View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by ethesis@nitr Color and Shape Recognition Smruti Saurav Shasani (111CS0607) Ramiya Ranjan Meher (111CS0137) Manoj Kumar Patra (111CS0465) Department of Computer Science and Engineering National Institute of Technology Rourkela Rourkela { 769 008, Odisha, India Color and Shape Recognition Thesis submitted in partial fulfillment of the requirements for the degree of Bachelor Of Technology in Computer Science and Engineering by Smruti Saurav Shasani (111CS0607) Ramiya Ranjan Meher (111CS0137) Manoj Kumar Patra (111CS0465) under the supervision of Dr. Pankaj Kumar Sa Department of Computer Science and Engineering National Institute of Technology Rourkela Rourkela { 769 008, Odisha, India May 2015 Department of Computer Science and Engineering National Institute of Technology, Rourkela Certificate This is to certify that the work in the thesis entitled Color and Shape Recognition by Smruti Saurav Shasani (111CS0607), Ramiya Ranjan Meher (111CS0137), and Manoj Kumar Patra (111CS0465), is a record of an original research work carried out by them under my supervision and guidance in partial fulfillment of the requirements for the award of the degree of Bachelor of Technology in Computer Science and Engineering. Date: May 11, 2015 Dr. Pankaj Kumar Sa Assistant Professor CSE Department NIT Rourkela Acknowledgement This thesis has been possible due to the help and endeavor of many people. Many individuals have helped throughout for the finishing of this task and each of their inputs has been valuable. Our deepest and sincerest gratitude goes to our supervisor, Dr. Pankaj Kumar Sa, Assis- tant Professor, Department of Computer Science & Engineering, for his guidance and encouragement throughout the period of the undertaking. He stimulated us to work on the topic and provided valuable informa- tion which helped in completing the project through various stages. We are also thankful to NIT Rourkela for the academic resources it has supplied us through the course of the project. We want to thank the staff from the office who have been sufficiently benevolent to extend their help towards the completion of this project. We would also like to take this opportunity to express our sincere gratitude to everyone who has provided us with inspirational words, ideas, constructive criticism, and above all, their invaluable time. Abstract The object "car" and "cat" can be easily distinguished by humans, but how these labels are assigned? Grouping these images is easy for a person into different categories, but its very tedious for a computer. Hence, an object recognition system finds objects in the real world from an image. Object recognition algorithms rely on matching, learning or pattern recognition algorithms using appearance-based or feature-based techniques. In this thesis, the use of color and shape attributes as an explicit color and shape representation respectively for object detection is pro- posed. Color attributes are dense, computationally effective, and when joined with old-fashioned shape features provide pleasing results for ob- ject detection. The procedure of shape detection is actually a natural extension of the job of edge detection at the pixel level to the diffi- culty of global contour detection. A tool for a systematic analysis of edge based shape detection is provided by this filtering scheme. This enables us to find distinctions between objects based on color and shape. Keywords: Color Models, Edge-based Shape Detection Contents 1 Introduction 1 1.1 Computer Vision . 2 1.1.1 OpenCV (the tool) . 2 1.2 Motivation . 3 1.3 Objective . 3 1.4 Thesis Organization . 4 2 Color Recognition 5 2.1 Color Models . 5 2.1.1 RGB Color Model . 5 2.1.2 CMYK Color Model . 8 2.1.3 HSV Color Model . 9 2.1.4 Grayscale . 10 3 Edge-Based Shape detection 11 3.1 Edge Detection . 13 3.1.1 Sobel Filter . 13 3.2 Line Detection . 14 3.2.1 Hough Transform . 15 3.2.2 Advantages and Disadvantages . 15 3.3 Regular Polygon Detection . 16 3.4 Circle Detection . 16 3.4.1 Circular Hough Transform . 16 4 Simulation and Results 19 4.1 Color Recognition . 19 4.2 Shape Recognition . 20 4.2.1 Polygon Recognition . 20 4.2.2 Circle Detection . 22 5 Conclusion and Future Work 24 List of Figures 2.1 RGB Color Wheel. 6 2.2 Schematic of RGB Color Cube. 7 2.3 CMY Color Wheel. 9 2.4 HSV Color Wheel. 10 3.1 Sobel Filter. 14 4.1 Test Image I for COLOR Recognition . 19 4.2 Test Image II for COLOR Recognition . 19 4.3 Input Image for POLYGON Detection . 20 4.4 Output Image I for POLYGON Detection . 21 4.5 Output Image II for POLYGON Detection . 21 4.6 Input Image I for CIRCLE Detection . 22 4.7 Output Image I for CIRCLE Detection . 22 4.8 Input Image II for CIRCLE Detection . 23 4.9 Output Image II for CIRCLE Detection . 23 List of Tables 2.1 RGB Numeric Representations . 8 2.2 RGB and HSV values of some primary colors . 10 Chapter 1 Introduction A solitary image conveys a ton of information in a brief while in light of the fact that we see an image at the same time, though reading or listening to frequently takes essentially more to process the same information. Colors and shapes work in amicability with one another to impart. Accordingly, a comprehension of shapes is key to comprehension the power of color. Shape detection obliges pre-programming in a mathematical depic- tion database of the shapes to detect. For instance, assume composing a program which can recognize a triangular shape, a square shape, and a circular shape. We can do it this way: Run contour identification for discovering the boundary line of every shape considered. Then the number of continuous edges is counted. A sharp deviation in line de- tection implies an alternate line does this by determining the average vector between adjoining pixels. In the event that three lines identi- fied, then it's a triangle. In the event that four lines, then a square. In the event that one closed line, then it is a circle. You can focus more information by measuring angles between lines (rhombus, square, equilateral triangle, etc.) Complex shapes obliges pattern recognition that is probability anal- ysis. For instance, assume our algorithm expected to perceive between 10 distinct natural products (just by shape), for example, an orange, a bunch of grapes, an apple, a cherry, and so on. How will this be achieved? Since, all of these are circular, however none of them is seamlessly circular. Also, neither all fruits appear to be identical. By 1 utilizing probability, an investigation can be run that says 'gracias, this natural product matches 85% of the attributes of a cherry, yet just 65% the qualities of an apple, so it's more probable a cherry. It is the computational form of an 'educated guess.' We can likewise say if some specific characteristic is available, then it has a 30% higher likelihood of being an orange. The characteristic can be a stem, for example, spikes like on a pineapple, or fuzziness like on a coconut, and so on. This strategy is known as feature detection. 1.1 Computer Vision It is the study of machines that have the capacity to extract infor- mation from an image that is important to tackle some assignment. As a scientific discipline, computer vision is concerned with the hypothesis behind artificial systems that extract information from images. The image information can take numerous structures, for example, video sequences, sees from different cams, or multi-dimensional information from a medicinal scanner. As a technological discipline, computer vi- sion tries to apply its speculations and models to the development of computer vision frameworks. Illustrations of uses of computer vision include system for: 1. Controlling processes (e.g., an industrial robot or an autonomous vehicle). 2. Detecting events (e.g., for visual surveillance or people counting). 3. Organizing information (e.g., for indexing databases of images and mage sequences). 4. Modelling objects or environments (e.g., industrial inspection, med- ical image analysis or topographical modelling). 5. Interaction (e.g., as the input to a device for computer-human interaction). 1.1.1 OpenCV (the tool) The OpenCV Library is principally gone for real-time computer vi- sion. Some sample zones would be Human-Computer Interaction (HCI); 2 Segmentation, Object detection, and Recognition; Gesture Recognition; Face Recognition; Motion Tracking, Ego Motion, and Motion Under- standing; SFM (Structure from Motion); and Mobile Robotics. The OpenCV Library is a gathering of low-overhead, elite operations performed on images. The OpenCV actualizes a wide mixture of tools for image interpretation. It is good with IPL (Intel Image Processing Library) that actualizes low-level operations on computerized images. Notwithstanding primitives, for example, filtering, banalization, pyra- mids, image statistics, OpenCV is basically an high-level library imple- menting algorithms for Camera Calibration (calibration techniques), Optical Flow (tracking), and feature (Feature detection), motion anal- ysis (Motion Templates, Estimators), Geometry, Contour Processing (shape analysis), View Morphing (3D reconstruction), object segmen- tation and recognition (Embedded Hidden Markov Models, Histogram, Eigen Objects). 1.2 Motivation There is a clear distinction between the two fruits \apple" and \ba- nana", the identification of this distinction for a human is easy, but how can a computer find the difference between those two objects. Identi- fication of an object from its two dimensional image brought up the real interest for this project. This is a paramount topic in the field of computer vision. 1.3 Objective In this project object recognition has been performed considering color and shape as its prime entities.