Ear Contour Detection and Modeling Using Statistical Shape Models

Ear detection is an actively growing area of research because of its applications in human head tracking and biometric recognition. In head tracking, it is used to augment face detectors and to perform pose estimation. In biometric systems, it is used both as an independent modality and in multi-modal biometric recognition. The ear shape is the preferred feature used to perform detection because of its unique structure in both 2D color images and 3D range images. Ear shape models have also been used in literature to perform ear detection, but at a cost of a loss in information about the exact ear structure. In this thesis, we seek to address these issues in existing methods by a combination of techniques including Viola Jones Haar Cascades, Active Shape Models (ASM) and Dijkstra\u27s shortest path algorithm to devise a shape model of the ear using geometric parameters and mark an accurate contour around the ear using only 2D color images. The Viola Jones Haar Cascades classifier is used to mark a rectangular region around the ear in a left side profile image. Then a set of key landmark points around the ear including the ear outer helix, the ear anti-helix and the ear center is extracted using the ASM. This set of landmarks is then fed into Dijkstra\u27s shortest path algorithm which traces out the strongest edge between adjacent landmarks, to extract the entire ear outer contour, while maintaining a high computational efficiency

Scheduling task dependence graphs with variable task execution times onto heterogeneous multiprocessors

ABSTRACT We present a statistical optimization approach for scheduling a task dependence graph with variable task execution times onto a heterogeneous multiprocessor system. Scheduling methods in the presence of variations typically rely on worst-case timing estimates for hard real-time applications, or average-case analysis for other applications. However, a large class of soft real-time applications require only statistical guarantees on latency and throughput. We present a general statistical model that captures the probability distributions of task execution times as well as the correlations of execution times of different tasks. We use a Monte Carlo based technique to perform makespan analysis of different schedules based on this model. This approach can be used to analyze the variability present in a variety of soft real-time applications, including a H.264 video processing application. We present two scheduling algorithms based on statistical makespan analysis. The first is a heuristic based on a critical path analysis of the task dependence graph. The other is a simulated annealing algorithm using incremental timing analysis. Both algorithms take as input the required statistical guarantee, and can thus be easily re-used for different required guarantees. We show that optimization methods based on statistical analysis show a 25-30% improvement in makespan over methods based on static worst-case analysis

An Automated Exploration Framework for FPGA-Based Soft Multiprocessor Systems

FPGA-based soft multiprocessors are viable system solutions for high performance applications. They provide a software abstraction to enable quick implementations on the FPGA. The multiprocessor can be customized for a target application to achieve high performance. Modern FPGAs provide the capacity to build a variety of micro-architectures composed of 20-50 processors, complex memory hierarchies, heterogeneous interconnection schemes and custom co-processors for performance critical operations. However, the diversity in the architectural design space makes it difficult to realize the performance potential of these systems. In this paper we develop an exploration framework to build efficient FPGA multiprocessors for a target application. Our main contribution is a tool based on Integer Linear Programming to explore micro-architectures and allocate application tasks to maximize throughput. Using this tool, we implement a soft multiprocessor for IPv4 packet forwarding that achieves a throughput of 2 Gbps, surpassing the performance of a carefully tuned hand design

Circular Convolutional Learned ISTA for Automotive Radar DOA Estimation

Radar is imperative for many automotive applications in detecting targets. Accurate direction of arrival (DOA) estimation is essential for maximizing the reliability of radar by improving the angular resolution. And a lightweight algorithm with a small memory footprint is desired considering that limited computational resources are accessible for automotive radar. Conventionally, iterative algorithms such as iterative shrinkage thresholding algorithm (ISTA) were used for DOA estimation. However, algorithms like ISTA can require many iterations to converge, and a lot of manual parameter tuning is required to obtain optimal performance. Learned ISTA (LISTA) has been used to approximate ISTA with fewer iterations without the necessity of manual tuning by unfolding the iterative algorithm as a neural network. But directly using LISTA is not suitable for DOA estimation due to the large size of the matrices that need to be learned. The large number of learning parameters require a lot of training data, a long training time, and heavy computation. This work proposes to use circular convolutions to reduce the number of learning parameters in the model as well as computation. We show that the circular convolution-based ISTA has better performance metrics than the traditional ISTA

Biological control of surface temperature in the Arabian Sea

By far the dominant variable parameter controlling the absorption cross-section for short-wavelength solar radiation incident on the ocean surface is the concentration of photosynthetic pigment contained in phytoplankton cells. The abundance of phytoplankton depends on the intensity of incident radiation and on the supply of essential nutrients (nitrogen in particular). A higher abundance increases absorption of radiation and thus enhances the rate of heating at the ocean surface. In the Arabian Sea, the southwest monsoon promotes seasonal upwelling of deep water, which supplies nutrients to the surface layer and leads to a marked increase in phytoplankton growth. Using remotely sensed data on ocean colour, we show here that the resulting distribution of phytoplankton exerts a controlling influence on the seasonal evolution of sea surface temperature. This results in a corresponding modification of ocean-atmosphere heat exchange on regional and seasonal scales. Thus we show that this biological mechanism may provide an important regulating influence on ocean-atmosphere interactions

Has quality of suicide reporting by print media changed in India? A re-examination of previous findings

Background: Suicide is a serious mental health problem in India, and suicide rates in India have risen over the past decades. Reporting of suicide by the media is a common cause for spurts of suicides that may occur. Methodology: Suspected suicide by the renowned actor Sushant Singh Rajput was selected as the reference case. The top two Indian daily newspapers published in English having the highest circulation as per data provided by the Registrar of Newspapers, Government of India, were selected to be part of the study. The authors screened all news stories in the two newspapers within a 6-month period (3 months prior and 3 months post the date of the reference suicide case), and these news reports were evaluated as per the suicide reporting guidelines for media laid down by the Indian Psychiatric Society. The data were analyzed using Chi-square test and descriptive statistics where appropriate. Results: Our search yielded 158 articles from a period of 6 months, with 50 articles published before the suspected celebrity suicide and 108 published after. 29.7% had the word suicide in headline, 14.6% of them had news printed on the first page, 17.7% had a suicide note mentioned, whereas 1.9% mentioned prior attempts by victim. Conclusions: There is no change in media trend toward reporting suicide as noted following postcelebrity suicide, and so it is essential that media follow guidelines stringently when reporting a serious problem like suicide