SCALE INVARIANT FEATURE TRANSFORM … INVARIANT FEATURE TRANSFORM ALGORITHM FORIDENTIFICATION OF QUALITY OBJECTS FORPALLETIZATION APPLICATION J.Sai Sirisha1, Mr.Mohammad Khadir2 1Student(M.Tech),

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Global Journal of Advanced Engineering Technologies Volume 6, Issue 3- 2017ISSN (Online): 2277-6370 & ISSN (Print):2394-0921

SCALE INVARIANT FEATURE TRANSFORM ALGORITHM FOR IDENTIFICATION OF QUALITY

OBJECTS FOR PALLETIZATION APPLICATIONJ.Sai Sirisha1, Mr.Mohammad Khadir 2

1Student (M.Tech), Department of ECE, Institute Of Aeronautical Engineering, Dundigal, Rangareddy, Telangana, India.2 Assistant Professor ,Department of ECE, Institute Of Aeronautical Engineering, Dundigal, Rangareddy, Telangana, India.

Abstract: This paper proposes a new approach forsolving well-known industrial automation problemssuch as Quality Control and Palletization (QCP). Anintelligent four-bar mechanism has been designed as a mechanical Palletizer. It has been modeled as asingular quadrilateral mechanism whose intelligence is sourced from an image processing algorithm targeted for Field Programmable Gate Array (FPGA) real-time processing system. In thisproposed approach the algorithms are implementedusing MATLAB and Simulink packages. The criticalsystem blocks of the Simulink model are the serial pixel data generator and the thresh holder whose functions is to compute threshold value of all pixels for binarization. All the hardware description language (HDL) codes generated from the Simulink model show no behavioral deviation from the original MATLAB version of the algorithm. The recognition rate results are high and the whole system is very fast at 50 MHz clock frequency.Key words: FPGA; Mechanical Palletizer; EdgeDetection; BLOB; Robotics and Automation

I. INTRODUCTIONIn Present days due the advancements in sensors and digital technology, industries have been shifting their focus towards full automation. Processes like Quality Control and Palletization are among the most recurring routines in the manufacturing industries. Palletizing refers to the process of stacking boxes of products coming from a production line onto pallets for the purpose of storage and distribution.. There are two main types of pelletizes: mechanical and robotic palletizes. Mechanical pelletizes are fast but inflexible, while robotic palletizes are slower but offer higher flexibility. The challenge in robotic palletizing is to make robotic pelletizes faster operating, while maintaining their flexibility and availability. Machine vision has been one of the hot research topics that have received high level of attention in industrial applications. The vision system in turn consists of huge calculations and processing of the images of a process captured by high resolution industrial Charged Couple

Device (CCD) image sensors. The need becomes even much high when the application is performed in real-time where the stream of image frames must be processed by the hardware and the result is passed to the next process. Traditionally, General Purpose Processor (GPP) based processing have been the norms for years, however with high demands in speed to process colored images, GPP-based processing grapples with so many challenges. FPGA to be used in many different applications in real-time but for large designs, FPGAs implementation is difficult then the System on Chip (SoC) design. Now-a-days increasing of the low power and area consumptionrequirements System on Chip (SoC) implementation is designed instead of FPGA. SoC is an Integrated Circuit(IC) that integrates all components of a computer or other electronic system into a single chip. Due to integrating whole system design on a single chip area and power will be reduced. SoCs are most common in the mobile electronics market because of their low power consumption a typical application in the area of VLSI systems and embedded systems. For an efficient implementation of this process, an FPGA hardware architectural design is proposed in this paper. The rest of this paper is organized as follows: Section 2 presents an overview of object recognition, CCA and mechanical palletizers. Section 3 presents the proposed system and the implementation methodologies. Section 4 demonstrates experimental results of the object recognition and orientation computation. In Section 5 simulation results from mechanical Palletizer models are presented. Finally, Section 6 concludes the findings.

II. THEOROTICAL OVER VIEWIn this paper, the critical areas including the objectrecognition CCA, FPGA architecture and theMechanical Palletizer modeling are presented. Usually,the object recognition parts provide information forquality control actuators. Results obtained from theCCA are used in the object recognition classifier andgeometrical feature extractions to actuate the



mechanical Palletizer. And the entire process isimplemented using FPGA.A) Object RecognitionThe main advantage of any object recognition algorithm is its ability to identify key points on the object and the unique features surrounding those points.These features are stored in feature vectors which are subsequently used with a classifier to compare with an object of interest stored in the database..Basically, classifiers are the final decision makers of the algorithm and can either be rule-based or artificial intelligence-based. The most familiar amongst the rule based classifier is the cascaded one using Viola-Jones algorithm [8]. If at any stage an object is classified negative, the classification stops there and does not proceed to the next stage. An object is reported positive at the current window location in the final stage of the classifier only when the detector classifies the image region to be positive. To train the classifier, a sufficient set of positive and negative samples of the target object must be supplied to the classifier. The number of positive samples (PS) required to train each stage is computed by:

PS=floor

1)

B) Connected Component AnalysisConnected Component algorithms are applied tocolored, grayscale and binary images. Initially, theentire image is scanned pixel by pixel; row and columnwise from the left top to the right bottom corners In a binary image, any pixel encountered during the scan and found to have a value of 1, is considered as anobject pixel while 0 values indicate non-object pixels. In a certain grayscale range of intensity values (i.e. [0, 50] and [60,80]), to indicate an object pixel; any pixel with intensity within that range can be assigned as an output object. If any of the neighbors is logic 1, it will also be burnt and assigned same label as the pixel K, whereas if all the neighboring pixels are logic 1s, they all get burnt and get the same labeling as pixel K. The operation continues until all the pixels are processed and the second scan is performed to sort out connected objects into equivalent classes each with distinct label. The algorithm is often referred as Recursive Grass-FireAlgorithm [9]. Each labeled connected component object is treated as a unique Binary Large Object (BLOB) and stored in a vector with its labeled values and indices [10]. Considering a BLOB whose minimum and maximum X and Y coordinate of its pixels are denoted by Xmin, Ymin, X max and Y max,

respectively, if the total number of pixels within that BLOB is N, which is also equivalent to the BLOBs area, Eqns. (2) and (3) compute both the bounding box area (BB) and centroid (C) of the BLOB.

BB = (x max x min)*(y max y min) (2)

C=

(3)

The extracted geometrical features of the BLOB arehoused in object feature vectors which does not onlyprovide statistics about the BLOB but also become asimple classifier to identify the type and nature of theBLOB we are dealing with. For instance, circularitymeasure of BLOB in Eqn. (4) can help in distinguishingbetween circular and non-circular objects.

Circularity=

(4)

C) Modelling of the Mechanical PalletizerA four-bar linkage mechanism is modeled as themechanical Palletizer with planned trajectory to achieveits purpose. It is a movable chain with four linkagesconnected in series by four joints. Each joint has onedegree of freedom and could either be Revolute orPrismatic. In a planar quadrilateral linkage, as adoptedin this design, one link is fixed and is designated as thefixed link or frame (r1). The other two links connectedto both ends of the frame are the input (r2) and theoutput links (r4), respectively. The link connecting theinput and the output is the coupler link (r3). For a proper understanding of the mechanism operation and the desired trajectory on the conveyors plane, the linkslengths have to be synthesized so that the angularpositions, speed and acceleration of joints B and C arededuced. Consider Fig 2. Below, where P is any pointthat defines the trajectory of the coupler link and itsdistance from joint B is given as rp.

Figure 1: Schematic Diagram of a Four-bar Mechanism



III. PROPOSED SYSTEM & METHODOLOGYThe prototype of the proposed system is as depicted

in Fig. 2. The system consists of a network of three conveyor belts. Each of the products A, B and B with defects is routed into a separate conveyor after being analyzed. The images of the product moving on the conveyor are captured by the camera C. Proximity sensor S1 acknowledges the presence of objects. If the object passing is identified from the image processing algorithm to be product B or B with defect, Pneumatic Cylinder PC1 will be actuated to push the product into the vertical conveyor. The Orientation Device (OD) is only actuated if A is found to have distorted orientation in respect to the x-axis. For the products pushed onto the vertical conveyor, proximity sensor S2, detects their presence and if it is a product B with defect, PC2 is activated to push it into the horizontal conveyor that houses defected products, whereas, if it is normal B it continues its journey to another terminal.

Figure 2: Proposed System Diagram.A) SIFT ALGORITHMSIFT (Scale Invariant Feature Transform) algorithm proposed by Lower is one of the local image featuring algorithms. Although the scale of the objects changes and rotates, its feature does not change easily. For any object there are many features, interesting points on the object that can be extracted to provide a "feature" description of the object. This description can then be used when attempting to locate the object in an image containing many other objects. SIFT image features provide a set of features of an object that are not

affected by many of the complications experienced in other methods, such as object scaling and rotation.

Figure 3: Control Flow Chart

B) Gaussian KernelThe Gaussian kernel is defined in 1-D as,

(a) Where determines the width of kernel and is often referred to as the inner scale or in short scale. In statistical terms it is referred to as the standard deviation while the 2 is referred to as the variance.

Figure 4: Example for Gaussian curve



The term 1/ in front of the equation (a) is the normalization constant and it comes due the fact that the integral over the exponential function is not unity.Taking this constant in the equation makes it a normalized kernel with the integral unity for every an example for Gaussian curve with = 6.44 in 3 dimensional.C) Difference of Gaussian (DoG)DoG referred as the subtraction of one blurred version of an original image from another, less blurred version of the original. In the simple case of grayscale images, the blurred images are obtained by convolving the original grayscale images with Gaussian kernels having differing standard deviations. Once the Gaussian pyramid is constructed the next step is to compute the DoG pyramid.D) Extrema Detection

Figure 5: Detection of local Maxima and MinimaCandidate features are detected from the DoG scale-space by finding the local extrema. In order to detect the local maxima or minima from D(x, y, ) each sample in the DoG image is compared to its eight neighbors in the current image and nine corresponding neighbors at the adjacent scales. A sample point is selected as a candidate key point if it is either larger or smaller than all these neighbors. These extrema are

computed for all the octaves in the similar manner. Symbol x corresponds to a key point.

IV. IMAGE PROCESSING & RESULTSThe Simulink model is evaluated with ODE45

Simulink solver and conforms to the architecture, HDLcodes were automatically generated. Confirmed theworkability of the model with a negligible error. Theresults were obtained by averaging the simulations with 10 independent runs. The model was targeted for lowcost Altera Cyclone III EP3C120F780 FPGA with dualon-board oscillators for generating 50 MHz and 125MHz clock speed. Even at lower clock frequency of 50MHz, the resources of the hardware utilization would be very low with an extremely fast execution.Subsequently, it was correctly detected and extracted. Its exact location in the image was determined as its inclination with x-axis was computed as 31.1o. The lastresult was obtained when the image contains non-targetobject used. For all the scenarios tested, the detectionrate was always high and robust to the scaling androtation of the input image.RTL Schematic:

Figure 6: Schematic for proposed systemSimulation Result:

Figure 7: Result of Proposed System



VI.CONCLUSIONIn this project, a system is proposed for identification of the quality objects using SIFT algorithm for the Palletization process is designed in MATLAB using Xilinx System Generator. The feature point extractions of the images using SIFT algorithm is performed for different objects and the Verilog code is generated for the whole system. The generated Verilog code is compiled in Xilinx and obtained the simulation results.

REFERENCES[1] C. Diederichs and S. Fatikow, FPGA-based Object Detection and Motion Tracking in Micro and Nano Robotics, International Journal of IntelligentMechatronics and Robotics. March 27,2013, vol3, no.1,pp.27-37.[2] I. A. Qader and M. Maddix, Real-Time Edge Detection Using TMS320C6711 DSP, IEEE Transactions onImage Processing, May 2004, vol. 3, pp.306-309.[3] R. Harinarayan, R. Pannerselvam and M. Mubarak Ali, Feature Extraction of Digital Aerial Images byFPGA Based Implementation of edge detectionalgorithms.IEEE International Conference on Emerging Trends in Electrical andComputer Technology, ICETECT, Sept 2011,pp.131-135.[4] G. Orchard, J. G. Martin and R. J Vogelstein FastNeuro-mimetic Object Recognition Using FPGAOutperforms GPU Implementations, IEEE Transactions on Neural Networks and Learning Systems, August 2013, vol. 24, no. 8, pp. 1239 .[5] H. Yu, J. Shan and X. Zhu, Off line Programing and Remote Control for a Palletizing Robot'', IEEEInternational Conference on Computer Science and Automation Engineering, CSAE, 2011, vol.2,pp.58-589.[6] P. Dzitac and A. M Mazid, An Efficient ControlConfiguration Development for a High-speed RoboticPalletizing System, IEEE Conference on Robotics,Automation and Mechatronics, 2008, pp. 140 - 145.[7] A. Sultana and M. Meenakshi, Design and Development of FPGA based Adaptive Thresholder for Image Processing Applications, 2011 IEEE Relevant Advances in Intelligence 2011, pp. 633-637.[8] Z. Wang, H. Xiao, W. He and F. Wen, Real-time SIFTbased Object Recognition System,Proceedings of 2013 IEEE International Conference on Mechatronics and Automation, August 4-7, 2013, vol 5, pp.1361-1366.[9] S. Milan; H. Vaclav; and B. Roger.2008. ImageProcessing, Analysis and Machine Vision. Thompson.

Toronto,USA.[10] B. Alexander, H. Herpers and B.K. Kenneth, Hardware Acceleration of BLOB Detection for Image Processing, Third Internatinal Conference on Advances in Circuits, Electronics And Micro-electronics,2010,pp.28-33.[11] Z. Guo, W. Xu and Z. Chai, Image Edge Detection Based on FPGA, IEEE Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Science, Feb. 10, 2010, vol. 5, pp. 169-171.

AUTHOR DETAILS

Mr. MOHAMMAD KHADIRis an Assistant Professor in Institute Of AeronauticalEngineering.

J.Sai Sirisha is currently a PG scholar of VLSI System Design in ECE Department. She received B.TECH degree from JNTU.

SCALE INVARIANT FEATURE TRANSFORM … INVARIANT FEATURE TRANSFORM ALGORITHM FORIDENTIFICATION OF QUALITY OBJECTS FORPALLETIZATION APPLICATION J.Sai Sirisha1, Mr.Mohammad Khadir2 1Student(M.Tech),

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