Date fruit is among the major crops in the middle-east region, where millions of tons are harvested every year. Date is a healthy fruit, which involves sugars, minerals and vitamins. In addition, it helps preventing human body from several diseases such as cancer and heart diseases. Date sorting is a fundamental step in the date industry. However, manually conducting such an operation, by human labors, is expensive and time-consuming. In this paper, we propose a method for classifying the type of date fruit by incorporating supervised and unsupervised deep networks. Specifically, we use discriminant correlation analysis (DCA) algorithm to fuse features learned from convolution neural networks (VGG-F) and an unsupervised network called PCANet. DCA jointly performs feature fusion and dimensionality reduction with a low computational complexity. To carry out experiments, we introduce a new benchmark dataset of date fruit images from 20 date varieties. Our benchmark is, to the best of our knowledge, the largest one in terms of number of varieties. Note that the dataset is publicly available at https://unsat.000webhostapp.com/dataset . Experimental results demonstrate the utility of DCA as well as the complementarity of the fused features. It has also been shown the effectiveness of the proposed method compared to several relevant methods.

The accurate and efficient classification of steel surface defects is critical for ensuring product quality and minimizing production costs. This paper proposes a novel method based on wavelet transform and texture descriptors for the robust and precise classification of steel surface defects. By leveraging the multiscale analysis capabilities of wavelet transforms, our method extracts both broad and fine-grained textural features. It involves decomposing images using multi-level wavelet transforms, extracting a series set of statistical and textural features from the resulting coefficients, and employing Recursive Feature Elimination (RFE) to select the most discriminative features. A comprehensive series of experiments was conducted on two datasets, NEU-CLS and X-SDD, to evaluate the proposed method. The results highlight the effectiveness of the method in accurately classifying steel surface defects, outperforming the state-of-the-art techniques. Our method achieved an accuracy of 99.67% for the NEU-CLS dataset and 98.24% for the X-SDD dataset. Furthermore, we demonstrate the robustness of our method in scenarios with limited data, maintaining high accuracy, making it well-suited for practical industrial applications where obtaining large datasets can be challenging.

The growing adoption of telemedicine necessitates robust security measures for medical images during transmission. This paper proposes a novel blind watermarking system for medical images that utilizes both image gradients and the Discrete Wavelet Transform (DWT). Image gradients, acting as spatial derivatives, provide a “topological map” of the image, aiding in the identification of areas susceptible to disruption. The DWT, with its multi-resolution analysis, offers a favorable balance between robustness and imperceptibility. The proposed method embeds the watermark within the low–low band (LL) of the DWT-decomposed image, specifically in 3 × 3 block regions selected based on gradient information. The mathematical relationships between the gradient’s direction and magnitude are employed to extract the corresponding blocks and their codes adequately. These codes are then XORed with the watermark and embedded into the chosen blocks using the least significant bit (LSB) technique. Extensive experimentation on a medical image dataset evaluates the system’s performance against some attacks like filtering, noise, and scaling. The results demonstrate the efficacy of the proposed approach in hiding information while ensuring the security and integrity of watermarked medical images.

Teeth segmentation is a crucial and fundamental player for doctors in diagnosis and treatment planning in dentistry. Due to the blurred interdental boundaries, variations in noise, and the complexities arising from the orientation and overlapping of dental structures within oral images, the segmentation process becomes extremely challenging and time-consuming. Nowadays, computational tools have been introduced as promising strategies for automating teeth segmentation. As one of them, this paper presents a novel architecture called LCAT-Net, designed to address these challenges and improve teeth segmentation in panoramic X-rays. The proposed architecture incorporates several components to address the above challenges. Firstly, it leverages the main components of the Half-UNet for lightweight feature extraction, starting from ghost modules, unified channel numbers, and full-scale feature fusion. Secondly, to give our model the ability to focus on critical regions for improved differentiation in complex dental structures, a convolutional block attention module (CBAM) is integrated into the network. Thirdly, the architecture incorporates a novel multi-scale context fusion (MCF) module, our proposed MCF module extracts multi-scale spatial information through a spatial context fusion (SCF) block, followed by a CBAM block that learns to balance channel-wise features. The network uses a Dense skip connection module (DSM) to reduce the semantic gap. Experiments on three dental panoramic X-ray image datasets of Children, Adults, and Combined (Children and Adults) consisting of 193, 1776, and 1969 X-rays show that our model outperformed the SOTA models in teeth segmentation, with a high mean Dice-scores of 0.9235, 0.9444, 0.9405, respectively. While requiring significantly fewer parameters and floating-point operations (FLOPs) than existing methods.