Saliency detection aims to highlight the most relevant objects in an image. Methods using conventional models struggle whenever salient objects are pictured on top of a cluttered background while deep neural nets suffer from excess complexity and slow evaluation speeds. In this paper, we propose a simplified convolutional neural network which combines local and global information through a multi-resolution 4&#xd7;5 grid structure. Instead of enforcing spacial coherence with a CRF or superpixels as is usually the case, we implemented a loss function inspired by the Mumford-Shah functional which penalizes errors on the boundary. We trained our model on the MSRA-B dataset, and tested it on six different saliency benchmark datasets. Results show that our method is on par with the state-of-the-art while reducing computation time by a factor of 18 to 100 times, enabling near real-time, high performance saliency detection.

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Non-local Deep Features for Salient Object Detection

Using hand gestures is a natural method of interaction between humans and computers. We use gestures to express meaning and thoughts in our everyday conversations. Gesture-based interfaces are used in many applications in a variety of fields, such as smartphones, televisions (TVs), video gaming, and so on. With advancements in technology, hand gesture recognition is becoming an increasingly promising and attractive technique in human–computer interaction. In this paper, we propose a novel method for fingertip detection and hand gesture recognition in real-time using an RGB-D camera and a 3D convolution neural network (3DCNN). This system can accurately and robustly extract fingertip locations and recognize gestures in real-time. We demonstrate the accurateness and robustness of the interface by evaluating hand gesture recognition across a variety of gestures. In addition, we develop a tool to manipulate computer programs to show the possibility of using hand gesture recognition. The experimental results showed that our system has a high level of accuracy of hand gesture recognition. This is thus considered to be a good approach to a gesture-based interface for human–computer interaction by hand in the future.

Real-Time Hand Gesture Spotting and Recognition Using RGB-D Camera and 3D Convolutional Neural Network

Unmanned aerial vehicles (UAVs) play an important role in numerous technical and scientific fields, especially in wilderness rescue. This paper carries out work on real-time UAV human detection and recognition of body and hand rescue gestures. We use body-featuring solutions to establish biometric communications, like yolo3-tiny for human detection. When the presence of a person is detected, the system will enter the gesture recognition phase, where the user and the drone can communicate briefly and effectively, avoiding the drawbacks of speech communication. A data-set of ten body rescue gestures (i.e., Kick, Punch, Squat, Stand, Attention, Cancel, Walk, Sit, Direction, and PhoneCall) has been created by a UAV on-board camera. The two most important gestures are the novel dynamic Attention and Cancel which represent the set and reset functions respectively. When the rescue gesture of the human body is recognized as Attention, the drone will gradually approach the user with a larger resolution for hand gesture recognition. The system achieves 99.80% accuracy on testing data in body gesture data-set and 94.71% accuracy on testing data in hand gesture data-set by using the deep learning method. Experiments conducted on real-time UAV cameras confirm our solution can achieve our expected UAV rescue purpose.

Real-Time Human Detection and Gesture Recognition for On-Board UAV Rescue

The Sign language is a visual language used by the people with the speech and hearing disabilities for communication in their daily conversation activities. It is completely an optical communication language through its native grammar, be unlike fundamentally from that of oral languages. In this research paper, presented an optimal approach, whose major objective is to accomplish the transliteration of 24 static sign language alphabets and numbers of American Sign Language into humanoid or machine decipherable English manuscript. Pre-processing operations of the signed input gesture are done in the first phase. In the next phase, the various region properties of pre-processed gesture image is computed. In the final phase, based on the properties calculated of earlier phase, the transliteration of signed gesture into text has been carried out. This paper also presents the statistical result evaluation with the comparative graphical depiction of existing techniques and proposed technique.

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American Sign Language Recognition System: An Optimal Approach

A real-time fingertip-gesture-based interface is still challenging for human–computer interactions, due to sensor noise, changing light levels, and the complexity of tracking a fingertip across a variety of subjects. Using fingertip tracking as a virtual mouse is a popular method of interacting with computers without a mouse device. In this work, we propose a novel virtual-mouse method using RGB-D images and fingertip detection. The hand region of interest and the center of the palm are first extracted using in-depth skeleton-joint information images from a Microsoft Kinect Sensor version 2, and then converted into a binary image. Then, the contours of the hands are extracted and described by a border-tracing algorithm. The K-cosine algorithm is used to detect the fingertip location, based on the hand-contour coordinates. Finally, the fingertip location is mapped to RGB images to control the mouse cursor based on a virtual screen. The system tracks fingertips in real-time at 30 FPS on a desktop computer using a single CPU and Kinect V2. The experimental results showed a high accuracy level; the system can work well in real-world environments with a single CPU. This fingertip-gesture-based interface allows humans to easily interact with computers by hand.

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Real-time virtual mouse system using RGB-D images and fingertip detection

Hand gesture recognition is very significant for human-computer interaction. In this work, we present a novel real-time method for hand gesture recognition. In our framework, the hand region is extracted from the background with the background subtraction method. Then, the palm and fingers are segmented so as to detect and recognize the fingers. Finally, a rule classifier is applied to predict the labels of hand gestures. The experiments on the data set of 1300 images show that our method performs well and is highly efficient. Moreover, our method shows better performance than a state-of-art method on another data set of hand gestures.

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Real-time hand gesture recognition using finger segmentation.

Image saliency analysis plays an important role in various applications such as object detection, image compression, and image retrieval. Traditional methods for saliency detection ignore texture cues. In this paper, we propose a novel method that combines color and texture cues to robustly detect image saliency. Superpixel segmentation and the mean-shift algorithm are adopted to segment an original image into small regions. Then, based on the responses of a Gabor filter, color and texture features are extracted to produce color and texture sub-saliency maps. Finally, the color and texture sub-saliency maps are combined in a nonlinear manner to obtain the final saliency map for detecting salient objects in the image. Experimental results show that the proposed method outperforms other state-of-the-art algorithms for images with complex textures.

Image saliency detection using Gabor texture cues

The invention discloses a real-time gesture recognition method based on finger division and relates to a novel gesture recognition technology. The gesture recognition technology is one of hot topics in the human-computer interaction field. The real-time gesture recognition method based on finger division comprises the steps of extracting the whole hand region through a background subtraction method, and dividing a palm part and a finger part on the extracted hand region; judging finger types according to positions, angles and other information of fingers; performing gesture recognition on the whole gesture through a rule classifier. Related gesture recognition tests performed through a large number of experimental images prove that the real-time gesture recognition method based on finger division is high in speed and efficiency, and real-time effect can be achieved.

Jian-Ning Liang

Papers

Real-time hand gesture recognition using finger segmentation.

Image saliency detection using Gabor texture cues

Real-time gesture recognition method based on finger division