A four dukkha state-space model for hand tracking

In this paper, we propose a hand tracking method which was inspired by the notion of the four dukkha: birth, aging, sickness and death (BASD) in Buddhism. Based on this philosophy, we formalize the hand tracking problem in the BASD framework, and apply it to hand track hand gestures in isolated sign language videos. The proposed BASD method is a novel nature-inspired computational intelligence method which is able to handle complex real-world tracking problem. The proposed BASD framework operates in a manner similar to a standard state-space model, but maintains multiple hypotheses and integrates hypothesis update and propagation mechanisms that resemble the effect of BASD. The survival of the hypothesis relies upon the strength, aging and sickness of existing hypotheses, and new hypotheses are birthed by the fittest pairs of parent hypotheses. These properties resolve the sample impoverishment problem of the particle filter. The estimated hand trajectories show promising results for the American sign language

A Novel Method for Classification and Characterization of Urothelium Cell Culture Exposed to the Different PPARg Agonists

The main purpose of the thesis is to classify and characterize urothelium cell cultures under PPARg activator/inhibitor. In this project, the raw data are obtained from videos of three different cell cultures: TZ (PPARg activator), T0070907 (PPARg inhibitor) and a control culture. A cell tracking program based on the OpenCV computer vision library is applied to the videos to generate a dataset consisting of x,y coordinates of the tracked cells. The numerical computing environment MATLAB® is subsequently used to filter the data and extract features, which were applied to machine learning algorithms to classify the cell cultures. Results obtained indicate that the TZ/T0070907 addition can cause a change in the average behavior of cells, such as the number of cells in the culture, the speed of cells and the average clump size of cells. The work also demonstrates that there is a difference in single cell behavior among different cultures. In summary, it is proposed that the approach described in this project provides a potential way of analyzing the average behavior of cells in different cultures

Result Oriented Based Face Recognition using Neural Network with Erosion and Dilation Technique

It has been observed that many face recognition algorithms fail to recognize faces after plastic surgery and wearing the spec/glasses which are the new challenge to automatic face recognition. Face detection is one of the challenging problems in the image processing. This seminar, introduce a face detection and recognition system to detect (finds) faces from database of known people. To detect the face before trying to recognize it saves a lot of work, as only a restricted region of the image is analyzed, opposite to many algorithms which work considering the whole image. In This , we gives study on Face Recognition After Plastic Surgery (FRAPS )and after wearing the spec/glasses with careful analysis of the effects on face appearance and its challenges to face recognition. To address FRAPS and wearing the spec/glasses problem, an ensemble of An Optimize Wait Selection By Genetic Algorithm For Training Artificial Neural Network Based On Image Erosion and Dilution Technology. Furthermore, with our impressive results, we suggest that face detection should be paid more attend to. To address this problem, we also used Edge detection method to detect i/p image properly or effectively. With this Edge Detection also used genetic algorithm to optimize weight using artificial neural network (ANN)and save that ANN file to database .And use that ANN file to compare face recognition in future DOI: 10.17762/ijritcc2321-8169.16041

Energy efficient enabling technologies for semantic video processing on mobile devices

Semantic object-based processing will play an increasingly important role in future multimedia systems due to the ubiquity of digital multimedia capture/playback technologies and increasing storage capacity. Although the object based paradigm has many undeniable benefits, numerous technical challenges remain before the applications becomes pervasive, particularly on computational constrained mobile devices. A fundamental issue is the ill-posed problem of semantic object segmentation. Furthermore, on battery powered mobile computing devices, the additional algorithmic complexity of semantic object based processing compared to conventional video processing is highly undesirable both from a real-time operation and battery life perspective. This thesis attempts to tackle these issues by firstly constraining the solution space and focusing on the human face as a primary semantic concept of use to users of mobile devices. A novel face detection algorithm is proposed, which from the outset was designed to be amenable to be offloaded from the host microprocessor to dedicated hardware, thereby providing real-time performance and reducing power consumption. The algorithm uses an Artificial Neural Network (ANN), whose topology and weights are evolved via a genetic algorithm (GA). The computational burden of the ANN evaluation is offloaded to a dedicated hardware accelerator, which is capable of processing any evolved network topology. Efficient arithmetic circuitry, which leverages modified Booth recoding, column compressors and carry save adders, is adopted throughout the design. To tackle the increased computational costs associated with object tracking or object based shape encoding, a novel energy efficient binary motion estimation architecture is proposed. Energy is reduced in the proposed motion estimation architecture by minimising the redundant operations inherent in the binary data. Both architectures are shown to compare favourable with the relevant prior art

The Role of Adaptation in Archaeological Explanation

Adaptation, a venerable icon in archaeology, often is afforded the vacuous role of being an ex-post-facto argument used to »explain» the appearance and persistence of traits among prehistoric groups- A position that has seriously impeded development of a selectionist perspective in archaeology. Biological and philosophical definitions of adaptation- A nd by extension, definitions of adaptedness-vary considerably, but all are far removed from those usually employed in archaeology. The prevailing view in biology is that adaptations are features that were shaped by natural selection and that increase the adaptedness of an organism. Thus adaptations are separated from other features that may contribute to adaptedness but are products of other evolutionary processes. Analysis of adaptation comprises two stages: Showing that a feature was under selection and how the feature functioned relative to the potential adaptedness of its bearers. The archaeological record contains a wealth of information pertinent to examining the adaptedness of prehistoric groups, but attempts to use it will prove successful only if a clear understanding exists of what adaptation is and is not

Forward Collision Prediction with Online Visual Tracking

Safety is the key aspect when comes to driving. Self-driving vehicles are equipped with driver-assistive technologies like Adaptive Cruise Control, Forward Collision Warning system (FCW) and Collsion Mitigation by Breaking (CMbB) to ensure safety while driving. This paper proposes a method by following a lean way of multi-target tracking implementation and 3D bounding box detection without processing much visual information. Object Tracking is an integral part of environment sensing, which enables the vehicle to estimate the surrounding object’s trajectories to accomplish motion planning. The advancement in the object detection methods greatly benefits when following the tracking by detection approach. This will lead to less complex tracking methodology and thus decreasing the computational cost. Estimation based on particle filter is added to precisely associate the tracklets with detections. The model estimates and plots bounding box for the objects in its camera range and predict the 3D positions in camera coordinates from monocular camera data using a deep learning combined with geometric constraints using 2D bounding box, then the actual distance from the vehicle camera is calculated. The model is evaluated on the KITTI car dataset

Eugenic Ideology and the Institutionalization of the ‘Technofix’ on the Underclass

This scenario for the twenty-first century, in which China assumes world domination and establishes a world eugenic state, may well be considered an unattractive future. But this is not really the point. Rather, it should be regarded as the inevitable result of Francis Galton’s (1909) prediction made in the first decade of the twentieth century, that “the nation which first subjects itself to rational eugenical discipline is bound to inherit the earth” (p. 34)” (Lynn, 2001 p. 320)

Environmentally robust multiple camera tracking

A significant growth of the use of surveillance cameras has arisen from both the availability of low-cost home security and post-September 11th security measures. With such a plethora of surveillance cameras available and already in use, tracking a person or object from one field of view to another accurately is a challenging possibility; recognising the same person at different spatial locations, under different lighting conditions, at different scales and orientations. In order to address these challenges and provide a solution, a review of recent and past literature is provided. The main theme of this research is investigating methods to improve tracking of objects and people in dynamic environments and applying computational techniques to provide solutions to optimise such tracking systems. Image processing techniques are explored and refactored to adapt to currently available single-board computing power. Optimisation methods for speed of computing are investigated, presenting the paradigm of parallel programming during the design of “computationally intense” algorithms. The research also addresses cross-platform software/ server application design. In controlled environments current tracking systems perform well, however, this project explores methods to take multiple camera tracking to a higher level where they can, in real time, robustly cope with: rapid changes in lighting and track objects between indoor and outdoor scenarios at any time of day or in any weather conditions, severe image occlusion, rapid changes in direction, orientation and velocity of the object being tracked and be invariant to image clutter and noise. Thus the outputs are twofold: track a human/object across multiple cameras and ensure the algorithm is fast enough to run in real time on a modern processor. This research explores algorithms to deliver colour illumination invariance, also known as colour constancy. Colour illumination invariance can be applied as a pre-processing step to all cameras in a multi-camera environment. The research also investigates experimental assessment of multi-camera performance, focusing mainly on robustness to environmental changes. There are three main objectives for a tracking algorithm being used in the proposed system. Firstly, the tracking algorithm must accurately detect objects independently of their scale change and rotation. Secondly, the tracking algorithm must accurately detect objects across multiple cameras in different lighting conditions. The third objective for the tracking algorithm is that it must be able to attain a high level of colour constancy. The last objective can be implemented as a pre-processing step to such a tracking algorithm. This research explores the use of the Scale Invariant Feature Transform (SIFT) and the Speeded-Up Robust Features (SURF) algorithm. These algorithms are discussed in detail in the literature review as well as methods for providing colour illumination invariance