Machete

2021 ◽  
Vol 28 (1) ◽  
pp. 1-46
Author(s):  
Eugene M. Taranta II ◽  
Corey R. Pittman ◽  
Mehran Maghoumi ◽  
Mykola Maslych ◽  
Yasmine M. Moolenaar ◽  
...  
Keyword(s):  
Video Game ◽  
Recognition Accuracy ◽  
Training Sample ◽  
Low Latency ◽  
Computationally Efficient ◽  
Extensive Evaluation ◽  
Segmentation Accuracy ◽  
Alternative Techniques ◽  
High Recognition Accuracy ◽  
Continuous Dynamic

We present Machete, a straightforward segmenter one can use to isolate custom gestures in continuous input. Machete uses traditional continuous dynamic programming with a novel dissimilarity measure to align incoming data with gesture class templates in real time. Advantages of Machete over alternative techniques is that our segmenter is computationally efficient, accurate, device-agnostic, and works with a single training sample. We demonstrate Machete’s effectiveness through an extensive evaluation using four new high-activity datasets that combine puppeteering, direct manipulation, and gestures. We find that Machete outperforms three alternative techniques in segmentation accuracy and latency, making Machete the most performant segmenter. We further show that when combined with a custom gesture recognizer, Machete is the only option that achieves both high recognition accuracy and low latency in a video game application.

scholarly journals Low complexity hand gesture recognition

2014 ◽  
Author(s):  
Στέργιος Πουλαράκης
Keyword(s):  
Gesture Recognition ◽  
Video Game ◽  
Nearest Neighbor ◽  
Recognition Accuracy ◽  
Similarity Score ◽  
Cosine Similarity ◽  
Computational Time ◽  
Hand Detection ◽  
High Recognition Accuracy ◽  
Gesture Spotting

Gesture recognition is an expressive, alternative means for Human Computer Interaction(HCI), which recently drew signifcant attention after the release of mass consumer applicationsand devices, including gesture{controlled interactive TV systems (iDTV) andadvanced video{game environments. In this work, we propose a complete gesture recognitionframework for continuous streams of static postures and dynamic trajectories ofdigits and letters, targeting both high recognition accuracy and increased computationaleficiency. Special emphasis is given on four fundamental gesture recognition problems,i.e. hand detection and feature extraction, isolated recognition, gesture verification, andgesture spotting on continuous data streams.Specifically, we propose a novel finger detection method, based on geometrical handcontour features (apex detection) and show its importance in hand posture recognition.We then present our approach for isolated recognition, which is based on MaximumCosine Similarity (MCS) and a tree{based fast Nearest Neighbor (fastNN) technique,showing its high recognition accuracy and computational eficiency. Additionally, werelate the computational time required by fastNN for the classification of an unknownquery vector to its Mahalanobis distance and maximum cosine similarity with respectto the set of training examples. This property allows us to perform gesture verification,while it significantly reduces the search time.Finally, we design a complete framework for gesture spotting on continuous streams ofhand data, solving the joint problem of both gesture detection and recognition. Specifi-cally, we model subgesture relationships in a probabilistic way, using both the categoriesand the relative time positions of overlapping gesture candidates. Additionally, we introducea novel metric of ranking conicting gesture candidates, based on their timeduration and cosine similarity score, which oers high conict resolution results forsequences of digits and letters.In all cases, we support our arguments through thorough experiments on real and syntheticgesture datasets, as well as with real{time gesture spotting applications.

Ion-gating synaptic transistors with long-term synaptic weight modulation

2021 ◽  
Vol 9 (16) ◽  
pp. 5396-5402
Author(s):  
Youngjun Park ◽  
Min-Kyu Kim ◽  
Jang-Sik Lee
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Synaptic Weight ◽  
Recognition Accuracy ◽  
Artificial Neural ◽  
High Recognition Accuracy

This paper presents synaptic transistors that show long-term synaptic weight modulation via injection of ions. Linear and symmetric weight update is achieved, which enables high recognition accuracy in artificial neural networks.

scholarly journals Simulation and Recognition of Concrete Lining Infiltration Degree via an Indoor Experiment

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Dongsheng Wang ◽  
Jun Feng ◽  
Xinpeng Zhao ◽  
Yeping Bai ◽  
Yujie Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cement Mortar ◽  
Recognition Accuracy ◽  
Image Data ◽  
Concrete Lining ◽  
Learning Method ◽  
Recognition Method ◽  
Data Set ◽  
Recognition Model ◽  
High Recognition Accuracy

It is difficult to form a method for recognizing the degree of infiltration of a tunnel lining. To solve this problem, we propose a recognition method by using a deep convolutional neural network. We carry out laboratory tests, prepare cement mortar specimens with different saturation levels, simulate different degrees of infiltration of tunnel concrete linings, and establish an infrared thermal image data set with different degrees of infiltration. Then, based on a deep learning method, the data set is trained using the Faster R-CNN+ResNet101 network, and a recognition model is established. The experiments show that the recognition model established by the deep learning method can be used to select cement mortar specimens with different degrees of infiltration by using an accurately minimized rectangular outer frame. This model shows that the classification recognition model for tunnel concrete lining infiltration established by the indoor experimental method has high recognition accuracy.

Recognizing Hand-Printed Letters and Digits Using Backpropagation Learning

1991 ◽  
Vol 3 (2) ◽  
pp. 258-267 ◽  
Author(s):  
Gale L. Martin ◽  
James A. Pittman
Keyword(s):  
Character Recognition ◽  
High Performance ◽  
Network Capacity ◽  
Training Sample ◽  
Error Rates ◽  
Training Set ◽  
Set Size ◽  
Bank Checks ◽  
Recognition Systems ◽  
High Recognition Accuracy

We report on results of training backpropagation nets with samples of hand-printed digits scanned off of bank checks and hand-printed letters interactively entered into a computer through a stylus digitizer. Generalization results are reported as a function of training set size and network capacity. Given a large training set, and a net with sufficient capacity to achieve high performance on the training set, nets typically achieved error rates of 4-5% at a 0% reject rate and 1-2% at a 10% reject rate. The topology and capacity of the system, as measured by the number of connections in the net, have surprisingly little effect on generalization. For those developing hand-printed character recognition systems, these results suggest that a large and representative training sample may be the single, most important factor in achieving high recognition accuracy. Benefits of reducing the number of net connections, other than improving generalization, are discussed.

A Rapid Vote-Counting System Based on Image Understanding

2012 ◽  
Vol 490-495 ◽  
pp. 76-80
Author(s):  
Gang Xiao ◽  
Jing Jing Zhang ◽  
Yuan Ming Zhang ◽  
Jia Wei Lu ◽  
Zhi Ye
Keyword(s):  
Geometric Structure ◽  
High Speed ◽  
Recognition Accuracy ◽  
Logical Structure ◽  
Image Understanding ◽  
Counting System ◽  
Wide Applicability ◽  
High Recognition Accuracy ◽  
Vote Counting

This paper proposes a system, which can rapidly count votes in traditional elections based on image understanding. Firstly, the system gets ballot images through high-speed scanner and preprocesses the images. Then, it recognizes the geometric structure and layout of ballot image through detecting table lines. In addition, it also recognizes the logical structure of ballot image through analyzing the relative positions of candidates and vote symbols. Thirdly, it locates candidates and symbols on the ballot table, and recognizes the specific symbols based on run features. The system has been implemented, which shows high counting speed, high recognition accuracy with wide applicability.

scholarly journals Study on a Fault Identification Method of the Hydraulic Pump Based on a Combination of Voiceprint Characteristics and Extreme Learning Machine

Processes ◽  
2019 ◽  
Vol 7 (12) ◽  
pp. 894 ◽  
Author(s):  
Wanlu Jiang ◽  
Zhenbao Li ◽  
Jingjing Li ◽  
Yong Zhu ◽  
Peiyao Zhang
Keyword(s):  
Fault Diagnosis ◽  
Recognition Accuracy ◽  
Test Sample ◽  
Training Sample ◽  
Accuracy Rate ◽  
Diagnosis Method ◽  
Learning Machine ◽  
Hidden Layer ◽  
Axial Piston ◽  
Sample Set

Aiming at addressing the problem that the faults in axial piston pumps are complex and difficult to effectively diagnose, an axial piston pump fault diagnosis method that is based on the combination of Mel-frequency cepstrum coefficients (MFCC) and the extreme learning machine (ELM) is proposed. Firstly, a sound sensor is used to realize contactless sound signal acquisition of the axial piston pump. The wavelet packet default threshold denoises the original acquired sound signals. Afterwards, windowing and framing are added to the de-noised sound signals. The MFCC voiceprint characteristics of the processed sound signals are extracted. The voiceprint characteristics are divided into a training sample set and test sample set. ELM models with different numbers of neurons in the hidden layers are established for training and testing. The relationship between the number of neurons in the hidden layer and the recognition accuracy rate is obtained. The ELM model with the optimal number of hidden layer neurons is established and trained with the training sample set. The trained ELM model is applied to the test sample set for fault diagnosis. The fault diagnosis results are obtained. The fault diagnosis results of the ELM model are compared with those of the back propagation (BP) neural network and the support vector machine. The results show that the fault diagnosis method that is proposed in this paper has a higher recognition accuracy rate, shorter training and diagnosis times, and better application prospect.

scholarly journals Microstructure Diffusion Scalar Measures from Reduced MRI Acquisitions

2019 ◽  
Author(s):  
Santiago Aja-Fernández ◽  
Rodrigo de Luis-García ◽  
Maryam Afzali ◽  
Malwina Molendowska ◽  
Tomasz Pieciak ◽  
...  
Keyword(s):  
Diffusion Tensor ◽  
State Of The Art ◽  
B Value ◽  
Computationally Efficient ◽  
Huge Amount ◽  
Average Diffusion ◽  
Efficient Alternative ◽  
Alternative Techniques ◽  
Dense Sampling ◽  
Diffusion Properties

AbstractIn diffusion MRI, the Ensemble Average diffusion Propagator (EAP) provides relevant microstructural information and meaningful descriptive maps of the white matter previously obscured by traditional techniques like the Diffusion Tensor. The direct estimation of the EAP, however, requires a dense sampling of the Cartesian q-space. Due to the huge amount of samples needed for an accurate reconstruction, more efficient alternative techniques have been proposed in the last decade. Even so, all of them imply acquiring a large number of diffusion gradients with different b-values. In order to use the EAP in practical studies, scalar measures must be directly derived, being the most common the return-to-origin probability (RTOP) and the return-to-plane and return-to-axis probabilities (RTPP, RTAP).In this work, we propose the so-called “Apparent Measures Using Reduced Acquisitions” (AMURA) to drastically reduce the number of samples needed for the estimation of diffusion properties. AMURA avoids the calculation of the whole EAP by assuming the diffusion anisotropy is roughly independent from the radial direction. With such an assumption, and as opposed to common multi-shell procedures based on iterative optimization, we achieve closed-form expressions for the measures using information from one single shell. This way, the new methodology remains compatible with standard acquisition protocols commonly used for HARDI (based on just one b-value). We report extensive results showing the potential of AMURA to reveal microstructural properties of the tissues compared to state of the art EAP estimators, and is well above that of Diffusion Tensor techniques. At the same time, the closed forms provided for RTOP, RTPP, and RTAP-like magnitudes make AMURA both computationally efficient and robust.

scholarly journals Isolated Word Recognition Based on Combination of Multiple Noise-Robust Techniques

1970 ◽  
Vol 6 (1) ◽  
pp. 81-88
Author(s):  
Noboru Hayasaka
Keyword(s):  
Recognition Accuracy ◽  
Recognition System ◽  
Temporal Filtering ◽  
Low Snr ◽  
Robust Techniques ◽  
Noise Robust Speech Recognition ◽  
Isolated Word ◽  
High Recognition Accuracy ◽  
Noise Robust ◽  
Mean Variance

Although many noise-robust techniques have been presented, the improvement under low SNR condition is still insufficient. The purpose of this paper is to achieve the high recognition accuracy under low SNR condition with low calculation costs. Therefore, this paper proposes a novel noise-robust speech recognition system that makes full use of spectral subtraction (SS), mean variance normalization (MVN), temporal filtering (TF), and multi-condition HMMs (MC-HMMs). First, from the results of SS with clean HMMs, we obtained the improvement from 46.61% to 65.71% under 0 dB SNR condition. Then, SS+ MVN+TF with clean HMMs improved the recognition accuracy from 65.71% to 80.97% under the same SNR condition. Finally, we achieved the further improvement from 80.97% to 92.23% by employing SS+MVN+TF with MC-HMMs.

scholarly journals Towards Fog-Assisted Virtual Reality MMOG with Ultra-Low Latency

2020 ◽  
Vol 12 (6) ◽  
pp. 33-47
Author(s):  
YOSHIHARA Tsuyoshi ◽  
FUJITA Satoshi
Keyword(s):  
Virtual Reality ◽  
Video Game ◽  
Collision Detection ◽  
Line Of Sight ◽  
Low Latency ◽  
Prototype System ◽  
Head Mounted Display ◽  
3D Game ◽  
Cloud Server ◽  
Conventional Systems

In this paper, we propose a method to realize a virtual reality MMOG (Massively Multiplayer Online Video Game) with ultra-low latency. The basic idea of the proposed method is to introduce a layer consisting of several fog nodes between clients and cloud server to offload a part of the rendering task which is conducted by the cloud server in conventional cloud games. We examine three techniques to reduce the latency in such a fog-assisted cloud game: 1) To maintain the consistency of the virtual game space, collision detection of virtual objects is conducted by the cloud server in a centralized manner; 2) To reflect subtle changes of the line of sight to the 3D game view, each client is assigned to a fog node and the head motion of the player acquired through HMD (Head-Mounted Display) is directly sent to the corresponding fog node; and 3) To offload a part of the rendering task, we separate the rendering of the background view from that of the foreground view, and migrate the former to other nodes including the cloud server. The performance of the proposed method is evaluated by experiments with an AWS-based prototype system. It is confirmed that the proposed techniques achieve the latency of 32.3 ms, which is 66 % faster than the conventional systems.

scholarly journals Design and Image Research of Tennis Line Examination Based on Machine Vision Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Liu Yan ◽  
Sun Xin
Keyword(s):  
Machine Vision ◽  
Camera Calibration ◽  
Recognition Accuracy ◽  
Calibration Method ◽  
Recognition Method ◽  
Systematic Analysis ◽  
High Recognition Accuracy ◽  
Intelligent Recognition ◽  
Region Growth ◽  
Design Experiments

In view of the intelligent demand of tennis line examination, this paper performs a systematic analysis on the intelligent recognition of tennis line examination. Then, a tennis line recognition method based on machine vision is proposed. In this paper, the color region of the image recognition region is divided based on the region growth, and the rough estimation of the court boundary is realized. In order to achieve the effect of camera calibration, a fast camera calibration method which can be used for a variety of court types is proposed. On the basis of camera calibration, a tennis line examination and segmentation system based on machine vision analysis is constructed, and the experimental results are verified by design experiments. The results show that the machine vision analysis-based intelligent segmentation system of tennis line examination has high recognition accuracy and can meet the actual needs of tennis line examination.

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