Zhengya Xu

Bio: Zhengya Xu is an academic researcher from RMIT University. The author has contributed to research in topics: Image processing & Impulse noise. The author has an hindex of 5, co-authored 14 publications receiving 105 citations. Previous affiliations of Zhengya Xu include Melbourne Institute of Technology.

Geometric Features-Based Filtering for Suppression of Impulse Noise in Color Images

Abstract: A geometric features-based filtering technique, named as the adaptive geometric features based filtering technique (AGFF), is presented for removal of impulse noise in corrupted color images. In contrast with the traditional noise detection techniques where only 1D statistical information is used for noise detection and estimation, a novel noise detection method is proposed based on geometric characteristics and features (i.e., the 2-D information) of the corrupted pixel or the pixel region, leading to effective and efficient noise detection and estimation outcomes. A progressive restoration mechanism is devised using multipass nonlinear operations which adapt to the intensity and the types of the noise. Extensive experiments conducted using a wide range of test color images have shown that the AGFF is superior to a number of existing well-known benchmark techniques, in terms of standard image restoration performance criteria, including objective measurements, the visual image quality, and the computational complexity.

Colour image enhancement by virtual histogram approach

Abstract: This paper introduces a new hybrid image enhancement approach driven by both global and local processes on luminance and chrominance components of the image. This approach, based on the parameter-controlled virtual histogram distribution method, can enhance simultaneously the overall contrast and the sharpness of an image. The approach also increases the visibility of specified portions or aspects of the image whilst better maintaining image colour. The approach was compared with other well-known image enhancement techniques. The experimental results have shown the superiority of the proposed approach.

Smart video surveillance system

Abstract: A new approach is proposed for a smart video surveillance system in this paper. The proposed surveillance system uses cononical stereo configuration to set up a pair of statical cameras to support a salient map to control a Pan-Tilt-Zoom(PTZ) camera/cameras, which captures high definition image /video of the interesting moving object in the surveillance area for further forensic investigations. The other contribution of the proposed approach is that, 3D information generated by the set of static cameras is used to support reliable spatial-temporal based image segmentation and object detection. For object tracking, velocity field computation from two consecutive images is based on correspondence salient feature (points). Due to its low computational cost, it is potential to support a real time system to make a loop control for further using the information from the PTZ camera to support the object tracking in video captured by the static cameras.

Robust Shape-Feature-Vector-Based Face Recognition System

Abstract: This paper presents a feature-based approach for fast face recognition. A novel shape-based automatic reference control point and feature extraction technique is proposed for face representation, whereby the difference between two faces is measured by a set of extracted features, and 3-D features from a set of 2-D images are used for face template registration. Unlike holistic face recognition algorithms, the feature-based algorithm is relatively robust to variations of face expressions, illumination, and pose, due to invariance of its facial feature vector. The theoretical performance analysis of the proposed technique was provided by a probabilistic and statistical approach. The proposed approach is shown to achieve promising performance for face recognition using several subsets of face recognition databases.

Features Based Spatial and Temporal Blotch Detection for Archive Video Restoration

Abstract: In order to restore blotched archive video without causing distortion to areas of the frames that are not corrupted, the locations of the blotches must be identified. Blotch detection usually needs reliable motion estimation to avoid false detection of uncorrupted regions in existing techniques. In this paper a blotch artifacts detection technique, which is based on spatiotemporal blotch image features extraction to avoid the dependence on the motion estimation, is proposed. In order to greatly reduce false alarms due to motion estimation errors the proposed detection technique is first to detect blotch candidates based on their spatial features and then to detect blotches from the blotch candidates by their temporal intensity discontinuities. Experimental results show that the proposed technique significantly improves detection performance and outperforms existing techniques.

A Hybrid Feature Extraction Approach for Human Detection, Tracking and Activity Recognition Using Depth Sensors

Abstract: This paper presents spatiotemporal hybrid features, human tracking, and activity recognition into a single framework from video sequences captured by a RGB-D sensor. Initially, we received a sequence of depth maps to extract human silhouettes from the noisy background and track them using temporal human motion information from each frame. Then, hybrid features as optical flow motion features and distance parameters features are extracted from the depth silhouette region and used in an augmented form to work as patiotemporal features. In order to represent each activity in a better way, the augmented features are being clustered and symbolized by self-organization maps. Finally, these features are then processed by hidden Markov models to train and recognize human activities based on transition and emission probabilities values. The experimental results show the superiority of the proposed method over the state-of-the-art methods using two challenging depth images datasets.

A review of face recognition methods

Abstract: Face recognition has become more significant and relevant in recent years owing to it potential applications. Since the faces are highly dynamic and pose more issues and challenges to solve, researchers in the domain of pattern recognition, computer vision and artificial intelligence have proposed many solutions to reduce such difficulties so as to improve the robustness and recognition accuracy. As many approaches have been proposed, efforts are also put in to provide an extensive survey of the methods developed over the years. The objective of this paper is to provide a survey of face recognition papers that appeared in the literature over the past decade under all severe conditions that were not discussed in the previous survey and to categorize them into meaningful approaches, viz. appearance based, feature based and soft computing based. A comparative study of merits and demerits of these approaches have been presented.

Fuzzy Random Impulse Noise Removal From Color Image Sequences

Abstract: In this paper, a new fuzzy filter for the removal of random impulse noise in color video is presented. By working with different successive filtering steps, a very good tradeoff between detail preservation and noise removal is obtained. One strong filtering step that should remove all noise at once would inevitably also remove a considerable amount of detail. Therefore, the noise is filtered step by step. In each step, noisy pixels are detected by the help of fuzzy rules, which are very useful for the processing of human knowledge where linguistic variables are used. Pixels that are detected as noisy are filtered, the others remain unchanged. Filtering of detected pixels is done by blockmatching based on a noise adaptive mean absolute difference. The experiments show that the proposed method outperforms other state-of-the-art filters both visually and in terms of objective quality measures such as the mean absolute error (MAE), the peak-signal-to-noise ratio (PSNR) and the normalized color difference (NCD).

An Efficient Denoising Architecture for Removal of Impulse Noise in Images

Abstract: Images are often corrupted by impulse noise in the procedures of image acquisition and transmission. In this paper, we propose an efficient denoising scheme and its VLSI architecture for the removal of random-valued impulse noise. To achieve the goal of low cost, a low-complexity VLSI architecture is proposed. We employ a decision-tree-based impulse noise detector to detect the noisy pixels, and an edge-preserving filter to reconstruct the intensity values of noisy pixels. Furthermore, an adaptive technology is used to enhance the effects of removal of impulse noise. Our extensive experimental results demonstrate that the proposed technique can obtain better performances in terms of both quantitative evaluation and visual quality than the previous lower complexity methods. Moreover, the performance can be comparable to the higher,- complexity methods. The VLSI architecture of our design yields a processing rate of about 200 MHz by using TSMC 0.18 μm technology. Compared with the state-of-the-art techniques, this work can reduce memory storage by more than 99 percent. The design requires only low computational complexity and two line memory buffers. Its hardware cost is low and suitable to be applied to many real-time applications.

Adaptive defogging with color correction in the HSV color space for consumer video surveillance systems

Abstract: Consumer video surveillance systems often suffer from bad weather conditions, observed objects lose visibility and contrast due to the presence of atmospheric haze, fog, and smoke. In this paper, we present an image defogging algorithm with color correction in the HSV color space for video processing. We first generate a modified transmission map of the image segmentation using multiphase level set formulation from the intensity (V) values. We also estimate atmospheric light in the intensity (V) values. The proposed method can significantly enhance the visibility of foggy video frames using the estimated atmospheric light and the modified transmission map. Another contribution of the proposed work is the compensation of color distortion between consecutive frames using the temporal difference ratio of HSV color channels. Experimental results show that the proposed method can be applied to consumer video surveillance systems for removing atmospheric artifacts without color distortion.