Objective blur assessment based on contraction errors of local contrast maps

Blur distortion appears in multimedia content due to acquisition, compression or transmission errors. In this paper, a method is proposed to predict blur severity based on the contraction errors of local contrast maps. The proposed method is developed from the observation that histogram distribution of natural image would contract according to the degree of blur distortion. In order to quantify the level of contraction, an efficient method of determining local contrast boundaries is used. The upper and lower bounds of local histogram distribution are defined for the original image, and outlying points beyond these bounds are used to form the local contrast map. For the corresponding patch of a blur image, the same values of upper and lower bounds are used and the local contrast map for the blur image could be produced. Total difference between local contrast maps of the original and blur images is the contraction errors which are used to derive the blur score. The proposed method has advantages in terms of computation efficiency, and is performed in the spatial domain without the need of data transformation, conversion or filtering. In addition, prior training is not required at all for the model. Implementation of the proposed method as a multimedia tool is useful for estimating blur severity in multimedia content. The performance of the proposed method is verified by using three different blur databases and compared to popular state-of-the-artmethods. Experiment results show that the proposed blur metric has high correlation with human perception of blurriness

New spatiotemporal method for assessing video quality

The existence of temporal effects and temporal distortions in a video differentiate the way it is assessed from an image. Temporal effects and distortions can enhance or depress the visibility of spatial effects in a video. Thus, the temporal part of videos plays a significant role in determining the video quality. In this study, a spatiotemporal video quality assessment (VQA) method is proposed due to the importance of temporal effects and distortions in assessing video quality. Instead of measuring the frame quality on a frame basis, the quality of several averaged frames is measured. The proposed spatiotemporal VQA method is significantly improved compared with image quality assessment (IQA) methods applied on a frame basis. When combined with IQA methods, the proposed spatiotemporal VQA method has comparable performance with state-of-the-art VQA methods. The computational complexity of the proposed temporal method is also lower when compared with current VQA methods

Novel face recognition approach using bit-level information and dummy blank images in feedforward neural network

Bit-level information is useful in image coding especially in image compression. A digital image is constructed by multilevel information of bits, called as bit-plane information. For an 8-bits gray level digital image, bit-plane extraction has ability to extract 8 layers of bit-plane information. Conventional neural network-based face recognition usually used gray images as training and testing data. This paper presents a novel method of using bit-level images as input to feedforward neural network. CMU AMP Face Expression Database is used in the experiments. Experiment result showed improvement in recognition rate, false acceptance rate (FAR), false rejection rate (FRR) and half total error rate (HTER) for the proposed method. Additional improvement is proposed by introducing dummy blank images which consist of plain 0 and 1 images in the neural network training set. Experiment result showed that the final proposed method of introducing dummy blank images improve FAR by 3.5

Online Person Identification based on Multitask Learning

In the digital world, everything is digitized and data are generated consecutively over the times. To deal with this situation, incremental learning plays an important role. One of the important applications that needs an incremental learning is person identification. On the other hand, password and code are no longer the only way to prevent the unauthorized person to access the information and it tends to be forgotten.  Therefore, biometric characteristics system is introduced to solve the problems. However, recognition based on single biometric may not be effective, thus, multitask learning is needed. To solve the problems, incremental learning is applied for person identification based on multitask learning. Considering that the complete data is not possible to be collected at one time, online learning is adopted to update the system accordingly. Linear Discriminant Analysis (LDA) is used to create a feature space while Incremental LDA (ILDA) is adopted to update LDA. Through multitask learning, not only human faces are trained, but fingerprint images are trained in order to improve the performance. The performance of the system is evaluated by using 50 datasets which includes both male and female datasets. Experimental results demonstrate that the learning time of ILDA is faster than LDA. Apart from that, the learning accuracies are evaluated by using K-Nearest Neighbor (KNN) and achieve more than 80% for most of the simulation results. In the future, the system is suggested to be improved by using better sensor for all the biometrics. Other than that, incremental feature extraction is improved to deal with some other online learning problems

Online Person Identification based on Multitask Learning

In the digital world, everything is digitized and data are generated consecutively over the times. To deal with this situation, incremental learning plays an important role. One of the important applications that needs an incremental learning is person identification. On the other hand, password and code are no longer the only way to prevent the unauthorized person to access the information and it tends to be forgotten. Therefore, biometric characteristics system is introduced to solve the problems. However, recognition based on single biometric may not be effective, thus, multitask learning is needed. To solve the problems, incremental learning is applied for person identification based on multitask learning. Considering that the complete data is not possible to be collected at one time, online learning is adopted to update the system accordingly. Linear Discriminant Analysis (LDA) is used to create a feature space while Incremental LDA (ILDA) is adopted to update LDA. Through multitask learning, not only human faces are trained, but fingerprint images are trained in order to improve the performance. The performance of the system is evaluated by using 50 datasets which includes both male and female datasets. Experimental results demonstrate that the learning time of ILDA is faster than LDA. Apart from that, the learning accuracies are evaluated by using K-Nearest Neighbor (KNN) and achieve more than 80% for most of the simulation results. In the future, the system is suggested to be improved by using better sensor for all the biometrics. Other than that, incremental feature extraction is improved to deal with some other online learning problems

Face detection based on HSI information and tracking by Kalman filter

This project is to investigate the implementation of face detection and tracking in image sequences. The face detection is based on skin-color using hue, saturation and intensity (HSI) of the human skin to locate and find faces in images.Master of Science (Computer Control and Automation

An error-based video quality assessment method with temporal information

t Videos are amongst the most popular online media for Internet users nowadays. Thus, it is of utmost importance that the videos transmitted through the internet or other transmission media to have a minimal data loss and acceptable visual quality. Video quality assessment (VQA) is a useful tool to determine the quality of a video without human intervention. A new VQA method, termed as Error and Temporal Structural Similarity (EaTSS), is proposed in this paper. EaTSS is based on a combination of error signals, weighted Structural Similarity Index (SSIM) and difference of temporal information. The error signals are used to weight the computed SSIM map and subsequently to compute the quality score. This is a better alternative to the usual SSIM index, in which the quality score is computed as the average of the SSIM map. For the temporal information, the second-order time-differential information are used for quality score computation. From the experiments, EaTSS is found to have competitive performance and faster computational speed compared to other existing VQA algorithms

Face And Palmprint Multimod al Biometric System Based On Bit-Plane Decomposition Approach

Bit-plane decomposition approach has been introduced lately for single trait biometric system such as face, palmprint, and fingerprint recognition. This approach is able to provide promising high performance rate while reducing the data dimensionality. However, this approach has not been tested on multimodal biometric system which uses more than one biometric trait. Hence, this paper introduces a new multimodal biometric system based on face and palmprint fusion with bitplane decomposition approach. Pixel level fusion is applied by using simple averaging method before bit-plane feature extraction. Principal Component Analysis is also used on the hybrid face-palm bit planes for further dimension reduction before being classified by Feedforward Backpropagation Neural Network. The experimental results show that the proposed system is able to provide high recognition rate