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The revolution of RGB-D sensors is advancing towards mobile platforms for robotics, autonomous vehicles and consumer hand-helddevices. Strong pressures on power consumption and system price requirenew powerful algorithms that can robustly handle very low quality rawdata. In this paper we demonstrate the ability to reliably recover depth measurements from a variety of highly degraded depth modalities,...
AprilTags and other passive fiducial markers require specialized algorithms to detect markers among other features in a natural scene. The vision processing steps generally dominate the computation time of a tag detection pipeline, so even small improvements in marker detection can translate to a faster tag detection system. We incorporated lessons learned from implementing and supporting the AprilTag...
In this paper, we propose an image target tracking algorithm for an embedded platform. Our proposed can process 1280 × 720 resolution video sequences and provide accurate image tracking in real time. In the tracking algorithm, an adaptive local edge detection method is employed to extract the feature pixels of a tracked object. To reduce tracking errors, a region-based local binary pattern feature...
Robust estimation of linear structures such as edges and junction features in digital images is an important problem. In this paper, we use an adaptive robust structure tensor (ARST) method where the local adaptation process uses spatially varying adaptive Gaussian kernel that is initialized using the total least-squares structure tensor solution. An iterative scheme is designed with size, orientation,...
Accurate and efficient object tracking is an important aspect of various security and surveillance applications. In object tracking solutions which utilize intensity-based histogram feature methods for use on wide area motion imagery (WAMI), there currently exists tracking challenges due to object structural information distortions and pavement/background variations. The inclusion of structural target...
In order to solve the detection problem of bad real-time performance and robustness in complex scene, a new method for soft cascade classifier based on SVM was built. The image features can be extracted by the algorithm of using ORBP feature descriptor. Then, based on efficiently combining manifold features and cascaded threshold, a multistage classifier frame is introduced in detail. To ensure the...
Circles detection is an important part of object recognition in image processing and computer vision. In this paper, we propose an adaptive method based on Hough transform to detect the circle shapes in digital image. The Mexican Hat filter derived from edge filter is used to concentrate the peaks of Hough local maxima. So, the circle center and its radius can be extracted easily and accurate. The...
The number of new malwares is increasing everyday. Thus malware detection is nowadays a big challenge. The existing techniques for malware detection require a huge effort of engineering to manually extract the malicious behaviors. To avoid this tedious task, we propose in this paper an approach to automatically extract the malicious behaviors. We model a program using an API call graph, and we represent...
We propose a region base simple method to filter out strokes, which is brighter or darker than the background. And this method had good performance at blur, low contrast, and shadow. The method is implement by Difference of Gaussian (DOG), which is applied in edge detection, invariant feature. We use the properties of DOG, isotropic edge detect and sign value than background, to develop a stroke over...
Automatically identifying plants from images is a hot research topic due to its importance in production and science popularization. This process attempts to automatically identify the name of a plant with a known taxon from a given image. The majority of existing studies on automatic plant identification focus on identifying plants with a single organ, such as flower, leaf, or fruits. Plant identification...
This paper proposes three robust detection algorithms for locating the cutting line in an image captured by a panel-cutting system. All of the proposed methods contain two stages: edge detection and line fitting. In this paper, edge detection can search interest gradients depending on the intensity concentration. Meanwhile, the proposed line-fitting algorithm is able to precisely fit a line by minimizing...
In steel industry, one of the most critical problems that could ruin the process of slab or strip rolling in the hot roughing mill and the finishing line is the slab curvature, also known as camber. In case of its occurrence, the production process suffers a series of disastrous consequences which eventually necessitate instant production line stopping. In this paper, a real-time machine vision-based...
This paper proposes a novel interpolation method using edge orientation vector calculated by Hessian matrix. Existing polynomial-based interpolation methods cause blurring effects on edge. In addition, existing edge-based interpolation methods are suffered from edge aliasing and color distortion. To compensate for these problems, we propose an improved edge-based interpolation method considering edge...
Extracting meaningful structures from an image is an important task and benefits a wide range of image application tasks. However, it is typically very challenging to distinguish between noisy or textural patterns from image structures, especially when such patterns do not exhibit regularity (e.g., irregular textural patterns or those with varying scales). While existing edge-preserving image filters...
Edge-preserving smoothing is one of the most important topics for image and video processing. Recently, we presented a multiscale image decomposition method based on domain transform, which is an efficient edge-preserving smoothing method. It is robust to noise compared with the original domain transform. In this paper, we generalize the scheme so that it can be applied to not only domain transform...
A personal or enterprise collection of a large set of face images may contain many types of tags used for querying the collection. Often the tags have many irrelevant content that may not reflect the image content in terms of the facial characteristics. In this paper, we propose a data curation method to filter out the irrelevant face images using a face recognition based subgraph identification....
Circle detection from digital images is a necessary operation in many robotics and computer vision tasks to facilitate shape and object recognition. We propose and analyze a novel method, based on line segment detection and circle completeness verification, to detect circles in images. The key idea is to use line segments instead of raw edge pixels to get the circle candidates followed by a verification...
Adaptive normalized cross-correlation (ANCC) cost function works well between images under photometric distortions, but its heavy computational burden often limits its applications. To overcome this limitation, this paper proposes a robust and efficient computational framework, called ANCC flow, designed for establishing dense correspondences between images under severe photometric variations. We...
Document is unavailable: This DOI was registered to an article that was not presented by the author(s) at this conference. As per section 8.2.1.B.13 of IEEE's "Publication Services and Products Board Operations Manual," IEEE has chosen to exclude this article from distribution. We regret any inconvenience.
In this paper, we propose a robust edge indicator employing two eigenvalues of nonlocal structure tensor matrix. In our method, a new nonlocal structure tensor is first constructed. This structure tensor is robust to noise, which inherits from nonlocal means algorithm. Furthermore, based on the constructed nonlocal structure tensor, a new and edge indicator is built, which can effectively differentiate...
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