Emotion recognition in human-computer interaction

A control chart for detecting shifts in the variance of a process is developed for the case where the nominal value of the variance is unknown. As our approach does not require that the in-control variance be known a priori, it avoids the need for a lengthy Phase I data-gathering step before charting can begin. The method is a variance-change-point model, based on the likelihood ratio test for a change in variance with the conventional Bartlett correction, adapted for repeated sequential use. The chart may be used alone in settings where one wishes to monitor one-degree-of-freedom chi-squared variates for departure from control; or it may be used together with a parallel change-point methodology for the mean to monitor process data for shifts in mean and/or variance. In both the solo use and as the scale portion of a combined scheme for monitoring changes in mean and/or variance, the approach has good performance across the range of possible shifts.

A change-point model for a shift in variance

Multi-modal Medical Image Fusion based on Two-scale Image Decomposition and Sparse Representation

The objective of image contrast enhancement is to improve the contrast level of images, which are degraded during image acquisition. Image contrast enhancement is considered as an optimization problem in this paper and the artificial bee colony (ABC) algorithm is utilized to find the optimal solution for this optimization problem. The contribution of the proposed approach is two-fold. First, in view of that the fitness function is indispensable to evaluate the quality of the enhanced image, a new objective fitness function is proposed in this paper. Second, the image transformation function is critical to generate new pixel intensities for the enhanced image from the original input image; more importantly, it guides the searching movements of the artificial bees. For that, a parametric image transformation function is utilized in this paper so that only the optimal parameters used in the transformation function need to be searched by the ABC algorithm. This is in contrast to that the whole space of image intensity levels is used in the conventional ABC-based image enhancement approaches. Extensive experiments are conducted to demonstrate that the proposed approach outperforms conventional image contrast enhancement approaches to achieve both better visual image quality and higher objective performance measures.

Image contrast enhancement using an artificial bee colony algorithm

Image contrast is an essential visual feature that determines whether an image is of good quality. In computed tomography (CT), captured images tend to be low contrast, which is a prevalent artifact that reduces the image quality and hampers the process of extracting its useful information. A common tactic to process such artifact is by using histogram-based techniques. However, although these techniques may improve the contrast for different grayscale imaging applications, the results are mostly unacceptable for CT images due to the presentation of various faults, noise amplification, excess brightness, and imperfect contrast. Therefore, an ameliorated version of the contrast-limited adaptive histogram equalization (CLAHE) is introduced in this article to provide a good brightness with decent contrast for CT images. The novel modification to the aforesaid technique is done by adding an initial phase of a normalized gamma correction function that helps in adjusting the gamma of the processed image to avoid the common errors of the basic CLAHE of the excess brightness and imperfect contrast it produces. The newly developed technique is tested with synthetic and real-degraded low-contrast CT images, in which it highly contributed in producing better quality results. Moreover, a low intricacy technique for contrast enhancement is proposed, and its performance is also exhibited against various versions of histogram-based enhancement technique using three advanced image quality assessment metrics of Universal Image Quality Index (UIQI), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM). Finally, the proposed technique provided acceptable results with no visible artifacts and outperformed all the comparable techniques.

/pdf/an-innovative-technique-for-contrast-enhancement-of-computed-26shkdwiyh.pdf

An innovative technique for contrast enhancement of computed tomography images using normalized gamma-corrected contrast-limited adaptive histogram equalization

Histogram equalisation has been a much sought-after technique for improving the contrast of an image, which however leads to an over enhancement of the image, giving it an unnatural and degraded appearance. In this framework, a generalised contrast enhancement algorithm is proposed which is independent of parameter setting for a given dynamic range of the input image. The algorithm uses the modified histogram for spatial transformation on grey scale to render a better quality image irrespective of the image type. Added to this, two variants of the proposed methodology are presented, one of which preserves the brightness of original image while the other variant increases the image brightness adaptively, giving it a better look. Qualitative and quantitative assessments like degree of entropy un-preservation, edge-based contrast measure and structure similarity index measures are then applied to the 500 image data set for comparing the proposed algorithm with several existing state-of-the-art algorithms. Experimental results show that the proposed algorithm produces better or comparable enhanced images than several algorithms.

http://www.isical.ac.in/%7Esankar/paper/poddar_2013.pdf

Non-parametric modified histogram equalisation for contrast enhancement

Multiple Model Adaptive Complementary Filter for Attitude Estimation

A Comprehensive Overview of Inertial Sensor Calibration Techniques

With rapid advancements in the area of mobile robotics and industrial automation, a growing need has arisen towards accurate navigation and localization of moving objects. Camera based motion estimation is one such technique which is gaining huge popularity owing to its simplicity and use of limited resources in generating motion path. In this paper, an attempt is made to introduce this topic for beginners covering different aspects of vision based motion estimation task. The evolution of VO schemes over last few decades is discussed under two broad categories, that is, geometric and non-geometric approaches. The geometric approaches are further detailed under three different classes, that is, feature-based, appearance-based, and a hybrid of feature and appearance based schemes. The non-geometric approach is one of the recent paradigm shift from conventional pose estimation technique and is thus discussed in a separate section. Towards the end, a list of different datasets for visual odometry and allied research areas are provided for a ready reference.

Shashi Poddar

Papers

Non-parametric modified histogram equalisation for contrast enhancement

Multiple Model Adaptive Complementary Filter for Attitude Estimation

A Comprehensive Overview of Inertial Sensor Calibration Techniques

Evolution of Visual Odometry Techniques.

Window based Multiple Model Adaptive Estimation for Navigational Framework