Digital Coding of Waveforms

https://dspace.stir.ac.uk/bitstream/1893/25490/1/affective-computing-review.pdf

A review of affective computing

The Foundations of Statistics By Prof. Leonard J. Savage. (Wiley Publications in Statistics.) Pp. xv + 294. (New York; John Wiley and Sons, Inc.; London: Chapman and Hall, Ltd., 1954.) 48s. net.

Probability and Statistical Inference

Traditional and recent approaches in background modeling for foreground detection: An overview

Affect detection is an important pattern recognition problem that has inspired researchers from several areas The field is in need of a systematic review due to the recent influx of Multimodal (MM) affect detection systems that differ in several respects and sometimes yield incompatible results This article provides such a survey via a quantitative review and meta-analysis of 90 peer-reviewed MM systems The review indicated that the state of the art mainly consists of person-dependent models (622p of systems) that fuse audio and visual (556p) information to detect acted (522p) expressions of basic emotions and simple dimensions of arousal and valence (645p) with feature- (389p) and decision-level (356p) fusion techniques However, there were also person-independent systems that considered additional modalities to detect nonbasic emotions and complex dimensions using model-level fusion techniques The meta-analysis revealed that MM systems were consistently (85p of systems) more accurate than their best unimodal counterparts, with an average improvement of 983p (median of 660p) However, improvements were three times lower when systems were trained on natural (459p) versus acted data (127p) Importantly, MM accuracy could be accurately predicted (cross-validated R2 of 0803) from unimodal accuracies and two system-level factors Theoretical and applied implications and recommendations are discussed

A Review and Meta-Analysis of Multimodal Affect Detection Systems

The ever-growing numbers of medical digital images and the need to share them among specialists and hospitals for better and more accurate diagnosis require that patients’ privacy be protected. As a result of this, there is a need for medical image watermarking (MIW). However, MIW needs to be performed with special care for two reasons. Firstly, the watermarking procedure cannot compromise the quality of the image. Secondly, confidential patient information embedded within the image should be flawlessly retrievable without risk of error after image decompressing. Despite extensive research undertaken in this area, there is still no method available to fulfill all the requirements of MIW. This paper aims to provide a useful survey on watermarking and offer a clear perspective for interested researchers by analyzing the strengths and weaknesses of different existing methods.

/pdf/watermarking-techniques-used-in-medical-images-a-survey-2ntehh1yhk.pdf

Watermarking Techniques used in Medical Images: a Survey

This paper proposes a method for segmenting defects depicted in a thermal image of petrochemical equipments by means of passive thermography. The technique first enhances the contrast of the defects based on local neighborhood pixel intensity operation. This local intensity operation works in two modes, either brightening the pixels for detecting hot spots or darkening the pixels for detecting cold spots. The next step is to segment the defects using simple histogram-based thresholding techniques. We propose three thresholding methods: mean absolute thresholding (MAT), mean relative thresholding (MRT), and minimum frequency thresholding (MFT). Compared to existing techniques, we found that our proposed methods have better detection and success rate.

Defect detection in thermal image for nondestructive evaluation of petrochemical equipments

The ever-growing numbers of medical digital images and the need to share them among specialists and hospitals for better and more accurate diagnosis require that patients' privacy be protected. During the transmission of medical images between hospitals or specialists through the network, the main priority is to protect a patient's documents against any act of tampering by unauthorised individuals. Because of this, there is a need for medical image authentication scheme to enable proper diagnosis on patient. In addition, medical images are also susceptible to salt and pepper impulse noise through the transmission in communication channels. This noise may also be intentionally used by the invaders to corrupt the embedded watermarks inside the medical images. A common drawback of existing watermarking methods is their weakness against salt and pepper noise. The research carried out in this work addresses the issue of designing a new watermarking method that can withstand high density of salt and pepper noise for brain MRI images. For this purpose, combination of a spatial domain watermarking method, channel coding and noise filtering schemes are used. The region of non-interest (RONI) of MRI images from five different databases are used as embedding area and electronic patient record (EPR) is considered as embedded data. The quality of watermarked image is evaluated using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), and the accuracy of the extracted watermark is assessed in terms of Bit Error Rate (BER).

A robust medical image watermarking against salt and pepper noise for brain MRI images

In this paper, we present human emotion recognition systems based on audio and spatio-temporal visual features. The proposed system has been tested on audio visual emotion data set with different subjects for both genders. The mel-frequency cepstral coefficient (MFCC) and prosodic features are first identified and then extracted from emotional speech. For facial expressions spatio-temporal features are extracted from visual streams. Principal component analysis (PCA) is applied for dimensionality reduction of the visual features and capturing 97 % of variances. Codebook is constructed for both audio and visual features using Euclidean space. Then occurrences of the histograms are employed as input to the state-of-the-art SVM classifier to realize the judgment of each classifier. Moreover, the judgments from each classifier are combined using Bayes sum rule (BSR) as a final decision step. The proposed system is tested on public data set to recognize the human emotions. Experimental results and simulations proved that using visual features only yields on average 74.15 % accuracy, while using audio features only gives recognition average accuracy of 67.39 %. Whereas by combining both audio and visual features, the overall system accuracy has been significantly improved up to 80.27 %.

Human emotion recognition from videos using spatio-temporal and audio features

This paper presents an automatic segmentation of brain lesions from diffusion-weighted magnetic resonance imaging (DW-MRI or DWI) using region growing approach. The lesions are acute infarction, haemorrhage, tumour and abscess. Region splitting and merging is used to detect the lesion region. Then, histogram thresholding technique is applied to automate the seeds selection. The region is iteratively grown by comparing all unallocated neighbour pixels to the seeds. The difference between pixel’s intensity value and the region’s mean is used as the similarity measure. Evaluation is made for performance comparison between automatic and manual seeds selection. Overall, automated region growing algorithm provides comparable results with the semi-automatic segmentation.

Syed A.R. Abu-Bakar

Papers

Watermarking Techniques used in Medical Images: a Survey

Defect detection in thermal image for nondestructive evaluation of petrochemical equipments

A robust medical image watermarking against salt and pepper noise for brain MRI images

Human emotion recognition from videos using spatio-temporal and audio features

Automated region growing for segmentation of brain lesion in diffusion-weighted MRI