Vision-Based Lane Line Detection System

Since the 1990s, the rising key issue of the automobile industry is self-driving or driverless vehicles. Apparently, one of the most important challenges for smart self-driving cars comprises lane-detecting and lane-tracking capability to ensure safety and also decrease vehicle accidents for driver assistance systems. Since road lane detection is one of the most challenging tasks, driverless vehicles must learn to observe the road from a visual perspective in order to achieve automatic driving. Most of the research Works done so far can only detect the lanes or vehicles separately. However, in this paper, we propose a method to combine lane information and vehicle/obstacle information that can support the driver assistance system, driver warning system or the lane change assistant system so that it enhances the quality of results. For the lane changing system, the system detects or tracks the lane lines and detects the vehicles on all sides of a test vehicle. In lane detection, line detection algorithms such as the Canny Edge detection algorithm are used to detect the lane edges. Kalman filter will be used to track the vehicle detected from the vehicle detection algorithm. For vehicle detection, we use Otsu’s thresholding, horizontal edge filtering and vertical edge. The vertical edge filter and the Otsu’s thresholding are used to detect the vehicles on all sides of the test vehicles, then the horizontal edge is used to verify the vehicles detected.

Impact of Horizontal Edge–Interior and Vertical Canopy–Understory Gradients on the Abundance and Diversity of Bark and Woodboring Beetles in Survey Traps

Semiochemical-baited intercept traps are important tools used to collect information about the presence/absence and population dynamics of forest insects. The performance of these tools is influenced by trap location along both horizontal edge–interior and vertical understory–canopy gradients. Consequently, the development of survey and detection programs requires both the development of effective traps and semiochemical lures but also deployment protocols to guide their use. We used field trapping experiments to examine the impact of both horizontal edge–interior and vertical understory–canopy gradients and their interactions with the species richness and abundance of Buprestidae, Cerambycidae and Curculionidae. Both gradients had significant effects on the diversity and abundance of all three families collected in traps and the pattern of gradient effects differed between the two experiments. In the first experiment, traps were deployed along transects involving large (>100 m) forest gaps and in the second experiment traps transected small (ca. 15 m) forest gaps. These results were consistent with the idea that gradient effects on the abundance and diversity of these three families of forest Coleoptera are context dependent. The results of this study suggest that monitoring programs for bark and woodboring beetles should deploy traps at multiple locations along both vertical understory–canopy and horizontal edge–interior gradients.

Identification of Brain Tumor using MATLAB

Brain Tumor detection and removal is one medical issue that still remains challenging in the field of biomedicine. MRI is most often used for the detection of tumors, lesions, and other abnormalities in soft tissues, such as the brain. This project is about detecting Brain tumors from MRI images using an interface of GUI in Mat lab. Using the GUI, this program can use various combinations of segmentation, filters, and other image processing algorithms to realize the simplest results. Here We start the process by filtering the image with the help of Prewitt horizontal edge- emphasizing filter. The next step for detecting tumors is "watershed pixels." The most important part of this project is that all the Mat lab programs work with GUI “Matlab guide”