is the critical task for an automatic expression recognition system. Local Binary Pattern (LBP) is known to be a popular texture feature for facial expression recognition. However, only a few approaches utilize the relationship between local neighborhood pixels itself. This paper presents a Hybrid Local Texture Descriptor (HLTD) which is derived from the logical fusion of Local Neighborhood XNOR Patterns (LNXP) and LBP to investigate the potential of positional pixel relationship in automatic emotion recognition. The LNXP encodes texture information based on two nearest vertical and/or horizontal neighboring pixel of the current pixel whereas LBP encodes the center pixel relationship of the neighboring pixel. After logical feature fusion, the Deep Stacked Autoencoder (DSA) is established on the CK+, MMI and KDEF-dyn dataset and the results show that the proposed HLTD based approach outperforms many of the state of art methods with an average recognition rate of 97.5% for CK+, 94.1% for MMI and 88.5% for KDEF.

Combined with Local Neighborhood Characteristics and Remote Sensing Image Fusion Method of C-BEMD

For the sake of ameliorate the high resolution recognition capacity building remote sensing images, a remote sensing image fusion method based on local neighborhood characteristics and C-BEMD is advanced. The building remote sensing image acquisition model and the building remote sensing image picture element edge feature detection model are designed. The wavelet multi-scale denoising method is used to suppress the fuzzy spread of picture element feature points between image residual units, extract the geometric feature points of image sequence, and process the building remote sensing image block by block. The global residual learning and message fusion of building remote sensing image are implemented. The local neighborhood feature matching method is used to reconstruct the building remote sensing image region. Combined with the C-BEMD empirical mode decomposition method, the building remote sensing image fusion and feature point matching in affine region are implemented, and the block image template matching method is used to realize the automatic fusion and recognition of building remote sensing image. Simulation results show that this method has high precision in constructing remote sensing image fusion and good positioning performance in constructing remote sensing image feature points.

Learning Two-View Correspondences and Geometry via Local Neighborhood Correlation

Seeking quality feature correspondences (also known as matches) is a foundational step in computer vision. In our work, a novel and effective network with a stable local constraint, named the Local Neighborhood Correlation Network (LNCNet), is proposed to capture abundant contextual information of each correspondence in the local region, followed by calculating the essential matrix and camera pose estimation. Firstly, the k-Nearest Neighbor (KNN) algorithm is used to divide the local neighborhood roughly. Then, we calculate the local neighborhood correlation matrix (LNC) between the selected correspondence and other correspondences in the local region, which is used to filter outliers to obtain more accurate local neighborhood information. We cluster the filtered information into feature vectors containing richer neighborhood contextual information so that they can be used to more accurately determine the probability of correspondences as inliers. Extensive experiments have demonstrated that our proposed LNCNet performs better than some state-of-the-art networks to accomplish outlier rejection and camera pose estimation tasks in complex outdoor and indoor scenes.

Plant–plant interactions and local patterns of diversity from semi-arid to subalpine Mediterranean plant communities

An understanding of the diversity spatial organization in plant communities provides essential information for management and conservation planning. In this study we investigated, using a multi-species approach, how plant–plant interactions determine the local structure and composition of diversity in a set of Mediterranean plant communities, ranging from semi-arid to subalpine habitats. Specifically, we evaluated the spatial pattern of diversity (i.e., diversity aggregation or segregation) in the local neighborhood of perennial plant species using the ISAR (individual species–area relationship) method. We also assessed the local pattern of beta-diversity (i.e., the spatial heterogeneity in species composition among local assemblages), including the contribution of species turnover (i.e., species replacement) and nestedness (i.e., differences in species richness) to the overall local beta-diversity. Our results showed that local diversity segregation decreased in the less productive plant communities. Also, we found that graminoids largely acted as diversity segregators, while forbs showed more diverse neighborhoods than expected in less productive study sites. Interestingly, not all shrub and dwarf shrub species aggregated diversity in their surroundings. Finally, an increase in nestedness was associated with less segregated diversity patterns in the local neighborhood of shrub species, underlining their role in creating diversity islands in less productive environmental conditions. Our results provide further insights into the effect of plant–plant interactions in shaping the structure and composition of diversity in Mediterranean plant communities, and highlight the species and groups of species that management and conservation strategies should focus on in order to prevent a loss of biodiversity.