Crack Extraction for Polycrystalline Solar Panels

Crack extraction of solar panels has become a research focus in recent years. The cracks are small and hidden. In addition, there are particles of irregular shape and size on the surface of the polycrystalline solar panel, whose reflection position and direction are random. Therefore, there is a complex and uneven texture background on the solar panel image, which makes the crack extraction more difficult. In this paper, a crack extraction method combining image texture and morphological features is proposed. Firstly, the background texture and multi-scale details are suppressed by the linear filter and the Laplace pyramid decomposition method. Secondly, the edge can be extracted based on the modulus maximum method of the wavelet transform. Finally, cracks were extracted by using the improved Fuzzy C-means (FCM) clustering combining the morphological and texture features of the cracks. To make the extraction results more accurate and reasonable, an improved region growth algorithm is proposed to optimize the extraction results. All of the above research is closely centered on the accuracy and stability requirements of the solar cell crack detection, which is also the key point of this paper. The experimental results show that various improved or innovative algorithms proposed in this paper can accurately extract the position of cracks and obtain better extraction results. The detection results have good stability and can be faithful to the actual situation, which will promote the application of solar cells in more fields.

CO-OCCURRENCE MATRIX IN LONGITUDINAL DIFFERENTIATION OF MRI IMAGES OF PATIENTS WITH AD, MCI, AND HEALTHY INDIVIDUALS

Background: Texture analysis based on the gray level co-occurrence matrix (GLCM) has been applied to brain magnetic resonance images (MRI) to detect subtle differences among healthy and lesioned tissue occurring in several neurological conditions, but it has rarely been used in longitudinal AD studies. Objectives: To perform a longitudinal study by applying GLCM to brain MRI of AD and MCI patients and of healthy controls (HC), to verify the suitability of this technique to help detect the evolution of these conditions. Methods: Participants were 14 AD, 21 MCI and 17 HC, who had 2 T1-MRI obtained ~12 months apart. MRI were segmented using the AAL atlas. 3D GLCMs were computed for five voxel distances, for 16 regions per subject. A total of 55 texture parameters were extracted per region per subject. Statistical differences were evaluated using a t test. Results: Significant differences were found only for the MCI group, for the left precentral gyrus and left supplementary motor area, for which the correlation parameter decreased over time at different distances. Conclusions: This result could be due to a subtle motor loss in the MCI group before the onset of AD symptoms, or even, patients in the MCI group could progress to neurodegenerative diseases other than AD. The next step is to compare the obtained texture parameters between groups using analysis of covariance.

Pedestrian Dead Reckoning-Assisted Visual Inertial Odometry Integrity Monitoring

Visual inertial odometers (VIOs) have received increasing attention in the area of indoor positioning due to the universality and convenience of the camera. However, the visual observation of VIO is more susceptible to the environment, and the error of observation affects the final positioning accuracy. To address this issue, we analyzed the causes of visual observation error that occur under different scenarios and their impact on positioning accuracy. We propose a new method of using the short-time reliability of pedestrian dead reckoning (PDR) to aid in visual integrity monitoring and to reduce positioning error. The proposed method selects optimized positioning by automatically switching between outputs from VIO and PDR. Experiments were carried out to test and evaluate the proposed PDR-assisted visual integrity monitoring. The sensor suite of experiments consisted of a stereo camera and an inertial measurement unit (IMU). Results were analyzed in detailed and indicated that the proposed system performs better for indoor positioning within an environment that contains low illumination, little background texture information, or few moving objects.

Fabric Defect Detection Using L0 Gradient Minimization and Fuzzy C-Means

In this paper, we present a robust and reliable framework based on L0 gradient minimization (LGM) and the fuzzy c-means (FCM) method to detect various fabric defects with diverse textures. In our framework, the L0 gradient minimization is applied to process the fabric images to eliminate the influence of background texture and preserve sharpened significant edges on fabric defects. Then, the processed fabric images are clustered by using the fuzzy c-means. Through continuous iterative calculation, the clustering centers of fabric defects and non-defects are updated to realize the defect regions segmentation. We evaluate the proposed method on various samples, which include plain fabric, twill fabric, star-patterned fabric, dot-patterned fabric, box-patterned fabric, striped fabric and statistical-texture fabric with different defect types and shapes. Experimental results demonstrate that the proposed method has a good detection performance compared with other state-of-the-art methods in terms of both subjective and objective tests. In addition, the proposed method is applicable to industrial machine vision detection with limited computational resources.

Enhancing Motion-In-Depth Perception of Random-Dot Stereograms

Random-dot stereograms have been widely used to explore the neural mechanisms underlying binocular vision. Although they are a powerful tool to stimulate motion-in-depth (MID) perception, published results report some difficulties in the capacity to perceive MID generated by random-dot stereograms. The purpose of this study was to investigate whether the performance of MID perception could be improved using an appropriate stimulus design. Sixteen inexperienced observers participated in the experiment. A training session was carried out to improve the accuracy of MID detection before the experiment. Four aspects of stimulus design were investigated: presence of a static reference, background texture, relative disparity, and stimulus contrast. Participants’ performance in MID direction discrimination was recorded and compared to evaluate whether varying these factors helped MID perception. Results showed that only the presence of background texture had a significant effect on MID direction perception. This study provides suggestions for the design of 3D stimuli in order to facilitate MID perception.

Fabric Defect Detection via Un-Decimated Wavelet Decomposition and Gumbel Distribution Model

This paper investigates an unsupervised approach for fabric defect detection using un-decimated wavelet decomposition and simple statistical models. A novel data fusion scheme is proposed to merge the information from the different channels into a unique feature map in which potential defective regions will be highlighted distinctly. The distribution of the pixel values corresponding to the defect-free background texture in the feature map is modeled as per the Gumbel distribution model whose parameters are estimated by partitioning the feature map into a set of small patches. By calculating the log-likelihood value of each patch, a log-likelihood map (LLM) can be conveniently created, which provides a good cluster representation of the non-defective regions. A simple thresholding procedure then follows to discriminate between defective regions and homogeneous background in the LLM. The performance of the method has been extensively evaluated using a variety of real fabric samples, and the effectiveness of the proposed scheme has been verified by experimental results in comparison with other methods.