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Foreground detection is one of the well and widely studied research topic in the field of computer vision. However, it still fails to cope with the many practical issues such as illumination changes, dynamic backgrounds, and shadow. This paper proposes optimal color space based probabilistic foreground detector. The intuition is to employ two most widely used color spaces (RGB and YCbCr) one at a...
Tracking-Learning-Detection (TLD) is an excellent long-term tracking method which has the advantages of high accuracy of tracking rate and self-detection mechanism. Noting that TLD algorithm is sensitive to illumination change and clutter results in drift even missing, and the corresponding tracker designed based on the pyramid Lucaks-Kanade optical flow method needs vast computation. To overcome...
This paper proposes a novel inherently rotation invariant local descriptor which combined intensity information and gradient information of key feature. The CS-LBP shows a better performance than SIFT and do not need large computation. To further enhance its performance and robustness, we calculated the gradient of key feature and computed a combined histogram included intensity and gradient information...
Computer vision is one of the most active research fields in information technology today. Giving machines and robots the ability to see and comprehend the surrounding world at the speed of sight creates endless potential applications and opportunities. Feature detection and description algorithms can be indeed considered as the retina of the eyes of such machines and robots. However, these algorithms...
A boosted convolutional neural network (BCNN) system is proposed to enhance the pedestrian detection performance in this work. Being inspired by the classic boosting idea, we develop a weighted loss function that emphasizes challenging samples in training a convolutional neural network (CNN). Two types of samples are considered challenging: 1) samples with detection scores falling in the decision...
In this paper, we address the task of instance-level semantic boundary detection. To this end, we generate a large database consisting of more than 10k images (which is 20 bigger than existing edge detection databases) along with ground truth boundaries between 459 semantic classes including instances from both foreground objects and different types of background, and call it the PASCAL Boundaries...
Global fisheries and the future of sustainable seafood are predicated on healthy populations of various species of fish and shellfish. Recent developments in the collection of large-volume optical data by autonomous underwater vehicles (AUVs), stationary camera arrays, and towed vehicles has made it possible for fishery scientists to generate species-specific, size-structured abundance estimates for...
This paper deals with a design of specific marker detector and tracker for semiautonomous convoy purpose. The solution is based on computer vision. The detectors main requirement is the specific marker recognition ability for various environments. The next part of this paper deals with tracking of marker by KLT algorithm. This solution was primary designed for a semi-autonomous convoy task, where...
The Interest point detection algorithm plays a vital role in computer vision applications. The most commonly used interest point detector is scale invariant feature transform (SIFT). The SIFT algorithm fails to match interest points on the edge due to Gaussian filter. In order to overcome this failure a bilateral-Harris corner detector has been proposed. Bilateral filter is an edge-preserving, noise...
Detection of corner is the most essential process in a large number of computer vision and image processing applications. We have mentioned a number of popular contour-based corner detectors in our paper. Among all these detectors chord to triangular arm angle (CTAA) has been demonstrated as the most dominant corner detector in terms of average repeatability. We introduce a new effective method to...
The estimation of blurred regions is an important stage in several computer vision applications. In this paper an efficient training-free detector of local blurriness based on edge features is presented. Due to the intrinsic sparsity of edges in natural images a blur map is creating by using an approach based on the heat diffusion principle. A 2D point discrete Poisson solver is concatenated with...
Several methods are available in computer vision to recognize objects. Mostly, these methods consist of two well-separated parts: first the region of interest has to be recognized on the input image. After this, the visual representation of the object has to be compared with already-known samples: there are several ways, mostly based on the shape, color and pattern, or corner- or keypoints of the...
There exists a range of feature detecting and feature matching algorithms; many of which have been included in the Open Computer Vision (OpenCV) library. However, given these different tools, which one should be used? This paper discusses the implementation and comparison of a range of the library's feature detectors and feature matchers. It shows that the Speeded-Up Robust Features (SURF) detector...
Pedestrian detection is an active problem in computer vision research, with applications in robotics, self-driving cars and surveillance. It involves generating bounding boxes to indicate the location of every pedestrian in an input image. This paper proposes a method to augment a basic pedestrian detector with a Convolutional Neural Network. An implementation of the proposed algorithm was trained...
The existing pedestrian counting methods use the various keypoint detectors but there is no attempt to find a suitable keypoint detector for counting pedestrians. Therefore, in this paper, we compare the various keypoint detectors using a public dataset. Our evaluation framework uses the processing time of keypoint detection and matching as a performance measure. Also, we use the accuracy of moving...
The different strategies for feature extraction and synthesis employed by humans and computers are often complementary, hence combining the two into an integrated object recognition system may considerably improve performance over either used in isolation. Rapid Serial Visual Presentation (RSVP) is one well-established technique that has shown promise integrating human perception into a machine perception...
One challenge in a video surveillance system is the data rate required to represent digital video. Accordingly, the use of lossy video compression at a compression ratio of 100:1, or higher, is an essential part of any distributed live video system. The ensuing distortion can interfere with the goals of surveillance by confounding both human analysis and computer vision based processing. This paper...
In this paper, we present a novel corner detection method based on a new non-cornerness measure. The non-cornerness function is comprised of two steps: 1) eliminate any pixel located in a flat region and 2) remove a pixel that is positioned along an edge in any orientation. The experimental results show that our proposed method outperforms previous corner detectors.
With the development and application of the computer, the traditional paper documents can be stored and managed effectively after turning into digital images. In electronization of archives, a single camera is insufficient to gain a whole image clearly. At present, we generally shoot several images for stitching to obtain a complete and clear image. In document stitching, ghost has a quite large influence...
Tone mapping operators (TMO) have recently raised interest for their capability to handle illumination changes. However, these TMOs are optimized with respect to perception rather than image analysis tasks like key point detection. Moreover, no work has been done to analyze the factors affecting the optimization of TMOs for such tasks. In this paper, we investigate the influence of two factors-Correlation...
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