Automated inspections and intelligent image processing optimize quality control processes. Images captured in the industrial inspection system have low contrast and faint color. Many enhancement algorithms have recently been proposed to enhance visibility and restore color. We present a new image enhancement algorithm based on multi-scale block-rooting processing. The proposed method based on the frequency-domain coefficient correction of a set of images followed by their fusion based on the Laplacian pyramid. A new stage is presented in obtaining a local-global estimate of high-contrast images, also used in the general fusion model. The main idea is that enhancing the contrast of an image would create more high-frequency content in the enhanced image than in the original image. The experiment results on the test dataset confirmed the high efficiency of the proposed enhancement method compared to the state-of-the-art techniques for industrial inspection systems.
Automation of production processes using robots is a priority for developing many industrial enterprises. Human-machine interaction is a key component of such control infrastructure. The proposed algorithm is a four-stage procedure: (a) fusion information from multimodal sensors based on the quaternion model, (b) image preprocessing using a 3D Gabor filter, (c) a descriptor calculation using 3D local binary dense micro-block difference with skeleton points, and (d) classification. The proposed algorithm is based on capturing 3D sub-volumes located inside a video sequence patch and calculating the difference in intensities between these sub-volumes; for intensified motion, used the convolution with a bank of 3D arbitrarily oriented Gabor filters and calculating 3D local binary dense micro-block difference. A program was developed for transmitting information about the target points of the robot's movement through a virtual TCP / IP port and a script for working out the target points in the simulation environment. To test the effectiveness of the proposed algorithm, we simulate an action recognition system for Human-robot collaboration in the RoboGuide environment.
Image inpaintnig in textile manufacturing is a new emerging research topic in preprocessing for jacquard CAD systems. One of the most important aspects of a jacquard CAD system is the simulation of the appearance of a jacquard texture during inference. Jacquard image inpainting has become an indispensable process for the Jacquard CAD system. Jacquard image reconstruction is designed to restore a damaged image with missing information, so it is necessary to determine which parts of the image need to be repaired. Thus, this task includes two processing stages: the detection of defects and their recovery. This article presents a two-stage approach that combines new and traditional algorithms for detecting defects and repairing damaged areas. The first stage is a defect detection method based on a convolutional autoencoder (U-Net). The second stage is image inpainting based on exemplar-based concepts and the anisotropic gradient. Our system quantitatively outperforms state-of-the-art methods regarding reconstruction accuracy in the benchmark.
This article presents a two-stage approach, combining novel and traditional algorithms, to image segmentation and defect detection. The first stage is a new method for segmenting fabric images is based on Hamiltonian quaternions and the associative algebra and the active contour model with anisotropic gradient. To solve the problem of loss of important information about color, saturation, and other important information associated color, we use the quaternion framework to represent a color image to consider all three channels simultaneously when segmenting the RGB image. In the second stage, our crack and damage detection method are based on a convolutional autoencoder (U-Net) and deep feature fusion network (DFFN-Net). This solution allows localizing defects with higher accuracy compared to traditional methods of machine learning and modern methods of deep learning. All experiments were carried out using a public database with examples of damage to the TILDA fabric dataset.
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