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In most fabric industries fabric quality is assessed through manual inspection, which depends on an individual judgment. It is necessary to design an automatic fabric defect performance inspection system for the industry. This study aimed to develop a real-time, low-cost, and high-performance home textile fabric defect inspection machine system. The proposed system uses the Haar wavelet transform to reduce the information content of the fabric image. The brightness of the fabric image is compensated and the camera luminance is corrected in order to filter the image texture for fabric images with the Gaussian filter after correction. After that, the fabric defect classification was performed by using the random forest classifier. The designed system capability can detect and verify 10 kinds of fabrics with different colors. Moreover, the hardware cost of the machine is low and the average true defect recognition detection rate is more than 98.70%, with good adaptability. Meanwhile, the average processing detection time for a single image is 70 ms with a fabric defect inspection speed of 30 m/min. The efficiency of the machine is increased by five times compared with the traditional inspection. The designed inspection machine can also replace manual grading, cutting, and finishing in the processes of labeling defects. Eventually, it can reduced man power and overall mass production cost, so even small-scale home textile industries can afford a machine with high-precision defect detection.

Purpose Previously, we reported the octyl ester derivative of ginsenoside Rh2 (Rh2-O) had better antitumor and immunomodulatory effects than Rh2 in H22 tumor-bearing mice. Therefore, this study further explored the effects of Rh2-O on splenic lymphocytes in H22 tumor-bearing mice and the underlying mechanism. Methods Wild type and Tlr4-/- mice were selected to establish the H22 tumor-bearing mice model. After the treatment of Rh2-O (10 mg/kg by gavage) for 15 days, the sizes of tumor were measured. Subsequently, the splenic lymphocytes were isolated and the activities (eg. cell proliferation, cytotoxicity and cytokine secretion) were evaluated. Then, the proteins and mRNA expression levels of TRAF6 and NF-ĸB p65 in splenic lymphocytes were examined. Results The results showed that Rh2-O administration enhanced the proliferative capacity and cytotoxicity of splenic lymphocytes, and the effects were Tlr4-associated. Compared to WT mice, the up-regulation of cytokines secretion (eg. IFN-γ, IL-2 and IL-4) in isolated splenic lymphocytes after Rh2-O administration was lower in Tlr4-/- mice. Moreover, the results showed Rh2-O increased the expression of TRAF6 and the level of endonuclear NF-ĸB p65, which was inhibited in Tlr4-/- mice (P Conclusion Rh2-O could exert immunomodulatory effects on splenic lymphocytes with the partial participation of TLR4 in H22 tumor-bearing mice.

This study developed a 3-D measurement system based on flip-chip solder bump, used fringes with different modulation intensities in color channels, in order to produce color composite fringe with robustness, and proposed a multi-channel composite phase unwrapping algorithm, which uses fringe modulation weights of different channels to recombine the phase information for better measurement accuracy and stability. The experimental results showed that the average measurement accuracy is 0.43μm and the standard deviation is 1.38 µm. The results thus proved that the proposed 3-D measurement system is effective in measuring a plane with a height of 50 μm. In the flip-chip solder bump measuring experiment, different fringe modulation configurations were tested to overcome the problem of reflective coefficient between the flip-chip base board and the solder bump. The proposed system has a good measurement results and robust stability in the solder bump measurement, and can be used for the measurement of 3-D information for micron flip-chip solder bump application.

This study developed a 3D software and hardware measurement system, and proposes an innovative stripes adaptive detection algorithm. The fringe intensity is regulated automatically according to the reflection coefficient of different analytes, in order to avoid overexposure. For the measurement of the object in discontinuously changing height, a novel intensity difference coding unwrapping phase technology is used, thus overcoming the technological bottleneck of traditional phase unwrapping. In order to increase the measurement efficiency, the stripe pattern is combined with intensity coding pattern by three-channel color information, in order to generate an adaptive compound color stripe pattern. The measurement efficiency is increased by approximately two times compared with traditional gray stripe pattern. In order to increase the measurement accuracy, the uneven brightness is corrected by using brightness gain function. The three-channel intensity nonlinear response is corrected by cubic spline interpolation system response inverse function. The three-channel image is corrected by color cross-talk correction technology. The experiment proved that the system repeatability is 20 µm. The traditional phase-shifting profilometry is improved successfully, overcoming the technical measurement bottleneck of discontinuous change in the analyte height, so as to attain low cost, high measurement accuracy, efficiency and measurement reliability.

The traditional global template matching is time consuming, has low accuracy, and cannot be adapted to rotation and scale change. The template matching technique proposed in this study improves the time, accuracy and robustness for printed circuit boards (PCB). In order to shorten the image positioning time, the image preprocessing is implemented on PCB image and the image blocks are labeled to obtain the tagged image, and the feature vector is extracted and the marking point region image is selected. The feature vector with rotation change and scale change robustness is extracted from the tagged image after labeling in the PCB image by using artificial neural network, combined with image moments for training. The marking point region image in the PCB image is selected. The scale value of the marking point region image is estimated by parametric template vector matching. The deflection angle of marking point region image is calculated by Hough transform. The obtained scale value and deflection angle value are used for fast template matching to determine the marking point positioning. The three-dimensional (3D) parabolic curve fitting is implemented in marking point positioning and adjacent pixel position to reach the sub-pixel level accuracy. The experiment showed that the proposed template matching technique for the PCB image with or without noise or angle rotation, the average position accuracy error of each translated image is lower than 7 $$\upmu $$m, and the error standard deviation is lower than 5 $$\upmu $$m. The rotation angle error average and standard deviation of angular error of Hough transform are lower than 0.2$$^{\circ }$$, more accurate than orientation code (OC) method. The scale value estimation, relative error average and error standard deviation are lower than 0.004 and 0.006 for the image with or without noise. The average complete positioning time of PCB image at resolution of $$2500\times 2500$$ is only 0.55 s, which is better than the 3.97 s of traditional global template matching. The results prove that the template matching technique of this study not only has sub-pixel level high accuracy and short computing time, but also has the robustness of rotation change and scale change interference. It can implement rapid, efficient and accurate positioning.

This study focused on micro-defect recognition and classification in color filters. First, six types of defects were examined, namely grain, black matrix hole (BMH), indium tin oxide (ITO) defect, missing edge and shape (MES), highlights, and particle. Orthogonal projection was applied to locate each pixel in a test image. Then, an image comparison was performed to mark similar blocks on the test image. The block that best resembled the template was chosen as the new template (or matching adaptive template). Afterwards, image subtraction was applied to subtract the pixels at the same location in each block of the test image from the matching adaptive template. The control limit law employed logic operation to separate the defect from the background region. The complete defect structure was obtained by the morphology method. Next, feature values, including defect gray value, red, green, and blue (RGB) color components, and aspect ratio were obtained as the classifier input. The experimental results showed that defect recognition could be completed as fast as 0.154 s using the proposed recognition system and software. In micro-defect classification, back-propagation neural network (BPNN) and minimum distance classifier (MDC) served as the defect classification decision theories for the five acquired feature values. To validate the proposed system, this study used 41 defects as training samples, and treated the feature values of 307 test samples as the BPNN classifier inputs. The total recognition rate was 93.7%. When an MDC was used, the total recognition rate was 96.8%, indicating that the MDC method is feasible in applying automatic optical inspection technology to classify micro-defects of color filters. The proposed system is proven to successfully improve the production yield and lower costs.

The laryngeal video stroboscope is an important instrument to test glottal diseases and read vocal fold images and voice quality for physician clinical diagnosis. This study is aimed to develop a medical system with functionality of automatic intelligent recognition of dynamic images. The static images of glottis opening to the largest extent and closing to the smallest extent were screened automatically using color space transformation and image preprocessing. The glottal area was also quantized. As the tongue base movements affected the position of laryngoscope and saliva would result in unclear images, this study used the gray scale adaptive entropy value to set the threshold in order to establish an elimination system. The proposed system can improve the effect of automatically captured images of glottis and achieve an accuracy rate of 96%. In addition, the glottal area and area segmentation threshold were calculated effectively. The glottis area segmentation was corrected, and the glottal area waveform pattern was drawn automatically to assist in vocal fold diagnosis. When developing the intelligent recognition system for vocal fold disorders, this study analyzed the characteristic values of four vocal fold patterns, namely, normal vocal fold, vocal fold paralysis, vocal fold polyp, and vocal fold cyst. It also used the support vector machine classifier to identify vocal fold disorders and achieved an identification accuracy rate of 98.75%. The results can serve as a very valuable reference for diagnosis.

The traditional method of phase fringe generation often results in a lack of periodic consistency of the fringe, leading to increased phase errors due to projection lens distortion and projection angle deflection. This study proposed a novel method of homography fringe generation. The planar homography relationship between the coordinates of the projected image and the camera-captured coordinates was used to generate a fringe image on the captured image plane. The image was then converted and projected onto the projection plane to obtain a corrected fringe image of periodic consistency, thus effectively reducing phase errors caused by an inconsistent period. The proposed method can effectively generate a fringe image of periodic consistency by homography relationship without considering the complex projection angle deflection or the projection lens distortion model.

This study proposed an automatic LED defect detection system to investigate the defects of LED chips. Such defects include fragment chips, scratch marks and remained gold on the pad area, scratch marks on the luminous zone, and missing luminous zone respectively. The system was based on positioning and image acquisition, appearance feature recognition, and defect classification. The normalized correlation coefficient method was used to locate the chip and acquire its image, the K-means clustering method was used to distinguish the appearance, pad area, and luminous zone of chips. In terms of pad area detection, histogram equalization was used to enhance the pad image contrast, and statistical threshold selection and morphological closing were applied to modify the impure points in the pad. Feature values of the pad area were then calculated. The optimal statistical threshold separated the luminous zone and background from the substrate. After processed with closing operation, features of the luminous zone were extracted. Finally, features of each part were clarified by an efficient two-step back-propagation neural network, where a designed appearance classifier and an internal structure classifier were used for recognition. From experiments, total recognition rate of this study achieved 97.83 %, proving that the detection method proposed by this study can efficiently detect LED chip defects.

This series of studies proposed an automatic machine embroidery image color analysis system. Based on the machine embroidery image color separation system proposed in part I of this series of papers (Kuo CFJ, Jian BJ and Wu HC. Automatic machine embroidery image color analysis system, part I: using Gustafson-Kessel clustering algorithm in embroidery fabric color separation. Textil Res J 2011, submitted for publication), this paper presents an automatic repetitive pattern image recognition algorithm for machine embroidery. Firstly, the image pre-processing is implemented to eliminate image acquisition noise and smooth the texture of machine embroidery. After the completion of image pre-processing, the genetic algorithm (GA) is applied to obtain the color image of repetitive patterns. The novel fitness function proposed in this paper can increase the calculation of the color entropy value to improve the measurement block accuracy of color similarity in previous literature. When applied to the GA, the system can accurately identify and cut out images of repetitive patterns. In addition, since the proposed fitness function has been normalized, it can accurately calculate the optimal target function, indicating that the proposed algorithm can automatically and accurately identify and cut out the images of repetitive patterns in machine embroidery.

This series of studies aims to propose the automatic machine embroidery image color analysis system to solve the problem of lack of manpower for machine embroidery fabric drafting and to shorten drafting time. The studies included three parts: (1) machine embroidery image color separation, (2) search of repeated pattern images, (3) machine embroidery color analysis system integration. This study aimed to find the optimal clustering algorithm and cluster validity indices for the automatic color separation process of machine embroidery fabric drafting in order to shorten drafting time. To improve image quality for computer analysis, this study used the color hybrid median filter to filter noise and the color bilateral filter to smoothen fabric and embroidery texture for subsequent color separation. By extracting the color a* component and b* component of the machine embroidery image in CIE L*a*b* color system, this study used the Gustafson-Kessel clustering algorithm for color separation. The Gustafson-Kessel clustering algorithm in machine embroidery image color separation can improve color separation accuracy, and its result is compared with that of the clustering algorithms commonly used in the color separation of color images. This study implemented the chromatography of the color separation results, and used the cluster validity indices to prove that the application of Gustafson-Kessel clustering algorithm in the machine embroidery image color separation system has better results than K-means, K-medoid, fuzzy C-means (FCM), and self-organizing map (SOM) clustering algorithm. The results meet the classifications as expected by human eyes.

At present, computed tomography (CT) images of interest for medical diagnosis are manually selected among a series of images by physicians. This study uses image processing to automatically select the regions of the nasal vestibule and nasal septum using a back-propagation neural network. Three-dimensional (3D) image reconstructions of the nasal regions are conducted to obtain the 3D spatial information. 3D images of the nasal cavity, nasopharynx, and paranasal sinuses are also reconstructed. The representative points are labeled in three surgically risky locations, namely the anterior cranial fossa of the brain and the inferior and medial edges of the orbital rim. The nasal and paranasal sinus conformation and marked points are presented to assist surgeons in the preoperative evaluation. The overall recognition rate of the system for the nasal vestibule and nasal septum is 99.7%. The 3D images are considered to be beneficial by an otolaryngologist from Tri-Service General Hospital in Taiwan. The proposed method can facilitate the preoperative evaluation and improve the quality of medical care.