Aamir Saeed Malik

Consideration of illumination effects and optimization of window size for accurate calculation of depth map for 3D shape recovery

Obtaining an accurate and precise depth map is the ultimate goal for 3D shape recovery. For depth map estimation, one of the most vital parts is the initial selection of the focus measure and processing the images with the selected focus measure. Although, many focus measures have been proposed in the literature but not much attention has been paid to the factors affecting those focus measures as well as the manner the images are processed with those focus measures. In this paper, for accurate calculation of depth map, we consider the effects of illumination on the depth map as well as the selection of the window size for application of the focus measures. The resulting depth map can further be used in techniques and algorithms leading to recovery of three-dimensional structure of the object which is required in many high-level vision applications. It is shown that the illumination effects can directly result in incorrect estimation of depth map if proper window size is not selected during focus measure computation. Further, it is shown that the images need some kind of pre-processing to enhance the dark regions and shadows in the image. For this purpose, an adaptive enhancement algorithm is proposed for pre-processing. In this paper, we prove that without such pre-processing for image enhancement and without the use of proper window size for the estimation of depth maps, it is not possible to obtain the accurate depth map.

A novel algorithm for estimation of depth map using image focus for 3D shape recovery in the presence of noise

Three-dimensional shape recovery from one or multiple observations is a challenging problem of computer vision. In this paper, we present a new Focus Measure for the estimation of a depth map using image focus. This depth map can subsequently be used in techniques and algorithms leading to the recovery of a three-dimensional structure of the object, a requirement of a number of high level vision applications. The proposed Focus Measure has shown robustness in the presence of noise as compared to the earlier Focus Measures. This new Focus Measure is based on an optical transfer function implemented in the Fourier domain. The results of the proposed Focus Measure have shown drastic improvements in estimation of a depth map, with respect to the earlier Focus Measures, in the presence of various types of noise including Gaussian, Shot, and Speckle noises. The results of a range of Focus Measures are compared using root mean square error and correlation metric measures.

Survey of NLOS identification and error mitigation problems in UWB-based positioning algorithms for dense environments

In this survey, the currently available ultra-wideband-based non-line-of-sight (NLOS) identification and error mitigation methods are presented. They are classified into several categories and their comparison is presented in two tables: one each for NLOS identification and error mitigation. NLOS identification methods are classified based on range estimates, channel statistics, and the actual maps of the building and environment. NLOS error mitigation methods are categorized based on direct path and statistics-based detection.

Energy consumption balancing (ECB) issues and mechanisms in wireless sensor networks (WSNs): a comprehensive overview

Wireless sensor networks comprise of a large number of low cost sensor nodes that have strictly restricted sensing, computation and communication capabilities. In addition to this, sensor nodes have limited battery life which is not rechargeable in many applications. Due to resource limitations for the sensor nodes, it is important to use energy efficiently for each sensor node. This will result in prolonged network lifetime and functionality. Energy consumption balancing (ECB) property ensures that the average energy dissipation per sensor is equal for all sensors in the network. ECB can be considered as energy efficiency property that optimally manages energy consumption of sensors to prolong network lifetime. This paper investigates the ECB theory and ECB related mechanisms. A classification of ECB mechanism is given by surveying the current and state of the art research in this area. In addition, comparison and main constraints of the mechanisms are presented. Copyright

Acne analysis, grading and computational assessment methods: an overview

Introduction: This paper presents a comprehensive review of acne grading and measurement. Acne is a chronic disorder of the pilosebaceous units, with excess sebum production, follicular epidermal hyperproliferation, inflammation and Propionibacterium acnes activity. Most patients are affected with acne vulgaris, which is the prevalent type of acne. Acne vulgaris consists of comedones (whitehead and blackhead), papules, pustules, nodules and cysts.

Application of Passive Techniques for Three Dimensional Cameras

Depth estimation is an active area of research for 3-dimensional (3D) shape recovery. In this paper, we present a passive depth estimation method. Since the 3-dimensional (3D) cameras currently available are quite expensive, we propose the passive method as a means to decrease the high cost associated with 3D cameras. Our algorithm is based on the fuzzy-neuro approach. A fuzzy inference system (FIS) is designed and trained using the neural network for the calculation of the depth map. The proposed approximation technique yields good results when it is tested with several 3D objects.

A survey of methods used for source localization using EEG signals

Abstract The EEG source localization which is used to localize the electrical activity of brain has been an active area of research as it provides useful information for study of brains physiological, mental and functional abnormalities. This problem is called EEG inverse problem. The localization of the active sources needs the solution of ill posed EEG inverse problem. Since the foundation of this field till today, many methods have been developed with the aim of in-depth localization, high resolution, reduction in localization/energy error and decreased computational time. In this survey, EEG inverse problem is discussed with its primary to most developed and recent solutions. The introduction to the field along with the categorization of different solutions is provided. Also, the relative advantages and limitations for each method are discussed. Finally, the challenges and future recommendations are provided, in the end, for further improvement of EEG inverse problem in terms of resolution, computational power and localization error.

Comparison of stochastic filtering methods for 3D tracking

In the recent years, the 3D visual research has gained momentum with publications appearing for all aspects of 3D including visual tracking. This paper presents a review of the literature published for 3D visual tracking over the past five years. The work particularly focuses on stochastic filtering techniques such as particle filter and Kalman filter. These two filters are extensively used for tracking due to their ability to consider uncertainties in the estimation. The improvement in computational power of computers and increasing interest in robust tracking algorithms lead to increase in the use of stochastic filters in visual tracking in general and 3D visual tracking in particular. Stochastic filters are used for numerous applications in the literature such as robot navigation, computer games and behavior analysis. Kalman filter is a linear estimator which approximates systems dynamics with Gaussian model while particle filter approximates systems dynamics using weighted samples. In this paper, we investigate the implementation of Kalman and particle filters in the published work and we provide comparison between these techniques qualitatively as well as quantitatively. The quantitative analysis is in terms of computational time and accuracy. The quantitative analysis has been implemented using four parameters of the tracked object which are object position, velocity, size of bounding ellipse and orientation angle.

Tone mapping of HDR images: A review

Real world contains a wide range of intensities that cannot be captured with traditional imaging devices. Moreover, even if these images are captured with special procedures, existing display devices cannot display them. This paper presents a comparative study of most famous tone mapping algorithms. Tone mapping is the process of compressing high dynamic range images into a low dynamic range so they can be displayed by traditional display devices. The study implements six tone mapping algorithms and performs a comparison between them by visual rating. Independent participant were asked to rate these images based on a given rating scheme. The study concluded that Reinhard tone mapping operators are the best in term of visual pleasure and maintaining image integrity. In addition, exponential tone mapping operators have achieved better rating compared the logarithmic operators.

3D shape recovery from image defocus using wavelet analysis

We propose a new method for depth from defocus (DFD) using wavelet transform. Most of the existing DFD methods use inverse filtering to determine the measure of defocus. These methods suffer from inaccuracies in finding the frequency domain representation due to windowing and border effects. The proposed method uses wavelets that allow performing both the local analysis and windowing with variable-sized regions for images with varying textural properties. We show that normalized image ratio of wavelet power by Parsevals theorem is closely related to blur parameter and depth. Experimental results have been presented demonstrating that the proposed DFD method is faster and generates more accurate depth maps than the previous methods.