TECHNICAL FIELD
[001] The present disclosure relates to the determination of roughness of a surface.
More particularly, the present disclosure relates to a device for surface roughness measurement using images of an object.
BACKGROUND
[002] The background description includes information that may be useful in
understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[003] Surface roughness often shortened to roughness, is a component of surface
texture. It is quantified by deviations in the direction of normal vector of a real surface from its ideal form. In surface metrology, roughness is typically considered to be high-frequency, short-wavelength component of a measured surface image. However, in practice, it is often necessary to know both amplitude and frequency to ensure that a surface is fit for a purpose. Surface roughness evaluation is very important for many fundamental problems such as friction, contact deformation, heat and electric current conduction, tightness of contact joints and positional accuracy. Therefore, surface roughness has been a subject of experimental and theoretical investigations for many years. Many techniques have been developed for measuring surface finish ranging from the simple touch comparator to sophisticated optical techniques. In recent years, advent of high-speed general-purpose digital computers and vision systems has made image analysis easier and more flexible.
[004] A fractal dimension which has been surprisingly applied on engineering
application such as object tracking, dimension measurement, feature extraction, material
analysis, colour vision analysis and many more. Fractal theory played a vital role in image
analysis in terms of dimension estimation, material characterisation, and texture analysis.
[005] Existing techniques concerned with surface roughness fractal analysis in terms
of both grayscale and colour domain analysis. These existing techniques are dealing with algorithm (step-wise) approaches, but none of the techniques deals with device orientation which can be directly linked to image analysis. Another existing technique designed a system for measuring surface roughness of a magnetic pattern of thin-film magnetic disks by an angle of a first phase difference between first and second reflected polarised light signal

components. In an attempt to provide surface roughness measurement device was presented
based on splitting light of both first and second wavelengths reflectance for selecting a harder
and smoother surface. The surface roughness can be measured based on sensing fringe field
capacitance effects of electric circuit and electrodes. Afterwards, another method and
apparatus for accessing surface roughness were presented on processor-based optical system
and illumination of a planar surface. Recently, method for rapidly estimating fractal model
dimension were suggested for gully bottom-line total length based on computer basin
software tool. In context to same approach, recently counting were presented based on
algorithmic approach on grayscale image, where they were dealing with only use of
traditional algorithm mechanism, which was more tedious in dealing with real-life application
scenario and also failed to handle all influence factors present in real-time captured images.
[006] However, still, the surface measurement was one of the challenging issues for
complex objects found in nature, which was failed to analyse by Euclidian geometry in image analysis domain. The main issues that affect this estimation mainly of several influence factors like sampling process, similarity property, a region of interest, spatial resolution, spectral band, grayscale range, texture property, colour distance, colour property. Hence, none of the handheld devices was found yet to quantify the surface roughness accurately from the captured image directly.
[007] There is, therefore, a need in the art to provide a device for surface roughness
measurement of images that overcome the above-mentioned and other limitations of the existing solutions and utilise techniques, which are robust, accurate, fast, efficient, cost-effective and simple.
OBJECTS OF THE PRESENT DISCLOSURE
[008] Some of the objects of the present disclosure, which at least one embodiment
herein satisfy are as listed herein below.
[009] An object of the present disclosure is to provide a device for surface roughness
measurement of an object.
[0010] Another object of the present disclosure is to provide a device for surface
roughness measurement of an object mainly to help in engineering and health care for in-depth analysis.
[0011] Another object of the present disclosure is to provide a device for surface
roughness measurement of an object without any contact.

SUMMARY
[0012] This summary is provided to introduce a selection of concepts in a simplified
form to be further described below in the Detailed Description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
[0013] An aspect of the present disclosure relates to an electronic device. The
electronic device includes a non-transitory medium with a computer-readable program code
embodied therein. The computer readable program code for execution by one or more
processor to configure the electronic device to receive one or more first set of trigger inputs.
The electronic device access one or more remote database comprising a non-personalized
electronic documents and a personalized electronic documents upon detecting the receipt of
the one or more first set of trigger inputs and store an information at least partially relevant to
the one or more first set of trigger inputs from the non-personalized electronic
documents and the personalized electronic documents in a database of the electronic device.
[0014] The present disclosure relates to the determination of roughness of a surface.
More particularly, the present disclosure relates to a device for surface roughness measurement using images of an object.
[0015] According to an aspect of present disclosure, a device for surface roughness
measurement is disclosed. The device can include a camera for capturing one or more images of the object; and a control unit operatively coupled with the camera, the control unit comprises a processor coupled to a memory, the memory storing instructions executable by the processor to: receive the captured one or more images from the camera and define a Region of Interest (ROI) for each captured image, wherein the ROI is defined based on geometrical section of each of the captured one or more images, determine surface roughness in the ROI by analysing gradient value of edges for each of the captured one or more images; wherein the surface roughness of the object is analysed based on averaging of ROI over a complete span of the captured one or more images.
[0016] In an aspect, the geometric shape of the ROI is rectangular or square-shaped.
[0017] In an aspect, the ROI of the captured one or more images is resized within a
predefined range.
[0018] In an aspect, the device comprises a fractal dimension unit to determine the
space-filling capacity of the grid lines in the ROI of the captured one or more images.
[0019] In an aspect, the fractal dimension is configured to determine grid shifting
dimension in the geometrical section of the captured one or more images.

[0020] In an aspect, the device comprises a regression analysis unit to determine
surface roughness error by averaging grid lines in the ROI of each captured one or more
images.
[0021] In an aspect, the camera is selected from any or combination of standard
camera, a complementary metal-oxide-semiconductor (CMOS) camera, a digital single-lens
reflex (DSLR) camera.
[0022] In an aspect, the device comprises a display unit for displaying the determined
surface roughness.
[0023] In an aspect, the device comprises a set of batteries for supplying uninterrupted
power to the camera, the control unit, and the display unit.
[0024] Another aspect of the present disclosure provides a method for surface
roughness measurement of an object, said method includes: receiving, by one or more
processors of a control unit, one or more images captured by a camera operatively coupled to
the control unit; defining, by the one or more processors, a Region of Interest (ROI) for each
captured image, wherein the ROI is defined based on geometrical section of each of the
captured one or more images; and determining, by the one or more processors surface
roughness in the ROI by analysing gradient value of edges for each of the captured one or
more images, wherein analysing the surface roughness based on averaging of ROI over a
span of the captured one or more images.
[0025] Various objects, features, aspects and advantages of the present disclosure will
become more apparent from the following detailed description of preferred embodiments,
along with the accompanying drawing figures in which like numerals represent like features.
BRIEF DESCRIPTION OF THE DRAWINGS
[0026] In the figures, similar components and/or features may have the same
reference label. Further, various components of the same type may be distinguished by following the reference label with a second label that distinguishes among the similar components. If only the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label.
[0027] FIG. 1 illustrates architecture of a surface roughness measurement device to
illustrate its overall working in accordance with an embodiment of the present disclosure.
[0028] FIG. 2 illustrates an exemplary control unit in accordance with an embodiment
of the present disclosure.

[0029] FIG. 3 illustrates an exemplary grid shifting operation in accordance with an
embodiment of the present disclosure.
[0030] FIG. 4A illustrates a front view of a surface roughness measurement device in
accordance with an embodiment of the present disclosure.
[0031] FIG. 4B illustrates a rear view of a surface roughness measurement device in
accordance with an embodiment of the present disclosure.
[0032] FIG. 5 illustrates a method of working of proposed device in accordance with
an exemplary embodiment of the present disclosure.
DETAILED DESCRIPTION
[0033] In the following description, numerous specific details are set forth in order to
provide a thorough understanding of embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details.
[0034] Embodiments of the present invention include various steps, which will be
described below. The steps may be performed by hardware components or may be embodied in machine-executable instructions, which may be used to cause a general-purpose or special-purpose processor programmed with the instructions to perform the steps. Alternatively, steps may be performed by a combination of hardware, software, firmware and/or by human operators.
[0035] Embodiments of the present invention may be provided as a computer program
product, which may include a machine-readable storage medium tangibly embodying thereon instructions, which may be used to program a computer (or other electronic devices) to perform a process. The machine-readable medium may include, but is not limited to, fixed (hard) drives, magnetic tape, floppy diskettes, optical disks, compact disc read-only memories (CD-ROMs), and magneto-optical disks, semiconductor memories, such as ROMs, PROMs, random access memories (RAMs), programmable read-only memories (PROMs), erasable PROMs (EPROMs), electrically erasable PROMs (EEPROMs), flash memory, magnetic or optical cards, or other type of media/machine-readable medium suitable for storing electronic instructions (e.g., computer programming code, such as software or firmware).
[0036] Various methods described herein may be practiced by combining one or more
machine-readable storage media containing the code according to the present invention with appropriate standard computer hardware to execute the code contained therein. An apparatus

for practicing various embodiments of the present invention may involve one or more
computers (or one or more processors within a single computer) and storage systems
containing or having network access to computer program(s) coded in accordance with
various methods described herein, and the method steps of the invention could be
accomplished by modules, routines, subroutines, or subparts of a computer program product.
[0037] If the specification states a component or feature "may", "can", "could", or
"might" be included or have a characteristic, that particular component or feature is not required to be included or have the characteristic.
[0038] As used in the description herein and throughout the claims that follow, the
meaning of "a," "an," and "the" includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of "in" includes "in" and "on" unless the context clearly dictates otherwise.
[0039] Exemplary embodiments will now be described more fully hereinafter with
reference to the accompanying drawings, in which exemplary embodiments are shown. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. These embodiments are provided so that this invention will be thorough and complete and will fully convey the scope of the invention to those of ordinary skill in the art. Moreover, all statements herein reciting embodiments of the invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future (i.e., any elements developed that perform the same function, regardless of structure).
[0040] While embodiments of the present invention have been illustrated and
described, it will be clear that the invention is not limited to these embodiments only. Numerous modifications, changes, variations, substitutions, and equivalents will be apparent to those skilled in the art, without departing from the spirit and scope of the invention, as described in the claim.
[0041] The present disclosure relates to the determination of roughness of a surface.
More particularly, the present disclosure relates to a device for surface roughness measurement using images of an object.
[0042] According to an aspect of present disclosure, a device for surface roughness
measurement is disclosed. The device can include a camera for capturing one or more images of the object; and a control unit operatively coupled with the camera, the control unit comprises a processor coupled to a memory, the memory storing instructions executable by

the processor to: receive the captured one or more images from the camera and define a
Region of Interest (ROI) for each captured image, wherein the ROI is defined based on
geometrical section of each of the captured one or more images, determine surface roughness
in the ROI by analysing gradient value of edges for each of the captured one or more images;
wherein the surface roughness of the object is analysed based on averaging of ROI over a
complete span of the captured one or more images.
[0043] In an aspect, the geometric shape of the ROI is rectangular or square-shaped.
[0044] In an aspect, the ROI of the captured one or more images is resized within a
predefined range.
[0045] In an aspect, the device comprises a fractal dimension unit to determine the
space-filling capacity of the grid lines in the ROI of the captured one or more images.
[0046] In an aspect, the fractal dimension is configured to determine grid shifting
dimension in the geometrical section of the captured one or more images.
[0047] In an aspect, the device comprises a regression analysis unit to determine
surface roughness error by averaging grid lines in the ROI of each captured one or more
images.
[0048] In an aspect, the camera is selected from any or combination of standard
camera, a complementary metal-oxide-semiconductor (CMOS) camera, a digital single-lens
reflex (DSLR) camera.
[0049] In an aspect, the device comprises a display unit for displaying the determined
surface roughness.
[0050] In an aspect, the device comprises a set of batteries for supplying uninterrupted
power to the camera, the control unit, and the display unit.
[0051] Another aspect of the present disclosure provides a method for surface
roughness measurement of an object, said method includes: receiving, by one or more
processors of a control unit, one or more images captured by a camera operatively coupled to
the control unit; defining, by the one or more processors, a Region of Interest (ROI) for each
captured image, wherein the ROI is defined based on geometrical section of each of the
captured one or more images; and determining, by the one or more processors surface
roughness in the ROI by analysing gradient value of edges for each of the captured one or
more images, wherein analysing the surface roughness based on averaging of ROI over a
span of the captured one or more images.
[0052] FIG. 1 illustrates an architecture of a surface roughness measurement system
to illustrate its overall working in accordance with an embodiment of the present disclosure.

[0053] According to an embodiment, a device for surface roughness measurement of
an object 100 can be implemented in captured images. The device 100 can include a camera 102, a control unit 112 and an output unit 108. The camera 102 may be configured on rear side of the device, said camera 102 operatively coupled with the control unit 112, a memory unit 110, a controller unit 106, and a battery 104. The output unit 108 (interchangeably referred to as display unit 108 herein) for displaying surface roughness error estimation of the captured images. The control unit 112can include memory unit 110 and processing unit 114. For instance, processing unitll4 can be configured for pre-processing of the captured images through generalized fractal dimension 116, said generalized fractal dimension determines space filling capacity of grid lines in the Region of Interest (ROI) for determining surface roughness 118 of the captured images.
[0054] According to an embodiment, during pre-processing, the control unit 112can
receive one or more captured images from the camera 102 and defines a Region of Interest (ROI) based on geometrical section for each captured image, and each ROI is resized to grayscale and colour scale. In an embodiment, the control unit 112can detect surface roughness in the ROI of each captured images based on determination of grid lines repeated on all respective section of individual point image and averaging point image. The individual point image and the averaging point image can be obtained from the grayscale and the colour scale.
[0055] In an embodiment, the device for measuring surface roughness 100 includes
camera 102 such as standard camera, or complementary metal oxide semiconductor (CMOS) camera, single-lens reflex (DSLR) camera, camera, and scanner. In another embodiment, the camera 102 can be an optical instrument to capture still images or to record moving images, which are stored in a physical medium such as in a memory unit 110. In yet another embodiment, the camera 102can be configured at rear end of the device provides captured images, said captured images may also be an image created through any known synthetic techniques such as computer-generated animation, or maybe a combination of data which is acquired via memory unit 110. The input captured images may first undergo normalization, said normalization can process the captured image so it has a greater degree of compatibility and overall performance of image processing method with the device.
[0056] In an embodiment, the device 100 can include a processing unit 114. The
processing unit 114 can execute instructions and perform calculations on image data based upon computing instructions. The processing unit 114 containing executable instructions, and image data, can be stored wholly or partially in the memory unit 110, and transfer to the

processing unit 114 over a database. In another embodiment, control unit 112 operatively coupled to the camera 102, the control unit 112 including an processing unit 114 coupled to the memory unit 110, said memory unit 110 stores instructions executable by the control unit 112 to extract features of the captured images, said features pertain to visual features of the captured images to identify the geometry of image based on box height, region of interest, box area, and regression analysis. In an exemplary embodiment, regression analysis can detect surface roughness error by identifying the individual points and the averaging points, said individual points surface roughness image and the averaging points surface roughness image uses a statistical technique for the captured images.
[0057] FIG. 2 illustrates an exemplary control unit in accordance with an embodiment
of the present disclosure.
[0058] In an embodiment, the control unit 112 may comprise one or more processing
unit204. The one or more processing unit204 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that manipulate data based on operational instructions. Among other capabilities, the one or more processing unit204 is configured to fetch and execute computer-readable instructions stored in a memory 208 of the control unit 112. The memory 208 may store one or more computer-readable instructions or routines, which may be fetched and executed to create or share the data units over a network service. The memory 208 may comprise any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as EPROM, flash memory, and the like.
[0059] The control unit 112 may also comprise an interface(s) 206. The interface(s)
206 may comprise a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, and the like. The interface(s) 206 may facilitate communication of control unit 112 with various devices coupled to the control unit 112. The interface(s) 206 may also provide a communication pathway for one or more components of the control unit 112. Examples of such components include, but are not limited to, processing engine(s) 210 and data 220.
[0060] The processing engine(s) 210 may be implemented as a combination of
hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) 210. In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) 210 may be processor executable

instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine(s) 210 may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) 210. In such examples, the control unit 112 may comprise the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to control unit 112 and the processing resource. In other examples, the processing engine(s) 210 may be implemented by electronic circuitry.
[0061] The data 220 may comprise data that is either stored or generated as a result of
functionalities implemented by any of the components of the processing engine(s) 210. In an exemplary embodiment, the processing engine(s) 210 may comprise a pre-processing engine 212, a surface roughness detection engine 214, a surface roughness error detection engine 216 and other engine(s) 222.
[0062] It would be appreciated that engines being described are only exemplary
engines and any other engine or sub-engine may be included as part of the device 100 or the control unit 112. These engines too may be merged or divided into super-engines or sub-engines as may be configured.
Pre-processing Engine 212
[0063] In an embodiment, the pre-processing engine212 receives one or more
captured images from the camera 102, the received captured images may determine surface
roughness of the captured image for further processing.
[0064] In an embodiment, the pre-processing engine 212 defines the ROI for the
captured images. Those skilled in the art would appreciate that based on fractal theory, it has
been inferred that grid shifting operation will occur in an image. Therefore, according to an
embodiment of the present disclosure, the pre-processing engine 212 defines the ROI based
on geometrical section shifting of the captured images. The defined ROI may then be utilized
by other engines218for further processing.
[0065] In an embodiment, the ROI can be resized to gray scale and colour scale. The
surface roughness can be detected in the ROI of captured images based on determination of
grid lines repeated on all respective section of individual point surface roughness image and
averaging point surface roughness image, the individual point surface roughness image and

the averaging point surface roughness image being obtained from the gray scale and the colour scale respectively.
Surface roughness detection 214
[0066] FIG. 3 illustrates an exemplary grid shifting operation in accordance with an
embodiment of the present disclosure.
[0067] In an embodiment, initialise camera, display screen, and control panel, user
will be providing details from control panel such as focal length of captured image, image size, image resolution control panel, gray scale range. Surface roughness analysis or pre-processing of received captured image allows surface measurement factors including but not limited to box height, box area, regression analysis etc. During processing stage, the device can examine geometry of image, applying a grid that is geometrical section shifting mechanism. In an exemplary embodiment, if the captured image size can be square then equal square grid partitions are made on captured image surface until grid partition reaches size/2 (eg. 2, 4, 8, 16, 32; if image size is 64), every individual grid partition is taken into consideration for surface roughness analysis followed by grid shifting operation. If the captured images size can be rectangle then equal square partitions are made on the captured images surface until it reaches size/2 (eg. 2, 4, 8, 16, 32; if image size is 64), and individual grid further divided into a triangular partition to cop up with all the grids. Moreover, from every individual grid maximum surface roughness can be considered, and this process continues till all grids in image surface followed by grid shifting operation (i+1 j+1; i-1 j+1; i+l;j-l; i-lj-1). If image size can be rectangle then equal square partition made on image surface until it reaches size/2 (eg. 2, 4, 8, 16, 32; if image size is 64), and individual grid further divided into a triangular partition to cop up with all the grids. From every individual grid of rectangular captured images maximum surface roughness is considered, and this process continues till all the grids in the captured images surface is followed by grid shifting operation (i+1 j+1; i-1 j+1; i+1 j-1; i-lj-1).
Surface roughness error detection 216
[0068] In an embodiment, to evaluate fractal dimension of the captured image during
the grid point counting dimension. It is however contemplated that other fractal dimensions may be measured, such as correlation dimensions. Implementing in this example, therefore, the measurement of the grid point counting dimension, a grid with regular spacing is overlaid on the captured image. In an exemplary embodiment, the grid sizes range from the size of the

captured images, down to the geometric section size. However, the number of grid sizes and how they partition the images may be varied depending upon the accuracy and/or processing speed required. The number of points in the grid can be counted and stored for subsequent processing. In yet another embodiment, an integer can be an index to an array defining grid size, said integer can be incremented and a question is asked as to whether it is now greater than the largest value it may have. If not, then there is another grid size to use and control therefore returns. Eventually, all grid cell sizes can be used and so control will proceed to step, the regression analysis of the individual points and the averaging points on the captured image is evaluated.
[0069] In an embodiment, after successful evaluation of surface roughness of each
grid, finally, regression analysis can be related to a statistical technique that allows examining relationship between individual point surface roughness and average point surface roughness can be applied for surface roughness error estimation. The regression analysis can be related to the statistical technique for detecting the surface roughness error of one or more captured images by identifying the individual point surface roughness and the averaging point surface roughness image.
[0070] FIG. 4A illustrates a front view of a surface roughness measurement device in
accordance with an embodiment of the present disclosure.
[0071] FIG. 4B illustrates a rear view of a surface roughness measurement device in
accordance with an embodiment of the present disclosure.
[0072] In an embodiment, the device comprises of thin-film transistor liquid crystal
display (TFT-LCD) 402. During operation, the TFT-LCD 402 screen is used for viewing surface roughness of captured images of standard camera 426. The TFT-LCD 402 is operatively coupled with controllers 404 such as On/Off, Zoom in/out, save and mode, wherein mode helps user to configure settings of the TFT-LCD 402 such as brightness, contrast, sharpness, backlight etc. In another embodiment, the save controller enables the user to save the surface roughness image or normal image in the memory unit of a device for measurement of surface roughness error.
[0073] In an embodiment, a device for surface roughness measurement uses lithium-
ion batteries 422 for holding a huge amount of energy in small space that keeps the device inoperative condition. The battery 422 can be configured with the standard camera 426, control unit424, controller unit 404, and TFT-LCD 402for supplying power continuously for capturing real-time images by the standard camera 426, processing the captured images and measuring the surface roughness of the captured images.

[0074] FIG. 5 illustrates a method of working for a proposed device in accordance
with an exemplary embodiment of the present disclosure.
[0075] In an embodiment, present disclosure elaborates upon a method for surface
roughness measurement of images comprises, at block 502, receiving, by one or more processors of a control unit, one or more images captured by a camera operatively coupled to the control unit. Further, block 504 pertains to defining, by the one or more processors, a Region of Interest (ROI) for each captured image, wherein the ROI is defined based on geometrical section of each of the captured one or more images. Further, block 506 pertains to determining, by the one or more processors, surface roughness in the ROI by analysing gradient value of edges for each of the captured one or more images, wherein analysing the surface roughness based on averaging of ROI over a span of the captured one or more images.
[0076] Thus, it will be appreciated by those of ordinary skill in the art that the
diagrams, schematics, illustrations, and the like represent conceptual views or processes illustrating systems and methods embodying this invention. The functions of the various elements shown in the figures may be provided through the use of dedicated hardware as well as hardware capable of executing associated software. Similarly, any switches shown in the figures are conceptual only. Their function may be carried out through the operation of program logic, through dedicated logic, through the interaction of program control and dedicated logic, or even manually, the particular technique being selectable by the entity implementing this invention. Those of ordinary skill in the art further understand that the exemplary hardware, software, processes, methods, and/or operating systems described herein are for illustrative purposes and, thus, are not intended to be limited to any particular named.
[0077] While embodiments of the present invention have been illustrated and
described, it will be clear that the invention is not limited to these embodiments only. Numerous modifications, changes, variations, substitutions, and equivalents will be apparent to those skilled in the art, without departing from the spirit and scope of the invention, as described in the claim.
[0078] In the foregoing description, numerous details are set forth. It will be apparent,
however, to one of ordinary skill in the art having the benefit of this disclosure, that the present invention may be practiced without these specific details. In some instances, well-known structures and devices are shown in block diagram form, rather than in detail, to avoid obscuring the present invention.

[0079] As used herein, and unless the context dictates otherwise, the term "coupled
to" is intended to include both direct coupling (in which two elements that are coupled to each other contact each other)and indirect coupling (in which at least one additional element is located between the two elements). Therefore, the terms "coupled to" and "coupled with" are used synonymously. Within the context of this document terms "coupled to" and "coupled with" are also used euphemistically to mean "communicatively coupled with" over a network, where two or more devices can exchange data with each other over the network, possibly via one or more intermediary device.
[0080] It should be apparent to those skilled in the art that many more modifications
besides those already described are possible without departing from the inventive concepts herein. The inventive subject matter, therefore, is not to be restricted except in the spirit of the appended claims. Moreover, in interpreting both the specification and the claims, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms "comprises" and "comprising" should be interpreted as referring to elements, components, or steps in a non-exclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refer to at least one of something selected from the group consisting of A, B, C .... and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc.
[0081] While the foregoing describes various embodiments of the invention, other and
further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.
ADVANTAGES OF THE PRESENT DISCLOSURE
[0082] The present disclosure provides a device for surface roughness measurement
of an object.
[0083] The present disclosure provides a device for surface roughness measurement
of an object mainly to help in engineering and health care for in-depth analysis.

[0084] The present disclosure provides a device for surface roughness measurement
of an object without any contact.

We Claim:
1. A device for surface roughness measurement of an object, said device comprising:
a camera for capturing one or more images of the object; and a control unit operatively coupled with the camera, the control unit comprises a processor coupled to a memory, the memory storing instructions executable by the processor to:
receive the captured one or more images from the camera and define a Region of Interest (ROI) for each of the captured one or more images, wherein the ROI is defined based on geometrical section of each of the captured one or more images,
determine surface roughness in the ROI by analysing gradient value of edges for each of the capture done or more images; wherein the surface roughness of the object is analysed based on averaging of ROI over a complete span of the captured one or more images.
2. The device as claimed in claim 1, wherein the geometric shape of the ROI is rectangular or square-shaped.
3. The device as claimed in claim 1, wherein the ROI of the captured one or more images is resized within a predefined range.
4. The device as claimed in claim 1, wherein the device comprises with fractal dimension unit to determine the space-filling capacity of the grid lines in the ROI of the captured one or more images.
5. The device as claimed in claim 4, wherein the fractal dimension is configured to determine grid shifting dimension in the geometrical section of the captured one or more images.
6. The device as claimed in claim 1, wherein the device comprises a regression analysis unit to determine surface roughness error by averaging grid lines in the ROI of each captured one or more images.
7. The device as claimed in claim 1, wherein the camera is selected from any or combination of standard camera, a complementary metal-oxide-semiconductor (CMOS) camera, a digital single-lens reflex (DSLR) camera.
8. The device as claimed in claim 1, wherein the device comprises a display unit for displaying the determined surface roughness.

9. The device as claimed in claim 1, wherein the device comprises a set of batteries for supplying uninterrupted power to the camera, the control unit, and the display unit.
10. A method for surface roughness measurement of an object, said method comprising:
receiving, by one or more processors of a control unit, one or more images captured by a camera operatively coupled to the control unit;
defining, by the one or more processors, a Region of Interest (ROI) for each captured image, wherein the ROI is defined based on geometrical section of each of the captured one or more images; and
determining, by the one or more processors, surface roughness in the ROI by analysing gradient value of edges for each of the captured one or more images,
wherein analysing the surface roughness based on averaging of ROI over a span of the captured one or more images.