In the present research work, the use of geometrical figures have been made for the calculation of the value of pi. Instead of circle and square, ellipse and rectangle had been used to derive the value of pi. Ellipse can be considered as an extension of a circle where it had been stretched in two dimensions in unequal manner giving rise to the concept of major axis and minor axis. These axes are considered as the length and breadth of the considered rectangle. The ellipse has been considered within the rectangle and some random points are generated to see the position occurrence of the generated points. If the point lies within the ellipse, then the specific counter is incremented; otherwise, the counter for the rectangle is incremented.

Machine Intelligence of Pi From Geometrical Figures With Variable Parameters Using SCILab

Currently, the safety and security of people on the road has been an important concern area Every day, in newspapers and television a lot of news could be seen of mishaps on roads because of negligence This research has been carried out for providing a safe environment for drivers, visually impaired people This paper illustrates the experiment conducted on Roadside traffic symbols to increase the efficiency and accuracy The two algorithms named Regional proposal based Algorithm that is Faster RCNN and Regression Based Algorithm that is Single Short Multibox Detector are used respectively After the detection and identification of the traffic symbols a sound is produced which speaks out the recognized symbol name to the user The comparison between these algorithms is made to find which algorithm's performance is better based upon different parameters The different graphs for loss function, learning rate, accuracy, training and testing time are a few parameters for both the algorithms which shows that the Single Shot Multibox is better than Faster RCNN

An Acoustic based Roadside Symbols Detection and Identification using Faster RCNN and SSD

Identification of Temples via computer technology is very tough in end user images. Since, in the CV fewer data available to presume the discrepancy in the testing digital images. Entity recognition is exactly tough and sensibly helpful problem in the field of computer based tactic. In our projected paper, we present a computerized scheme for knowing temples in the captured digital icons. As we are very aware an enormous amount of effort has been applied on automobiles, Architectures, Creatures and many more. Therefore, here we planned to exertion designed for temple identification to facilitate integrated on authors assembled photos, Till time no tests had been intended for temple identification within pictures. From the CV sentiment, temple recognition is not easy because depiction or contours of a variety of Architectures are identical. Therefore, we directed a fresh slant to touch contour on temple identification in the digital photos using Scale Invariant Feature Transform. Here we also include a few scenes which have similar shape in digital images of temples and similar in building classification. In the planned effort employed sculpt in which contour attributes are in use for discover temple in lots of Architectures however identical figures are found within buildings.

Automatic Identification of Temples in Digital Images through Scale Invariant Feature Transform

Surface defect detection is essential and necessary for controlling the qualities of the products during manufacturing. The challenges in this complex task include: 1) collecting defective samples and manually labeling for training is time-consuming; 2) the defects' characteristics are difficult to define as new types of defect can happen all the time; 3) and the real-world product images contain lots of background noise. In this paper, we present a two-stage defect detection network based on the object detection model YOLO, and the normalizing flow-based defect detection model DifferNet. Our model has high robustness and performance on defect detection using real-world video clips taken from a production line monitoring system. The normalizing flow-based anomaly detection model only requires a small number of good samples for training and then perform defect detection on the product images detected by YOLO. The model we invent employs two novel strategies: 1) a two-stage network using YOLO and a normalizing flow-based model to perform product defect detection, 2) multi-scale image transformations are implemented to solve the issue product image cropped by YOLO includes many background noise. Besides, extensive experiments are conducted on a new dataset collected from the real-world factory production line. We demonstrate that our proposed model can learn on a small number of defect-free samples of single or multiple product types. The dataset will also be made public to encourage further studies and research in surface defect detection.

Computer Vision and Normalizing Flow Based Defect Detection.

Normalizing Flow Based Defect Detection with Motion Detection

Object detection plays vital role in image processing for finding the objects of interest Increase of image size and complexity has thrust for developing novel and robust object detection techniques. There are number of methods existing for detecting the objects in a particular scene. The focus of this paper is estimate the performance and effieciency of some existing object detection algorithms on the sampled images. Thus may give good initiative about the suitability of those algorithms on the images. Three existing algorithms are implemented for various images and compared under variety of situations to find which detector is robust under different conditions.

M. Senthilvadivu

Papers

Performance evaluation of object detection techniques for object detection