Enhanced Disease Detection through Image Fusion in Solanum Tuberosum L.
An Improved Technique for Enhancement of Satellite Image
Magnetic Resonance and Computer Tomography Image Fusion using Novel Weight Maps Obtained by using Median and Guided Filters
Thresholding Techniques in Computer Vision Applications
Advancement in Brain Tumour Detection using Deep Learning Technique
Identification of Volcano Hotspots by using Resilient Back Propagation (RBP) Algorithm Via Satellite Images
Data Hiding in Encrypted Compressed Videos for Privacy Information Protection
Improved Video Watermarking using Discrete Cosine Transform
Contrast Enhancement based Brain Tumour MRI Image Segmentation and Detection with Low Power Consumption
Denoising of Images by Wavelets and Contourlets using Bi-Shrink Filter
Natural disasters like floods often occur due to heavy rainfall, storms, melting snow and ice, overflowing rivers, dam failures, and urban drainage systems. Failing to evacuate flooded watery areas leads to drowning of objects into the water. While drowning in water people feel difficult to breathe and may not survive for a long period. Real-time Detection of persons and vehicles in heavy water flow is a challenging task. This paper proposes a framework to identify floating and almost drowned objects in the water and classify them whether they are humans or non-living objects using a series of Convolution Neural Network object detection models. Faster RCNN, Mask RCNN and, You Only Look Once (YOLOv5) network models are trained on the image dataset. In case of overlapped objects, object segmentation is also performed using Mask RCNN for predicting the shape of the drowning object. Faster RCNN and YOLOv5 models are validated using a test dataset and a decline in training loss is plotted on a tensor board. Results for evaluating object detection model show Faster RCNN as the best model for detecting and classifying objects in water than YOLOv5 and Mask RCNN. Distance between object is measured to find the shortest path to reach the object for faster rescue operations. Counting and Tracking of objects are performed to know the exact count of objects who need help in an emergency and to monitor their real-time position in the water.
This paper targets to develop a powerful algorithm for transacting money with high level security and high recognition rates in various environments. Haar Cascade algorithm has been applied for fast and easy face detection from the input image. The face image is then being converted into grayscale image. Finally the iris of the users are paired up and also the cost of each possible pairing is computed by a mixture of mathematical models. With human interaction, voice commands are used for transactions.
An adder is the basic computational circuit in Very Large Scale Integration (VLSI) digital design. To improve the design metrics of an adder, Approximate Adders (AAs) have been proposed. These adders have been applied and analyzed on 8x8 Dadda Multipliers (DMs). The design metrics of proposed AAs, Approximate Dadda Multipliers (ADMs) are synthesized in Cadence Register-Transfer Level (RTL) compiler and compares the design metrics with three different technology nodes. The HDL synthesis results shows that the delay to the output of the proposed 8-tap filter gains an improvement over the conventional method. The implementation is done using Verilog HDL. Simulation and synthesis are done with the Xilinx ISE tool.
To reduce the frequency of road accidents, Lane detection system can be implemented in four wheelers to identify lane borders on the road and further prompt the driver if he switches and moves to erroneous lane markings. There are many algorithms used in this system namely, perspective, sliding window algorithms and many more, so collectively these can be called as a library. Lane boundaries are detected using a camera that captures the view of the road. The camera mounted on the top of the car takes images which serves as the basic input.
In today’s world, security plays a major role in day-to-day life. In general, sensors are used to detect an intruder in secure places. The main problem with the sensor arises because of its specifications, limitations in aspect of temperature and humidity etc., In this paper, webcam is used instead of sensor to detect the moving object using myRIO. The motion of objects is detected by computing the difference between the images captured by the webcam. The detection process is achieved using LabVIEW software and myRIO
