FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003
COMPLETE SPECIFICATION
(See Section 10 and Rule 13)
Title of invention:
METHOD AND SYSTEM FOR DETECTING TOPOLOGICAL CONTOURS
Applicant
Tata Consultancy Services Limited A company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th floor,
Nariman point, Mumbai 400021,
Maharashtra, India
Preamble to the description
The following specification particularly describes the invention and the manner in
which it is to be performed.

TECHNICAL FIELD [001] The disclosure herein generally relates to interferometric profilometry, and, more particularly, to a method and system for detecting topological contours.
BACKGROUND
[002] A Fringe Projection Profilometry (FPP) is a non-contact and non-interferometric technique for surface profiling. The FPP is extensively utilized for micro and macro scale surface profiling in industrial manufacturing, virtual reality and computer vision. In FPP, a fringe pattern is projected on a surface under test (SUT) and an image of the fringe projected SUT is captured by utilizing an imaging device. The fringe pattern includes periodical fringes like a rectangular fringe and a sinusoidal fringe. A fringe pattern contains alternate dark and bright regions. The image of the fringe projected SUT is further analyzed to identify 3D shape of contours. For example, the contours includes a defect or anomaly or a feature associated with the SUT.
[003] Conventional methods for FPP focus on a methodology wherein a reference image/object is required for a standard surface reconstruction and are not suitable for real time applications, where a reference image does not exist. The conventional methods which do not require reference images follows standard approach, wherein the computation is performed for the entire image and the time complexity is more. Further, the conventional methods finally require manual intervention for verification of the contours.
SUMMARY [004] Embodiments of the present disclosure present technological
improvements as solutions to one or more of the above-mentioned technical problems
recognized by the inventors in conventional systems. For example, in one
embodiment, a method for detecting topological contours is provided. The method
includes receiving, a plurality of images of a Surface Under Test (SUT), wherein the

plurality of images are acquired by projecting a set of fringe patterns on the Surface Under Test with a plurality of orientations. Further, the method segments each of the plurality of images to obtain a set of image blocks based on a pre-defined block size. Further, the method computes a spatial fringe frequency coefficient associated with each image block from the set of image blocks based on an autoregressive technique. Further, the method identifies a set of anomalous image blocks by comparing the spatial fringe frequency coefficient corresponding to each image block from the set of the image blocks with a predetermined threshold. Furthermore, the method computes a contour map for each of the plurality of images based on a pixel by pixel analysis of the set of anomalous image blocks. Finally, the method integrates the contour map corresponding to each of the plurality of images to obtain a topological contour of the SUT.
[005] In another aspect, a system for detecting topological contours is provided. The system includes a computing device wherein the computing device includes, at least one memory comprising programmed instructions, at least one hardware processor operatively coupled to the at least one memory, wherein the at least one hardware processor is capable of executing the programmed instructions stored in the at least one memories and an image analysis unit, wherein the image analysis unit is configured to receive, a plurality of images of a Surface Under Test (SUT), wherein the plurality of images are acquired by projecting a set of fringe patterns on the Surface Under Test with a plurality of orientations. Further, the image analysis unit is configured to segment, each of the plurality of images to obtain a set of image blocks based on a pre-defined block size. Further, the image analysis unit is configured to compute a spatial fringe frequency coefficient associated with each image block from the set of image blocks based on an autoregressive technique. Further, the image analysis unit is configured to identify a set of anomalous image blocks by comparing the spatial fringe frequency coefficient corresponding to each image block from the set of image blocks with a predetermined threshold.

Furthermore, the image analysis unit is configured to compute a contour map for each of the plurality of images based on a pixel by pixel analysis of the set of anomalous image blocks. Finally, the image analysis unit is configured to integrate the contour map corresponding to each of the plurality of images to obtain a topological contour of the SUT.
[006] In yet another aspect, a computer program product comprising a non-transitory computer-readable medium having embodied therein a computer program for method and system for detecting topological contours is provided. The computer readable program, when executed on a computing device, causes the computing device to receive, a plurality of images of a Surface Under Test (SUT), wherein the plurality of images are acquired by projecting a set of fringe patterns on the Surface Under Test with a plurality of orientations. Further, the computer readable program, when executed on a computing device, causes the computing device to segment, each of the plurality of images to obtain a set of image blocks based on a pre-defined block size. Further, the computer readable program, when executed on a computing device, causes the computing device to compute, a spatial fringe frequency coefficient associated with each image from the set of image blocks based on an autoregressive technique. Further, the computer readable program, when executed on a computing device, causes the computing device to identify, a set of anomalous image blocks by comparing the spatial fringe frequency coefficient corresponding to each image block from the set of image blocks with a predetermined threshold. Furthermore, the computer readable program, when executed on a computing device, causes the computing device to compute, a contour map for each of the plurality of images based on a pixel by pixel analysis of the set of anomalous image blocks. Finally, the computer readable program, when executed on a computing device, causes the computing device to integrate the contour map corresponding to each of the plurality of images to obtain a topological contour of the SUT.

[007] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
BRIEF DESCRIPTION OF THE DRAWINGS
[008] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles:
[009] FIG. 1 illustrates an exemplary system for detecting topological contours of a Surface Under Test (SUT), according to some embodiments of the present disclosure.
[010] FIG. 2 is a functional block diagram of a computing device of the system for detecting the topological contours, according to some embodiments of the present disclosure.
[011] FIG. 3 illustrates an exemplary method for detecting topological contours, in accordance with some embodiments of the present disclosure.
[012] FIG. 4A and FIG. 4B illustrates a fringe projected image with 45 degree orientation corresponding to a defect free SUT and defective SUT, in accordance with some embodiments of the present disclosure.
[013] FIG. 5 is an exemplary flow diagram for a processor implemented method for detection of topological contours, according to some embodiments of the present disclosure. and
[014] FIG. 6A through FIG. 7C illustrates an exemplary experimental results of the method and system for detecting topological contours, according to some embodiments of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS
[015] Exemplary embodiments are described with reference to the
accompanying drawings. In the figures, the left-most digit(s) of a reference number
identifies the figure in which the reference number first appears. Wherever
convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the spirit and scope of the disclosed embodiments. It is intended that the following detailed description be considered as exemplary only, with the true scope and spirit being indicated by the following claims.
[016] Embodiments herein provide a method and system for detecting topological contours. Unlike the existing methods, the system detects the contours in a time efficient manner based on a 2D auto regression technique. Here, the block by block analysis of the image is performed prior to pixel by pixel analysis and a topological contours is obtained without any manual intervention. An implementation of the method and system for detecting topological contours is described further in detail with reference to FIGS. 1 through 7C.
[017] Referring now to the drawings, and more particularly to FIG. 1 through 7C, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method.
[018] FIG. 1 illustrates an exemplary system 100 for detecting topological contours of a Surface Under Test (SUT), according to an example embodiment of the present subject matter. The system 100 for detecting topological contours, includes a computing device 104, a projecting device 102, an imaging device 106 and a Surface Under Test (SUT) 108. In an embodiment, the SUT is varied based on the requirement. Here the computing device 104 can be one among a variety of

computing systems, such as a laptop computer, a desktop computer, a notebook, a workstation, a cloud-based computing device, a router, a network gateway, a sensor gateway, a wifi access point and the like. In one implementation, the computing device 104 may be implemented in a cloud-based environment. In another implementation, the system 100 can be implemented in a cloud-edge environment and in yet another implementation, the system 100 can be implemented in a cloud-fog environment. The projecting device 102, the imaging device 106 and the computing device 104 are communicatively coupled through a network.
[019] In an embodiment, the network may be a wireless or a wired network, or a combination thereof. In an example, the network can be implemented as a computer network, as one of the different types of networks, such as virtual private network (VPN), intranet, local area network (LAN), wide area network (WAN), the internet, and such. The network may either be a dedicated network or a shared network, which represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), and Wireless Application Protocol (WAP), to communicate with each other. Further, the network may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices. The network devices within the network 108 may interact with the each other through communication links.
[020] In an embodiment, imaging device 106 includes a Digital Single Lens Reflex (DSLR) camera. The projecting device 102 includes a digital projection device for projecting the structured pattern on the SUT.
[021] FIG. 2 illustrates a block diagram of the computing device 104, according to some embodiments of the present disclosure. The computing device 104 includes or is otherwise in communication with one or more hardware processors, such as a processor 202, at least one memory such as a memory 204, an I/O interface 222 and an image analysis unit 220. In an embodiment, the image analysis unit 220

comprising a segmentation module (not shown in FIG. 2), spatial fringe frequency calculation module (not shown in FIG. 2), a pixel by pixel analysis module (not shown in FIG. 2) and a contour integration module(not shown in FIG. 2). The processor 202, memory 204, and the I/O interface 222 may be coupled by a system bus such as a system bus 208 or a similar mechanism.
[022] The I/O interface 222 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The interfaces 222 may include a variety of software and hardware interfaces, for example, interfaces for peripheral device(s), such as a keyboard, a mouse, an external memory, the imaging device 106, the projecting device 102, a printer and the like. Further, the interfaces 222 may enable the computing device 104 to communicate with other devices, such as web servers and external databases. The interfaces 222 can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, local area network (LAN), cable, etc., and wireless networks, such as Wireless LAN (WLAN), cellular, or satellite. For the purpose, the interfaces 222 may include one or more ports for connecting a number of computing systems with one another or to another server computer. The I/O interface 222 may include one or more ports for connecting a number of devices to one another or to another server.
[023] The hardware processor 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the hardware processor 202 is configured to fetch and execute computer-readable instructions stored in the memory 204.
[024] The memory 204 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile

memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. In an embodiment, the memory 204 includes a plurality of modules 206 and a repository 210 for storing data processed, received, and generated by one or more of the modules 206 and the image analysis unit 220. The modules 206 may include routines, programs, objects, components, data structures, and so on, which perform particular tasks or implement particular abstract data types.
[025] The memory 204 also includes module(s) 206 and a data repository 210. The module(s) 206 include programs or coded instructions that supplement applications or functions performed by the system 100 for detecting topological contours. The modules 206, amongst other things, can include routines, programs, objects, components, and data structures, which perform particular tasks or implement particular abstract data types. The modules 206 may also be used as, signal processor(s), state machine(s), logic circuitries, and/or any other device or component that manipulates signals based on operational instructions. Further, the modules 206 can be used by hardware, by computer-readable instructions executed by a processing unit, or by a combination thereof. The modules 206 can include various sub-modules (not shown). The modules 206 may include computer-readable instructions that supplement applications or functions performed by the computing device 104 for detecting topological contours.
[026] The data repository 210 may include received images 212, a fringe pattern database 214, a contour map database 216 and other data 218. Further, the other data 218 amongst other things, may serve as a repository for storing data that is processed, received, or generated as a result of the execution of one or more modules in the module(s) 206 and the modules associated with the image analysis unit 220.
[027] Although the data repository 210 is shown internal to the computing device 104, it will be noted that, in alternate embodiments, the data repository 210 can also be implemented external to the computing device 104, where the data

repository 210 may be stored within a database (not shown in FIG. 1) communicatively coupled to the computing device 104. The data contained within such external database may be periodically updated. For example, new data may be added into the database (not shown in FIG. 1) and/or existing data may be modified and/or non-useful data may be deleted from the database (not shown in FIG. 1). In one example, the data may be stored in an external system, such as a Lightweight Directory Access Protocol (LDAP) directory and a Relational Database Management System (RDBMS). In another embodiment, the data stored in the data repository 210 may be distributed between the computing device 104 and the external database (not shown).
[028] FIG. 3 illustrates an exemplary method for detecting topological contours, in accordance with some embodiments of the present disclosure. Now referring to FIG. 3, a set of fringe patterns 304 each with different orientation is projected on the SUT 302 and a plurality of fringe projected images 306 are obtained. Further, each fringe projected image is processed by segmenting each fringe projected image into a plurality of image blocks. A spatial fringe frequency coefficient is computed for each image block and the spatial fringe frequency coefficient associated with each block is compared with a predetermined fringe frequency of the image block. The image blocks with the spatial frequency above the predetermined threshold are identified as anomalous image blocks. Further, a pixel by pixel analysis is performed on each anomalous image block corresponding to each of the plurality of fringe projected images 306 to obtain a contour map 308 corresponding to each of the fringe projected image 306. Further, each contour map 308 corresponding to each of the plurality of fringe projected image is integrated to obtain topological contours of the SUT.
[029] The image analysis unit 220 of the computing device 104 can be configured to receive a plurality of images of the Surface Under Test (SUT), wherein the plurality of images are acquired by projecting a set of fringe patterns on the SUT.

The set of fringe patterns are projected by utilizing a plurality of projection angles. The set of fringe pattern includes periodic fringe patterns, for example, sinusoidal fringe pattern and rectilinear fringe pattern. Further, the number of images in the set of images is proportional to the plurality of projection angles.
[030] In an embodiment, the fringe orientation is a crucial factor for identification of topological contours. A fixed fringe orientation is not effective for images with defects in arbitrary shapes and sizes. Here, multiple orientations are utilized by varying angles at a particular frequency and the frequency is further varied corresponding to each of the multiple orientations.
[031] In an embodiment, the intensity of each of the set of fringe pattern is given in equation 1.

where, a (x, y ) is background intensity, b (x, y ) is fringe amplitude, ωx and ωy are
fringe frequencies in the x and y directions respectively. The SUT without any
feature/defect, causes each fringe pattern projected on the SUT remains intact. For brevity of description, the term “defect” and “feature” can be used interchangeably. The SUT with any feature, causes a deviation in the fringe pattern projected on the SUT and is given in equation 2.

where, is the phase corresponding to the
deformation due to the defect or surface feature. The fringe pattern is modified in accordance with the size and shape of the defect in the SUT. In an embodiment, an exponential phase field (EPF) corresponding to the fringe pattern expressed in equation 2 is given as

where, In an embodiment, a transform based technique is utilized to
compute the EPF from equation 2. Since, the amplitude associated with the EPF is

unity, the information related to the SUT is encoded in the phase and operating with the EPF provides advantage of insensitivity against surface reflectivity variations.
[032] FIG. 4A and FIG. 4B illustrates a fringe projected image with 45 degree orientation corresponding to a defect free SUT and defective SUT , in accordance with some embodiments of the present disclosure. Here, FIG. 4A illustrates an image of a fringe projected SUT without any defect and FIG. 4B illustrates an image of a fringe projected SUT with a defect 402. Now referring to FIG. 4B, a phase change is visible in the fringe pattern created by the defect 402.
[033] Further, the image analysis unit 220 of the computing device 104 can be configured to segment, each of the plurality of images to obtain a set of image blocks/patches based on a pre-defined block size. In an embodiment, the image block size is 5 x 5, 7 x 7.
[034] Further, the image analysis unit 220 of the computing device 104 can be configured to compute, a spatial fringe frequency coefficient associated with each of the set of image blocks based on a 2D autoregressive technique.
[035] In an embodiment, estimating the spatial fringe frequency coefficient corresponding to each of the set of image blocks based on autoregressive technique is explained as follows: For brevity of description, “the spatial fringe frequency coefficient” and “the autoregressive coefficient” can be used interchangeably. A 2D autoregressive technique/model of the EPF within each image block is given in equation 4

Where, represents the order of the AR model and
is a complex 2D white zero-mean stationary field with variance . The AR
model is assumed causal and stable and with quarter plane support with a pre-defined model order. The power spectral density corresponding to the 2D autoregressive model is given in equation 5.


Here, since the EPF is considered within a small size image block, wherein the phase variation can be approximated to be linear, the EPF can be modeled as a two dimensional complex sinusoid. The power spectral density given in equation 5 includes a single peak corresponding to the sinusoid frequencies. In order to determine the precise spatial fringe frequency, accurate computation of the 2D autoregressive coefficients is essential. Here, the 2D autoregressive coefficient is calculated based on Yule-Walker equations as given in equation 6.

where, is a
2D autocorrelation function, E is an expectation operator and δ (p, q ) is a Kronecker
delta function. The least square estimate of autoregressive coefficient is obtained by utilizing the equation 7

Where,R is sized matrix; r and a are (KL+K+L)X1
sized vectors. The above matrix and vector sizes are considered after
removing the contribution of a (0,0 ) coefficient. The estimated coefficients are
plugged into Eq. (5) to estimate the frequency.
[036] Further, the image analysis unit 220 of the computing device 104 can be configured to identify the set of anomalous image blocks by comparing the spatial fringe frequency coefficient corresponding to each of the image block with a

predetermined threshold. The plurality of image blocks with the spatial fringe frequency coefficient outside the predetermined threshold are the anomalous image blocks.
[037] Further, the image analysis unit 220 of the computing device 104 can be configured to compute, a contour map for each of the plurality of images based on a pixel by pixel analysis of the set of anomalous image blocks. Here an autoregressive coefficient is calculated for each pixel associated with each anomalous image block from the set of anomalous image blocks. Further, the autoregressive coefficient associated with each pixel is compared with the predetermined pixel threshold. If the autoregressive coefficient associated with each pixel is greater than the predetermined pixel threshold, the pixel is considered as part of the contour and marked as 1. If the autoregressive coefficient associated with each pixel it is equal to or less than the predetermined pixel threshold, the pixel value is considered to be defect-free and marked 0. Further, all the pixels marked as 1, corresponding to each image from the plurality images are utilized to identify the defect/feature contour for each image from the plurality of images.
[038] Further, the image analysis unit 220 of the computing device 104 is configured to integrate, the contour map corresponding to each of the plurality of images to obtain the topological contour of the SUT. In an embodiment, the contour map corresponding to each of the plurality of images are merged together by utilizing a logical `OR' operation resulting in an enhanced detection accuracy.
[039] FIG. 5 is an exemplary flow diagram for a processor implemented method for detection of topological contours using computing device of system of FIG. 1, according to some embodiments of the present disclosure. The method 500 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, etc., that perform particular functions or implement particular abstract data types. The method 500 may

also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communication network. The order in which the method 500 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method 500, or an alternative method. Furthermore, the method 500 can be implemented in any suitable hardware, software, firmware, or combination thereof.
[040] At 502, the computing device 104 receives, by a one or more
hardware processors, the plurality of images of a Surface Under Test (SUT), wherein
the plurality of images are acquired by projecting the set of fringe patterns on the
Surface Under Test with a plurality of orientations. Here, the set fringes includes
periodic fringes, for example, sinusoidal fringes and rectilinear fringes. Here, the
number of images in the set of images is proportional to the number of orientations of
the fringe pattern. Further, the set of fringe patterns are projected by utilizing a
plurality of projection angles At 504, computing device 104 segments, by the one or
more hardware processors, each of the plurality of images to obtain a set of image
blocks based on a pre-defined block size. At 506, computing device 104 computes, by
the one or more hardware processors, the spatial fringe frequency coefficient
associated with each of the set of image blocks based on an autoregressive technique.
At 508, the computing device 104 identifies by the one or more hardware processors,
a set of anomalous image blocks by comparing the spatial fringe frequency
coefficient corresponding to each of the image block with a predetermined threshold.
At 510, the computing device 104 computes, by the one or more hardware
processors, a contour map for each of the plurality of images based on a pixel by pixel analysis of the set of anomalous image blocks. At 512, the computing device 104 integrates, by the one or more hardware processors, the contour map corresponding to each of the plurality of images to obtain the topological contour of the SUT.

[041] Experimentation: In an embodiment, the experimental setup includes the projecting device/projector for generating illumination patterns, the surface under test (SUT), and the imaging device/camera. A set of fringe patterns are projected on the SUT by the projecting device. Each fringe pattern includes an array of sinusoidal fringes oriented at varying angles. Correspondingly, the plurality of images of the SUT with each pattern superimposed on the SUT at a unique angle is captured on the camera/imaging device. Further, the method for detecting topological contours is applied to process each of these images to yield the contours of the plurality of images on the SUT. In an embodiment, the contours includes a defect on the SUT.
[042] In an embodiment, the present disclosure is implemented for 4 fringe frequencies and 5 sets of fringe orientations. The simulation was performed with signal to noise ratio of 30 dB. The five sets of fringe orientations were considered in the proposed multiple-angle (in degrees) fringe projection as {0, 90}, {0, 45, 90}, {0, 22.5, 45, 67.5, 90}, {0, 22.5, 33.75, 45, 56.25, 67.5, 90}, and {0, 11.25, 22.5, 33.75, 45, 56.25, 67.5, 78.75, 90}. For each combination of fringe frequency and fringe orientation, the number of unidentified image blocks associated with defect region is identified as the error measure and percentage accuracy is computed as depicted in Table 1.
Table 1

Fringe Frequency (radians) Fringe orientation set 5 86 86
86 86

1 2 3 4

0.1227 42 86 86 86

0.1841 42 86 86 86

0.2454 39 86 86 86

0.3068 42 86 86 86

[043] In an embodiment, two important observations are apparent from the calculated accuracy values provided in Table 1: (1) Defect identification based on only vertically and horizontally projected patterns do not yield high accuracy (2) the accuracy of the algorithm saturates/does not improve significantly at a certain number

of multiply oriented fringes. In this example, three fringe projection with three orientations (0, 45 and 90 degrees) were found to yield sufficiently accurate results.
[044] FIG. 6A through FIG. 7C illustrates exemplary experimental results of the method and system for detection topological contours, in accordance with some embodiments of the present disclosure.
[045] FIG. 6A illustrates an SUT with defects of different shapes and sizes, in accordance with some embodiments of the present disclosure and FIG 6B illustrates the fringe projected SUT with defects of different shapes and sizes, , in accordance with some embodiments of the present disclosure.
[046] FIG. 6C illustrates the contour map for the fringes projected from multiple angles, in accordance with some embodiments of the present disclosure. Now referring to FIG. 6C, three image columns corresponds to three fringe frequencies with increasing values from top to bottom. Each row of the FIG. 6C illustrates a particular set of fringe orientations.
[047] FIG. 7A illustrates a metal plate SUT with defects of different shapes and sizes, in accordance with some embodiments of the present disclosure and FIG 7B illustrates the fringe projected metal plate SUT with defects of different shapes and sizes, in accordance with some embodiments of the present disclosure.
[048] FIG. 7C illustrates the contour map of the metal plate SUT with the fringes projected from multiple angles, in accordance with some embodiments of the present disclosure. Now referring to FIG. 7C, three image columns corresponds to three fringe frequencies with increasing values from top to bottom. Each row of the FIG. 7C illustrates a particular set of fringe orientations. The orientations are {0, 90},
{0, 45, 90}, and {0, 11.25, 22.5, 33.75, 45, 56.25, 67.5, 78.75, 90}.
[049] The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do

not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.
[050] The embodiments of present disclosure herein addresses unresolved problem of automated structured illumination based phase imaging approach for detecting topological contours of an image. Further, the present disclosure reduces the time complexity by utilizing an autoregressive technique to identify anomalous image blocks prior to applying pixel by pixel analysis. Here, the pixel by pixel analysis is performed only on anomalous image blocks rather than for entire images. Furthermore, integration of a plurality of contour maps of different orientation to obtain a single contour map improves the accuracy of the method and system for detection of topological contours.
[051] It is to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g. any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g. hardware means like e.g. an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software modules located therein. Thus, the means can include both hardware means and software means. The method embodiments described herein could be implemented in hardware and software. The device may also include software means. Alternatively, the embodiments may be implemented on different hardware devices, e.g. using a plurality of CPUs.

[052] The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various modules described herein may be implemented in other modules or combinations of other modules. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
[053] The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments. Also, the words “comprising,” “having,” “containing,” and “including,” and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.
[054] Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which

information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e. non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.
[055] It is intended that the disclosure and examples be considered as exemplary only, with a true scope and spirit of disclosed embodiments being indicated by the following claims.

WE CLAIM:
1. A processor implemented method, the method comprising:
receiving, by a one or more hardware processors, a plurality of images of a Surface Under Test (SUT), wherein the plurality of images are acquired by projecting a set of fringe patterns on the SUT with a plurality of orientations;
segmenting, by the one or more hardware processors, each of the plurality of images to obtain a set of image blocks based on a pre-defined block size;
computing, by the one or more hardware processors, a spatial fringe frequency coefficient associated with each image block from the set of image blocks based on an autoregressive technique;
identifying, by the one or more hardware processors, a set of anomalous image blocks by comparing the spatial fringe frequency coefficient corresponding to each image block from the set of the image blocks with a predetermined threshold;
computing, by the one or more hardware processors, a contour map for each of the plurality of images based on a pixel by pixel analysis of the set of anomalous image blocks; and
integrating, by a one or more hardware processors, the contour map corresponding to each of the plurality of images to obtain a topological contours of the SUT.
2. The method as claimed in claim 1, wherein set of the fringe patterns comprises periodic fringe patterns.
3. The method as claimed in claim 1, wherein the number of images in the set of images is proportional to the number of orientations of the fringe pattern.

4. The method as claimed in claim 1, wherein the set of fringe patterns are projected by utilizing a plurality of projection angles.
5. A system (100), the system (100) comprising:
a computing device (104), wherein the computing device (104) comprising: at least one memory (204) storing programmed instructions;
one or more hardware processors (202) operatively coupled to the at least one memory, wherein the one or more hardware processors (202) are capable of executing the programmed instructions stored in the at least one memory (204); and
an image analysis unit (220), wherein the image analysis unit (220) is configured to:
receive, by the one or more hardware processors (202), a plurality of images of a Surface Under Test (SUT), wherein the plurality of images are acquired by projecting a set of fringe patterns on the Surface Under Test with a plurality of orientations;
segment, by the one or more hardware processors (202), each of the plurality of images to obtain a set of image blocks based on a pre-defined block size;
compute, by the one or more hardware processors (202), a spatial fringe frequency coefficient associated with each image block from the set of image blocks based on an autoregressive technique;
identify, by the one or more hardware processors (202), a set of
anomalous image blocks by comparing the spatial fringe frequency coefficient
corresponding to each image block from the image block with a
predetermined threshold;
compute, by the one or more hardware processors (202), a contour map for each of the plurality of images based on a pixel by pixel analysis of the set of anomalous image blocks; and

integrate, by the one or more hardware processors (202), the contour map corresponding to each of the plurality of images to obtain a topological contours of the SUT.
6. The system as claimed in claim 5, wherein the set of fringe patterns comprises periodic fringe patterns.
7. The system as claimed in claim 5, wherein the number of images in the set of images is proportional to the number of orientations of the fringe pattern.
8. The system as claimed in claim 5, wherein the set of fringe patterns are projected by utilizing a plurality of projection angles.