218 research outputs found
CNN Based Posture-Free Hand Detection
Although many studies suggest high performance hand detection methods, those
methods are likely to be overfitting. Fortunately, the Convolution Neural
Network (CNN) based approach provides a better way that is less sensitive to
translation and hand poses. However the CNN approach is complex and can
increase computational time, which at the end reduce its effectiveness on a
system where the speed is essential.In this study we propose a shallow CNN
network which is fast, and insensitive to translation and hand poses. It is
tested on two different domains of hand datasets, and performs in relatively
comparable performance and faster than the other state-of-the-art hand
CNN-based hand detection method. Our evaluation shows that the proposed shallow
CNN network performs at 93.9% accuracy and reaches much faster speed than its
competitors.Comment: 4 pages, 5 figures, in The 10th International Conference on
Information Technology and Electrical Engineering 2018, ISBN:
978-1-5386-4739-
Hand gesture based digit recognition
Recognition of static hand gestures in our daily plays an important role in human-computer interaction. Hand gesture recognition has been a challenging task now a days so a lot of research topic has been going on due to its increased demands in human computer interaction. Since Hand gestures have been the most natural communication medium among human being, so this facilitate efficient human computer interaction in many electronics gazettes . This has led us to take up this task of hand gesture recognition. In this project different hand gestures are recognized and no of fingers are counted. Recognition process involve steps like feature extraction, features reduction and classification. To make the recognition process robust against varying illumination we used lighting compensation method along with YCbCr model. Gabor filter has been used for feature extraction because of its special mathematical properties. Gabor based feature vectors have high dimension so in our project 15 local gabor filters are used instead of 40 Gabor filters. The objective in using fifteen Gabor filters is used to mitigate the complexity with improved accuracy. In this project the problem of high dimensionality of feature vector is being solved by using PCA. Using local Gabor filter helps in reduction of data redundancy as compared to that of 40 filters. Classification of the 5 different gestures is done with the use of one against all multiclass SVM which is also compared with Euclidean distance and cosine similarity while the former giving an accuracy of 90.86%
Development of Single Camera Finger Touch Position Detection System
This research proposes a Single Camera Finger Touch Position Detection System. Image processing based single camera figure touch position detection approach has several significant advantages: (1) No sensing devices need to be instrumented on the surface of the touch screen. (2) Minimum sensor construction can reduce the failure rate to realize maintenance free system. (3) This approach enables an easy installation and a low-cost touch sensing. The problem of using a single camera is how to detect the touch action and the position of the finger from a single view image. In order to solve this problem, we use the reflected fingertip image appears on the back of the screen. Detecting the fingertip and the reflected image on the screen effectively, we enable to detect touch position only using single camera. In this paper, we provide concrete method, algorithm, implement details, and several experimental results
3D hand posture recognition using multicam
This paper presents the hand posture
recognition in 3D using the MultiCam, a monocular 2D/3D
camera developed by Center of Sensorsystems (ZESS). The
:VlultiCam is a camera which is capable to provide high
resolution of color data acquired from CMOS sensors and low
resolution of distance (or range) data calculated based on timeof-
flight (ToF) technology using Photonic Mixer Device (PMD)
sensors. The availability of the distance data allows the hand
posture to be recognized in z-axis direction without complex
computational algorithms which also enables the program to
work in real-time processing as well as eliminates the
background effectively. The hand posture recognition will
employ a simple but robust algorithm by checking the number
of fingers detected around virtually created circle centered at
the Center of Mass (CoM) of the hand and therefore classifies
the class associated with a particular hand posture. At the end
of this paper, the technique that uses intersection
between the circle and fingers as the method to classify
the hand posture which entails the MultiCam capability
is proposed. This technique is able to solve the problem
of orientation, size and distance invariants by utilizing
the distance data
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Efficient Kinect Sensor-based Kurdish Sign Language Recognition Using Echo System Network
Sign language assists in building communication and bridging gaps in understanding. Automatic sign language recognition (ASLR) is a field that has recently been studied for various sign languages. However, Kurdish sign language (KuSL) is relatively new and therefore researches and designed datasets on it are limited. This paper has proposed a model to translate KuSL into text and has designed a dataset using Kinect V2 sensor. The computation complexity of feature extraction and classification steps, which are serious problems for ASLR, has been investigated in this paper. The paper proposed a feature engineering approach on the skeleton position alone to provide a better representation of the features and avoid the use of all of the image information. In addition, the paper proposed model makes use of recurrent neural networks (RNNs)-based models. Training RNNs is inherently difficult, and consequently, motivates to investigate alternatives. Besides the trainable long short-term memory (LSTM), this study has proposed the untrained low complexity echo system network (ESN) classifier. The accuracy of both LSTM and ESN indicates they can outperform those in state-of-the-art studies. In addition, ESN which has not been proposed thus far for ASLT exhibits comparable accuracy to the LSTM with a significantly lower training time
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