scholarly journals A method for real-time memory efficient implementation of blob detection in large images

2017 ◽  
Vol 14 (1) ◽  
pp. 67-84
Author(s):  
Vladimir Petrovic ◽  
Jelena Popovic-Bozovic
Keyword(s):  
Real Time ◽  
Hardware Implementation ◽  
State Of The Art ◽  
Input Image ◽  
Text Recognition ◽  
Blob Detection ◽  
Motion Imagery ◽  
Speed Up ◽  
Feature Based ◽  
Memory Efficient

In this paper we propose a method for real-time blob detection in large images with low memory cost. The method is suitable for implementation on the specialized parallel hardware such as multi-core platforms, FPGA and ASIC. It uses parallelism to speed-up the blob detection. The input image is divided into blocks of equal sizes to which the maximally stable extremal regions (MSER) blob detector is applied in parallel. We propose the usage of multiresolution analysis for detection of large blobs which are not detected by processing the small blocks. This method can find its place in many applications such as medical imaging, text recognition, as well as video surveillance or wide area motion imagery (WAMI). We explored the possibilities of usage of detected blobs in the feature-based image alignment as well. When large images are processed, our approach is 10 to over 20 times more memory efficient than the state of the art hardware implementation of the MSER.

Real-time feature-based image morphing for memory-efficient impostor rendering and animation on GPU

2012 ◽  
Vol 29 (2) ◽  
pp. 131-140 ◽  
Author(s):  
Kamer Ali Yuksel ◽  
Alp Yucebilgin ◽  
Selim Balcisoy ◽  
Aytul Ercil
Keyword(s):  
Real Time ◽  
Image Morphing ◽  
Feature Based ◽  
Memory Efficient

Feature-Based Real-Time Visual SLAM Using Kinect

2014 ◽  
Vol 989-994 ◽  
pp. 2651-2654
Author(s):  
Yan Song ◽  
Bo He
Keyword(s):  
Real Time ◽  
3D Models ◽  
Image Features ◽  
Microsoft Kinect ◽  
Depth Information ◽  
Visual Slam ◽  
Depth Data ◽  
Localization And Mapping ◽  
Speed Up ◽  
Feature Based

In this paper, a novel feature-based real-time visual Simultaneous localization and mapping (SLAM) system is proposed. This system generates colored 3-D reconstruction models and 3-D estimated trajectory using a Kinect style camera. Microsoft Kinect, a low priced 3-D camera, is the only sensor we use in our experiment. Kinect style sensors give RGB-D (red-green-blue depth) data which contains 2D image and per-pixel depth information. ORB (Oriented FAST and Rotated BRIEF) is the algorithm used to extract image features for speed up the whole system. Our system can be used to generate 3-D detailed reconstruction models. Furthermore, an estimated 3D trajectory of the sensor is given in this paper. The results of the experiments demonstrate that our system performs robustly and effectively in both getting detailed 3D models and mapping camera trajectory.

scholarly journals OLBVH: octree linear bounding volume hierarchy for volumetric meshes

2020 ◽  
Vol 36 (10-12) ◽  
pp. 2327-2340 ◽  
Author(s):  
Daniel Ströter ◽  
Johannes S. Mueller-Roemer ◽  
André Stork ◽  
Dieter W. Fellner
Keyword(s):  
Data Structure ◽  
State Of The Art ◽  
Memory Consumption ◽  
Bounding Volumes ◽  
Bounding Volume ◽  
Speed Up ◽  
Slicing Method ◽  
Testing Speed ◽  
Bounding Volume Hierarchy ◽  
Memory Efficient

Abstract We present a novel bounding volume hierarchy for GPU-accelerated direct volume rendering (DVR) as well as volumetric mesh slicing and inside-outside intersection testing. Our novel octree-based data structure is laid out linearly in memory using space filling Morton curves. As our new data structure results in tightly fitting bounding volumes, boundary markers can be associated with nodes in the hierarchy. These markers can be used to speed up all three use cases that we examine. In addition, our data structure is memory-efficient, reducing memory consumption by up to 75%. Tree depth and memory consumption can be controlled using a parameterized heuristic during construction. This allows for significantly shorter construction times compared to the state of the art. For GPU-accelerated DVR, we achieve performance gain of 8.4$$\times $$ × –13$$\times $$ × . For 3D printing, we present an efficient conservative slicing method that results in a 3$$\times $$ × –25$$\times $$ × speedup when using our data structure. Furthermore, we improve volumetric mesh intersection testing speed by 5$$\times $$ × –52$$\times $$ × .

UL-CNN: An Ultra-Lightweight Convolutional Neural Network Aiming at Flash-Based Computing-In-Memory Architecture for Pedestrian Recognition

2020 ◽  
pp. 2150022
Author(s):  
Chen Yang ◽  
Jingyu Zhang ◽  
Qi Chen ◽  
Yi Xu ◽  
Cimang Lu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Design Methodology ◽  
Hardware Implementation ◽  
State Of The Art ◽  
Memory Architecture ◽  
Storage Overhead ◽  
Speed Up ◽  
Memory Bottleneck ◽  
On Chip

Pedestrian recognition has achieved the state-of-the-art performance due to the progress of recent convolutional neural network (CNN). However, mainstream CNN models are too complicated to emerging Computing-In-Memory (CIM) architectures for hardware implementation, because enormous parameters and massive intermediate processing results may incur severe “memory bottleneck”. This paper proposed a design methodology of Parameter Substitution with Nodes Compensation (PSNC) to significantly reduce parameters of CNN model without inference accuracy degradation. Based on the PSNC methodology, an ultra-lightweight convolutional neural network (UL-CNN) was designed. The UL-CNN model is a specially optimized convolutional neural network aiming at a flash-based CIM architecture (Conv-Flash) and to apply for recognizing person. The implementation result of running UL-CNN on Conv-Flash shows that the inference accuracy is up to 94.7%. Compared to LeNet-5, on the premise of the similar operations and accuracy, the amounts of UL-CNN’s parameters are less than 37% of LeNet-5 at the same dataset benchmark. Such parameter reduction can dramatically speed up the training process and economize on-chip storage overhead, as well as save the power consumption of the memory access. With the aid of UL-CNN, the Conv-Flash architecture can provide the best energy efficiency compared to other platforms (CPU, GPU, FPGA, etc.), which consumes only 2.2[Formula: see text] 105J to complete pedestrian recognition for one frame.

scholarly journals Superpixel-Based Feature Tracking for Structure from Motion

2019 ◽  
Vol 9 (15) ◽  
pp. 2961
Author(s):  
Mingwei Cao ◽  
Wei Jia ◽  
Zhihan Lv ◽  
Liping Zheng ◽  
Xiaoping Liu
Keyword(s):  
Structure From Motion ◽  
Feature Tracking ◽  
State Of The Art ◽  
Input Image ◽  
Ratio Test ◽  
Feature Correspondences ◽  
Speed Up ◽  
Joint Approach ◽  
Speed Up Robust Feature ◽  
Locality Sensitive Hash

Feature tracking in image collections significantly affects the efficiency and accuracy of Structure from Motion (SFM). Insufficient correspondences may result in disconnected structures and incomplete components, while the redundant correspondences containing incorrect ones may yield to folded and superimposed structures. In this paper, we present a Superpixel-based feature tracking method for structure from motion. In the proposed method, we first propose to use a joint approach to detect local keypoints and compute descriptors. Second, the superpixel-based approach is used to generate labels for the input image. Third, we combine the Speed Up Robust Feature and binary test in the generated label regions to produce a set of combined descriptors for the detected keypoints. Fourth, the locality-sensitive hash (LSH)-based k nearest neighboring matching (KNN) is utilized to produce feature correspondences, and then the ratio test approach is used to remove outliers from the previous matching collection. Finally, we conduct comprehensive experiments on several challenging benchmarking datasets including highly ambiguous and duplicated scenes. Experimental results show that the proposed method gets better performances with respect to the state of the art methods.

scholarly journals A Micro SLAM System Based on ORB for RGB-D Cameras

2018 ◽  
Vol 160 ◽  
pp. 07001
Author(s):  
Fei Wang ◽  
Xiaogang Ruan ◽  
Pengfei Dong ◽  
OUATTARA SIE
Keyword(s):  
3D Reconstruction ◽  
Real Time ◽  
State Of The Art ◽  
Estimation Error ◽  
Bundle Adjustment ◽  
Optimization Strategy ◽  
Localization And Mapping ◽  
Benchmark Datasets ◽  
Speed Up

In this paper, a micro SLAM system based on ORB features for RGB-D cameras has been proposed. With only a RGB-D sensor, this method can be applied in small environment for localization and mapping. Furthermore, the task of 3D reconstruction can also be accomplished by using the approach. The pose graph based on Bundle Adjustment is adopted for reducing the estimation error. In order to further speed up computing to meet the requirement of real-time, we have proposed the piecewise optimization strategy. The approach is evaluated on public benchmark datasets. Compared with several state-of-the-art scheme, this method has proven to work well in these environments.

Current Issues: Advanced Digital Technology for Supervising Graduate Clinicians

2010 ◽  
Vol 20 (1) ◽  
pp. 9-13 ◽  
Author(s):  
Glenn Tellis ◽  
Lori Cimino ◽  
Jennifer Alberti
Keyword(s):  
Real Time ◽  
Digital Technology ◽  
Clinical Training ◽  
State Of The Art ◽  
Clinical Skills ◽  
Slow Motion ◽  
Video Capture ◽  
Microsoft Excel ◽  
Clinical Supervisors ◽  
Training And Supervision

Abstract The purpose of this article is to provide clinical supervisors with information pertaining to state-of-the-art clinic observation technology. We use a novel video-capture technology, the Landro Play Analyzer, to supervise clinical sessions as well as to train students to improve their clinical skills. We can observe four clinical sessions simultaneously from a central observation center. In addition, speech samples can be analyzed in real-time; saved on a CD, DVD, or flash/jump drive; viewed in slow motion; paused; and analyzed with Microsoft Excel. Procedures for applying the technology for clinical training and supervision will be discussed.

scholarly journals CoCoX: Generating Conceptual and Counterfactual Explanations via Fault-Lines

2020 ◽  
Vol 34 (03) ◽  
pp. 2594-2601
Author(s):  
Arjun Akula ◽  
Shuai Wang ◽  
Song-Chun Zhu
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Input Image ◽  
Classification Model ◽  
Learning Models ◽  
Fault Line ◽  
Semantic Level ◽  
Explainable Ai ◽  
Fault Lines ◽  
Classification Category

We present CoCoX (short for Conceptual and Counterfactual Explanations), a model for explaining decisions made by a deep convolutional neural network (CNN). In Cognitive Psychology, the factors (or semantic-level features) that humans zoom in on when they imagine an alternative to a model prediction are often referred to as fault-lines. Motivated by this, our CoCoX model explains decisions made by a CNN using fault-lines. Specifically, given an input image I for which a CNN classification model M predicts class cpred, our fault-line based explanation identifies the minimal semantic-level features (e.g., stripes on zebra, pointed ears of dog), referred to as explainable concepts, that need to be added to or deleted from I in order to alter the classification category of I by M to another specified class calt. We argue that, due to the conceptual and counterfactual nature of fault-lines, our CoCoX explanations are practical and more natural for both expert and non-expert users to understand the internal workings of complex deep learning models. Extensive quantitative and qualitative experiments verify our hypotheses, showing that CoCoX significantly outperforms the state-of-the-art explainable AI models. Our implementation is available at https://github.com/arjunakula/CoCoX

Review on biomass feedstocks, pyrolysis mechanism and physicochemical properties of biochar: State-of-the-art framework to speed up vision of circular bioeconomy

2021 ◽  
Vol 297 ◽  
pp. 126645
Author(s):  
Gajanan Sampatrao Ghodake ◽  
Surendra Krushna Shinde ◽  
Avinash Ashok Kadam ◽  
Rijuta Ganesh Saratale ◽  
Ganesh Dattatraya Saratale ◽  
...  
Keyword(s):  
Physicochemical Properties ◽  
State Of The Art ◽  
Pyrolysis Mechanism ◽  
Biomass Feedstocks ◽  
Speed Up

scholarly journals Pooled testing with replication as a mass testing strategy for the COVID-19 pandemics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Julius Žilinskas ◽  
Algirdas Lančinskas ◽  
Mario R. Guarracino
Keyword(s):  
Real Time ◽  
Real Time Pcr ◽  
Group Testing ◽  
Testing Strategy ◽  
Testing Technique ◽  
Testing Procedures ◽  
Pooled Testing ◽  
Replication Scheme ◽  
Speed Up ◽  
Multiple Patients

AbstractDuring the COVID-19 pandemic it is essential to test as many people as possible, in order to detect early outbreaks of the infection. Present testing solutions are based on the extraction of RNA from patients using oropharyngeal and nasopharyngeal swabs, and then testing with real-time PCR for the presence of specific RNA filaments identifying the virus. This approach is limited by the availability of reactants, trained technicians and laboratories. One of the ways to speed up the testing procedures is a group testing, where the swabs of multiple patients are grouped together and tested. In this paper we propose to use the group testing technique in conjunction with an advanced replication scheme in which each patient is allocated in two or more groups to reduce the total numbers of tests and to allow testing of even larger numbers of people. Under mild assumptions, a 13 ×  average reduction of tests can be achieved compared to individual testing without delay in time.

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