Catheter Navigation System with RL
The typical catheter navigation system relies on fluoroscopy, which exposes patients to dangerous irradiation. To limit the dose throughout the operation, endoscopic cameras are used to guide catheter near the target site of the procedure. When vessel diameter becomes too small to insert an endos...
Read More
Intrusion Detection with Deep Learning
Detecting physical and virtual intrusions is a key process in ensuring information and property security. Physical intrusion detection refers to all attempts at break-ins to a building, warehouse, or other perimeters by an unauthorized person, where access is granted to only limited personnel. Ba...
Read More
GAN for non-rigid object tracking
Object identification and tracking remains a challenging task in computer vision, despite advances in hardware, computational, and algorithmic developments. Difficulties arise, in part, due to the non-rigid nature of objects’ motion, where continuous shape morphing during motion is observed. This...
Read More
Chest CT Scan Analysis with Deep Learning
Chest radiography, with modalities such as X-Ray and CT, is now the common practice for the detection and analysis of the progression of lung tumors, tuberculosis and other pulmonary abnormalities. To date, most analysis are done by expert radiographers, who analyze resulting scans and estimate p...
Read More
Object Tracking at High fps
Object tracking in video sequences is a classical challenge in computer vision, which finds applications in nearly all domains of the industry: from assembly line automation, security, traffic control, automatic driving assistance systems and agriculture. Presently state of the art algorithms per...
Read More
High Resolution Image Reconstruction
Recovering a high-resolution (HR) image from a low resolution one is a classical problem in computer vision for which many algorithms have been developed to date. Most notably, methodologies using sparse coding: these techniques have achieved current state-of-the-art results, but suffer from long...
Read More
Temporal point process sampling in video
Object identification and tracking in a sequence of frames (video) consists of sampling of the scene, by e.g raster or uniform scatter, to extract features and compute their descriptors for target objects identification. This raster scanning procedure can by resource intensive, especially if ever...
Read More
RGB-D SLAM building 3D models from depth cameras
In the past few years, depth cameras became common and easy to get. Several product are available in the market at a reasonable price, e.g. Microsoft Kinect and Intel RealSense. Some recent smartphone also have depth cameras. In this project, we demonstrate our system for creating 3D models from ...
Read More
Automated Defect Inspection Using Deep Learning
Defect detection during production is a necessary step to ensure product quality. Although manual human inspections are still being employed, automated visual inspection has practically replaced manual labor in almost all major production lines and is ubiquitous in mechanical parts manufacturing,...
Read More
Deep Learning in Cardiology
1.1 Segmentation tasks [10] suggest a new fully convolutional network architecture for the task of cardiovascular MRI segmentation. The architecture is based on the idea of network blocks in which each layer is densely connected with auxiliary side paths (skip connections) to all the following la...
Read More
Deep Learning in Pulmonology
Deep learning has been successfully applied in various applications in pulmonary imaging, including CT registration, airway mapping, real time catheter navigation, and pulmonary nodule detection. Some of these applications are still in ongoing development, and here we review few of the most recen...
Read More
Deep Learning in Ophthalmology
Recent works suggest novel deep learning tools for detection, segmentation and characterization of eye disorders. Accurate segmentation of retinal fundus lesions and anomalies in imaging data is an important technical step for early detection and treatment of common eye disorders, and a central a...
Read More
Deep Learning in Brain Imaging
In this article we discuss several recent leading works about Deep Learning in brain imaging and brain microscopy. We organize the works in subsections according to the general algorithmic tasks: segmentation, registration, classification, image enhancement or other tasks. The categories are not ...
Read More
Deep Learning in Medical Imaging
Medical imaging and medical image data analysis are rapidly growing fields. The increasing amounts of available data due to advances and ubiquity of imaging technologies give rise to new medical applications and to new requirements in existing applications, and lead to an increasing demand for ne...
Read More
Wafer Macro Defects Detection and Classification
Defect detection is an integral part of wafer (chip) fabrication process. It enables defect detection and classification along the process to increase the fab yield (amount of good chips out of total wafers processed). Every detected defect is handled as an indicator of some process malfunction. ...
Read More
Echo Cancellation Using Deep Learning
Complete cancellation of returned acoustic echo signal is still an unresolved issue in signal processing. When a signal from a speaker in one end of a room returns and is fed into a microphone, a delayed and distorted version of the input signal is registered and transferred to the transmitting e...
Read More
Pattern Matching Algorithms
Pattern matching in computer vision refers to a set of computational techniques which enable the localization of a template pattern in a sample image or signal. Such template pattern can be a specific facial feature, an object of known characteristics or a speech pattern such as a word. Many of t...
Read More
Classification and Segmentation of Dendritic Cells
Dry eye disease (DED) is one of the most common ophthalmic disorders. Inflammation of the ocular surface is controlled by corneal antigen-presenting cells called dendritic cells (DCs), which induce T-cell activation, and play a critical role in the pathogenesis of dry eye disease. The density of ...
Read More
Extracting Features for Fingerprint Recognition and Matching
Fingerprint matching is used extensively in biometric identity verification for purposes ranging from forensic to recreational. The set of geometrical patterns, such as the ridges, whorls, and twists, enables to uniquely identify individuals (as far as we know): datasets of known fingerprints hav...
Read More
Fingerprint Segmentation Using Deep Learning
Automatic fingerprint recognition systems are based on the extraction of features from scanned fingerprint image. A successful preprocessing of the scan is an important first step towards a successful recognition, that is, comparison against a known database or the extraction of information chara...
Read More
