Implementation Segmentation of Color Image with Detection of Color to Detect Object

Detection of objects in an image 2 dimensional is a process which is quite complex to do. Object detection required a computer vision approach to the desired part of the object can be recognizable computer accurately. The method in this research using waterfall method. This research will describe the application of color segmentation method with the color detection of a digital image to produce image segment object in the form of blob so that the computer can be detected. The object detection process will process the resulting color segment by the process of segmentation so that can know the number of detected objects, the area and the center of the object. This application can take pictures with laptop or notebook webcam. The result of color segmentation based on color detection is strongly influenced by color samples and color tolerance values to which the segmentation process is based. Lighting, location, texture, and contour of objects or background image will greatly affect the results of segmentation and object detection

Color Recognition in Challenging Lighting Environments: CNN Approach

Light plays a vital role in vision either human or machine vision, the perceived color is always based on the lighting conditions of the surroundings. Researchers are working to enhance the color detection techniques for the application of computer vision. They have implemented proposed several methods using different color detection approaches but still, there is a gap that can be filled. To address this issue, a color detection method, which is based on a Convolutional Neural Network (CNN), is proposed. Firstly, image segmentation is performed using the edge detection segmentation technique to specify the object and then the segmented object is fed to the Convolutional Neural Network trained to detect the color of an object in different lighting conditions. It is experimentally verified that our method can substantially enhance the robustness of color detection in different lighting conditions, and our method performed better results than existing methods

Color and Shape Recognition

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 proposed. Color attributes are dense, computationally effective, and when joined with old-fashioned shape features provide pleasing results for object detection. The procedure of shape detection is actually a natural extension of the job of edge detection at the pixel level to the difficulty 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

Particle Filter with Integrated Multiple Features for Object Detection and Tracking

Considering objects in the environments (or scenes), object detection is the first task needed to be accomplished to recognize those objects. There are two problems needed to be considered in object detection. First, a single feature based object detection is difficult regarding types of the objects and scenes. For example, object detection that is based on color information will fail in the dark place. The second problem is the object’s pose in the scene that is arbitrary in general. This paper aims to tackle such problems for enabling the object detection and tracking of various types of objects in the various scenes. This study proposes a method for object detection and tracking by using a particle filter and multiple features consisting of color, texture, and depth information that are integrated by adaptive weights. To validate the proposed method, the experiments have been conducted. The results revealed that the proposed method outperformed the previous method, which is based only on color information

High-for-Low and Low-for-High: Efficient Boundary Detection from Deep Object Features and its Applications to High-Level Vision

Most of the current boundary detection systems rely exclusively on low-level features, such as color and texture. However, perception studies suggest that humans employ object-level reasoning when judging if a particular pixel is a boundary. Inspired by this observation, in this work we show how to predict boundaries by exploiting object-level features from a pretrained object-classification network. Our method can be viewed as a "High-for-Low" approach where high-level object features inform the low-level boundary detection process. Our model achieves state-of-the-art performance on an established boundary detection benchmark and it is efficient to run. Additionally, we show that due to the semantic nature of our boundaries we can use them to aid a number of high-level vision tasks. We demonstrate that using our boundaries we improve the performance of state-of-the-art methods on the problems of semantic boundary labeling, semantic segmentation and object proposal generation. We can view this process as a "Low-for-High" scheme, where low-level boundaries aid high-level vision tasks. Thus, our contributions include a boundary detection system that is accurate, efficient, generalizes well to multiple datasets, and is also shown to improve existing state-of-the-art high-level vision methods on three distinct tasks

KENDALI VISUAL DUAL ARM ROBOT MENGGUNAKAN PENDEKATAN CENTER OF GRAVITY

The robot arm is very popular in the world of robotics for the future. Robot arm has several different kinds of functions. Where the robot arm in addition to functioning as a human arm, also serves as a tool in the industry, robot manipulators. In this final project will be made a Dual Arm Robot with ten degrees of freedom where there are five degrees of freedom in each arm. Dual Arm Robot Control requires that appropriate controls so that the movement of Dual Arm Robot move well and achieve the expected goals, as well as providing a bit error in the system. Therefore Control of Dual Robot Arm using web cameras can produce X and Y axis position on the detection of color. Control of Dual Robot Arm using a web camera when the data already obtained form the midpoint of the X and Y axes of the color detection using OpenCV, Dual Arm Robot to follow the movement the position of the colored object detection. Keywords: Dual Arm Robot, Web Camera, and OpenC