A Convolutional Neural Network (CNN) based Pill Image Retrieval System

Several works have been done in the area of image retrieval systems, and many are still trying to provide improvements for a better model for retrieving said images. Image segmentation using clustering techniques is one of the most used approaches. There are various clustering methods available, but the non-linear k-means clustering technique is the most used method. In the following research, a model of retrieving images using a non-linear classifier aided with a convolutional neural network is proposed. Both algorithms were exploited and paired in terms of feature extraction and classification. Comprehensive evaluations over a dataset containing over 7,000 pill images of 1,000 pill types obtained from the National Library of Medicine database demonstrate significant success during the data classification using the proposed model

Nuclei segmentation of histology images based on deep learning and color quantization and analysis of real world pill images

Medical image analysis has paved a way for research in the field of medical and biological image analysis through the applications of image processing. This study has special emphasis on nuclei segmentation from digitized histology images and pill segmentation. Cervical cancer is one of the most common malignant cancers affecting women. This can be cured if detected early. Histology image feature analysis is required to classify the squamous epithelium into Normal, CIN1, CIN2 and CIN3 grades of cervical intraepithelial neoplasia (CIN). The nuclei in the epithelium region provide the majority of information regarding the severity of the cancer. Segmentation of nuclei is therefore crucial. This paper provides two methods for nuclei segmentation. The first approach is clustering approach by quantization of the color content in the histology images uses k-means++ clustering. The second approach is deep-learning based nuclei segmentation method works by gathering localized information through the generation of superpixels and training convolutional neural network. The other part of the study covers segmentation of consumer-quality pill images. Misidentified and unidentified pills constitute a safety hazard for both patients and health professionals. An automatic pill identification technique is essential to address this challenge. This paper concentrates on segmenting the pill image, which is crucial step to identify a pill. A color image segmentation algorithm is proposed by generating superpixels using the Simple Linear Iterative Clustering (SLIC) algorithm and merging the superpixels by thresholding the region adjacency graphs. The algorithm manages to supersede the challenges due to various backgrounds and lighting conditions of consumer-quality pill images --Abstract, page iii

Shape and Text Imprint Recognition of Pill Image Taken with a Smartphone

학위논문 (석사)-- 서울대학교 대학원 공과대학 기계항공공학부, 2017. 8. 이건우.삶을 이롭게 하기 위해 수 많은 약들이 제조 · 판매되지만 이러한 약들은 오용되거나 남용될 경우 사람에게 치명적인 문제를 일으킬 수 있다. 인터넷과 스마트폰을 통해 알약을 검색하고 그에 대한 정보를 얻을 수 있으나, 모양과 색상, 글자를 직접 입력 해야하고 특수한 마커를 배경으로 사용해야하는 등 접근성이 낮다. 따라서 특수한 마커를 사용하지 않으면서도 알약 이미지로부터 글자 정보까지 획득할 필요가 있다. 본 논문에서는 스마트폰으로 촬영된 알약 영상에서 글자와 형상을 인식하는 방법을 제안한다. 알약이 포함된 영상에서 알약 영역을 특정 짓기 위해 Saliency Map을 이용한 뒤, 빛 효과와 그림자 효과를 제거한다. 이렇게 획득된 알약 영역에서 Zernike Moment를 통해 형상 정보를 얻는다. Gaussian Filter, Gradient Filter, Binarization을 통해 알약의 글자를 감싸는 박스를 추출하고, CNN Deep Learning을 통해 학습된 각인 글자 인식기를 사용하여 최종적인 글자 정보를 획득한다. 본 논문에서 제안하는 알고리즘을 테스트하기위해 NLM 데이터베이스의 알약 이미지 총 500개를 사용하였다. 또한, 1) Shape Matching Rate, 2) Text Box Detection Rate, 3) Character Recognition Rate, 4) Text Recognition Rate, 5) Recognition Success Rate로 총 5가지 항목에 대해 알고리즘을 평가하였다. 그 결과는 각각 75.5%, 87.5%, 0.786, 73%, 58.4%였으며, 기존의 알약 인식 알고리즘과 비교하였을 때, 상당히 개선된 결과를 얻었다.제 1 장 서론 1 제 2 장 관련연구 4 2.1 모양 또는 색상 등 일부만 인식 4 2.2 모양과 색상, 글자 모두 인식 5 제 3 장 제안된 인식 시스템 10 3.1 전체 시스템 개요 10 3.2 알약 주변부 획득 11 3.3 알약 영역 추출 13 3.4 형상 정보 획득 14 3.5 글자부 획득 19 3.6 글자 학습 및 인식 22 제 4 장 실험 및 결과 25 4.1 Database 25 4.2 Shape Matching Rate 26 4.3 Text Box Detection Rate 29 4.4 Character Recognition Rate 30 4.5 Text Recognition Rate & Recognition Success Rate 32 제 5 장 결론 34 참고문헌 35 Abstract 39Maste

Identification of unique microbial signatures pre- and post-coitus in male-female pairings by massively parallel sequencing and its potential to detect sexual contact

Background: The capture of male DNA, post-assault, is important in sexual assault investigation, particularly where an offender is unknown to the victim. The recovery of DNA often occurs when the female victim undergoes a forensic medical assessment. Analysis regularly results in mixed autosomal DNA profiles. As these results contain both victim and perpetrator DNA, they are often difficult to interpret a searchable male profile. While STR profiling of the male Y-chromosome is often used to overcome this, the successful identification of an individual can be hindered by the paternal inheritance pattern of Y-STRs. An adjunct method of perpetrator identification lies with microbiome analysis using massively parallel sequencing. Aims: This study aimed to identify ASVs that were unique to each participant and compare the bacterial communities found on the genitals pre- and post-coitus. From the sequence data derived, statistical analysis was performed to investigate if bacteria sequences could be used to infer contact between each male-female pairing. Content: Samples were collected from 14 male-female pairings across two recruitment cohorts. Volunteers were asked to self-collect samples pre- and post-coitus. Samples were extracted using PureLink™ Microbiome DNA Purification Kit. Extracted DNA underwent library preparation using primers targeting the V1-V9 hypervariable regions of the bacterial 16S rRNA gene (~1,449 bp). Libraries were sequenced by PacBio® SMRT Sequel II sequencing platform. Unique bacterial signatures were detected in low frequencies (<1%) in male and female participants pre-coitus. The data indicates a disruption to microbial composition post-coitus. Further genomic analysis is needed to confirm species and subspecies classification of bacteria

Fish behavior and its use in the capture and culture of fishes

Fishery management, Behaviour, Food fish, Fish culture, Conferences

Quantifying Quality of Life

Describes technological methods and tools for objective and quantitative assessment of QoL Appraises technology-enabled methods for incorporating QoL measurements in medicine Highlights the success factors for adoption and scaling of technology-enabled methods This open access book presents the rise of technology-enabled methods and tools for objective, quantitative assessment of Quality of Life (QoL), while following the WHOQOL model. It is an in-depth resource describing and examining state-of-the-art, minimally obtrusive, ubiquitous technologies. Highlighting the required factors for adoption and scaling of technology-enabled methods and tools for QoL assessment, it also describes how these technologies can be leveraged for behavior change, disease prevention, health management and long-term QoL enhancement in populations at large. Quantifying Quality of Life: Incorporating Daily Life into Medicine fills a gap in the field of QoL by providing assessment methods, techniques and tools. These assessments differ from the current methods that are now mostly infrequent, subjective, qualitative, memory-based, context-poor and sparse. Therefore, it is an ideal resource for physicians, physicians in training, software and hardware developers, computer scientists, data scientists, behavioural scientists, entrepreneurs, healthcare leaders and administrators who are seeking an up-to-date resource on this subject

Extracting Physiological Measurements from Thermal Images

Multiple techniques are used to extract physiological signals from the human body. These signals provide a reliable method to identify the physical and mental state of a person at any given point in time. However, these techniques require contact and cooperation of the individual as well as human effort for connecting the devices and collecting the needed measurement. Moreover, these methods can be invasive, timeconsuming, and infeasible in many cases. Recent efforts have been made in order to find alternatives to extract these measurements using noncontact and efficient techniques. One of these alternatives is the use of thermal cameras for health monitoring. Our work explores reliable methods for extracting respiration rate, skin temperature and heart rate from thermal video. These methods leverage a combination of image processing and signal processing techniques in order to extract and filter physiological signals from the thermal domain. Finally, we review the use of thermal imaging in several applications, such as deception detection, stress detection and emotion recognition.Master of ScienceComputer and Information Science, College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/167385/1/Christian Hessler Final Thesis.pdfDescription of Christian Hessler Final Thesis.pdf : Thesi

Quantifying Quality of Life

Describes technological methods and tools for objective and quantitative assessment of QoL Appraises technology-enabled methods for incorporating QoL measurements in medicine Highlights the success factors for adoption and scaling of technology-enabled methods This open access book presents the rise of technology-enabled methods and tools for objective, quantitative assessment of Quality of Life (QoL), while following the WHOQOL model. It is an in-depth resource describing and examining state-of-the-art, minimally obtrusive, ubiquitous technologies. Highlighting the required factors for adoption and scaling of technology-enabled methods and tools for QoL assessment, it also describes how these technologies can be leveraged for behavior change, disease prevention, health management and long-term QoL enhancement in populations at large. Quantifying Quality of Life: Incorporating Daily Life into Medicine fills a gap in the field of QoL by providing assessment methods, techniques and tools. These assessments differ from the current methods that are now mostly infrequent, subjective, qualitative, memory-based, context-poor and sparse. Therefore, it is an ideal resource for physicians, physicians in training, software and hardware developers, computer scientists, data scientists, behavioural scientists, entrepreneurs, healthcare leaders and administrators who are seeking an up-to-date resource on this subject

Multi-Dimensional Task Recognition for Human-Robot Teaming

Human-robot teams involve humans and robots collaborating to achieve tasks under various environmental conditions. Successful teaming requires robots to adapt autonomously in real-time to a human teammate's state. An important element of such adaptation is the ability for the robot to infer the tasks performed by their human teammates. Human-robot teams often perform a wide variety of tasks, involving multiple activity components, and may even perform two or more tasks concurrently. A robot’s ability to recognize the human’s composite tasks that occur concurrently is a key requirement for realizing successful collaboration. Existing task recognition algorithms are not viable for human-robot teams, as they only detect tasks from a subset of activity components and rarely detect concurrent, composite tasks. This dissertation developed a multi-dimensional task recognition algorithm capable of detecting concurrent, composite tasks across the cognitive, speech, auditory, visual, gross motor, fine-grained motor, and tactile components by incorporating metrics that are sensitive, versatile, and suitable across human-robot teaming paradigms. The developed algorithm addresses a foundational problem of understanding an individual's task engagement state in human-robot teams operating in dynamic, unstructured environments

Enhancing numerical modelling efficiency for electromagnetic simulation of physical layer components.

The purpose of this thesis is to present solutions to overcome several key difficulties that limit the application of numerical modelling in communication cable design and analysis. In particular, specific limiting factors are that simulations are time consuming, and the process of comparison requires skill and is poorly defined and understood. When much of the process of design consists of optimisation of performance within a well defined domain, the use of artificial intelligence techniques may reduce or remove the need for human interaction in the design process. The automation of human processes allows round-the-clock operation at a faster throughput. Achieving a speedup would permit greater exploration of the possible designs, improving understanding of the domain. This thesis presents work that relates to three facets of the efficiency of numerical modelling: minimizing simulation execution time, controlling optimization processes and quantifying comparisons of results. These topics are of interest because simulation times for most problems of interest run into tens of hours. The design process for most systems being modelled may be considered an optimisation process in so far as the design is improved based upon a comparison of the test results with a specification. Development of software to automate this process permits the improvements to continue outside working hours, and produces decisions unaffected by the psychological state of a human operator. Improved performance of simulation tools would facilitate exploration of more variations on a design, which would improve understanding of the problem domain, promoting a virtuous circle of design. The minimization of execution time was achieved through the development of a Parallel TLM Solver which did not use specialized hardware or a dedicated network. Its design was novel because it was intended to operate on a network of heterogeneous machines in a manner which was fault tolerant, and included a means to reduce vulnerability of simulated data without encryption. Optimisation processes were controlled by genetic algorithms and particle swarm optimisation which were novel applications in communication cable design. The work extended the range of cable parameters, reducing conductor diameters for twisted pair cables, and reducing optical coverage of screens for a given shielding effectiveness. Work on the comparison of results introduced ―Colour maps‖ as a way of displaying three scalar variables over a two-dimensional surface, and comparisons were quantified by extending 1D Feature Selective Validation (FSV) to two dimensions, using an ellipse shaped filter, in such a way that it could be extended to higher dimensions. In so doing, some problems with FSV were detected, and suggestions for overcoming these presented: such as the special case of zero valued DC signals. A re-description of Feature Selective Validation, using Jacobians and tensors is proposed, in order to facilitate its implementation in higher dimensional spaces