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Although light field data provides abundant cues for depth estimation, light field depth estimation suffers from occlusion and uncertain edges. In this paper, we propose occlusion robust light field depth estimation using segmentation guided bilateral filtering. First, we calculate refocused images from light field data using digital refocusing. Second, we perform support vector machines (SVM) classification...
Saliency detection aims to find the useful and attractive regions from an image. In many situations, there may be multiple objects in the image, and these objects may have equal attractiveness. Moreover, the appearance of pixels in one object may demonstrate large difference, which could lead to lose the object integrality when detecting saliency. To this end, this paper proposes a multi-saliency...
In recent years there have been many proposals for automated applications in vegetable harvesting. There are two major challenges: estimation of yield and harvesting of fruit trees. This research proposes a new algorithm that allows accurate counting of the number of fruits on a tree to accurately estimate the output of a dragon fruit. The algorithm consists of the following main steps: image segmentation,...
Skull extraction from Magnetic Resonance (MR) head image datasets is the process of segmentation of brain tissues from other tissues (e.g., skin, bone, fat) and has an important role in computer-assisted operations. Because, the accuracy of skull extraction affects the next stages of digital image processing or analysis. Semi-/full automated detection and extraction of the skull from MR datasets is...
Traversable region estimation is the fundamental enabler in autonomous navigation. In this paper, we propose a traversable region segmentation algorithm using stereo vision. We address this problem mainly in road scenes for the goal of autonomous driving. Using only geometry information, our approach has the advantages of effectiveness and robustness. The proposed approach is based on a cascaded framework...
A vast majority of consumer cameras operate the rolling shutter mechanism, which often produces distorted images due to inter-row delay while capturing an image. Recent methods for monocular rolling shutter compensation utilize blur kernel, straightness of line segments, as well as angle and length preservation. However, they do not incorporate scene geometry explicitly for rolling shutter correction,...
Existing methods for 3D scene flow estimation often fail in the presence of large displacement or local ambiguities, e.g., at texture-less or reflective surfaces. However, these challenges are omnipresent in dynamic road scenes, which is the focus of this work. Our main contribution is to overcome these 3D motion estimation problems by exploiting recognition. In particular, we investigate the importance...
Stereo matching is important in the area of computer vision and photogrammetry. We present a stereo matching algorithm to refine depth map by using stereo image pair. The reference image is segmented by using hill-climbing algorithm and Scale Invariant Feature Transform (SIFT) feature descriptor with Sum of Absolute Difference (SAD) local stereo matching is performed. Next, we extract a set of disparity...
Brain tumour diagnosis is usually a vital use of medical image processing, where clustering technique commonly used with medical application especially regarding brain tumour diagnosis with magnetic resonance imaging (MRI). In this MRI has been considered because it provides accurate visualization of anatomical structure of tissues. The conventional mean shift technique utilizes radially symmetric...
Estimating correspondence between two images and extracting the foreground object are two challenges in computer vision. With dual-lens smart phones, such as iPhone 7+ and Huawei P9, coming into the market, two images of slightly different views provide us new information to unify the two topics. We propose a joint method to tackle them simultaneously via a joint fully connected conditional random...
Numerous computer vision problems such as stereo depth estimation, object-class segmentation and fore-ground/background segmentation can be formulated as per-pixel image labeling tasks. Given one or many images as input, the desired output of these methods is usually a spatially smooth assignment of labels. The large amount of such computer vision problems has lead to significant research efforts,...
In this paper we propose a fast method for detecting the ground plane in 3D scenes for an arbitrary roll angle rotation of a stereo vision camera. The method is based on the analysis of the disparity map and its “V-disparity” representation. First, the roll angle of the camera is identified from the disparity map. Then, the image is rotated to a zero-roll angle position and the ground plane is detected...
This paper describes the measurement to take traffic lines of pedestrian flows outdoors and the investigation of dynamic characteristics by using the density, direction, velocity distribution and so on. These parameters become indexes of the congestion and the dangerousness. And, the time transition of these characteristics evaluate the field dynamically. The arrangement of structures and public facilities...
Following paper presents new approach to the problem of automotive Ki-67 proliferation factor estimation in breast cancer biopsy images. This method is based on context filtering with designated neighborhood masks for edge detection. Proposed method was designed to compensate two major problems: cell shape divergence from ellipse and variability of color hue along with intensity in cell staining....
This paper describes an iterative data-driven algorithm for automatically labeling coronary vessel segments in MDCT images. Such techniques are useful for effective presentation and communication of findings on coronary vessel pathology by physicians and computer-assisted diagnosis systems. The experiments are done on the 18 sets of coronary vessel data in the Rotterdam Coronary Artery Algorithm Evaluation...
In recent years, monitoring of coral reef status and health are done with the assist from image processing technique. Since underwater images are always suffer from major drawbacks, research in this area is still active. In this paper, we propose to use edge based segmentation where we modify the original canny edge detector and then use the blob processing technique to extract dominant features from...
Estimating human pose, shape, and motion from images and videos are fundamental challenges with many applications. Recent advances in 2D human pose estimation use large amounts of manually-labeled training data for learning convolutional neural networks (CNNs). Such data is time consuming to acquire and difficult to extend. Moreover, manual labeling of 3D pose, depth and motion is impractical. In...
Surveillance video parsing, which segments the video frames into several labels, e.g., face, pants, left-leg, has wide applications [41, 8]. However, pixel-wisely annotating all frames is tedious and inefficient. In this paper, we develop a Single frame Video Parsing (SVP) method which requires only one labeled frame per video in training stage. To parse one particular frame, the video segment preceding...
We present an improved version of the Simple Linear Iterative Clustering (SLIC) superpixel segmentation. Unlike SLIC, our algorithm is non-iterative, enforces connectivity from the start, requires lesser memory, and is faster. Relying on the superpixel boundaries obtained using our algorithm, we also present a polygonal partitioning algorithm. We demonstrate that our superpixels as well as the polygonal...
SAR image segmentation is the pre-process for SAR image application. This paper presents a new SAR image segmentation algorithm combining both the the bias field and Markov random field(MRF) characteristic with fuzzy clustering model(FCM) called BMFCM. The MRF characteristic of an image includes the spatial information of the image and the bias filed estimation is introduced to deal with the grey...
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