毕业设计的翻译英文文献-关于图像分割.pdf

Microprocessors and Microsystems 36 2012 215 231 Contents lists available at SciVerse ScienceDirect Microprocessors and Microsystems j o u r n a l h o m e p a g e w w w e l s ev i e r c o m l o c a t e m i c p r o An embedded software reconfigurable color segmentation architecture for image processing systems Grigorios Chrysos a Apostolos Dollas a Nikolaos Bourbakis a b a Technical University of Crete ECE Dept Chania Crete Greece b Wright State University Engr College ATR Center Dayton OH 45435 USA a r t i c l ei n f o Article history Available online 17 December 2011 Keywords Reconfigurable architectures Image segmentation Embedded systems a b s t r a c t Image segmentation is one of the first important and difficult steps of image analysis and computer vision and it is considered as one of the oldest problems in machine vision Lately several segmentation algorithms have been developed with features related to thresholding edge location and region growing to offer an opportunity for the development of faster image video analysis and recognition systems In addition fuzzy based segmentation algorithms have essentially contributed to synthesis of regions for bet ter representation of objects These algorithms have minor differences in their performance and they all perform well Thus the selection of one algorithm vs another will be based on subjective criteria or driven by the application itself Here a low cost embedded reconfigurable architecture for the Fuzzy like reason ing segmentation FRS method is presented The FRS method has three stages smoothing edge detection and the actual segmentation The initial smoothing operation is intended to remove noise The smoother and edge detector algorithms are also included in this processing step The segmentation algorithm uses edge information and the smoothed image to find segments present within the image In this work the FRS segmentation algorithm was selected due to its proven good performance on a variety of applications face detection motion detection Automatic Target Recognition ATR and has been developed in a low cost reconfigurable computing platform aiming at low cost applications In particular this paper presents the implementation of the smoothing edge detection and color segmentation algorithms using Stretch S5000 processors and compares them with a software implementation using the Matlab The new architec ture is presented in detail in this work together with results from standard benchmarks and comparisons to alternative technologies This is the first such implementation that we know of having at the same time high throughput excellent performance at least in standard benchmarks and low cost 2011 Elsevier B V All rights reserved 1 Introduction 1 1 Segmentation Many computer vision pattern recognition image analysis and object extraction systems have been developed during the last thirty years At the same time fuzzy and semi fuzzy clustering algorithms have been also presented for the extraction and recog nition of an object s features In order for these systems and algo rithms to be successful they generally have to start with a robust smoothing and or segmentation technique Thus image segmenta tion is an important starting step for almost all vision and pattern recognition methodologies Several studies have been done to cat egorize segmentation into classes based on characteristics such as thresholding or clustering edge detection region growing merging Corresponding author E mail addresses chrysos mhl tuc gr G Chrysos dollas mhl tuc gr A Dollas nikolaos bourbakis wright edu N Bourbakis 0141 9331 see front matter 2011 Elsevier B V All rights reserved doi 10 1016 j micpro 2011 12 004 and others 1 3 In particular Lee and Chung 4 showed that thresholding would usually produce good results in bimodal images only where the images comprise of only one object and its background However when the object area is small compared to the background area or when both the object and background have a broad range of gray levels selecting a good threshold is dif ficult Another weakness of this technique occurs when multiple objects are present within the image In such cases finding sharp valleys within the histogram is further complicated and segmenta tion results may be very poor Edge detection is another approach associated to image segmentation 5 An edge is defined as a loca tion where a sharp change in gray level or color is detected How ever in this method it is difficult to maintain the continuity of the detected edges a segment must always be enclosed by a continu ous edge Region growing or merging is a third approach for image segmentation 6 In this case large easy to find continuous re gions or segments are detected first Afterwards small regions may be merged by using homogeneity criteria 7 8 One disadvan tage of region growing and merging is the inherently sequential 216G Chrysos et al Microprocessors and Microsystems 36 2012 215 231 nature of this approach Often the regions produced depend upon the order in which those regions grow or merge 1 2 Color segmentation architectures The literature reports different approaches for color segmenta tion An important color segmentation method is the development of dichromatic reflection model 15 16 which describes the color of reflected light as a linear combination of the color of surface reflection highlights and body reflection object color Use of this model to the region growing and merging method 6 17 produced impressive results In this method highlighted areas were merged with the matte areas of an object However using hard thresholds throughout degraded the performance of this technique within its intermediate stages There are segmentation methods 18 19 which do not segment the color image in the RGB color space as it does not closely model the psychological understanding of color Instead of they choose other color spaces like HIS or YUV which produce better results than the RGB color space Some of these image segmentation pro cesses were fused with the edge location method to produce better results 20 21 Segmentation based on the theory of approximate reasoning or fuzzy like reasoning produced promising results 22 23 Huntsberger 5 defined color edges as the zero crossing of differences between the membership values of each pixel The fuzzy membership values are generated by using an iterative c mean segmentation algorithm although it is time consuming due to its iterative nature Lim 24 presented an automated coarse to fine segmentation method This approach is based on histogram thresholds for each color and the c means algorithm 25 26 An interesting approach proposed by Lambert and Carron 27 com bined the color space where hue was explicitly defined and pro cessed according to its relevance to chroma and symbolic representations and rule based systems using color and lumi nance features to determine homogeneity among pixels Recently more segmentation techniques based on color and texture have been introduced using features commonly observed in most images especially in color textured images of natural scenes Extensive research results on human perception of color and texture are also available in the literature e g uniform color spaces 64 or filter banks 35 37 For all these reasons most seg mentation methods use color or texture as key features for image segmentation Recently several attempts to combine color and texture have been made to enhance the basic performance of color or texture segmentation These attempts namely color texture segmentation include region growing approaches 38 40 wa tershed techniques 41 edge flow techniques 42 and stochastic model based approaches 43 44 The application of Markov mod els on color segmentation has also been studied 45 46 Lastly the Boyokov et al 47 49 approach to color texture segmentation is based on graph cut techniques which find an optimal color texture segmentation of a color textured image by regarding it as a mini mum cut problem in a weighted graph There are many architectures and hardware implementations of color segmentation algorithms in literature Perez and Koch 28 proposed the use of a simplified hue description suitable for imple mentation in analog VLSI They designed and fabricated for the first time an analog CMOS VLSI circuit that computes normalized color and hue Stichling and Kleinjohann 29 present a hardware imple mentation of color segmentation algorithm using region growing and merging methods implemented on a Philips Trimedia micro controller The system processes 25 frames per sec rate for small images and using a HW SW system Leclerq and Braunl 30 imple mented a color segmentation algorithm on a 32 bit Motorola con troller for 80 60 images The system was used for the Robocup competition and identifies small objects in about 0 02 s Saffiotti 31 presents the implementation of a seeded region growing seg mentation algorithm on a Sony AIBO robot using the specific device CDT that uses threshold technique Johnston et al 32 present a system that implements color segmentation and object tracking using an FPGA Spartan II and offering real time processing Koo et al 33 present a system that analyzes magnetic resonance images The system was implemented on a high performance reconfigurable computer using 4 FPGAs and achieves a 5 speedup of the algorithm Dillinger et al 34 built an FPGA based coproces sor which implements a 3 D image segmentation achieving high performance Yamaoka et al 35 present a novel algorithm imple mented on an FPGA tracking up to 220 objects on 80 60 video pictures 1 3 Segmentation for image processing based systems Image processing systems such as Automatic Target Recogni tion ATR Face Recognition and Motion Detection 14 50 54 62 require a robust and fast segmentation algorithm Thus these systems use a process for object of features extraction and recogni tion applied to still images and or video 9 13 For instance an ATR system consist of a combination of algorithms such as smoothing heuristic segmentation edge detection thinning region growing fractals etc appropriately selected to recognize targets under various conditions These algorithms especially the smoothing segmentation and edge detection consume a signifi cant amount of computing time needed for the software comple tion in an ATR system Color segmentation is a much studied problem 45 57 58 as it is used in applications such as face recog nition 55 56 Thus the contribution of this work is an architecture and de tailed hardware design for the implementation of the three time consuming parts of the FRS methodology smoothing edge detec tion and color segmentation 7 8 22 23 36 which were developed as independent in hardware as black boxes to perform a specific procedure The final result is an image divided into its objects which are colored with the same color This piece of information can be used by the subsequent steps of the ATR system to perform feature extraction of the objects in the image The complete system was fully designed in a reconfigurable processor using the technol ogy of Stretch Inc This is a low cost technology which leads to an easily embeddable subsystem As will be shown in this paper the tight coupling of an embedded processor with reconfigurable fab ric allows for an efficient implementation of the algorithm how ever the vast amounts of data that need to be transferred between the memory the processor and the reconfigurable part pose challenges which will be presented in depth in this work The Stretch company 51 has developed the series of S5000 and S6000 software configurable processors which is based on the Tensilica core RISC processor with a small embedded reconfigura ble part The design flow comprises of system development in C C profiling of the code and mapping its critical sections to the reconfigurable fabric as special hardware implemented instructions The C C language is used to program the S5000 pro cessors Stretch C is a C like language which includes some exten sions for hardware implementation Stretch C is the programming language which maps the critical parts of the design in the recon figurable parts of the processor The rest of this paper is organized as follows Section 2 de scribes the FSR segmentation methodology that was implemented Section 3 describes the new architecture its major subsystems their interconnection and its mapping on the Stretch technology Section 4 has performance results and a detailed comparison to previously published implementations Finally Section 5 has some conclusions from this work G Chrysos et al Microprocessors and Microsystems 36 2012 215 231217 2 The FRS segmentation methodology Segmentation is a process used to facilitate the extraction of objects that form an image The FRS methodology which is studied in this paper consists of three steps prior to the recognition it self smoothing edge detection and color segmentation The data flow of segmentation process is described in Fig 1 In this work as will be shown below the HIS hue intensity saturation model is used from original RGB images an approach which is quite typical and has been shown in literature see Section 1 to work well 2 1 Smoothing algorithm The images contain noise introduced either by the camera or because of the image s transmission over a noisy medium In either case the noise must be removed before any further image process ing is applied The most common way of noise removal is the use of filters An important concept for a smoothing algorithm is the neighborhood between two pixels This algorithm allows for a fuz zy degree of neighborhood in which for each neighboring pixel there is the corresponding degree of neighborhood as shown in Fig 2 Each pixel s color is compared with the color of each of its neighboring blocks as shown in Fig 3 The size of blocks for our implementation was 3 3 which results to a strong smoothing of the image The average color for each of the neighboring blocks was calculated taking into account the neighborhood membership function as shown in the Eq 1 For smoothing the color contrast between the center pixel and all of the surrounding blocks must be measured Th