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A novel extension to Hızlı B-ESA object detection algorithm is proposed in order to learn convolutional context features for determining boundaries of objects better. For input images, the hypothesis windows and their context around those windows are learned through convolutional layers as two parallel networks. The resulting object and context feature maps are combined in such a way that they preserve...
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...
Feature fusion methods have been demonstrated to be effective for many computer vision based applications. These methods generally use multiple hand-crafted features. However, in recent days, features extracted through transfer leaning procedures have been proved to be robust than the hand-crafted features in myriad applications, such as object classification and recognition. The transfer learning...
A vehicle detection method in tunnel video is proposed based on ViBe (Visual Background Extractor) algorithm. The proposed method consists of three steps: firstly, histogram equalization is used to preprocessing the video aiming at improving the quality of the video images; secondly, the classic ViBe algorithm is adopted to model the background and detect the moving vehicles; finally, morphological...
Semantic image segmentation is now an exciting area of research owing to its various useful applications in daily life. This paper introduces a hierarchical joint-guided network (HJGN) which is mainly composed of proposed hierarchical joint learning convolutional networks (HJLCNs) and proposed joint-guided and making networks (JGMNs). HJLCNs exhibit high robustness in the segmentation of unseen objects...
This paper adapts a popular image quality measure called structural similarity for high precision registration based tracking while also introducing a simpler and faster variant of the same. Further, these are evaluated comprehensively against existing measures using a unified approach to study registration based trackers that decomposes them into three constituent sub modules - appearance model,...
Classifying the various shapes and attributes of a glioma cell nucleus is crucial for diagnosis and understanding of the disease. We investigate the automated classification of the nuclear shapes and visual attributes of glioma cells, using Convolutional Neural Networks (CNNs) on pathology images of automatically segmented nuclei. We propose three methods that improve the performance of a previously-developed...
Underwater 3D shape scanning technique becomes popular because ofseveral rising research topics, such as map making ofsubmarine topography for autonomous underwater vehicle (UAV), shape measurement of live fish, motion capture of swimming human, etc. Structured light systems (SLS) based active 3D scanning systems are widely used in the air and also promising to apply underwater environment. When SLS...
Finding the camera pose is an important step in many egocentric video applications. It has been widely reported that, state of the art SIAM algorithms fail on egocentric videos [1, 2, 3, 4]. In this paper, we propose a robust method for camera pose estimation, designed specifically for egocentric videos. In an egocentric video, the camera views the same scene point multiple times as the wearer's head...
The high quality of computer vision (CV) software has a great impact on the usability of the overall CV systems in real world scenarios. As the usage of standardized quality assurance methods, metrics and tools can ease the work of any CV developer and quickly improve the overall process, we report here on the introduction of code coverage analysis in the field. Our initial motivations were both to...
One technical challenge in the field of computer vision is to acquire information in three dimensions from an arbitrary environment. There have been successful algorithms that focus on the precision and robustness, the 3D vision, or a generalized vision system, yet none of them performs very well in all these aspects and approach the level of human vision. The research problem of this project is 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...
Lip features and contour detection is an important aspect of computer vision, having many application domains. This research conveys various aspects of the related topics and presents an elliptical mathematical approach followed by image processing methods such as morphological operation to extract lip contour. Lip contour detection starts from mouth area which is segmented from face image area and...
The human detection and tracking in a video plays major roll in security systems. This paper proposes an approach to detect and track the persons in a video. This approach uses Gaussian Mixture Model to detect the person and Kalman filter to track the detected person. The processing time to detect the person is reduced by performing the detection operation on down-sampled video. After detecting the...
The quality of outdoor captured images by camera may be degraded due to the occurrence of haze in the atmosphere. The process of enhancing the image by removing the haze is call dehazing. In this paper, dark channel prior and fuzzy enhancement based method is applied to remove haze from a hazy image. The performance of the proposed method is evaluated by comparing average information content and natural...
In this paper, we introduce computer vision to the study of gender politics and present a data-driven method to measure the impact of the ‘woman card’ exchange between Hillary Clinton and Donald Trump. Building from a unique dataset of the two candidates' Twitter followers, we first examine the transition dynamics of the two candidates' Twitter followers one week before the exchange and one week after...
Exploiting simple actions to recognize complex actions instead of using complex actions as training samples can save labor expenses and time consumption. Each complex action is composed of a sequence of simple actions and different manners of combinations of simple actions can form different complex actions. Thus, in this paper, we focus on temporal order information (TOI), which can be used to improve...
Despite the recent advances in computer vision and the proliferation of applications for tracking, image classification, and video analysis, very little applied work has been done to improve techniques for underwater video. Object detection and classification for underwater environments is critical in domains like marine biology, where scientist study populations of underwater species. Most applications...
This paper presents a new algorithm for automatic segmentation of moving objects in video based on spatiotemporal saliency and laplacian coordinates (LC). Our algorithm exploits the saliency and the motion information to build a spatio-temporal saliency map, used to extract a moving region of interest (MRI). This region is used to provide automatically the seeds for the segmentation of the moving...
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...
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