[[alternative]]An Efficient Shape-Representation Method for Content Based Image Retrieval

計畫編號：NSC93-2213-E032-006研究期間：200408~200507研究經費：593,000[[abstract]]以內容為基礎之影像查詢(CBIR)的研究可分為特徵選取、物件表示以及結果比 對。假如以物件的外形輪廓表示物件的特徵，那麼邊緣點偵測就是抽取這類特徵的第 一個步驟。當找完了邊緣點後，一個好的物件表示法必須能夠克服物件在影像中的移 位、旋轉、以及放大或縮小等問題。甚至對於物件外形在一定程度內的損毀下也必須 能夠有好的比對結果。這些問題都是在利用物件外形特徵來表示物件時以及比對過程 中相當重要的議題。 因此本計畫將提出一個有效率及強健的以物件外形特徵為基礎的影像查詢系統。 我們使用一快速的邊緣點偵測演算法來偵測出影像中所有可能的邊緣點，並提出一新 的物件表示法—爬山式序列表示法(Mountain Climbing Sequence (MCS))。此表示法 對於前面所提之影像中的移位、旋轉、以及放大或縮小等問題都可以達到不變的效果。 另外，由於邊緣點的偵測就目前的研究經驗上並無法保証能夠找一物件的完整外形， 因此我們也將嘗試在現有的外形特徵表示法下，克服物件外形不完整抽取的情況，甚 至於在物件少部份被遮蔽的狀況也能得到好的比對結果。[[sponsorship]]行政院國家科學委員

[[alternative]]Image Retrieval by Shape and Spatial Similarity

計畫編號：NSC91-2213-E032-015研究期間：200208~200307研究經費：502,000[[sponsorship]]行政院國家科學委員

A Robust Method for AOI System Design

[[abstract]]Abstract Automated optical inspection (AOI) systems suffer a lot of problems in the illumination control and unstable working environment. In this paper, we proposed a tailored design method which combines a keypoint based image alignment and an illumination adjusted comparison algorithms for the automated optical inspection systems. Further, an error tolerance control method for inherited noises and computation errors is also introduced.[[sponsorship]]Tatung University, Taipei, Taiwan[[conferencetype]]國內[[conferencedate]]17~ 19 July, 2015[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Tatung University, Taipei, Taiwa

Robust and Effective Banknote Recognition Model for Aiding Visual Impaired People

Visual disabled Ethiopians find great difficulty in recognizing banknotes. Each Ethiopian banknote has an identical feel, with no Braille markings, irregular edges, or other tangible features that make it easily recognizable by blind persons. In Ethiopia, there's only one device available that will assist blind people to acknowledge their notes. Internationally, there are devices available; however, they're expensive, complex, and haven't been developed to cater to Ethiopian currency. Because of these facts, visually impaired people may suffer from recognizing each folding money. This fact necessitates a higher authentication and verification system that will help visually disabled people to simply identify and recognize the banknotes. This paper presents a denomination-specific component-based framework for a banknote recognition system. Within the study, the dominant color of the banknotes was first identified and so the exclusive feature for every denomination-specific ROI was calculated. Finally, the Colour-Momentum, dominant color, and GLCM features were calculated from each denomination-specific ROI. Designing the recognition system by thereby considering the denomination-specific ROI is simpler as compared to considering the entire note in collecting more class-specific information and robust in copying with partial occlusion and viewpoint changes. The performance of the proposed model was verified by using a larger dataset of which containing banknotes in several conditions including occlusion, cluttered background, rotation, and changes of illumination, scaling, and viewpoints. The proposed algorithm achieves a 98% recognition rate on our challenging datasets

Template Matching Advances and Applications in Image Analysis

In most computer vision and image analysis problems, it is necessary to define a similarity measure between two or more different objects or images. Template matching is a classic and fundamental method used to score similarities between objects using certain mathematical algorithms. In this paper, we reviewed the basic concept of matching, as well as advances in template matching and applications such as invariant features or novel applications in medical image analysis. Additionally, deformable models and templates originating from classic template matching were discussed. These models have broad applications in image registration, and they are a fundamental aspect of novel machine vision or deep learning algorithms, such as convolutional neural networks (CNN), which perform shift and scale invariant functions followed by classification. In general, although template matching methods have restrictions which limit their application, they are recommended for use with other object recognition methods as pre- or post-processing steps. Combining a template matching technique such as normalized cross-correlation or dice coefficient with a robust decision-making algorithm yields a significant improvement in the accuracy rate for object detection and recognition

Template Matching Advances and Applications in Image Analysis

In most computer vision and image analysis problems, it is necessary to define a similarity measure between two or more different objects or images. Template matching is a classic and fundamental method used to score similarities between objects using certain mathematical algorithms. In this paper, we reviewed the basic concept of matching, as well as advances in template matching and applications such as invariant features or novel applications in medical image analysis. Additionally, deformable models and templates originating from classic template matching were discussed. These models have broad applications in image registration, and they are a fundamental aspect of novel machine vision or deep learning algorithms, such as convolutional neural networks (CNN), which perform shift and scale invariant functions followed by classification. In general, although template matching methods have restrictions which limit their application, they are recommended for use with other object recognition methods as pre- or post-processing steps. Combining a template matching technique such as normalized cross-correlation or dice coefficient with a robust decision-making algorithm yields a significant improvement in the accuracy rate for object detection and recognition

A study of smart device-based mobile imaging and implementation for engineering applications

Title from PDF of title page, viewed on June 12, 2013Thesis advisor: ZhiQiang ChenVitaIncludes bibliographic references (pages 76-82)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2013Mobile imaging has become a very active research topic in recent years thanks to the rapid development of computing and sensing capabilities of mobile devices. This area features multi-disciplinary studies of mobile hardware, imaging sensors, imaging and vision algorithms, wireless network and human-machine interface problems. Due to the limitation of computing capacity that early mobile devices have, researchers proposed client-server module, which push the data to more powerful computing platforms through wireless network, and let the cloud or standalone servers carry out all the computing and processing work. This thesis reviewed the development of mobile hardware and software platform, and the related research done on mobile imaging for the past 20 years. There are several researches on mobile imaging, but few people aim at building a framework which helps engineers solving problems by using mobile imaging. With higher-resolution imaging and high-performance computing power built into smart mobile devices, more and more imaging processing tasks can be achieved on the device rather than the client-server module. Based on this fact, a framework of collaborative mobile imaging is introduced for civil infrastructure condition assessment to help engineers solving technical challenges. Another contribution in this thesis is applying mobile imaging application into home automation. E-SAVE is a research project focusing on extensive use of automation in conserving and using energy wisely in home automation. Mobile users can view critical information such as energy data of the appliances with the help of mobile imaging. OpenCV is an image processing and computer vision library. The applications in this thesis use functions in OpenCV including camera calibration, template matching, image stitching and Canny edge detection. The application aims to help field engineers is interactive crack detection. The other one uses template matching to recognize appliances in the home automation system.Introduction -- Background and related work -- Basic imaging processing methods for mobile applications -- Collaborative and interactive mobile imaging -- Mobile imaging for smart energy -- Conclusion and recommendation

Segmentação, seguimento e avaliação automática de bactérias em imagens de microscópio

Dissertação para obtenção do Grau de Mestre em Engenharia BiomédicaAo longo de várias décadas, os diversos trabalhos realizados no campo da microscopia caracterizaram-se por um vasto conjunto de procedimentos de análise em imagens microscópicas. A quantificação celular das imagens em estudo é normalmente um procedimento lento, podendo apresentar uma percentagem de erro significativa, devido essencialmente à elevada quantidade de observações a serem efetuadas. Neste sentido, o desenvolvimento de algoritmos de processamento de imagem e de sistemas de reconhecimento, permite a criação de processos de quantificação automática de muitas das imagens microscópicas em estudo. A dissertação de mestrado aqui apresentada descreve e avalia o desenvolvimento de um algoritmo que tem como objetivo efetuar a segmentação e avaliação de um conjunto de imagens microscópicas. O protótipo projetado constitui um sistema de contabilização automática do número de bactérias E.coli visualizado em cada uma das imagens consideradas. As imagens foram disponibilizadas pelo Laboratory of Biosystem Dynamics da Tampere University of Technology tendo sido adquiridas através de microscópios confocais. O algoritmo desenvolvido pode dividir-se em três passos distintos. Inicialmente é aplicado um pré-processamento sobre as imagens em estudo constituído por um conjunto de transformações que retiram alguma da informação desnecessária da imagem e ao mesmo tempo melhoram os contornos dos segmentos constituintes. Seguidamente é implementado um processo de Template matching, que efetua a detecção da localização de cada uma das bactérias. Neste passo, as bactérias são povoadas individualmente por um conjunto de marcas que são posteriormente utilizadas no terceiro e último passo. Neste último passo é aplicada uma técnica de segmentação baseada no método Watershed, um dos mais estudados métodos de segmentação, inseridos na área do processamento de imagem. O protótipo foi testado nas imagens disponibilizadas, tendo obtido um grau de eficiência bastante satisfatório. De forma a avaliar a versatilidade do software desenvolvido, este foi também aplicado a imagens fornecidas por um dos mais eficientes softwares existentes no mercado e os seus resultados comparados