研究生: |
陳郁蓁 Yu-Chen Chen |
---|---|
論文名稱: |
基於局部限制稀疏表示的貓身分識別 Cat Recognition Based on Locality-constrained Sparse Representation |
指導教授: |
花凱龍
Kai-Lung Hua |
口試委員: |
楊傳凱
Chuan-Kai Yang 鄧惟中 Wei-Chung Teng 鄭文皇 Wen-Huang Cheng |
學位類別: |
碩士 Master |
系所名稱: |
電資學院 - 資訊工程系 Department of Computer Science and Information Engineering |
論文出版年: | 2015 |
畢業學年度: | 103 |
語文別: | 英文 |
論文頁數: | 40 |
中文關鍵詞: | 生物識別 、貓識別 、稀疏表示 、字典學習 、資料局部性 |
外文關鍵詞: | biometrics, cat recognition, sparse representation, dictionary learning, data locality |
相關次數: | 點閱:224 下載:4 |
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貓(家貓)於我們的社會扮演重要的角色,牠們對於飼主而言就像是伴侶、家人甚至是小孩,陪伴並給人心靈上的滿足。遺失、調換、遭竊等狀況是全球性的問題,因此對於擁有貓的人來說可靠的辨識是很有效的管理工具。傳統的辨識方法還不夠完備,不夠保險,且這些方法可能會對於貓有不利的影響。儘管以動物外表來識別的方式在近年來備受關注,然而這些方式仍不足以用來辨識貓。
因此在本篇論文我們提出了一個新的生物識別方法,以貓的鼻子來識別貓。我們蒐集了70隻貓,共700張貓鼻子影像來建立貓資料庫,基於這個貓資料庫,我們建立了有代表性且具資料局部性限制的字典來辨識貓。與一些基於特徵的演算法相比,實驗結果證明我們的方法來辨識貓較為有效。
Cat (Felis catus) plays an important social role within our society and can provide considerable emotional support for their owners. Missing, swapping, theft, and false insurance claims of cat have become global problem throughout the world. Reliable cat identification is thus an essential factor in the effective management of the owned cat population. The traditional cat identification methods by permanent (e.g., tattoos, microchip, ear tips/notches, and freeze branding), semi-permanent (e.g., identification collars and ear tags), or temporary (e.g., paint/dye and radio transmitters) procedures are not robust to provide adequate level of security. Moreover, these methods might have adverse effects on the cats. Though the work on animal identification based on their phenotype appearance (face and coat patterns) has received much attention in recent years, however none of them specifically targets cat.
In this paper, we therefore propose a novel biometrics method to recognize cat by exploiting their noses that are believed to be a unique identifier by cat professionals. As the pioneer of this research topic, we first collect a Cat Database that contains 700 cat nose images from 70 different cats. Based on this dataset, we design a representative dictionary with data locality constraint for cat identification. Experimental results well demonstrate the effectiveness of the proposed method compared to several state-of-the-art feature-based algorithms.
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