Optical navigation (OPNAV) is the use of the on-board imaging data to provide a direct measurement of the image coordinates of the target as navigation information. Among the optical observables in deep-space, the edge of the celestial body is an important feature that can be utilized for locating the planet centroid. However, traditional edge detection algorithms like Canny algorithm cannot be applied directly for OPNAV due to the noise edges caused by surface markings. Moreover, due to the constrained computation and energy capacity on-board, light-weight image-processing algorithms with less computational complexity are desirable for real-time processing. Thus, to fast and accurately extract the edge of the celestial body from high-resolution satellite imageries, this paper presents an algorithm-hardware co-design of real-time edge detection for OPNAV. First, a light-weight edge detection algorithm is proposed to efficiently detect the edge of the celestial body while suppressing the noise edges caused by surface markings. Then, we further present an FPGA implementation of the proposed algorithm with an optimized real-time performance and resource efficiency. Experimental results show that, compared with the traditional edge detection algorithms, our proposed one enables more accurate celestial body edge detection, while simplifying the hardware implementation.

The quality of edge detection is related to detection angle, scale, and threshold. There have been many algorithms to promote edge detection quality by some rules about detection angles. However these algorithm did not form rules to detect edges at an arbitrary angle, therefore they just used different number of angles and did not indicate optimized number of angles. In this paper, a novel edge detection algorithm is proposed to detect edges at arbitrary angles and optimized number of angles in the algorithm is introduced. The algorithm combines singularity detection with Gaussian wavelet transform and edge detection at arbitrary directions and contain five steps: 1) An image is divided into some pixel lines at certain angle in the range from 45° to 90° according to decomposition rules of this paper. 2) Singularities of pixel lines are detected and form an edge image at the certain angle. 3) Many edge images at different angles form a final edge images. 4) Detection angles in the range from 45° to 90° are extended to range from 0° to 360°. 5) Optimized number of angles for the algorithm is proposed. Then the algorithm with optimized number of angles shows better performances.

This paper presents a novel defense scheme for DDoS attacks that uses an image processing method. This scheme especially focused on the prevalence of adjacent neighbor spoofing, called subnet spoofing. It is rarely studied and there is few or no feasible approaches than other spoofing attacks. The key idea is that a “DDoS attack with IP spoofing” is represented as a specific pattern such as a “line” on the spatial image planes, which can be recognized through an image processing technique. Applying the clustering technique to the lines makes it possible to identify multiple attack source networks simultaneously. For the identified networks in which the zombie hosts reside, we then employ a signature-based pattern extraction algorithm, called a pivoted movement, and the DDoS attacks are filtered by correlating the IP and media access control pairing signature. As a result, this proposed scheme filters attacks without disturbing legitimate traffic. Unlike previous IP traceback schemes such as packet marking and path fingerprinting, which try to diagnose the entire attack path, our proposed scheme focuses on identifying only the attack source. Our approach can achieve an adaptive response to DDoS attacks, thereby mitigating them at the source, while minimizing the disruption of legitimate traffic. The proposed scheme is analyzed and evaluated on the IPv4 and IPv6 network topology from CAIDA, the results of which show its effectiveness.

Considering the non-linear properties of the human visual system, many non-linear operators and models have been developed, particularly the logarithmic image processing (LIP) model proposed by Jourlin and Pinoli, which has been proved to be physically justified in several laws of the human visual system and has been successfully applied in image processing areas. Recently, several modifications based on this logarithmic mathematical framework have been presented, such as parameterized logarithmic image processing (PLIP), pseudo-logarithmic image processing, homomorphic logarithmic image processing. In this paper, a new single parameter logarithmic model for image processing with an adaptive parameter-based Sobel edge detection algorithm is presented. On the basis of analyzing the distributive law, the subtractive law, and the isomorphic property of the PLIP model, the five parameters in PLIP are replaced by a single parameter to ensure the completeness of the model and physical constancy with the nature of an image, and then an adaptive parameter-based Sobel edge detection algorithm is proposed. By using an image noise estimation method to evaluate the noise level of image, the adaptive parameter in the single parameter LIP model is calculated based on the noise level and grayscale value of a corresponding image area, followed by the single-parameter LIP-based Sobel operation to overcome the noise-sensitive problem of classical LIP-based Sobel edge detection methods, especially in the dark area of an image, while retaining edge sensitivity. Compared with the classical LIP and PLIP model, the given single parameter LIP achieves satisfactory results in noise suppression and edge accuracy.

In this paper, we develop a novel two-sample test statistic for edge detection in CT image. This test statistic involves the non-parametric estimate of the samples' probability density functions (PDF's) based on the kernel density estimator and the calculation of the mean square error (MSE) distance of the estimated PDF's. In order to extract single-pixel-wide edges, a generic detection scheme cooperated with the non-maximum suppression is also proposed. This new method is applied to a variety of noisy images, and the performance is quantitatively evaluated with edge strength images. The experiments show that the proposed method provides a more effective and robust way of detecting edges in CT image compared with other existing methods.

In H.264/AVC intra frame coding, the rate-distortion optimization (RDO) is employed to select the optimal coding mode to achieve the minimum rate-distortion cost. Due to a large number of combinations of coding modes, the computational burden of Rate distortion optimization (RDO) becomes extremely high in intra prediction. In this paper, we proposed an efficient selective intra block size decision (SIB) that selects the appropriate block size for intra prediction, further proposed fast intra prediction algorithm reduces a number of modes required for RDO that significantly reduces the encoder complexity. Experimental results show that the proposed fast mode decision algorithm reduces the encoding time by up to 68% with negligible video quality degradation.

Quantification of the hip cartilages is clinically important. In this study, we propose an automatic technique for segmentation and visualization of the acetabular and femoral head cartilages based on clinically obtained multi-slice T1-weighted MR data and a hybrid approach. We follow a knowledge based approach by employing several features such as the anatomical shapes of the hip femoral and acetabular cartilages and corresponding image intensities. We estimate the center of the femoral head by a Hough transform and then automatically select the volume of interest. We then automatically segment the hip bones by a self-adaptive vector quantization technique. Next, we localize the articular central line by a modified canny edge detector based on the first and second derivative filters along the radial lines originated from the femoral head center and anatomical constraint. We then roughly segment the acetabular and femoral head cartilages using derivative images obtained in the previous step and a top-hat filter. Final masks of the acetabular and femoral head cartilages are automatically performed by employing the rough results, the estimated articular center line and the anatomical knowledge. Next, we generate a thickness map for each cartilage in the radial direction based on a Euclidian distance. Three dimensional pelvic bones, acetabular and femoral cartilages and corresponding thicknesses are overlaid and visualized. The techniques have been implemented in C++ and MATLAB environment. We have evaluated and clarified the usefulness of the proposed techniques in the presence of 40 clinical hips multi-slice MR images.

Salient edge detection which is mentioned less frequently than salient point detection is another important cue for subsequent processing in computer vision. How to find the salient edges in natural images is not an easy work. This paper proposes a simple method for salient edge detection which preserves the edges with more salient points on the boundaries and cancels the less salient ones or noise edges in natural images. According to the Gestalt Principles of past experience and entirety, we should not detect the whole edges in natural images. Only salient ones can be an advantageous tool for the following step just like object tracking, image segmentation or contour detection. Salient edges can also enhance the efficiency of computing and save the space of storage. The experiments show the promising results.

In this paper, a fast mode decision method for intra-prediction is proposed to reduce the computational complexity of H.264/AVC encoders. With edge information, we propose a novel fast estimation algorithm to reduce the computation overhead of H.264/AVC for mode selection, where the edge direction of each coding block is detected from only part of the transformed coefficients. Hence, the computation complexity is greatly reduced. Experimental results show that the proposed fast mode decision method can eliminate about 81.34% encoding time for all intra-frame sequences with acceptable degradation of averaged PSNR and bitrates.

The research on displacement vector detection has gained increasing attention in recent years. However, no relationship between displacement vectors and the outlines of objects in motion has been established. We describe a new method of detecting displacement vectors through edge segment detection by emphasizing the correlation between displacement vectors and their outlines. Specifically, after detecting an edge segment, the direction of motion of the edge segment can be inferred through the variation in the values of the Laplacian-Gaussian filter at the position near the edge segment before and after the motion. Then, by observing the degrees of displacement before and after the motion, the displacement vector can be calculated. The accuracy compared to other methods of displacement vector detection demonstrates the feasibility of this method.

For real time image processing, a readout circuit for an infrared focal plane array (IRFPA) involving a new edge detection technique has been proposed in this letter. A non-uniformity correction unit (NUC), essential in an IRFPA because of bad non-uniformity characteristics of IR sensors is eliminated in this circuit by using a noise tolerant edge detection technique. In addition, real time edge detection can be possible, because of pixel-level integration and parallel processing. The proposed readout circuit shows an approximately three to nine times better edge error rate than other available methods using pixel-level parallel processing.

UWB pulse radars enable us to measure a target location with high range-resolution, and so are applicable for measurement systems for robots and automobile. We have already proposed a robust and fast imaging algorithm with an envelope of circles, which is suitable for these applications. In this method, we determine time delays from received signals with the matched filter for a transmitted waveform. However, scattered waveforms are different from transmitted one depending on the target shape. Therefore, the resolution of the target edges deteriorates due to these waveform distortions. In this paper, a high-resolution imaging algorithm for convex targets is proposed by iteration of the shape and waveform estimation. We show application examples with numerical simulations and experiments, and confirm its capability to detect edges of an object.

A 3 dimensional (3D) error diffusion method based on edge detection for flat panel display (FPD) is presented. The new method diffuses errors to the neighbor pixels in current frame and the neighbor pixel in the next frame. And the weights of error filters are dynamically adjusted based on the results of edge detection in each pixel's processing, which makes the weights coincide with the local edge feathers of input image. The proposed method can reduce worm artifacts and improve reproduction precision of image details.

Text detection in color images has become an active research area in the past few decades. In this paper, we present a novel approach to accurately detect text in color images possibly with a complex background. The proposed algorithm is based on the combination of connected component and texture feature analysis of unknown text region contours. First, we utilize an elaborate color image edge detection algorithm to extract all possible text edge pixels. Connected component analysis is performed on these edge pixels to detect the external contour and possible internal contours of potential text regions. The gradient and geometrical characteristics of each region contour are carefully examined to construct candidate text regions and classify part non-text regions. Then each candidate text region is verified with texture features derived from wavelet domain. Finally, the Expectation maximization algorithm is introduced to binarize each text region to prepare data for recognition. In contrast to previous approach, our algorithm combines both the efficiency of connected component based method and robustness of texture based analysis. Experimental results show that our proposed algorithm is robust in text detection with respect to different character size, orientation, color and language and can provide reliable text binarization result.

A network for the detection of an approaching object with simple-shape recognition is proposed based on lower animal vision. The locust can detect an approaching object through a simple process in the descending contralateral movement detector (DCMD) in the locust brain, by which the approach velocity and direction of the object is determined. The frog can recognize simple shapes through a simple process in the tectum and thalamus in the frog brain. The proposed network is constructed of simple analog complementary metal oxide semiconductor (CMOS) circuits. The integrated circuit of the proposed network is fabricated with the 1.2 µm CMOS process. Measured results for the proposed circuit indicate that the approach velocity and direction of an object can be detected by the output current of the analog circuit based on the DCMD response. The shape of moving objects having simple shapes, such as circles, squares, triangles and rectangles, was recognized using the proposed frog-visual-system-based circuit.

We propose a robust edge detection method based on independent component analysis (ICA). It is known that most of the basis functions extracted from natural images by ICA are sparse and similar to localized and oriented receptive fields, and in the proposed edge detection method, a target image is first transformed by ICA basis functions and then the edges are detected or reconstructed with sparse components only. Furthermore, by applying a shrinkage algorithm to filter out the components of noise in the ICA domain, we can readily obtain the sparse components of the original image, resulting in a kind of robust edge detection even for a noisy image with a very low SN ratio. The efficiency of the proposed method is demonstrated by experiments with some natural images.

We propose a new method to precisely detect pupil contours in face images. Pupil contour detection is necessary for various applications using face images. It is, however, difficult to detect pupils precisely because of their weak edges or lack of edges. The proposed method is based on minimizing the energy of pattern and edge. The basic idea of this method is that the energy, which consists of the pattern and the edge energy, has to be minimized. An efficient search method is also introduced to overcome the underlying problem of efficiency in energy minimization methods. "Guide patterns" are introduced for this purpose. Moreover, to detect pupils more precisely we use an ellipse model as pupil shape in this paper. Experimental results show the effectiveness of the proposed method.

Edge detection has been an essential step in image processing, and there has been much work undertaken to date. This paper inspects a fuzzy mathematical morphology in order to reach a higher-level of edge-image processing. The proposed scheme uses a fuzzy morphological gradient to detect object boundaries, when the boundaries are roughly defined as a curve or a surface separating homogeneous regions. The automatic edge detection algorithm consists of two major steps. First, a new version of anisotropic diffusion is proposed for edge detection and image restoration. All improvements of the new version use fuzzy mathematical morphology to preserve the edge accuracy and to restore the images to homogeneity. Second, the fuzzy morphological gradient operation detects the step edges between the homogeneous regions as object boundaries. This operation uses geometrical characteristics contained in the structuring element in order to extract the edge features in the set of edgeness, a set consisting of the quality values of the edge pixels. This set is prepared with fuzzy logic for decision and selection of authentic edge pixels. For experimental results, the proposed method has been tested successfully with both synthetic and real pictures.

In this paper, a new image synthesis model based on a set of wavelet bases is proposed. In the proposed model, images are approximated by the sum of synthesis functions that are translated to image edge positions. By applying the proposed model to sketch-based image coding, no iterative image recovery procedure is required for image decoding. In the design of the synthesis functions, we define the synthesis functions as a linear combination of wavelet bases. The coefficients for wavelet bases are obtained from an iterative procedure. The vector quantization is applied to the vectors of the coefficients to limit the number of the synthesis functions. We apply the proposed synthesis model to the sketch-based image coding. Image coding experiments by eight synthesis functions and a comparison with the orthogonal transform methods are also given.

This paper describes the implementation of a proposed image filter into a Discrete-Time Cellular Neural Network (DT-CNN). The three stages that compose the filter are described, showing that the resultant filter is capable of (1) erasing or detecting several concentric shapes simultaneously, (2) thresholding and (3) thinning of gray-scale images. Because the DT-CNN has to fill certain conditions for this filter to be implemented, it becomes a modified version of a DT-CNN. Those conditions are described and also experimental results are clearly shown.