Filtering is a required task in surface metrology for the identification of the components relevant for automated quality control. The calculation of real-time features about the surface is crucial to determining the mechanical and physical properties of the inspected product. The computation efficiency of the filtering operations is a major challenge in surface metrology, as...
For smart mobility, and autonomous vehicles (AV), it is necessary to have a very precise perception of the environment to guarantee reliable decision-making, and to be able to extend the results obtained for the road sector to other areas such as rail. To this end, we introduce a new single-stage monocular real-time 3D object detection convolutional neural network (CNN) based on...
Aerial surveillance and tracking have gained significant traction in recent years for both civilian applications and military reconnaissance. Disaster analysis, emergency medical response, pandemic spread analysis, etc. have significantly improved with the availability of aerial data. The next big step is to push the system for autonomous detection and tracking of targets beyond...
Multi-image super-resolution (MISR) usually outperforms single-image super-resolution (SISR) under a proper inter-image alignment by explicitly exploiting the inter-image correlation. However, the large computational demand encumbers the deployment of MISR in practice. In this work, we propose a distributed optimization framework based on data parallelism for fast large-scale...
Determining the optical flow of a video is a compute-intensive task essential for computer vision. For achieving this processing in real time, the whole algorithm deployment chain must be thought of for efficiency first. The development is usually divided into two parts: first, designing an algorithm that meets precision constraints, then, implementing and optimizing its...
Skin color plays an important role in color image processing and human–computer interaction. However, factors such as rapidly changing illumination, various color styles, and camera characteristics also make skin detection a challenging task. In particular, the real-time requirement of practical applications is a challenging task in skin detection. In this paper, face detection...
The existing pedestrian tracking applications are challenging to balance real-time performance and accuracy. We propose a detection–tracking–correction strategy based on the improved single-shot multi-box detector (SSD), Deep-SORT, and the improved multi-stage object detection architecture (Cascade-R-CNN), which takes both real-time performance and accuracy into consideration...
Real-time detection of moving objects in a resource-constrained environment, such as a personal mobile device, is a challenging task. Nowadays, cameras of cell phones and other mobile devices produce high-resolution videos. In addition, possible camera motion which is inherent to mobile devices adds further complexity to the image processing. Real-time analysis of those videos...
This paper presents an incremental stereo algorithm designed to calculate a real-time disparity image. The algorithm is designed for stereo video sequences and uses previous information to reduce computation time and improve disparity image quality. It is based on the semi-global matching stereo algorithm but modified to reuse previous calculation information. Storing and reusing...
The advent of machine learning techniques and image processing techniques has led to new research opportunities in this area. Machine learning has enabled automatic extraction and analysis of information from images. The convergence of machine learning with image processing is useful in a variety of security applications. Image processing plays a significant role in physical as...
Machine learning algorithms based on convolutional neural networks (CNNs) have recently been explored in a myriad of object detection applications. Nonetheless, many devices with limited computation resources and strict power consumption constraints are not suitable to run such algorithms designed for high-performance computers. Hence, a novel smartphone-based architecture...
Training of convolutional neural networks (CNNs) on embedded platforms to support on-device learning has become essential for the future deployment of CNNs on autonomous systems. In this work, we present an automated CNN training pipeline compilation tool for Xilinx FPGAs. We automatically generate multiple hardware designs from high-level CNN descriptions using a multi-objective...
Image dehazing is an increasingly widespread approach to address the degradation of images of the natural environment by low-visibility weather, dust and other phenomena. Advances in autonomous systems and platforms have increased the need for low-complexity, high-performing dehazing techniques. However, while recent learning-based image dehazing approaches have significantly...
The digital video coding process imposes severe pressure on memory traffic, leading to considerable power consumption related to frequent DRAM accesses. External off-chip memory demand needs to be minimized by clever architecture/algorithm co-design, thus saving energy and extending battery lifetime during video encoding. To exploit temporal redundancies among neighboring frames...
Real-time object tracking is an important step of many modern image processing applications. The efficient hardware design of real-time object tracker must achieve the desired accuracy while satisfying the frame rate requirements for a variety of image sizes. The existing methods of visual tracking employ sophisticated algorithms and challenge the capabilities of most embedded...
Melanoma is considered the deadliest skin cancer and when it is in an advanced state it is difficult to treat. Diagnoses are visually performed by dermatologists, by naked-eye observation. This paper proposes an augmented reality smartphone application for supporting the dermatologist in the real-time analysis of a skin lesion. The app augments the camera view with information...
Face alignment is a crucial component in most face analysis systems. It focuses on identifying the location of several keypoints of the human faces in images or videos. Although several methods and models are available to developers in popular computer vision libraries, they still struggle with challenges such as insufficient illumination, extreme head poses, or occlusions...