A ROI Focusing Mechanism for Digital Cameras

Transcription

1 A ROI Focusing Mechanism fo Digital Cameas Chu-Hui Lee, Meng-Feng Lin, Chun-Ming Huang, and Chun-Wei Hsu Abstact With the development and application of digital technologies, the digital camea is moe popula than othe electonic poducts fo all ages The opeations of digital cameas ae facile and the use inteface is simila in most bands Auto-focusing is a common but impotant featue both fo still image photogaphy and video photogaphy Taditionally, focusing sensos ae utilized fo auto-focusing systems; in this study, we popose a new auto-focusing mechanism which uses the image senso diectly fo measuing and estimating the focal distance Afte the image is captued by Chage Coupled Device (CCD) o Complementay Metal-Oxide-Semiconducto (CMOS) image sensos, uses will select Region of Inteest (ROI) and the system can exploe the in-focus image automatically The expeimental esults show the poposed mechanism has supeio pecision and elasticity of auto-focusing Index Tems Auto-focusing, Digital Camea, Discete Cosine Tansfom (DCT), Standad Deviation (STD), Region of Inteesting (ROI) I INTRODUCTION UE to the development of digital technologies, the Delectonic poducts ae extensively poduced in ou life, such as cell phones, digital cameas, GPSes, PDAs and noteboos [] The manipulation concept and technique of the taditional negative cameas ae moe complicated than those of the digital cameas Besides, developing films is inconvenient and difficult The digital camea is becoming one of the popula electonic poducts gadually, and futhemoe, the digital image can be applied to medical pathologic slides image, image post-pocessing and gowth ecod The design of evey band is innovated and developed continually The techniques of digital camea ae developed speedily and applied to mobile devices including cell phones, PDAs and noteboos The egion and numbe of the focusing sensos fo digital camea ae fix and limited fo most bands If the inteesting object of uses is not in the devised location of This wo was suppoted by National Science Council unde Gant NSC 99--E Chu-Hui Lee is with the Depatment of Infomation Management, Chaoyang Univesity of Technology, Wufong Township Taichung County, 4349 Taiwan (ROC) Phone: ext 4388; fax: ; chlee@ cyutedutw Meng-Feng Lin is with the Gaduate Institute of Infomatics, Chaoyang Univesity of Technology, Wufong Township Taichung County, 4349 Taiwan (ROC) s953390@cyutedutw Chun-Ming Huang is with the National Applied Reseach Laboatoies, National Chip Implementation Cente, Science Pa Hsinchu City, 300 Taiwan (ROC) cmhuang@cicogtw Chun-Wei Hsu is with the Depatment of Infomation Management, Chaoyang Univesity of Technology, Wufong Township Taichung County, 4349 Taiwan (ROC) s984605@cyutedutw focusing senso, the image of the inteesting object will be not in the focus Besides, the pice of digital cameas is getting moe expensive with the apidly inceasing numbe of focusing senso which is a buden of use This pape poposes a new auto-focusing mechanism which uses the image senso diectly fo measuing and estimating the focal distance Fo a given scene, we will fist fix the shutte speed and iis value, then captue seies of pictues with diffeent focal distance, afte that, we select the Region of Inteesting (ROI) fom the pictue and pefom spectum analysis on ROI egion; finally, this pape can estimate the coect focal distance fo each seies of ROIs by selecting the ROI with highest enegy fo high fequency component The emainde of this pape is stuctued as follows Section II discusses elated wos, including Discete Cosine Tansfom (DCT) and Standad Deviation (STD) The details of the poposed mechanism ae descibed in Section III Section IV discusses the expeimental esults to test the poposed mechanism is pesented in Finally, Section V summaizes the conclusions of the pape II RELATED WORKS This section descibes the elated technologies fo the poposed mechanism, including DCT and STD A Discete Cosine Tansfom The DCT is one of multimedia pocessing methods which can tansfom the spatial o time domain of an image into spectum domain [] It is simila to Discete Fouie Tansfom (DFT), but the dissimilaity is that DCT only uses eal numbes with DFT In geneal, the tansfomation is applied to n n aays and the typically fom is sized to 88 aays in image pocessing [3] The capability and complexity of calculations in DCT not only can be impoved and educed, but also obtains the simila effect The DCT is a wothy method and is applied to image compession fo JPEG and MPEG files [4] Theefoe, the DCT has been embedded in digital cameas Afte DCT, the fequency components which can epesent the image ae impotant fo obseving the detail in the pictue Seveal studies have pesented that the image content is applied and analyzed vaiously to auto-focusing Baina and Dublet poposed a mechanism which can evaluate the best setting of the lens and diaphagm by DCT [] Chaifi et al descibes the cuves of DCT ae pesented diffeently between the focusing images and bluing images [5] Lee et al poposed an auto-focusing method fo cell phones based on intemediate fequency of DCT [6] Pesent studies demonstate the focus of digital cameas

2 and DCT have an insepaable elation The numbe of the components afte DCT is numeous and has to be futhe mashal B Standad Deviation The STD is a measuement concept which is used to obseve the spead of one data set fom the mean value To undestand this concept of STD, the lage STD shows that the most numbes ae fa apat fom mean in the data set Compaatively, the smalle STD descibes the numbes ae elatively close to mean in the data set [7] It can be used to compae between the anges of the data sets when the mean values of the data sets ae equal Fo instance, the following,5,8,0,6 0,0,0,0,0, two sets ae and espectively The mean values of two sets ae 0 identically, but the fist has the lage STD which speads out moe clealy [7] Accodingly, the STD can epesent the spead degee of the obseved data It is usually accepted in some appaisal because it is easily used to calculate Fig The algoithms of the poposed mechanism III AUTO-FOCUSING MEASURE OF ROI Figue pesents the poposed Auto-Focusing Measue of ROI (AFMR) based on DCT, which is used to analyze the featues of ROI Fo a given scene, we will fist fix the shutte speed and iis value, then captue seies of pictues with diffeent focal distance The ROI egion is selected by uses Each ROI is cut into seveal i j sub-egions The impotant Focus Value ( FV ) of sub-egions is acquied though DCT Finally, auto-focusing measuement will base on the maximum on FV The following subsections intoduce the details Fig The ROI of use A Pe-pocessing Thee ae totally m images Each image has the same scene including ten chais and with diffeent focal distance The poposed mechanism will pic out the best in-focus image fo each ROI Let image denote the image database of this pape, shown as Eq image image ; fo to m () B Opting the ROI The use can select the inteesting egion of each image in this pape The blac ectangle mas the ROI in Fig Let M denote the inteesting egion of uses in the image The size of M is b b Fig 3 An example of 88 sub-egions C Cutting the Seveal Sub-egions M is segmented into i j sub-egions The size of each sub-egion is b a espectively b and b b a a, whee i and j ae a and ae the size of M The size a is an influence facto of pefomance of auto-focusing ( x, Fig 4 The spatial matix of example

3 C u,v Fig 5 The spectum matix of example Fig 6 An image of the image database D Discete Cosine Tansfom Since the DCT has a concentate chaacteistic of enegy The two-dimensional DCT (-D DCT) can tansfom the spatial domain of an image into spectum domain The -D DCT is pesented as Eq and 3 [7] Colo is one of the most dominant and distinguished pats to human peception fo CBIR The HSV (Hue, Satuation, Value) colo attibute model ae used to pesent colo featues Each sub-egion is extacted the value of HSV, whee ( x, is an input pixel of the image Fig 3 displays an example of 88 sub-egions The spatial matix ( x, is shown in Fig 4 Though -D DCT, the spatial coefficient matix ( x, is tansfomed into the spectum matix C ( u,, as shown in Fig 5 C( u, n n Sv x0 y0 u v x, y n fo u, v 0 cos n x u x v cos n u 0 v 0 x 0 y 0 () u, v n and (3) fo u, v 0 E Calculating the Focus Value of Image An in-focus image contains moe details than an out-focus image This pape uses STD to obseve the value of DCT coefficients If the image has complicated content, the data of DCT coefficient is abundant The highe value of a specific c( u, means the image has concentate on a specific DCT basis This pape uses the SDT to evaluate the vaiation of DCT coefficient The vaiation is lage means detail infomation is moe plenty In othe wods, the details of egion ae pellucid in an in-focus image The divegence of DCT coefficient is bigge than out-focus image In the poposed mechanism, afte each AC component is divided by DC component, the STD of all AC components is calculated The coefficients of C( u, can be aanged in linea, epesented by X, X,, X,, X X N is the DC value and othes X ae the AC values The mean value X and the STD of sub-egion (, whee i and j specify the egion, ae defined as Eq 4 and 5, espectively The sub-egion i j is defined as Eq 5 FV, i j of a N X X (4) N X N i, ( (5) N FV j X X X The spectum matix of all sub-egions of each ROI fo each image is calculated as FV in ou poposed eseach The FV, i j cubic matix specify the egion, of each ROI of each image is stoed in the T i, j shown as Eq 6, whee i and j FV i, j i j in image indicates the image Hence denotes the focus value fo the egion INO( defined as Eq 7, which ecods the image numbe of lagest FV in each sub-egion, whee the dimension is i j The image is a best in-focus image when it is the majoity in the INO( i, TABLE I THE REALISTIC EXAMPLE OF THE PROPOSED MECHANISM ROI- ROI- ROI-3 I _ FV = 87 I _ FV = 75 I _ FV = 3

4 ROI TABLE II THE EXPERIMENTAL RESULTS FOR FIVE DIFFERENT FOCUSING DEGREES OF ROI Degee Image- Image- Image- Image-3 Image-4 I _ FV = 30 I _ FV = 48 I _ FV = 75 I _ FV = 40 I _ FV = 3 Image-5 Image-5 Image-5 Image-35 Image-45 I _ FV = 8 I _ FV = 56 I _ FV = 70 I _ FV = 3 I _ FV = 36 3 Image- Image- Image-3 Image-4 Image-5 I _ FV = I _ FV = 5 I _ FV = 67 I _ FV = 57 I _ FV = 3 4 Image-4 Image-4 Image-34 Image-44 Image-54 I _ FV = I _ FV = 3 I _ FV = 05 I _ FV = 3 I _ FV = 4 5 Image-9 Image-9 Image-39 Image-49 Image-57 I _ FV = 0 I _ FV = 8 I _ FV = 97 I _ FV = 3 I _ FV = 4 The FV of image i, denoted as I _ FV, is define as the count in INO( i, If the I _ FV of image i is n, then it means image i appeas in INO( n times T i j FV, j (6) m INO( image numbe of (max{ T }) i, j IV EXPERIMENTAL RESULTS This section pesents the esults of the poposed mechanism Ou expeiment has 57 images in the image database, which ae captued by the diffeent focal distance The Image- has the shotest focal distance and the Image-57 has the longest focal distance Each image contains ten objects with diffeent depths All the images ae taen by Canon EOS-Ds with dimensions pixels Fig 6 illustates an example image in database Regading expeiment envionment, it was as follow The hadwae was Inte Coe Quad Q800 CPU and 00GB RAM, unning Micosoft Windows XP All pogams wee implemented in MATLAB 009b The fist expeiment pesents a ealistic example of the poposed auto-focusing mechanism in Table I with the (7) 88 egion size The small blac ectangles ae the best focus egions In this ealistic example, the I _ FV of ROI labeled //3 ae 87/75/3, espectively That means the fist image has moe detail infomation than othes two The second expeiment shows the expeiment esults of the five diffeent ROIs with the egion size, showing the I _ FV of each image in the poposed mechanism, as shown in Table II Fo instance, the best focusing image of ROI-5 is Image-39 and the I _ FV is 97 in the fifth ow and the thid column These five ROI of chais ae aanged fom nea to fa The best focus image ae Image- fo the fist, Image-5 fo the second, Image-3 fo the thid, Image-34 fo the fouth, and Image-39 fo the fifth The chais of ROI ae selected fom nea to fa Also the in-focus image numbe is inceasing That is comfot to the image captued condition that is image has shotest focal distance with smalle image numbe Obseving all the image, the selected image by ou method is also the best in-focus image V CONCLUSION This pape poposed a new auto-focusing mechanism fo the digital camea images And the expeimental esults also demonstated the poposed mechanism can select the best focus image Howeve, the size of sub-egion in ROI is

5 an inteesting topic fo futhe eseach The diffeent scene has its pope egion size It is an impotant facto of pocess time The futue wo is to exploe how to educe the time complexity of calculation base on diffeent egion size REFERENCE [] J Baina & J Dublet, Automatic Focus and Iis Contol fo Video Cameas, in Poc the 5th Intenational Confeence on Image Pocessing and its Applications, pp3-35, 995 [] N Ahmed, T Nataajan & KR Rao, Discete cosine tansfom, IEEE Tansactions on Compute, Vol 3, No, pp90-93, 974 [3] 94009pdf Available: [4] M Tayyab, MF Zafa & SS Al Pefomance Compaison of D-Discete Cosine Tansfom and D-Discete Wavelet Tansfom fo Neual Netwo-Based Face Detection, in Poc the 009 Soft Computing and Patten Recognition, pp387-39, 009 [5] M Chaf A Nyec & A Tose, Focusing Citeion, IEE Electonics Lettes, pp33-35, 99 [6] SY Lee, SS Pa, CS Kim, Y Kuma & SW Kim, Low-Powe Auto Focus Algoithm Using Modified DCT fo The Mobile Phones, in Poc the Intenational Confeence on Consume Electonics, pp67-68, 006 [7] Standad_deviation Available: [8] SA Khayam, The discete cosine tansfom: theoy and application Technical Repot ECE 80-60: Infomation theoy and coding, Depatment of Electical & Compute Engineeing, Michigan State Univesity, Michigan, USA, 003