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This paper introduces a novel object recognition approach based on the Gabor Wavelet representation of the binarized image that makes use of fuzzy logic for determining the 'soft' class label of the color test images with respect to the gray training templates. The fuzzy membership function used is a Generalized Gaussian function whose exponent value is determined empirically. The use of simple computations...
Cell structures analyze and cell organelles play important roles in bio-manipulation. However, it is difficult to image the live cell organelles without damage by using the common microscopy. Polarized light microscopy provides unique opportunities for analyzing the architectural dynamics of living cells, tissues, and whole organisms. In this paper, a novel cell spindle positioning method with the...
In this paper, an improved implicit shape model is presented for on-road vehicle and pedestrian detection. Implicit shape model (ISM) is widely used for object detection and categorization. The training of ISM usually consists of three components: interest point detector, local feature descriptor, codebook generation. We evaluate six common interest point detectors to determine the best detector for...
This paper presents a new method for object detection by edge grouping. This method can detect the boundaries of objects under complex background where the object contours are partly occluded or missing during contour extraction. Our method is adapted to detect the objects with not only closed boundaries but also open-boundaries. There are three contributions in this work. First, the shape of an object...
This paper proposes a video surveillance system to position ships in a ship lock A background subtraction method is employed to extract the foreground of all ships. Subsequently, the DBSCAN clustering algorithm is applied to isolate each ship from the curves scanned from the extracted foreground regions. The system can locate adjacent ships and identify each one in real time. Theoretical analyses...
This paper presents an approach to detect moving and static objects occurring in a video by a novel model-based tracking. The method exploits the spatial and motion coherence of objects across image frames that results from the known bounded shape distortion and object's velocity between two consecutive frames. The interframe transformation space is thus reduced to a reasonable small space of only...
A novel contour grouping method was recently proposed for the difficult task of detecting and delineating unexpected multi-part objects of unknown specific shape and appearance in a variety of natural images. This method, in many ways original and unique, was generally able to obtain object-level groups of quite good quality for a variety of objects and images. For each tested image, a number of object-level...
Object detection and classification are key tasks in computer vision that can facilitate high-throughput image analysis of microscopy data. We present a set of local image descriptors for three-dimensional (3D) microscopy datasets inspired by the well-known Haar wavelet framework. We add orientation, illumination and scale information by assuming that the neighborhood surrounding points of interests...
This paper describes an efficient approach for irregular moving object detecting and tracking in real-time system based on color and shape information of the target object from realistic environment. Firstly, the data is gotten from a real-time camera system at a stable frame rate. And then, each frame is processed by using proposed method to detect and track the target object immediately in consecutive...
Conventional object detection methods often use local features based on object shape, of which the HOG feature is typical. In recent years, Color Self-Similarity (CSS) has been proposed as a local feature that uses color information. CSS involves computing color similarity as a basis for deciding the sameness of objects, and thus represent a feature that is effective for object detection. It has also...
This paper presents a method named Depth-Assisted Rectification of Contours (DARC) for detection and pose estimation of texture-less planar objects using RGB-D cameras. It consists in matching contours extracted from the current image to previously acquired template contours. In order to achieve invariance to rotation, scale and perspective distortions, a rectified representation of the contours is...
The development of cell manufacturing process using object recognition has been interested in automated factory. But it is not trivial work to recognize object because features transformed from illumination and diversified field needs have caused challenge problem in object detection and recognition. The recognition reliability in real world environment can be increased by object, which preserves...
This paper proposes a hand detection methodbased on statistical learning training way. Using Microsoft's Kinect sensor, to get the depth information. Through the analysis of the characetristics of hands, put out a kind of new features for statistical learning which approximate with Harr-like feature. The new feature is good at describing complex hand shape degeneration. With the help of Adaboost statistical...
This paper presents a novel object detection and segmentation method utilizing an inpainting algorithm. Inpainting is a concept of recovering missing image regions based on their surroundings, which were originally used for restoration of damaged paintings. In this paper, we newly utilize inpainting to judge whether an object candidate region includes the foreground object or not. The key idea is...
We present a new pedestrian detection algorithm that considers multiple information sources. Appearance-based detection methods face difficulties such as appearance variations and occlusions. Shape-based methods can have false positives on shadows since they usually have similar shapes with foreground objects. To deal with these problems, we use appearance, motion, and shadow information simultaneously...
One popular approach for multi-camera detection of pedestrians or other objects of interest in surveillance scenes is to perform background subtraction and project the resulting foreground mask images to a common scene plane using homographies. As the complexity of the scene increases, it is unavoidable that so called "ghost" detections should occur. These are false positives, indicating...
This paper presents a method of learning global and reconfigurable part-based models (RPM) for object detection. Recently, deformable part-based model (DPM) is widely used. A DPM consists of a root node and a collection of part nodes, which is learned under the latent SVM formulation by treating part nodes as hidden variables. Although the configuration of parts (i.e., the shapes, sizes and locations...
This study proposes a method to detect and mark the target object removed from the monitoring scene and the unknown object left in the monitoring scene. The present method uses the timeliness background to extract the foreground object and to mask the part that was unwanted. The foreground object was compared with the current frame, thus, the unreliable pixels were filtered out. By the identification...
The performance of part-based object detectors generally degrades for highly flexible objects. The limited topological structure of models and pre-specified part shapes are two main factors preventing these detectors from fully capturing large deformations. To better capture the deformations, we propose a novel approach to integrate the detections from a family of part-based detectors with patches...
In this paper we propose an approach to holistic scene understanding that reasons jointly about regions, location, class and spatial extent of objects, presence of a class in the image, as well as the scene type. Learning and inference in our model are efficient as we reason at the segment level, and introduce auxiliary variables that allow us to decompose the inherent high-order potentials into pairwise...
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