Jing-Wein Wang

Nested image steganography scheme using QR-barcode technique

In this paper, QR bar code and image processing techniques are used to construct a nested steganography scheme. There are two types of secret data lossless and lossy embedded into a cover image. The lossless data is text that is first encoded by the QR barcode; its data does not have any distortion when comparing with the extracted data and original data. The lossy data is a kind of image; the face image is suitable for our case. Because the extracted text is lossless, the error correction rate of QR encoding must be carefully designed. We found a 25% error correction rate is suitable for our goal. In image embedding, because it can sustain minor perceptible distortion, we thus adopted the lower nibble byte discard of the face image to reduce the secret data. When the image is extracted, we use a median filter to filter out the noise and obtain a smoother image quality. After simulation, it is evident that our scheme is robust to JPEG attacks. Compared to other steganogra- phy schemes, our proposed method has three advantages: i the nested scheme is an enhanced security system never previously developed; ii our scheme can conceal lossless and lossy secret data into a cover image simultaneously; and iii the QR barcode used as secret data can widely extend this methods application fields.

Enhanced Ridge Structure for Improving Fingerprint Image Quality Based on a Wavelet Domain

Fingerprint image enhancement is one of the most crucial steps in an automated fingerprint identification system. In this paper, an effective algorithm for fingerprint image quality improvement is proposed. The algorithm consists of two stages. The first stage is decomposing the input fingerprint image into four subbands by applying two-dimensional discrete wavelet transform. At the second stage, the compensated image is produced by adaptively obtaining the compensation coefficient for each subband based on the referred Gaussian template. The experimental results indicated that the compensated image quality was higher than that of the original image. The proposed algorithm can improve the clarity and continuity of ridge structures in a fingerprint image. Therefore, it can achieve higher fingerprint classification rates than related methods can.

Face Recognition Based on Projected Color Space With Lighting Compensation

In this letter, we propose a novel color space conversion method called adaptive projection color space (APCS). This method includes two portions: adaptive singular value decomposition and an inner product conversion algorithm for color images. We employed images from the Color FERET and CMU-PIE databases for training and experiment. The results revealed that the recognition rates from our proposed APCS approach were higher than other color spaces and those of methods proposed in relevant studies.

Inferior sleeve fracture of the patella.

Patella sleeve fracture is a rare fracture that only occurs in children. Diagnosis is difficult both clinically and radiologically. A high-riding patella and hemarthrosis are important signs when diagnosing this fracture. We report a case of an 11-year-old boy who suffered from patella sleeve fracture without visible bony fragments on a lateral radiograph. Open reduction with transosseous tunneling and patellotibial cerclage wiring for anastomosis protection was performed. Early weight bearing was achieved together with a satisfactory range of knee motion. Premature anterior physeal arrest was noted because of insertion of cerclage wire in the open physis. However, no genu recurvatum was present 2 years after the initial operation. An awareness of sleeve fracture, together with its characteristic clinical and radiological features, is important to avoid misdiagnosis and treatment delay. If the cerclage wire technique is used, care should be taken not to disturb the proximal tibial apophysis.

Color face image enhancement using adaptive singular value decomposition in fourier domain for face recognition

Face recognition is still a challenging problem because of large intra-class variability, small inter-class variability, and the presence of lighting variation. To deal with these difficulties, an illumination compensation method, adaptive singular value decomposition in the two-dimensional discrete Fourier domain (ASVDF) and an efficient brightness detector for lighting detection, for face image enhancement are proposed in this paper. The proposed enhancement algorithm involves three steps: In the first step, uniform lighting is rapidly distinguished from lateral lighting in the image by using the brightness detector, which is based on the percentage ratio of pixels among the three RGB color channels. ASVDF is then globally performed for the uniform lighting image, whereas ASVDF is applied block-by-block for the lateral lighting image. In addition, to reduce computing time, a region-based ASVDF method is introduced; the ASVDF method is applied to four regions of the face image. Experimental results for the CMU-PIE, Color FERET, and FEI face databases show that the method considerably improves the quality of face images, even lateral lighting, thereby improving the accuracy of face recognition substantially. Adaptive singular value decomposition in the Fourier domain is proposed for face recognition.Self-adapted illumination compensation is devised to overcome lighting variation.Experimental results are demonstrated on CMU, FERET, and FEI databases to verify the effectiveness.

Contrast enhancement of x-ray image based on singular value selection

Low-contrast profile images are frequently encountered in medical practice, and the correct interpretation of these images is of vital importance. This study introduces a contrast enhancement technique based on singular value decomposition (SVD) to enhance low-contrast fracture x-ray images. We propose a development of the traditional singular value solution by applying a feature selection process on the extracted singular values. The proposal calls for the establishment of a feature space in which the interpretability or perception of information in images for human viewers is enhanced, while noise and blurring are reduced. In this approach, the area of interest is manually cropped, and histogram equalization (HE) and singular value selection procedures are then conducted for comparative study. This approach exploits the spectral property of SVD, and the singular value selection algorithm is developed based on the corresponding Fourier domain technique for high frequency enhancement. The proposed method can generate more enhanced views of the target images than HE processing. Ten physicians confirm the performance of the proposed model using the visual analog scale (VAS). The average VAS score improves from 2.5 with HE to 8.3 using the proposed method. Experimental results indicate that the proposed method is helpful in fracture x-ray image processing.

Three-step approach for wafer sawing lane inspection

Wafer sawing performance must be closely monitored to ensure a satisfactory integrated circuits manufacturing yield. The inspection must allow the GO/NG decision to be fast and reliable, while also assuring that the training of the inspector is simple and not time consuming. The traditional neural-network approach to inspect images, while simple to implement, presents some disadvantages, including training efficiency and model effectiveness. Based on contour detection of the sawing lane, this work proposes a novel method combined with cross-center localization of sawing lanes, detection of sawing track, and four signatures to detect the abnormality of sawing effectively and timely. Our method does not need pretraining but runs faster and provides a better method with more effectiveness, higher flexibility, and immediate feedback to the sawing operation. An experiment using real data collected from an international semiconductor package factory is conducted to validate the performance of the proposed framework. The accurate acceptance rate and the accurate rejection rate are both 100%, while the false acceptance rate and false rejection rate are both zero as well. The results demonstrate that the proposed method is sound and useful for sawing inspection in industries.

Securing traffic at QoS-aware residential gateway using biometric signatures

Home networks are becoming ubiquitous. Multimedia is a primary home network application that is challenging bandwidth capabilities with respect to QoS and also creating issues regarding authorized access to the home network and its digital content. To deal with these issues, this study presents an embedded QoS-aware residential gateway (EmQRG) in the home network which classifies forwarded traffic for optimal use of bounded network bandwidth resources and, instead of traditional password methodology, requires bimodal biometric recognition of users. A laboratory EmQRG testbed with bimodal biometric recognition system (BBRS) is implemented via class-based queuing (CBQ) bandwidth management in a 3-PC network including a real DiffServ-capable CBQ-capable router, CCD camera and optical fingerprint reader. Novel computational methods are used for face and fingerprint analysis and recognition. It is confirmed that combining two biometric modalities improves performance, particularly for the false acceptance rate (FAR) that is considered the most serious home security problem. Experimental results demonstrate efficiency and robustness of both the EmQRG and the BBRS.

Framework combined face segmentation with recognition

An elliptic face segmentation algorithm, called a facial component extractor (FCExtractor), was recently proposed. The algorithm is based on a novel overcomplete wavelet template, a support vector machine (SVM) classifier, and wavelet entropy filtering. It is designed to consistently detect and segment the eyes-nose-mouth T-shaped facial region via ellipse. Thereafter, head orientation is estimated by using the ratio of cheeks. To evaluate the effectiveness of the FCExtractor, we introduce a face detection measure based on the distance between the expected and segmented eye-mouth triangle circumscribed circle areas. We then apply the local description of the segmented face through normalization, illumination normalization, log-polar mapping, and self-eigenface to achieve recognition. The novelty of this approach for face representation comes from the derivation of the likelihood fitness function for self-eigenface selection of a discriminative subset and the adaptive threshold value. The approach maximizes the differences amidst face images of different persons, and it also minimizes the expression and pose variations of the same person. Experimental results on available databases and a live sequence show that our method is superior to conventional methods based on rectangular face segmentation against complex scenes.

Recognition based on two separated singular value decomposition-enriched faces

Abstract. In previous studies on human face recognition, illumination pretreatment has been considered to be among the most crucial steps. We propose the illumination compensation algorithm two separated singular value decomposition (TSVD). TSVD consists of two parts, namely the division of high- and low-level images and singular value decomposition, which are implemented according to self-adapted illumination compensation to resolve the problems associated with strong variation of light and to improve face recognition performance. The mean color values of the three color channels R, G, and B are used as the thresholds, and two subimages of two types of light levels are then input with the division of the maximal mean and minimal mean, which are incorporated with light templates at various horizontal levels. The dynamic compensation coefficient is proportionately adjusted to reconstruct the subimages. Finally, two subimages are integrated to achieve illumination compensation. In addition, we combined TSVD and the projection color space (PCS) to design a new method for converting the color space called the two-level PCS. Experimental results demonstrated the efficiency of our proposed method. The proposed method not only makes the skin color of facial images appear softer but also substantially improves the accuracy of face recognition, even in facial images that were taken under conditions of lateral light or exhibit variations in posture.