15Nov

Intrusion Detection with Deep Learning

By R. A. / 15/11/2018 / RSIP Vision Learns / No Comments

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...

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14Nov

GAN for non-rigid object tracking

By R. A. / 14/11/2018 / RSIP Vision Learns / No Comments

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...

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16Oct

Chest CT Scan Analysis with Deep Learning

By R. A. / 16/10/2018 / RSIP Vision Learns / No Comments

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...

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15Oct

Object Tracking at High fps

By R. A. / 15/10/2018 / RSIP Vision Learns / No Comments

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...

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10Oct

High Resolution Image Reconstruction

By R. A. / 10/10/2018 / RSIP Vision Learns / No Comments

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...

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3Sep

Temporal point process sampling in video

By R. A. / 03/09/2018 / RSIP Vision Learns / No Comments

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...

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22Aug

RGB-D SLAM building 3D models from depth cameras

By R. A. / 22/08/2018 / RSIP Vision Learns / No Comments

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 ...

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20Aug

Automated Defect Inspection Using Deep Learning

By R. A. / 20/08/2018 / RSIP Vision Learns / No Comments

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,...

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14Aug

Deep Learning in Cardiology

By R. A. / 14/08/2018 / RSIP Vision Learns / No Comments

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...

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13Aug

Deep Learning in Pulmonology

By R. A. / 13/08/2018 / RSIP Vision Learns / No Comments

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...

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12Aug

Deep Learning in Ophthalmology

By R. A. / 12/08/2018 / RSIP Vision Learns / No Comments

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...

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9Aug

Deep Learning in Brain Imaging

By R. A. / 09/08/2018 / RSIP Vision Learns / No Comments

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 ...

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8Aug

Deep Learning in Medical Imaging

By R. A. / 08/08/2018 / RSIP Vision Learns / No Comments

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...

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7Aug

Wafer Macro Defects Detection and Classification

By R. A. / 07/08/2018 / RSIP Vision Learns / No Comments

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. ...

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24Jul

Echo Cancellation Using Deep Learning

By R. A. / 24/07/2018 / RSIP Vision Learns / No Comments

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...

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22Jul

Pattern Matching Algorithms

By R. A. / 22/07/2018 / RSIP Vision Learns / No Comments

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...

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18Jul

Classification and Segmentation of Dendritic Cells

By R. A. / 18/07/2018 / RSIP Vision Learns / No Comments

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 ...

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17Jul

Extracting Features for Fingerprint Recognition and Matching

By R. A. / 17/07/2018 / RSIP Vision Learns / No Comments

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...

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15Jul

Fingerprint Segmentation Using Deep Learning

By R. A. / 15/07/2018 / RSIP Vision Learns / No Comments

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...

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12Jul

Magnetic Resonance Imaging software

By R. A. / 12/07/2018 / RSIP Vision Learns / No Comments

Magnetic resonance imaging is a ubiquitous clinical imaging modality, in which patients are placed in a device inducing a magnetic field, and the relaxation of exited hydrogen atoms are collected to form an image of an organ or their entire body. The size of MRI machines makes them practically im...

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