feature descriptor
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This paper presents a novel feature descriptor termed principal component analysis (PCA)-based Advanced Local Octa-Directional Pattern (ALODP-PCA) for content-based image retrieval. The conventional approaches compare each pixel of an image with certain neighboring pixels providing discrete image information. The descriptor proposed in this work utilizes the local intensity of pixels in all eight directions of its neighborhood. The local octa-directional pattern results in two patterns, i.e., magnitude and directional, and each is quantized into a 40-bin histogram. A joint histogram is created by concatenating directional and magnitude histograms. To measure similarities between images, the Manhattan distance is used. Moreover, to maintain the computational cost, PCA is applied, which reduces the dimensionality. The proposed methodology is tested on a subset of a Multi-PIE face dataset. The dataset contains almost 800,000 images of over 300 people. These images carries different poses and have a wide range of facial expressions. Results were compared with state-of-the-art local patterns, namely, the local tri-directional pattern (LTriDP), local tetra directional pattern (LTetDP), and local ternary pattern (LTP). The results of the proposed model supersede the work of previously defined work in terms of precision, accuracy, and recall.

Establishing an effective local feature descriptor and using an accurate key point matching algorithm are two crucial tasks in recognizing and registering on the 3D point cloud. Because the descriptors need to keep enough descriptive ability against the effect of noise, occlusion, and incomplete regions in the point cloud, a suitable key point matching algorithm can get more precise matched pairs. To obtain an effective descriptor, this paper proposes a Multi-Statistics Histogram Descriptor (MSHD) that combines spatial distribution and geometric attributes features. Furthermore, based on deep learning, we developed a new key point matching algorithm that could identify more corresponding point pairs than the existing methods. Our method is evaluated based on Stanford 3D dataset and four real component point cloud dataset from the train bottom. The experimental results demonstrate the superiority of MSHD because its descriptive ability and robustness to noise and mesh resolution are greater than those of carefully selected baselines (e.g., FPFH, SHOT, RoPS, and SpinImage descriptors). Importantly, it has been confirmed that the error of rotation and translation matrix is much smaller based on our key point matching algorithm, and the precise corresponding point pairs can be captured, resulting in enhanced recognition and registration for three-dimensional surface matching.

An automated Computer Aided Diagnosis (CAD) system for glaucoma diagnosis using fundus images is developed. The various glaucoma image classification schemes using the supervised and unsupervised learning approaches are reviewed. The research paper involves three stages of glaucoma disease diagnosis. First, the pre-processing stage the texture features of the fundus image is recorded with a two-dimensional Gabor filter at various sizes and orientations. The image features are generated using higher order statistical characteristics, and then Principal Component Analysis (PCA) is used to select and reduce the dimension of the image features. For the performance study, the Gabor filter based features are extracted from the RIM-ONE and HRF database images, and then Support Vector Machine (SVM) classifier is used for classification. Final stage utilizes the SVM classifier with the Radial Basis Function (RBF) kernel learning technique for the efficient classification of glaucoma disease with accuracy 90%.

Presentation attacks on face recognition systems are classified into two categories: physical and digital. While much research has focused on physical attacks such as photo, replay, and mask attacks, digital attacks such as morphing have received limited attention. With the advancements in deep learning and computer vision algorithms, several easy-to-use applications are available where with few taps/clicks, an image can be easily and seamlessly altered. Moreover, generation of synthetic images or modifying images/videos (e.g. creating deepfakes) is relatively easy and highly effective due to the tremendous improvement in generative machine learning models. Many of these techniques can be used to attack the face recognition systems. To address this potential security risk, in this research, we present a novel algorithm for digital presentation attack detection, termed as MagNet, using a “Weighted Local Magnitude Pattern” (WLMP) feature descriptor. We also present a database, termed as IDAgender, which consists of three different subsets of swapping/morphing and neural face transformation. In contrast to existing research, which utilizes sophisticated machine learning networks for attack generation, the databases in this research are prepared using social media platforms that are readily available to everyone with and without any malicious intent. Experiments on the proposed database, FaceForensic database, GAN generated images, and real-world images/videos show the stimulating performance of the proposed algorithm. Through the extensive experiments, it is observed that the proposed algorithm not only yields lower error rates, but also provides computational efficiency.

Abstract Aiming at the problem of inaccurate extraction of feature points by the traditional image matching method, low robustness, and problems such as diffculty in inentifying feature points in area with poor texture. This paper proposes a new local image feature matching method, which replaces the traditional sequential image feature detection, description and matching steps. First, extract the coarse features with a resolution of 1/8 from the original image, then tile to a one-dimensional vector plus the positional encoding, feed them to the self-attention layer and cross-attention layer in the Transformer module, and finally get through the Differentiable Matching Layer and confidence matrix, after setting the threshold and the mutual closest standard, a Coarse-Level matching prediction is obtained. Secondly the fine matching is refined at the Fine-level match, after the Fine-level match is established, the image overlapped area is aligned by transforming the matrix to a unified coordinate, and finally the image is fused by the weighted fusion algorithm to realize the unification of seamless mosaic of images. This paper uses the self-attention layer and cross-attention layer in Transformers to obtain the feature descriptor of the image. Finally, experiments show that in terms of feature point extraction, LoFTR algorithm is more accurate than the traditional SIFT algorithm in both low-texture regions and regions with rich textures. At the same time, the image mosaic effect obtained by this method is more accurate than that of the traditional classic algorithms, the experimental effect is more ideal.

Hyperspectral Images (HSIs) have been utilized in many fields which contain spatial and spectral features of objects simultaneously. Hyperspectral image matching is a fundamental and critical problem in a wide range of HSI applications. Feature descriptors for grayscale image matching are well studied, but few descriptors are elaborately designed for HSI matching. HSI descriptors, which should have made good use of the spectral feature, are essential in HSI matching tasks. Therefore, this paper presents a descriptor for HSI matching, called HOSG-SIFT, which ensembles spectral features with spatial features of objects. First, we obtain the grayscale image by dimensional reduction from HSI and apply it to extract keypoints and descriptors of spatial features. Second, the descriptors of spectral features are designed based on the histogram of the spectral gradient (HOSG), which effectively preserves the physical significance of the spectral profile. Third, we concatenate the spatial descriptors and spectral descriptors with the same weights into a new descriptor and apply it for HSI matching. Experimental results demonstrate that the proposed HOSG-SIFT performs superior against traditional feature descriptors.

The rejoining of oracle bone rubbings is a fundamental topic for oracle research. However, it is a tough task to reassemble severely broken oracle bone rubbings because of detail loss in manual labeling, the great time consumption of rejoining, and the low accuracy of results. To overcome the challenges, we introduce a novel CFDA&CAP algorithm that consists of the Curve Fitting Degree Analysis (CFDA) algorithm and the Correlation Analysis of Pearson (CAP) algorithm. First, the orthogonalization system is constructed to extract local features based on the curve features analysis. Second, the global feature descriptor is depicted by using coordinate points sequences. Third, we screen candidate curves based on the features as well as the CFDA algorithm, so the search range of the candidates is narrowed down. Finally, image recommendation libraries for target curves are generated by adopting the CAP algorithm, and the rank for each target matching curve generates simultaneously for result evaluation. With experiments, the proposed method shows a good effect in rejoining oracle bone rubbings automatically: (1) it improves the average accuracy rate of curve matching up to 84%, and (2) for a low-resource task, the accuracy of our method has 25% higher accuracy than that of other methods.

With the exponential increasement of 3D models, 3D model classification is crucial to the effective management and retrieval of model database. Feature descriptor has important influence on 3D model classification. Voxel descriptor expresses surface and internal information of 3D model. However, it does not contain topological structure information. Shape distribution descriptor expresses geometry relationship of random points on model surface and has rotation invariance. They can all be used to classify 3D models, but accuracy is low due to insufficient description of 3D model. This paper proposes a 3D model classification algorithm that fuses voxel descriptor and shape distribution descriptor. 3D convolutional neural network (CNN) is used to extract voxel features, and 1D CNN is adopted to extract shape distribution features. AdaBoost algorithm is applied to combine several Bayesian classifiers to get a strong classifier for classifying 3D models. Experiments are conducted on ModelNet10, and results show that accuracy of the proposed method is improved.

In order to deal with the problems of low detection accuracy and efficiency existing in traditional detection methods of body behavior characteristics in sports training, this paper proposes a new detection method of body behavior characteristics in sports training based on the grey correlation model. According to this method, the foreground binary image is obtained by the interframe difference method, and the action information and contour information are extracted, firstly. And then, the behavior feature descriptor is obtained. After the description of body behavior characteristics in sports training is completed, the grey correlation coefficient is calculated, and the complete observation equation of time delay estimation of image grey value under complex sports training background is established to complete the detection of body behavior characteristics. The experimental results show that, compared with the traditional methods, the detection accuracy and efficiency of the text method can always maintain a high level, indicating that the practical application performance of this method is strong. In fact, the grey analysis does not attempt to find the best solution but does provide techniques for determining a good solution.