A Model for Self Similar Search in Image Database with Scars

In Thailand, the entire Thai citizens are required to carry a national identity card. Therefore, a personal data of each person such as date of birth, weight, height, blood type, religion, occupation, registered address and individual photograph image are kept in the Central Registration Database Systems (CRDS) operated by the Department of Local Administration under the supervision of the Ministry of Interior. And in order to increase the efficiency of the internal administration, this Central Registration Database can be utilized and shared by other authorized government agencies at their best interest. The Thai Royal Police Department and the Crime Combat Section are one of the government agencies that share the information from the CRDS. Frequently, individual registration database and photographs are needed for the crime investigation. In previous research, the author proposed a method called “self-similarity search” to handle searching for photographs image database (PID) to support police officers when searching criminal records [18]. By using the proposed method in comparison with the sequential search employed a database search. As a result the proposed method is faster than sequential searching and requires less space. In this research, it is aimed at the refinement of attribute, which will guarantee much faster result when using our proposed searching method provided that scars of suspected person has been earlier foun

Deep learning for cardiac image segmentation: A review

Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research

Fingerabdruckswachstumvorhersage, Bildvorverarbeitung und Multi-level Judgment Aggregation

Im ersten Teil dieser Arbeit wird Fingerwachstum untersucht und eine Methode zur Vorhersage von Wachstum wird vorgestellt. Die Effektivität dieser Methode wird mittels mehrerer Tests validiert. Vorverarbeitung von Fingerabdrucksbildern wird im zweiten Teil behandelt und neue Methoden zur Schätzung des Orientierungsfelds und der Ridge-Frequenz sowie zur Bildverbesserung werden vorgestellt: Die Line Sensor Methode zur Orientierungsfeldschätzung, gebogene Regionen zur Ridge-Frequenz-Schätzung und gebogene Gabor Filter zur Bildverbesserung. Multi-level Jugdment Aggregation wird eingeführt als Design Prinzip zur Kombination mehrerer Methoden auf mehreren Verarbeitungsstufen. Schließlich wird Score Neubewertung vorgestellt, um Informationen aus der Vorverarbeitung mit in die Score Bildung einzubeziehen. Anhand eines Anwendungsbeispiels wird die Wirksamkeit dieses Ansatzes auf den verfügbaren FVC-Datenbanken gezeigt.Finger growth is studied in the first part of the thesis and a method for growth prediction is presented. The effectiveness of the method is validated in several tests. Fingerprint image preprocessing is discussed in the second part and novel methods for orientation field estimation, ridge frequency estimation and image enhancement are proposed: the line sensor method for orientation estimation provides more robustness to noise than state of the art methods. Curved regions are proposed for improving the ridge frequency estimation and curved Gabor filters for image enhancement. The notion of multi-level judgment aggregation is introduced as a design principle for combining different methods at all levels of fingerprint image processing. Lastly, score revaluation is proposed for incorporating information obtained during preprocessing into the score, and thus amending the quality of the similarity measure at the final stage. A sample application combines all proposed methods of the second part and demonstrates the validity of the approach by achieving massive verification performance improvements in comparison to state of the art software on all available databases of the fingerprint verification competitions (FVC)

Efficiency in utilizing ICT infrastructure in developing countries: a case study of the Royal Thai Police\u27s attitudes to the adoption of an image retrieval application for eyewitness identification

One of the most important aspects of decision and policy making is the timely access to accurately and relevant information. At present the situation in some developing countries is that communication and exchange of information between the government agencies are still paper-based. It can often take weeks or months for one government agency to obtain the records it requires from another agency. The lack of communication between these agencies often results in duplication of efforts and inefficiencies. This lack of communication also means that agencies often produce more of the irrelevant albeit sophisticated information (such as the Statistics Division) than the essential information that is critical and actually needed by other agency for decision making. (World Bank report, 1998) In order to bring people into information society, to have access to information, it is crucial to have appropriate technology and applications that compatible with both old and new technologies—given that majority can not afford to keep up with new technologies being introduced everyday--as well as quality programming in indigenous languages, To create an information society in developing countries, we must first have knowledge of their past, understand their present. Only then participating in their future can be more probable and possible (Matsepe-Casaburri 1996) The overall aim of this study is to add value to the process of information sharing among the government departments in Thailand. It does this by analyzing the opportunity to integrate existing technology with the data available in existing databases and make it more valuable for future use. A case study of the Department of Local Administration, under the Ministry of Interior, and the Royal Thai Police Department is used to develop an understanding of how the utilization of data to a full extent can be beneficial in the government service. In Thailand, every Thai citizen is required to carry a national identity card. Personal data of each person such as date of birth, height, blood type, religion and occupation, including registered address and individual photograph image, are kept in the Central Registration Database Systems (CRDS). The CRDS is operated by the Department of Local Administration under the supervision of the Ministry of Interior. In order to maximize the benefits from this database, the CRDS is shared by other authorized government agencies. The Royal Thai Police Department is one of the government agencies that also share the information from the CRDS. Frequently, the individual registration database and photographs from CRDS are needed to support crime investigation. This research therefore, investigates how the Thai government could utilize the existing database system to aid in the crime investigation process. It then suggests an effective method of image retrieval to support police officers when searching criminal records from a Central Registration Database Systems. The research begins with an exploratory study of the use and sharing of information amongst the government agencies in developing countries. It examines the use of existing technology and how the Government uses technology to access information. There are two major objectives in this research. The first one addresses how value can be added to the present data in the existing system. The author chooses to focus on the area of crime investigation and evaluate two existing image retrieval methods, in order to determine the most suited one from crime investigation process in Thailand. The second objective is to examine and evaluate the attitudes and perceptions of the Thai police towards acceptance of IT usage in the crime investigation process. The results are then was compared with the literature on barriers to the adoption of IT and some of the more recently developed Technology Acceptance Models, which is also used to explain the findings

Cancer diagnosis using deep learning: A bibliographic review

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements

Design of Environment Aware Planning Heuristics for Complex Navigation Objectives

A heuristic is the simplified approximations that helps guide a planner in deducing the best way to move forward. Heuristics are valued in many modern AI algorithms and decision-making architectures due to their ability to drastically reduce computation time. Particularly in robotics, path planning heuristics are widely leveraged to aid in navigation and exploration. As the robotic platform explores and navigates, information about the world can and should be used to augment and update the heuristic to guide solutions. Complex heuristics that can account for environmental factors, robot capabilities, and desired actions provide optimal results with little wasted exploration, but are computationally expensive. This thesis demonstrates results of research into simplifying heuristics that maintains the performance improvements from complicated heuristics. The research presented is validated on two complex robotic tasks: stealth planning and energy efficient planning. The stealth heuristic was created to inform a planner and allow a ground robot to navigate unknown environments in a less visible manner. Due to the highly uncertain nature of the world (where unknown observers exist) this heuristic implemented was instrumental to enabling the first high-uncertainty stealth planner. Heuristic guidance is further explored for use in energy efficient planning, where a machine learning approach is used to generate a heuristic measure. This thesis demonstrates effective learned heuristics that simplify convergence time and accounts for the complexities of environment. A reduction of 60% in required compute time for planning was found

20 year evolution of Glyaderm® dermal regeneration matrix:the first non-commercial dermal regeneration matrix

The research of Dr. Pirayesh encompassed a series of carefully planned studies ranging from laboratory research to clinical applications. The goal was to develop and validate Glyaderm® as an effective dermal replacement. Long-term studies have shown that Glyaderm® can be effectively integrated with autologous skin grafting after wound bed preparation, resulting in reduced costs and morbidity.In a prospective, randomized clinical study, the effectiveness of Glyaderm® and autologous skin grafting was confirmed. This combination was found to be non-inferior to standard treatment in terms of graft uptake, scar scales and color. In addition, the two-stage procedure with Glyaderm® showed superior elasticity and better results in visual scar evaluation.Glyaderm® has proven to be an effective choice for burn treatment, with beneficial outcomes and a positive impact on patients' quality of life

Automation and robotics for the Space Exploration Initiative: Results from Project Outreach

A total of 52 submissions were received in the Automation and Robotics (A&R) area during Project Outreach. About half of the submissions (24) contained concepts that were judged to have high utility for the Space Exploration Initiative (SEI) and were analyzed further by the robotics panel. These 24 submissions are analyzed here. Three types of robots were proposed in the high scoring submissions: structured task robots (STRs), teleoperated robots (TORs), and surface exploration robots. Several advanced TOR control interface technologies were proposed in the submissions. Many A&R concepts or potential standards were presented or alluded to by the submitters, but few specific technologies or systems were suggested