Colour Detection Based Robotic Cart Using Raspberry Pi

One of the most critical subjects of embedded vision is color tracking in real time. Many computer vision applications begin by detecting and tracking moving objects in video scenes. Customers arriving at hypermarkets may benefit from this concept. A color detection algorithm locates pixels in an image that fit a predetermined color scheme. To differentiate detected pixels from the rest of the image, the color of the detected pixels can be modified. The robot is programmed to track objects by turning left and right to keep the target in view and driving forward and backward to keep the distance between the robot and the object steady. By maintaining a surrounding distance, detection of other objects of the same color pattern is ignored. By keeping a safe distance between the user and the robot, other objects of the same color pattern are not detected. The camera on an ARM11 Raspberry Pi computer attached to the robot is used to capture images. Using inbuilt python files, the acquired image is processed to locate the color using RGB varying pattern methodology. To make the product work smarter, this system also includes automatic billing via RFID reader and tag. The new concept of image processing domain is based on this device theory.

Kinetic Energy Calculation in Granite Particles Comminution Considering Movement Characteristics and Spatial Distribution

Profound knowledge of the movement characteristics and spatial distribution of the particles under compression during the crushing of rocks and ores is essential to further understanding kinetic energy release law. Various experimental methods such as high-speed camera technology, the coordinate method, and the color tracking method were adopted to improve the understanding of particles’ movement characteristics and spatial distribution in rock comminution. The average horizontal velocities of the four size particles α, β, γ, and δ are statistically calculated. The descending order of the particles’ average velocity is γ, β, α, and δ. In comparison, the descending order of the particles’ kinetic energy is α, β, γ, and δ. Moreover, the contribution of α particles to the total kinetic energy exceeds 70%. The spatial distribution characteristics of coarse and fine particles show different results. The probability of fine particles appearing in the range closer to the center area is greater, while the position of large particles appears to be more random. The color tracking results show that super-large particles generated by crushing are on the specimen’s surface, while small particles are generally produced from inside. The above results indicate a connection between the particle generation mechanism, movement characteristics, and spatial distribution in the comminution process.

Control Design for Attitude and Position Based on Real Time Color Tracking System with Image Processing Technique

The paper mainly emphasizes on the control design for attitude and position based on real time color tracking system with image processing technique. The research problem in this study is to observe the high accuracy of the tracking system in image processing areas. The solution for this problem is to control the attitude and position of the object based on real time color tracking system. The objective of this study is to implement the image processing algorithms for autonomous tracking system. The specific objective of this study was fulfilled the experimental studies for contribution of real time color tracking for motion detection system in reality based on this study. This system is used the high performance camera to improve the enactment of tracking of a target and estimation of a motion. An image processing system consists of a light source to illuminate the sense, a sensor system, an interface between the sensor system and the computer. Then, color component analysis is used for color tracking system. MATLAB is competently used for tracking the ball and controlling the attitude and position of the ball.

Quantitative Assessment of Color Tracking and Gray Tracking in Color Medical Displays

The goal of this study is to develop quantitative metrics for evaluating color tracking and gray tracking in a color medical display. Color tracking is the chromaticity consistency of the red, green, or blue shades. Gray tracking is the chromaticity consistency of the gray shades. Color tracking and gray tracking are the most important colorimetric responses of a color medical display because they directly indicate the color calibration quality and can therefore be used to compare color performance between displays. Two metrics, primary purity and gray purity, are defined to measure the color shift of the primary shades and gray shades of a color display, respectively. The area under the curves of primary purity and gray purity can then represent the quality of color tracking (C_AUC) and gray tracking (G_AUC), respectively. Fifteen displays including medical, professional-grade, consumer-grade, mobile, and special displays were tested to compare their C_AUC and G_AUC. The OLED displays have the greatest C_AUC values. The medical and professional-grade displays have the greatest combinations of C_AUC and G_AUC values. Most consumer-grade displays have lower C_AUC and G_AUC values, but some show better gray tracking than color tracking. The special displays exhibit particularly poor color and gray tracking. Using C_AUC and G_AUC together can quantitatively predict and compare color performance of different displays.

The Mismatch Problem for Tracking Theories

One prominent theory of intentionality is the tracking theory, on which original intentionality arises from tracking, where tracking is detecting, carrying information about or having the function of carrying information about, or otherwise appropriately corresponding to items in the environment. This chapter argues that tracking theories cannot accommodate certain paradigm cases of intentionality; in these mismatch cases, the contents ascribed by the tracking theory fail to match the contents that we have theory-independent reason to ascribe. This chapter focuses on one of the most obvious mismatch cases, that of perceptual representations of color: Tracking theories predict that perceptual color representations represent surface reflectance profiles or the like, while theory-independent considerations suggest that they represent primitive colors, which, it happens, are probably uninstantiated.

Stochastic Modeling and Diagnosis of Leak Areas for Surface Assembly

Assembly through mating a pair of machined surfaces plays a crucial role in many manufacturing processes such as automotive powertrain production, and the mating errors during the assembly (i.e., gaps between surfaces) can cause significant internal leakage and functional performance problems. The surface mating errors are difficult to diagnose because they are not measurable. Current in-plant quality control for surface mating focuses on controlling the surface flatness of each individual part before they are mated, and the mating errors are indirectly evaluated by a pressurized sealing test to check whether any pressure drop occurs. However, it does not provide any clue to engineers about the origins and the root cause of the internal leakage. To address these limitations, this paper presents a pressurized color-tracking method to directly measure internal leak areas. By using the measurements of leak areas and the profiles of surfaces mated as training data along with Hagen–Poiseuille law, this paper develops a novel diagnostic method to predict potential leak areas (leakage paths) given the measurements on the profiles of mating surfaces. The effectiveness and robustness of the proposed method are verified by a simulation study and an experiment. The approach provides practical guidance for the subsequent assembly process as well as troubleshooting in surface machining processes.