Image Processing

Computer vision and image processing have made significant progress in many real-world applications, including environmental monitoring and protection. Recent studies have shown that computer vision and image processing can be used to quantify water turbidity, a crucial physical parameter in water quality assessment. This paper presents a procedure to determine water turbidity using deep learning methods, specifically, convolutional neural network (CNN). At first, water samples were located inside a dark cabin before digital images of the samples were captured with a smartphone camera.

Categories:
1599 Views

Here, a dataset used in manuscript "Wide-Area Land Cover Mapping with Sentinel-1 Imagery using Deep Learning Semantic Segmentation Models" Scepanovic et al. (https://doi.org/10.1109/JSTARS.2021.3116094) is published. The data contains preprocessed SAR backscatter digital numbers as 7000 geotiff image patches of size 512x512 (about 10 km x 10 km size) sampled from several wide-area SAR mosaics compiled from all summer Sentinel-1A images  acquired over Finland in the summer of 2018.

Categories:
669 Views

ABSTRACT

Europe is covered by distinct climatic zones which include semiarid, the Mediterranean, humid subtropical, marine,

humid continental, subarctic, and highland climates. Land use and land cover change have been well documented in the

past 200 years across Europe1where land cover grassland and cropland together make up 39%2. In recent years, the

agricultural sector has been affected by abnormal weather events. Climate change will continue to change weather

Categories:
488 Views

MATLAB code for the proposed Single-shot Super-Resolution Phase Retrieval (SSR-PR) algorithm.

Categories:
124 Views

MI3

Surveillance video captured by Multi-intensity infrared illuminator.

GT(ground-truths) :bounding boxes of 'person' in channel 2,4 and 6 by following the Pascal VOC format.

Categories:
751 Views

The iSAID-Reduce100 is a reduced version of the DOTA dataset for instance segmentation task, including 1400 training samples and 1362 validation samples. The images are captured from multiple sensors and cropped to (512, 512).  

Categories:
297 Views

Most of Facial Expression Recognition (FER) systems rely on machine learning approaches that require large databases (DBs) for an effective training. As these are not easily available, a good solution is to augment the DBs with appropriate techniques, which are typically based on either geometric transformation or deep learning based technologies (e.g., Generative Adversarial Networks (GANs)). Whereas the first category of techniques have been fairly adopted in the past, studies that use GAN-based techniques are limited for FER systems.

Categories:
2117 Views

Computer vision systems are commonly used to design touch-less human-computer interfaces (HCI) based on dynamic hand gesture recognition (HGR) systems, which have a wide range of applications in several domains, such as, gaming, multimedia, automotive, home automation. However, automatic HGR is still a challenging task, mostly because of the diversity in how people perform the gestures. In addition, the number of publicly available hand gesture datasets is scarce, often the gestures are not acquired with sufficient image quality, and the gestures are not correctly performed.

Categories:
9976 Views

We provide two folders: 

(1)The shallow depth of field image data set folder consists of 27 folders from 1 to 27. 

In folder 1-27, each folder contains two test images and two word files. Img1 is the shallow depth of field image with the best focusing state taken with a 300 mm long focal lens, and img2 is the overall blurred image. 

Categories:
427 Views

The network attacks are increasing both in frequency and intensity with the rapid growth of internet of things (IoT) devices. Recently, denial of service (DoS) and distributed denial of service (DDoS) attacks are reported as the most frequent attacks in IoT networks. The traditional security solutions like firewalls, intrusion detection systems, etc., are unable to detect the complex DoS and DDoS attacks since most of them filter the normal and attack traffic based upon the static predefined rules.

Categories:
7961 Views

Pages