Face Recognition Through Multi-Resolution Images

In this paper, we introduce a new method for face recognition in multi-resolution images. The proposed method is composed of two phases: an off-line phase and an inference phase. In the off-line phase, we built the Kernel Partial Least Squares (KPLS) regression model to map the LR facial features to HR ones. The KPLS predictor was then used in the inference phase to map HR features from LR features. We applied in both phases the Block-Based Discrete Cosine Transform (BBDCT) descriptor to enhance the facial feature description. Finally, the identity matching was carried out with the K-Nearest Neighbor (KNN) classifier. Experimental study was conducted on the AR and ORL databases and the obtained results proved the efficiency of the proposed method to deal with LR and VLR face recognition problem.

The Feature Description of Formal Context Based on the Relationships among Concept Lattices

Three-way concept analysis (3WCA) is extended research of formal concept analysis (FCA) by combining three-way decision. The three-way object oriented concept lattice (OEOL) is one of the important data structures which integrates rough set, concept lattice and three-way decision in 3WCA. In the paper, we investigate the characteristics of formal context based on the isomorphic relationship among the kinds of concept lattices with OEOL. Firstly, II-dual intersectable attributes and II-dual intersectable context are proposed and the relationship between the type I-dual intersectable context(dual intersectable context) and the type II-dual intersectable context are studied. In addition, the relationship among the kinds of concept lattices with OEOL are studied when the formal context is both I-dual intersectable context and II-dual intersectable context. Finally, the inverse problems of the above conclusions are discussed and the following two conclusions are obtained: (1) the formal context is the type II-dual intersectable context, when the object oriented concept lattice and OEOL are isomorphic. (2) In addition, the formal context is the type I-dual intersectable context, when the concept lattice and OEOL are anti-isomorphic.

IFRAD: A Fast Feature Descriptor for Remote Sensing Images

Feature description is a necessary process for implementing feature-based remote sensing applications. Due to the limited resources in satellite platforms and the considerable amount of image data, feature description—which is a process before feature matching—has to be fast and reliable. Currently, the state-of-the-art feature description methods are time-consuming as they need to quantitatively describe the detected features according to the surrounding gradients or pixels. Here, we propose a novel feature descriptor called Inter-Feature Relative Azimuth and Distance (IFRAD), which will describe a feature according to its relation to other features in an image. The IFRAD will be utilized after detecting some FAST-alike features: it first selects some stable features according to criteria, then calculates their relationships, such as their relative distances and azimuths, followed by describing the relationships according to some regulations, making them distinguishable while keeping affine-invariance to some extent. Finally, a special feature-similarity evaluator is designed to match features in two images. Compared with other state-of-the-art algorithms, the proposed method has significant improvements in computational efficiency at the expense of reasonable reductions in scale invariance.

A Performance Analysis of Feature Extraction Algorithms for Acoustic Image-Based Underwater Navigation

In underwater navigation, sonars are useful sensing devices for operation in confined or structured environments, enabling the detection and identification of underwater environmental features through the acquisition of acoustic images. Nonetheless, in these environments, several problems affect their performance, such as background noise and multiple secondary echoes. In recent years, research has been conducted regarding the application of feature extraction algorithms to underwater acoustic images, with the purpose of achieving a robust solution for the detection and matching of environmental features. However, since these algorithms were originally developed for optical image analysis, conclusions in the literature diverge regarding their suitability to acoustic imaging. This article presents a detailed comparison between the SURF (Speeded-Up Robust Features), ORB (Oriented FAST and Rotated BRIEF), BRISK (Binary Robust Invariant Scalable Keypoints), and SURF-Harris algorithms, based on the performance of their feature detection and description procedures, when applied to acoustic data collected by an autonomous underwater vehicle. Several characteristics of the studied algorithms were taken into account, such as feature point distribution, feature detection accuracy, and feature description robustness. A possible adaptation of feature extraction procedures to acoustic imaging is further explored through the implementation of a feature selection module. The performed comparison has also provided evidence that further development of the current feature description methodologies might be required for underwater acoustic image analysis.

A Modified Binary Descriptor for Object Detection

The reliability of computer vision applications highly depends on the extraction of compact, fast, and accurate and robust feature description. This paper presents a better and modified binary descriptor based on ORB (oriented and rotated brief) with the SVM-RBF-RFE (support vector machine-radial basis function-recursive feature elimination) to achieve a better extraction and representation of local binary descriptors. This work presents the extensive comparison of the proposed modified descriptor with the state-of-the-art binary descriptors on various datasets. The results show that the proposed descriptor is highly distinctive and efficient as compared to the other state-of-the-art binary descriptors. The experiments were performed on the four benchmark datasets PASCAL, CALTECH, COIL, and OXFORD to demonstrate the robustness and effectiveness of the proposed descriptor. The robustness and effectiveness of the proposed descriptor is tested under the various transformations like scaling, rotation, noise, intensity variation.