16 accepted papers
Knowledge distillation involves a lightweight student model learning from the high-performance teacher model. Traditional feature-based knowledge distillation has limitations as it makes the student model mimic the teacher’s features. This approach lacks flexibility, particularly when there are sign…
Face recognition has recently achieved remarkable performance with the help of deep learning networks, but there is still a domain gap between frontal and profile face recognition. Generally speaking, we utilize pose-invariant face recognition methods or incorporate additional depth information to h…
Convolutional neural networks can effectively learn features and predict the depth by considering different scene types. However, previous studies have not accurately predicted the depth in cases wherein the objects or scenes were small and the background was complex. These studies have used the bil…
Three-dimensional (3D) object reconstruction is among the most important research areas in the field of computer vision. Its purpose is to reconstruct the overall shape of an object from its twodimensional (2D) image. With the development of deep learning, many methods based on convolutional neural…
In this paper, fast and accurate two paths CNN architecture was designed in hardware-oriented manner. Our proposed network is composed of RGB and depth path for gesture recognition by fusing RGB and depth features, following the pre-defined constraints on dedicated hardware. The RTL simulation resul…
Object detection technology has received increasing research attention with recent developments in automation technology. Most studies in this field, however, use RGB images as input to deep-learning classifiers, and they rarely use depth information.So, in this paper, we use images with both RGB an…
In recent years, the rapid development of depth cameras and wide application scenarios. The depth image information becomes more influential in face identification. In the proposed architecture, we implement the networks in dual CNN paths for color and depth images separately. Moreover, we design in…
Recently, convolutional neural networks (CNNs) have seen great progress in classifying images. Action recognition is different from still image classification; video data contains temporal information that plays an important role in video understanding. Currently, most CNN-based approaches for actio…
Object detection is an important research area in the field of computer vision. Its purpose is to find all objects in an image and recognize the class of each object. Since the development of deep learning, an increasing number of studies have applied deep learning in object detection and have achie…
Plenty of pores can solve the lack of feature points problem on high-resolution partial fingerprints. Pore-based features are similar, so the neighbor ridge features are also taken into account. However, lots of feature points cause heavy computation required. We propose a binary descriptor, Pore-Va…
Traffic sign recognition (TSR) is one of the Advanced Driver Assistance System (ADAS) device in modern cars. We propose a high efficiency hardware implementation for TSR, which is divided into two stages. In the detection stage, we use Normalized RGB color transform and Single-Pass Connected Compone…
In recent years, face recognition has become a popular topic in academia and industry. Current local methods such as the local binary pattern (LBP), and scale invariant feature transform (SIFT) perform better than holistic methods, but their high complexity levels limit their application. In additio…
Foreground segmentation/background subtraction is a vital step in many high-level video analysis applications. While many methods have been proposed for foreground segmentation, most assume the cameras to be stationary. With this assumption, they are unable to handle the movements caused by camera r…
Fingerprint is one of the unique biometric features for the application of identity security. Minutiae cylinder code (MCC) constructs a cylinder for each minutia to record the contribution of the neighbor minutiae, which has great performance on fingerprint recognition. However, the computation time…
In this paper we present a new fingerprint matching method which combines different features, including minutiae and ridge features. The ridge features contain ridge count, ridge length, ridge curvature direction and ridge frequency. All ridge features are extracted in blocks around each minutia. Th…
Scale-invariant feature transform (SIFT) is a feature point based method using the orientation descriptor for pattern recognition. It is robust under the variation of scale and rotation changes, but the computation cost increases with its feature points. Local binary pattern (LBP) is a pixel based t…