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Detection and segmentation of small renal mass (SRM) in renal CT images are important pre-processing for computer-aided diagnosis of renal cancer. However, the task is known to be challenging due to its variety of size, shape, and location. In this paper, we propose an automated method for detecting and segmenting SRM in contrast-enhanced CT images using texture and context feature classification...
Co-registration of point clouds is critical when a scene is measured several times. We present a novel feature based solution, where features are described by combining local shape context and local intensity (or image) context. The Euclidean distances of such shape and intensity combined context descriptors are used to identify candidate correspondences, which are then used as input to the final...
Hand activity is a critical monitoring component in understanding a driver's behavior within the car. Current vision-based hand detection algorithms perform poorly in naturalistic settings, due to various challenges such as global illumination changes and constant hand deformation and occlusion. To achieve a more accurate and robust hand detection system, this paper presents a hierarchical context-aware...
Automated prostate diagnoses and treatments have gained much attention due to the high mortality rate of prostate cancer. In particular, unsupervised (automatic) prostate segmentation is an active and challenging research. Most conventional works usually utilize handcrafted (low-level) features for prostate segmentation; however they often fail to extract the intrinsic structure of the prostate, especially...
A novel method for shape context realization has been presented in this paper and specific application of it in hand-drawn character recognition has been shown. In preprocessing stage, the endpoints of the digital straight line segments defining the object boundary are extracted. The four vertices of the minimum area rectangle bounding these extracted points are used as the reference points for shape...
Zooplankton are the key components of marine food webs. The abundance of it influences the ocean ecological balance. To efficiently monitor species richness of zooplankton and protect marine environment, marine biologists and computer vision experts started to research automated zooplankton classification system with computer vision technologies. Most current research focuses on achieving high classification...
The paper proposes a dynamic shape context retrieval algorithm based on statistic. It selects shape's feature point's number dynamically, and statistics the number of target shape's feature points in different area block to form a contour feature point histogram. Finally, measuring the similarity is used dynamic programming algorithm. This method can selects the number of feature points adaptively,...
This paper is interested in shape representation and recognition with a particular target to technical and line-drawing symbols. Specifically, two sorts of directional and spatial features are explored to construct a new descriptor for symbol matching and recognition. These features are rotation-, translation- and scale-invariant and can be extracted with a low cost of computation. The descriptor...
To effectively discriminate the so called “significant motion”, actions with subtle differences, such as minimum inertia among running, jogging and walking, are approximated by a new local posture descriptor. First, each human pose from action sequences is divided into multiple local rigid body-parts (LRBPs) by the multi-group 2-simplex templates. Second, a new local posture descriptor is proposed...
Human action recognition is a challenging task not only because of the factors like changes in intensity, background, etc but also because of the variability in the behavioural patterns among the objects in the image which in turn affects the recognition accuracy. Analyzing all those factors and identifying the action is termed as activity recognition. In this paper, we present an approach of activity...
The retrieval of visual cultural symbols is an important research field of inheriting and carrying forward Chinese traditional culture in digital way. Generally visual cultural symbols are foregrounds of natural images, so using shape features in image retrieval that needs image segmentation in advance has great advantages. At present, image segmentation is mostly interactive, which is quite subjective,...
This paper presents a retrieval method for the image of Chinese characters calligraphy. The precision and recall rate are main indicators to measure the quality of image retrieval algorithm. Starting from the two aspects, there are two parts in the process of image retrieve which is proposed in this paper. In this paper, carry out the retrieve with the Hu invariant moment matching algorithm, which...
Chinese calligraphy is the indispensable part of the traditional culture. Facing the present situation that ancient calligraphy existing in form of inscriptions is destroyed, we propose a complete technique for inscription corrupted by weathering or corrosion. Firstly, we use histogram projection to get single Chinese character. Then, Chinese characters of inscription should be analyzed to extract...
Depression is a typical mood disorder, which affects people in mental and even physical problems. People who suffer depression always behave abnormal in visual behavior and the voice. In this paper, an audio visual based multimodal depression scale prediction system is proposed. Firstly, features are extracted from video and audio are fused in feature level to represent the audio visual behavior....
This paper proposed a fusion of color features, shape features and SURF image recognition algorithm. Traditional SURF algorithm take greyscale image as input to extract local extreme value points as characteristics points, ignoring color and shape information. The paper introduced the global color histogram information to make up the lack of color information loss, at the same time introduced shape...
The natural contour extraction during non-rigid object tracking is a challenging task in computer vision. Most tracking-by-detection methods are based on rectangular bounding-boxes, and this leads to compounding tracking errors in subsequent frames. This paper present an accurate natural contour tracking method for non-rigid object in video, there are three main contributions. Firstly, we combined...
In this paper, we present a new shape-based system for person re-identification. The silhouette shape is represented by a Point Distribution Model (PDM) aligned on the body. We improve a fitting model which iteratively adjusts the shape by maximizing a boosted score of local features: the "Boosted Deformable Model". We modify the training procedure with a ranking structure to find how the...
This paper proposes an innovative method to detect micro aerial vehicles (MAVs) and estimate their relative pose in formation using a monocular on-board camera. Haar classifier is trained for autonomously detecting MAV in open scenes, like grasslands or obstruct-free playgrounds. In order to increase the robustness of the detection, a Kaiman filter has been employed to conduct image tracking. Contours...
The crucial problem of multisensor remote sensing image registration is how to establish the reliable correspondences between the features extracted from two images. The feature similarity based methods fail when similar local regions exist, and the spatial relationship methods fail to match small portion of pair wise correspondences out of the total number of features. In this paper, we proposed...
This paper proposes a robust minutiae based fingerprint image hashing technique. The idea is to incorporate the orientation and descriptor in the minutiae of fingerprint images using SIFT-Harris feature points. A recent shape context based perceptual hashing method has been compared against the proposed technique. Experimentally, the proposed technique has been shown to deliver better robustness against...
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