As the number of vehicles soared, demand for parking spaces became more intense. Although there are many horizontal parking spaces located outdoor, many existing methods cannot detect horizontal parking spaces effectively due to the complex environment of outdoor and the long span of horizontal parking space. In this paper, a method based on vehicle-mounted fisheye image is proposed for outdoor parking space detection. While a car goes through a parking space at idle speed, fisheye images are captured by vehicle-mounted fisheye camera. Image processing algorithms are used to remove noise and classify light intensity. Then, different grayscale thresholds are used to enhance image contrast and Canny operator is utilized to detect contour information of the parking space line. Next, Hough transform is performed to detect straight-line segments. Angles of the line segments are calculated to determine whether the segments are perpendicular to each other. If right angle is detected in two consecutive frames, the second frame is selected as signal of starting or ending position of parking space. Different distances between space line and wheels are selected to verify that the method has good adaptability to distance. Experiments show that the proposed method can detect parking line effectively and meet real-time requirement.
Constant false alarm rate (CFAR) detection is an important method which is widely used in radar automatic detection. However, the performance of CFAR detection degrades dramatically in non-homogeneous environment because of the existence of interference targets and clutter in the reference window of the test cell. To the end, an intelligent multistrategy fusion (IMSF) CFAR detection algorithm is proposed in this paper. By combining the preprocessing results of FOCA-CFAR, BOCA-CFAR and OSVI-CFAR, IMSF-CFAR can utilize more suitable independent, identically distributed reference cells than conventional CFAR method, which means better detection performance. The simulation results reveal that the IMSF-CFAR maintains stable detection performance both in the homogeneous and heterogeneous environments.
With the popularity of commercial unmanned aerial vehicles (UAVs), people have easy access to UAV. However, people’s privacy and safety can be threatened if UAV flies at airports, private yards, etc. It is important to be able to detect the illegal UAV accurately and promptly on these vulnerable sites. However, motion blur, occlusion and truncation occur frequently due to fast movement of UAV. It is hard to make correct predictions because of the small size of UAV in images. In this paper, we propose an anchor-free one-stage method for UAV detection. The method eliminates the anchor boxes that are used in most existing detectors, which makes our method simpler and more efficient. We improve the detection accuracy in the following two ways. First, a new multi-scale feature fusion method is proposed to enhance the semantic information exchange between different scales. Second, a reasonable loss function is adopted to increase the proportion of small UAV’s loss. Experimental results validate the effectiveness of our improvements and our proposed detector achieve a superior performance.
The Screen content images (SCIs) are images containing textual and pictorial regions, which have become more and more connected with our daily life with the widespread adoption of multimedia applications. In particular, the image quality assessment (IQA) of SCIs is important because of its good property to guide and optimize lots of image processing systems. However, the no-reference (NR) IQA algorithms receive little attention and achieve unsatisfactory performance. Hence, this paper proposes a novel no-reference IQA method based on patch-wise multi-order derivatives for SCIs. This method includes two stages: patch-wise image quality evaluation and quality pooling. The first stage focuses on learning visual quality of local regions. Two features of image patches are extracted: multi-order derivative statistics, multi-order derivative histograms, which respectively describe the global and local information of the multiorder derivatives. Then the support vector regression (SVR) is applied to measure visual quality of image patches given a set of extracted features. The second stage aims at pooling patch-wise quality to an overall quality score with weights derived from entropy of gradient information of SCIs. Experimental results show that our method obtains superior performance against state-of-the-art NR-IQA approaches on the SIQAD database of SCIs, and also achieves competitive performance against state-of-the-art FR-IQA methods for SCIs.
Unimodal analysis of finger-vein (FV) and finger dorsal texture (FDT) has been investigated intensively for personal recognition. Unfortunately, it is not robust to segmentation error and noise. Motivated by distribution trait of FV and FDT in a finger, we present a multimodal recognition method, called weighted sparse fusion for identification (WSFI), which uses FV and FDT images with fusion applied at the pixel level. Firstly, a new fused test sample, a weighted sum of FV and FDT images per-pixel, is obtained, the weight values are computed according to the reconstruction error of each FV and FDT pixels. And a new dictionary associated with the fused test sample is constructed in the same manner. Secondly, for every new fused test sample and the dictionary associated with it, the sparse representation based classification (SRC) is implemented for recognition. Experiments show that comparing with state-of-art techniques, our method achieves significant improvement in terms of accuracy rate (AR) equal error rate (EER).
Video security has become more and more important with the widespread use of videos. The encryption algorithm developed to protect text data may not be suitable to encrypt videos because of large data size and high real-time demand. Light weight algorithms, especially selective encryption algorithms, are attractive. However, existing algorithms can’t meet requirements in cryptography security, encryption efficiency and compression efficiency at the same time. We propose a fast selective encryption algorithm in this paper. Our algorithm selects data randomly instead of selecting key information. This is achieved by generating several pseudo-random sequences using RC4 with separate keys. The sequences are labeled with ‘0’ and ‘1’, and whether one bit in video data is encrypted is determined by the corresponding bit in the sequences. Our algorithm is at least as safe as naive algorithm for ciphertext-only attack, knownplaintext attack and chosen-plaintext attack. On the other hand, our computational cost is less than 7 percent compared with naive algorithm. Furthermore, our algorithm doesn’t enlarge video size and keeps video codec and video format compliant.
This paper proposes an interactive psoriasis lesion segmentation algorithm based on Gaussian Mixture Model (GMM). Psoriasis is an incurable skin disease and affects large population in the world. PASI (Psoriasis Area and Severity Index) is the gold standard utilized by dermatologists to monitor the severity of psoriasis. Computer aid methods of calculating PASI are more objective and accurate than human visual assessment. Psoriasis lesion segmentation is the basis of the whole calculating. This segmentation is different from the common foreground/background segmentation problems. Our algorithm is inspired by GrabCut and consists of three main stages. First, skin area is extracted from the background scene by transforming the RGB values into the YCbCr color space. Second, a rough segmentation of normal skin and psoriasis lesion is given. This is an initial segmentation given by thresholding a single gaussian model and the thresholds are adjustable, which enables user interaction. Third, two GMMs, one for the initial normal skin and one for psoriasis lesion, are built to refine the segmentation. Experimental results demonstrate the effectiveness of the proposed algorithm.